John von Neumann

Turing's Cathedral
by George Dyson
Published 6 Mar 2012

Mariette von Neumann to John von Neumann, September 22, 1937, in Frank Tibor, “Double Divorce: The Case of Mariette and John von Neumann,” Nevada Historical Society Quarterly 34, no. 2 (1991): 361. 12. Mariette von Neumann to John von Neumann, n.d., 1937, in ibid. 13. Klára von Neumann to John von Neumann, November 11, 1937, KVN. 14. John von Neumann to Stanislaw Ulam, April 22, 1938, SFU. 15. Klára von Neumann, Two New Worlds. 16. John von Neumann to Klára von Neumann, September 14, 1938, KVN. 17. John von Neumann to Klára von Neumann, September 6, 1938, KVN. 18. John von Neumann to Klára von Neumann, September 5, 1938, KVN; John von Neumann to Klára von Neumann, September 13, 1938, KVN. 19.

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Nicholas Vonneumann, interview with author. 10. Vonneumann, John von Neumann as Seen by His Brother, pp. 23, 16. 11. Ibid., p. 24. 12. Nicholas Vonneumann, interview with author. 13. Stanislaw Ulam, “John von Neumann: 1903–1957,” Bulletin of the American Mathematical Society 64, no. 3, part 2 (May 1958): 1. 14. Klára von Neumann, Johnny, ca. 1963, KVN; Ulam, “John von Neumann: 1903–1957,” 2:37. 15. John von Neumann to Stan Ulam, December 9, 1939, SFU; Oskar Morgenstern, in John von Neumann, documentary produced by the Mathematical Association of America, 1966. 16. Klára von Neumann, Johnny. 17. John von Neumann and Oskar Morgenstern, Theory of Games and Economic Behavior (Princeton, N.J.: Princeton University Press, 1944), p. 2; Samuelson, “A Revisionist View of Von Neumann’s Growth Model,” in M.

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Jack Rosenberg, interview with author, February 12, 2005; Marina von Neumann Whitman, interview with author, February 9, 2006. 29. Klára von Neumann to John von Neumann, n.d., ca. 1949, KVN. 30. Klára von Neumann, Johnny. 31. Ibid. 32. John von Neumann and Oswald Veblen to Frank Aydelotte, March 23, 1940, IAS. 33. Ibid. 34. Klára von Neumann, Johnny. 35. John von Neumann to Stanislaw Ulam, April 2, 1942, VNLC; John von Neumann to Clara [Klára] von Neumann, April 13, 1943, KVN; S. W. Hubbel [Office of Censorship] to Clara [Klára] von Neumann, April 13, 1943, IAS. 36. Klára von Neumann, Johnny. 37. John von Neumann to Klára von Neumann, May 8, 1945, KVN; John von Neumann to Klára von Neumann, May 11, 1945, KVN. 38.

The Man From the Future: The Visionary Life of John Von Neumann
by Ananyo Bhattacharya
Published 6 Oct 2021

Fellner would initially study the subject for similar reasons. All three would drop it quite soon after finishing their degrees to pursue their true passions. 23. Quoted in Stanisław Ulam, ‘John von Neumann 1903–1957’, Bulletin of the American Mathematical Society, 64 (1958), pp. 1–49. 24. John von Neumann, ‘Eine Axiomatisierung der Mengenlehre’, Journal für die reine und angewandte Mathematik, 154 (1925), pp. 219–40. 25. John von Neumann, ‘Die Axiomatisierung der Mengenlehre’, Mathematische Zeitschrift, 27 (1928), pp. 669–752. 26. Quoted in Dyson, Turing’s Cathedral. CHAPTER 3: THE QUANTUM EVANGELIST 1.

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Quoted in Dyson, Turing’s Cathedral. 18. https://libertyellisfoundation.org/passenger-details/czoxMzoiOTAxMTk4OTg3MDU0MSI7/czo4OiJtYW5pZmVzdCI7. 19. Details of Meitner’s life drawn from Ruth Lewin Sime, 1996, Lise Meitner: A Life in Physics, University of California Press, Berkeley. 20. John von Neumann, 2005, John von Neumann: Selected Letters, ed. Miklós Rédei, American Mathematical Society, Providence, R.I. 21. Subrahmanyan Chandrasekhar and John von Neumann, 1942, ‘The Statistics of the Gravitational Field Arising from a Random Distribution of Stars. I. The Speed of Fluctuations’, Astrophysical Journal, 95 (1942), pp. 489–531. 22. Thomas Haigh and Mark Priestly have recently made the case that von Neumann was not much influenced by Turing when it came to computer design, based on the text of three lectures they discovered: ‘Von Neumann Thought Turing’s Universal Machine Was “Simple and Neat”.

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This chapter is particularly indebted to Dyson; Thomas Haigh, Mark Priestley and Crispin, Rope, 2016, ENIAC in Action: Making and Remaking the Modern Computer, MIT Press, Cambridge, Mass., and William Aspray, 1990, John von Neumann and the Origins of Modern Computing, MIT Press, Cambridge, Mass. 2. A slightly longer excerpt is quoted in Leonard, Von Neumann, Morgenstern, and the Creation of Game Theory Cambridge University Press, Cambridge. 3. See Macrae, John von Neumann. 4. Earl of Halsbury, ‘Ten Years of Computer Development’, Computer Journal, 1 (1959), pp. 153–9. 5. Brian Randell, 1972, On Alan Turing and the Origins of Digital Computers, University of Newcastle upon Tyne Computing Laboratory, Technical report series. 6. Quoted in Aspray, John von Neumann and the Origins of Modern Computing. 7.

Darwin Among the Machines
by George Dyson
Published 28 Mar 2012

Berkeley, Giant Brains (New York: John Wiley, 1949), 5. 45.John von Neumann, 1948, “The General and Logical Theory of Automata,” in Lloyd A. Jeffress, ed., Cerebral Mechanisms in Behavior: The Hixon Symposium (New York: Hafner, 1951), 31. 46.Stanislaw Ulam, Adventures of a Mathematician (New York: Scribner’s, 1976), 242. 47.John von Neumann, 1948, response to W. S. McCulloch’s paper “Why the Mind Is in the Head,” Hixon Symposium, September 1948, in Jeffress, Cerebral Mechanisms, 109–111. 48.John von Neumann to Oswald Veblen, memorandum, 26 March 1945, “On the Use of Variational Methods in Hydrodynamics,” reprinted in John von Neumann, Theory of Games, Astrophysics, Hydrodynamics and Meteorology, vol. 6 of Collected Works, ed.

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Dyson, Disturbing the Universe (New York: Harper & Row, 1979), 194. 2.Stanislaw Ulam, Adventures of a Mathematician (New York: Scribner’s, 1976), 231. 3.Nicholas Vonneumann, “John von Neumann: Formative Years,” Annals of the History of Computing 11, no. 3 (1989): 172. 4.Eugene P. Wigner, “John von Neumann—A Case Study of Scientific Creativity,” Annals of the History of Computing. 11, no. 3 (1989): 168. 5.Edward Teller, in Jean R. Brink and Roland Haden, “Interviews with Edward Teller and Eugene P. Wigner,” Annals of the History of Computing 11, no. 3 (1989): 177. 6.Stanislaw Ulam, “John von Neumann, 1903–1957,” Bulletin of the American Mathematical Society 64, no. 3 (May 1958): 1. 7.Eugene Wigner, “Two Kinds of Reality,” The Monnist 49, no. 2 (April 1964); reprinted in Symmetries and Reflections (Cambridge: MIT Press, 1967), 198. 8.John von Neumann, statement on nomination to membership in the AEC, 8 March 1955, von Neumann Papers, Library of Congress; in William Aspray, John von Neumann and the Origins of Modern Computing (Cambridge: MIT Press, 1990), 247. 9.John von Neumann, as quoted by J.

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Wigner,” Annals of the History of Computing 11, no. 3 (1989): 177. 6.Stanislaw Ulam, “John von Neumann, 1903–1957,” Bulletin of the American Mathematical Society 64, no. 3 (May 1958): 1. 7.Eugene Wigner, “Two Kinds of Reality,” The Monnist 49, no. 2 (April 1964); reprinted in Symmetries and Reflections (Cambridge: MIT Press, 1967), 198. 8.John von Neumann, statement on nomination to membership in the AEC, 8 March 1955, von Neumann Papers, Library of Congress; in William Aspray, John von Neumann and the Origins of Modern Computing (Cambridge: MIT Press, 1990), 247. 9.John von Neumann, as quoted by J. Robert Oppenheimer in testimony before the AEC Personnel Security Board, 16 April 1954, In the Matter of J. Robert Oppenheimer (Washington, D.C.: Government Printing Office, 1954; reprint, Cambridge: MIT Press, 1970), 246 (page citation is to the reprint edition). 10.Nicholas Metropolis, “The MANIAC,” in Nicholas Metropolis, J.

Prisoner's Dilemma: John Von Neumann, Game Theory, and the Puzzle of the Bomb
by William Poundstone
Published 2 Jan 1993

The Anchor Books edition is published by arrangement with Doubleday, a division of Random House, Inc. Anchor Books and colophon are registered trademarks of Random House, Inc. The quotes from letters of John von Neumann on pp. 22, 65, 75, 140–41, and 180 are from materials in the John von Neumann archives, Library of Congress, and are used with permission of Marina von Neumann Whitman. The excerpts from “The Mathematician” by John von Neumann on pp. 28–29 are used with permission of the University of Chicago Press. Copyright © 1950. The quotations from letters of J. D. Williams on pp. 94–95 are used with permission of Evelyn Williams Snow.

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Thanks for recollections, assistance, or advice must also go to Paul Armer, Robert Axelrod, Sally Beddow, Raoul Bott, George B. Dantzig, Paul Halmos, Jeane Holiday, Cuthbert Hurd, Martin Shubik, John Tchalenko, Edward Teller, and Nicholas A. Vonneuman. CONTENTS Cover Other Books by this Author Title Page Dedication Acknowledgments 1 DILEMMAS The Nuclear Dilemma John von Neumann Prisoner’s Dilemma 2 JOHN VON NEUMANN The Child Prodigy Kun’s Hungary Early Career The Institute Klara Personality The Sturm und Drang Period The Best Brain in the World 3 GAME THEORY Kriegspiel Who Was First? Theory of Games and Economic Behavior Cake Division Rational Players Games as Trees Games as Tables Zero-Sum Games Minimax and Cake Mixed Strategies Curve Balls and Deadly Genes The Minimax Theorem N-Person Games 4 THE BOMB Von Neumann at Los Alamos Game Theory in Wartime Bertrand Russell World Government Operation Crossroads The Computer Preventive War 5 THE RAND CORPORATION History Thinking About the Unthinkable Surfing, Semantics, Finnish Phonology Von Neumann at RAND John Nash The Monday-Morning Quarterback 6 PRISONER’S DILEMMA The Buick Sale Honor Among Thieves The Flood-Dresher Experiment Tucker’s Anecdote Common Sense Prisoner’s Dilemmas in Literature Free Rider Nuclear Rivalry 7 1950 The Soviet Bomb The Man from Mars Urey’s Speech The Fuchs Affair The Korean War The Nature of Technical Surprise Aggressors for Peace Francis Matthews Aftermath Public Reaction Was It a Trial Balloon?

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Today, with East-West tensions relaxing, preventive war seems a curious aberration of cold-war mentality. Yet the same sorts of issues are very much with us today. What should a nation do when its security conflicts with the good of all humanity? What should a person do when his or her interests conflict with the common good? JOHN VON NEUMANN Perhaps no one exemplifies the agonizing dilemma of the bomb better than John von Neumann (1903–1957). That name does not mean much to most people. The celebrity mathematician is almost a nonexistent species. Those few laypersons who recognize the name are most likely to place him as a pioneer of the electronic digital computer, or as one of the crowd of scientific luminaries who worked on the Manhattan Project.

The Man Who Invented the Computer
by Jane Smiley
Published 18 Oct 2010

Presper Eckert, only eighteen, was applying to college at MIT, though in the end he went to business school at the University of Pennsylvania. Konrad Zuse, in Berlin, had already built one computer (the Z1) in his parents’ apartment. He later said that if the building had not been bombed, he would not have been able to get his machine out of the apartment. John von Neumann, born in Hungary but living in Princeton, New Jersey, had become so convinced that war in Europe was inevitable that he had applied for U.S. citizenship. He received his naturalization papers in December 1937. Von Neumann was one of the most talented mathematicians of his day, but he wasn’t yet involved with computers.

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In some ways, Alan Turing was Atanasoff’s precise opposite, drawn to pure mathematics rather than practical physics, educated to think rather than to tinker, disorganized in his approach rather than systematic, never a family man and required by his affections and his war work to be utterly secretive. His figure is now so mysterious and tragically evocative that he has become the most famous of our inventors. The man who was best known in his own lifetime, John von Neumann, has retreated into history, more associated with the atomic bomb and the memory of the cold war than with the history of the computer, but it was von Neumann who made himself the architect of that history without, in some sense, ever lifting a screwdriver (in fact, his wife said that he was not really capable of lifting a screwdriver).

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If, at the University of Florida and Iowa State, and even at the University of Wisconsin, Atanasoff was always more or less at the periphery of both the mathematics and physics establishments, at King’s College Turing was at the exact heart, especially of mathematics. He took courses from astrophysicist Arthur Eddington and mathematicians G. H. Hardy and Max Born. He met John von Neumann there—many mathematicians fleeing conditions in Germany and the East passed through Cambridge on their way to settling elsewhere. And it was Max Newman, who was lecturing on topology—the study of relationships between geometric spaces as they are transformed by such operations as stretching, but not such operations as cutting—who introduced him to the Hilbert problem that would make his career.

A Beautiful Mind
by Sylvia Nasar
Published 11 Jun 1998

Kuhn, interview. 27. Ibid. 28. Milnor, interview, 9.26.95. 7: John von Neumann 1. See, for example, Stanislaw Ulam, “John von Neumann, 1903–1957,” Bulletin of the American Mathematical Society, vol. 64, no. 3, part 2 (May 1958); Stanislaw Ulam, Adventures of a Mathematician (New York: Scribner’s, 1983); Paul R. Halmos, “The Legend of John von Neumann,” American Mathematical Monthly, vol. 80 (1973); William Poundstone, Prisoner’s Dilemma, op. cit.; Ed Regis, Who Got Einstein’s Office?, op. cit. 2. Poundstone, op. cit. 3. Ulam, “John von Neumann,” op. cit.; Poundstone, op. cit., pp. 94–96. 4. Harold Kuhn, interview, 1.10.96. 5.

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Nash shared von Neumann’s interest in game theory, quantum mechanics, real algebraic variables, hydrodynamic turbulence, and computer architecture. 6. See, for example, Ulam, “John von Neumann,” op. cit. 7. Norman McRae, John von Neumann (New York: Pantheon Books, 1992), pp. 350–56. 8. John von Neumann, The Computer and the Brain (New Haven: Yale University Press, 1959). 9. See, for example, G. H. Hardy, A Mathematician’s Apology (Cambridge, U.K.: Cambridge University Press, 1967), with a foreword by C. P. Snow. 10. Ulam, “John von Neumann,” op. cit. 11. Poundstone, op. cit. 12. Poundstone, Prisoner’s Dilemma, p. 190. 13. Clay Blair, Jr., “Passing of a Great Mind,” Life (February 1957), pp. 89–90, as quoted by Poundstone, op. cit., p. 143. 14.

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Clay Blair, Jr., “Passing of a Great Mind,” Life (February 1957), pp. 89–90, as quoted by Poundstone, op. cit., p. 143. 14. Poundstone, op. cit. 15. Ulam, “John von Neumann,” op. cit. 16. Harold Kuhn, interview, 3.97. 17. Paul R. Halmos, “The Legend of John von Neumann,” op. cit. 18. Ibid. 19. Poundstone, op. cit. 20. Halmos, op. cit. 21. Ibid. 22. Poundstone, op. cit. 23. Ulam, Adventures of a Mathematician, op. cit. 24. Ulam, “John von Neumann,” op. cit. 25. Ibid. 26. Ibid., p. 10; Robert J. Leonard, “From Parlor Games to Social Science,” op. cit. 27. Richard Duffin, interview, 10.94. 28. Halmos, op. cit. 29. Ulam, “John von Neumann,” op. cit., pp. 35–39. 30. Interviews with Donald Spencer, 11.18.95; David Gale, 9.20.95; and Harold Kuhn, 9.23.95. 31.

Singularity Rising: Surviving and Thriving in a Smarter, Richer, and More Dangerous World
by James D. Miller
Published 14 Jun 2012

Von Neumann made Stalin unwilling to risk war because von Neumann shaped U.S. weapons policy—in part by pushing the United States to develop hydrogen bombs—to let Stalin know that the only human life Stalin actually valued would almost certainly perish in World War III. 20 Johnny helped develop a superweapon, played a key role in integrating it into his nation’s military, advocated that it be used, and then made sure that his nation’s enemies knew that in a nuclear war they would be personally struck by this superweapon. John von Neumann could himself reasonably be considered the most powerful weapon ever to rest on American soil. Now consider the strategic implications if the Chinese high-tech sector and military acquired a million computers with the brilliance of John von Neumann, or if, through genetic manipulation, they produced a few thousand von Neumann-ish minds every year. Contemplate the magnitude of the resources the US military would pour into artificial intelligence if it thought that a multitude of digital or biological von Neumanns would someday power the Chinese economy and military.

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But whole brain emulation is still a path to the Singularity that could work, even if a Kurzweilian merger proves beyond the capacity of bioengineers. If we had whole brain emulations, Moore’s Law would eventually give us some kind of Singularity. Imagine we just simulated the brain of John von Neumann. If the (software adjusted) speed of computers doubled every year, then in twenty years we could run this software on computers that were a million times faster and in forty years on computers that were a trillion times faster. The innovations that a trillion John von Neumanns could discover in one year would change the world beyond our current ability to imagine. 3.Clues from the Brain Even if we never figure out how to emulate our brains or merge them with machines, clues to how our brains work could help scientists figure out how to create other kinds of human-level artificial intelligence.

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If the following diagram is right, we would likely have a lot of warning time between when AIs reach chimp level and when they become ultra-intelligent. But this first diagram might overstate differences in intelligence. The basic structure of my brain is pretty close to that of chimps and shockingly similar to John von Neumann’s. Perhaps, under some grand theory of intelligence, once you’ve reached chimp level it doesn’t take all that many more tweaks to go well past John von Neumann. If this next diagram has the relative sizes of intelligences right, then it might take very little time for an AI to achieve superintelligence once it becomes as smart as a chimp. As I’ll discuss in Chapter 7, there are a huge number of genes, each of which slightly contributes to a person’s intelligence.

The Pentagon's Brain: An Uncensored History of DARPA, America's Top-Secret Military Research Agency
by Annie Jacobsen
Published 14 Sep 2015

The computer designed by John von Neumann played an important role in allowing Livermore scientists to model new nuclear weapons designs before building them. In the summer of 1955, John von Neumann was diagnosed with cancer. He had slipped and fallen, and when doctors examined him, they discovered that he had an advanced, metastasizing cancerous tumor in his collarbone. By November his spine was affected, and in January 1956 von Neumann was confined to a wheelchair. In March he entered a guarded room at Walter Reed Hospital, the U.S. Army’s flagship medical center, outside Washington, D.C. John von Neumann, at the age of fifty-four, racked with pain and riddled with terror, was dying of a cancer he most likely developed because of a speck of plutonium he inhaled at Los Alamos during the war.

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Air Force brawn: Abella, photographs, (unpaginated). 2 game pieces scattered: Leonard, 339. 3 “credibility”: York, Making Weapons, 89. 4 remarkable child prodigy: S. Bochner, John Von Neumann, 1903–1957, National Academy of Sciences, 442–450. 5 “unsolved problem”: P. R. Halmos, “The Legend of John Von Neumann,” Mathematical Association of America, Vol. 80, No. 4, April 1973, 386. 6 “He was pleasant”: York, Making Weapons, 89. 7 “I think”: Kaplan, Wizards of Armageddon, 63. 8 “all-out atomic war”: Whitman, 52. 9 maximum kill rate: “Citation to Accompany the Award of the Medal of Merit to Dr. John von Neumann,” October 1946, Von Neumann Papers, LOC. 10 “a mentally superhuman race”: Dyson, Turing’s Cathedral, 45. 11 Prisoner’s Dilemma: Poundstone, 8-9, 103-106. 12 something unexpected: Abella, 55–56; Poundstone, 121-123. 13 “How can you persuade”: McCullough, 758. 14 Goldstine explained: Information on Goldstine comes from Jon Edwards, “A History of Early Computing at Princeton,” Princeton Alumni Weekly, August 27, 2012. 15 von Neumann declared: Dyson, Turing’s Cathedral, 73. 16 “Our universe”: George Dyson, “‘An Artificially Created Universe’: The Electronic Computer Project at IAS,” Institute for Advanced Study, Princeton (Spring 2012), 8-9. 17 secured funding: Maynard, “Daybreak of the Digital Age,” Princeton Alumni Weekly, April 4, 2012. 18 he erred: Jon Edwards, “A History of Early Computing at Princeton,” Princeton Alumni Weekly, August 27, 2012, 4. 19 Wohlstetter’s famous theory: Wohlstetter, “The Delicate Balance of Terror,” 1-12. 20 Debris: Descriptions of shock wave and blast effects are described in Garrison, 23-29. 21 Georg Rickhey: Information on Rickhey comes from Bundesarchiv Ludwigsburg and RG 330 JIOA Foreign Scientist Case Files, NACP.

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Competition was valued and encouraged at RAND, with scientists and analysts always working to outdo one another. Lunchtime war games included at least one person in the role of umpire, which usually prevented competitions from getting out of hand. Still, tempers flared, and sometimes game pieces scattered. Other times there was calculated calm. Lunch could last for hours, especially if John von Neumann was in town. In the 1950s, von Neumann was the superstar defense scientist. No one could compete with his brain. At the Pentagon, the highest-ranking members of the U.S. armed services, the secretary of defense and the Joint Chiefs of Staff, all saw von Neumann as an infallible authority. “If anyone during that crucial period in the early and middle-fifties can be said to have enjoyed more ‘credibility’ in national defense circles than all the others, that person was surely Johnny,” said Herb York, von Neumann’s close friend.

Journey to the Edge of Reason: The Life of Kurt Gödel
by Stephen Budiansky
Published 10 May 2021

CHAPTER 5: UNDECIDABLE TRUTHS 1.KG, “Vollständigkeit des Logikkalküls”; Wang, “Facts about Gödel,” 654 n. 2. 2.KG, “Vollständigkeit der Axiome.” 3.Sacks, “Reflections on Gödel.” 4.Rudolf Gödel, “Chronik der Familie,” 58. 5.Herbert Feigl to KG, 29 March 1929, KGP, 1c/45. 6.KG, “Protokolle,” 10; KG to MG, 30 September 1956. 7.Rudolf Gödel, “Chronik der Familie,” 58. 8.KG income/expense ledger, KGP, 13b/31. 9.Stadler, Vienna Circle, 153–55. 10.Program of the Second Conference on the Epistemology of the Exact Sciences, Königsberg, September 5–7, 1930, reprinted in Stadler, Vienna Circle, 162–63. 11.Sigmund, Exact Thinking, 221. 12.Von Plato, “Sources of Incompleteness,” 4047. 13.Rudolf Carnap diary quoted in Wang, Reflections on Gödel, 84. 14.Rudolf Carnap diary quoted in Wang, Reflections on Gödel, 85. 15.KG, “Lecture at Königsberg,” CW, 3:28. 16.Von Plato, “Sources of Incompleteness,” 4047; Hahn et al., “Diskussion zur Grundlegung,” 148. 17.Hahn et al., “Diskussion zur Grundlegung,” 148. 18.KG’s postscript in Hahn et al., “Diskussion zur Grundlegung,” 147–51; KG, “Über unentscheidbare Sätze.” 19.Rucker, Infinity and Mind, 182; Engelen, ed., Notizbücher, 376, 390. 20.Enzensberger, “Hommage à Gödel,” reprinted in W&B, 25 (my translation). 21.KG, “Existence of Undecidable Propositions,” 6. 22.KG, “Existence of Undecidable Propositions,” 6–7. 23.KG, “Situation in Foundations of Mathematics,” CW, 3:50–51. 24.KG, “Existence of Undecidable Propositions,” 8–9. 25.KG, “Undecidable Propositions of Formal Systems,” CW, 1:355. 26.KG, “Existence of Undecidable Propositions,” 14. 27.KG, “Undecidable Propositions of Formal Systems,” CW, 1:359. 28.Kleene, “Kurt Gödel,” 154. 29.Vinnikov, “Hilbert’s Apology.” 30.Heinrich Scholz to Rudolf Carnap, 16 April 1931, quoted in Mancosu, “Reception of Gödel’s Theorem,” 33; Marcel Natkin to KG, 27 June 1931, KGP, 2c/114. 31.Goldstine, Pascal to von Neumann, 167–68. 32.John von Neumann to KG, 20 November 1930, CW, 5:336–39. 33.Drafts of KG to John von Neumann, late November 1930, quoted in von Plato, “Sources of Incompleteness,” 4050–51. 34.John von Neumann to KG, 29 November 1930, CW, 5:338. 35.Von Plato, “Sources of Incompleteness,” 4054. 36.Ulam, Adventures of a Mathematician, 80; Goldstine, Pascal to von Neumann, 174. 37.Statement in Connection with the First Presentation of the Albert Einstein Award to Dr.

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Philip Erlich File on Kurt Gödel, 1, 8, and 15 March 1977, KGP, 27/1. 50.Simotta, “Marriage and Divorce in Austria,” 526 and n. 6. 51.Power of Attorney, 29 August 1938, KGP, 13a/13; receipt, Rathauskeller, 20 September 1938, KGP, 13b/21. 52.Karl Menger to Oswald Veblen, [n.d., 1938], Veblen, Papers, 8/10; Karl Menger to KG, [December 1938], CW, 5:125. 53.KG to Karl Menger, 25 June, 19 October, and 11 November 1938, quoted in Menger, Reminiscences, 218–19. 54.Menger, Reminiscences, 220–21. 55.Menger, Reminiscences, 224. 56.Menger, Reminiscences, 224–25; KG to Karl Menger, 30 August 1939, CW, 5:124–26; OMD, 19 March 1972. 57.KG to Oswald Veblen, draft letter, November 1939, KGP, 13c/197. 58.John von Neumann to Abraham Flexner, 27 September 1939, quoted in Dyson, Turing’s Cathedral, 96; von Neumann to KG, telegram, 5 October 1939, IAS, Faculty Files, Pre-1953. 59.John von Neumann to Abraham Flexner, 16 October 1939, IAS, Visa-Immigration. 60.Ash, “Universität Wien,” 124–25; Friedrich Plattner to Rektor der Universität, 12 August 1939, reproduced in GA, 67–68. 61.Arthur Marchet to Rektor der Universität, 30 September 1939, reproduced in GA, 72. 62.Dawson, Logical Dilemmas, 140; KG to Devisenstelle Wien, 29 July 1939, reproduced in GA, 65–66. 63.KG to Oswald Veblen, draft letter, November 1939, KGP, 3c/197; Menger, Reminiscences, 224; Kreisel, “Kurt Gödel,” 155. 64.Frank Aydelotte to Chargé d’Affaires, German Embassy, 1 December 1939, IAS, Faculty Files, Pre-1953. 65.Der Dekan to Rektor der Universität, 27 November 1939, reproduced in GA, 71. 66.KG to Frank Aydelotte, 5 January 1940, IAS, Faculty Files, Pre-1953; KG to Institute for Advanced Study, telegram, 15 January 1940, ibid.; KG passport, KGP, 13a/8. 67.KG to MG, 29 November 1965 (“I still recall the suitcase of things that Adele brought back from there in 1940”). 68.KG to RG, 31 March 1940; KG to Institute for Advanced Study, telegram, 5 March 1940, IAS, Faculty Files, Pre-1953. 69.OMD, 10 March 1940.

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Philip Erlich Walking with Einstein Railroad line to Brünn, 1838 Brünn’s city theater Austro-Hungarian Monarchy, 1906 (map) Vienna’s medieval glacis The Ringstraße, nearing completion Vienna’s prototype anti-Semite, Karl Lueger Portrait of Margaret Stonborough-Wittgenstein by Gustav Klimt, 1905 Self-portrait of Ernst Mach Brünn’s textile mills, 1915 Brünn (map) Gödel as a baby The Gödel family Gödel Villa in Brünn Realgymnasium, Brünn Gödel’s school report card Gödel’s Vienna (map) Café Arkaden Albert Einstein in Vienna, 1921 University of Vienna Olga Taussky Philipp Furtwängler Gödel as a student Hans Hahn Anti-Semitic attacks at the university The Bärenhöhle Justizpalast attack, 1927 Moritz Schlick Café Josephinum Karl Menger Rudolf Carnap Ludwig Wittgenstein At tea with Olga Taussky and foreign visitors With Adele in Vienna Adele on stage, age nineteen Marked-up proof page of the Incompleteness Theorem Gödel’s lecture fees, 1937 Hiking near the Rax Josefstädter Straße Oswald Veblen Princeton, 1930s John von Neumann Nazi students and faculty at the University of Vienna, 1931 With Alfred Tarski, 1935 Purkersdorf Sanatorium Receipt for stay at Purkersdorf Rekawinkel Sanatorium Portrait of Adele, 1932 Moritz Schlick’s murder Hans Nelböck on trial Gödel’s 1937–38 shorthand diary Grinzing, 1938 Nazi takeover at the University of Vienna, 1938 Adele’s NSDAP application Wedding portrait, September 1938 Passport and cables, 1940 Crossing the Pacific with Adele Fuld Hall Verena Huber-Dyson With Albert Einstein With Oskar Morgenstern Receiving the Einstein Award, 1951 With Adele at Linden Lane Working outdoors With the flamingo With Rudi and Marianne Gödel in Princeton Gödel’s office in the new library Student protests in Princeton, 1969 At the Institute for Advanced Study garden party, 1973 JOURNEY to the EDGE of REASON At the Institute for Advanced Study, 1956 PROLOGUE MARCH 1970.

Complexity: A Guided Tour
by Melanie Mitchell
Published 31 Mar 2009

“When his mother once stared rather aimlessly”: Macrae, N., John von Neumann. New York: Pantheon, 1992, p. 52. “the greatest paper on mathematical economics”: Quoted in Macrae, N., John von Neumann. New York: Pantheon, 1992, p. 23. “the most important document ever written on computing and computers”: Goldstine, H. H., The Computer, from Pascal to von Neumann. Princeton, NJ: Princeton University Press, first edition, 1972, p. 191. “Five of Hungary’s six Nobel Prize winners”: Macrae, N., John von Neumann. New York: Pantheon, 1992, p. 32. “The [IAS] School of Mathematics”: Quoted in Macrae, N., John von Neumann. New York: Pantheon, 1992, p. 324.

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In fact, it is one of the simplest systems to capture the essence of chaos: sensitive dependence on initial conditions. The logistic map was brought to the attention of population biologists in a 1971 article by the mathematical biologist Robert May in the prestigious journal Nature. It had been previously analyzed in detail by several mathematicians, including Stanislaw Ulam, John von Neumann, Nicholas Metropolis, Paul Stein, and Myron Stein. But it really achieved fame in the 1980s when the physicist Mitchell Feigenbaum used it to demonstrate universal properties common to a very large class of chaotic systems. Because of its apparent simplicity and rich history, it is a perfect vehicle to introduce some of the major concepts of dynamical systems theory and chaos.

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This simple-sounding problem turns out to have echos in the work of Kurt Gödel and Alan Turing, which I described in chapter 4. The solution also contains an essential means by which biological systems themselves get around the infinite regress. The solution was originally found, in the context of a more complicated problem, by the twentieth-century Hungarian mathematician John von Neumann. Von Neumann was a pioneer in fields ranging from quantum mechanics to economics and a designer of one of the earliest electronic computers. His design consisted of a central processing unit that communicates with a random access memory in which both programs and data can be stored. It remains the basic design of all standard computers today.

Tools for Thought: The History and Future of Mind-Expanding Technology
by Howard Rheingold
Published 14 May 2000

At the age of forty-two, he committed suicide, hounded cruelly by the same government he helped save. John von Neumann spoke five languages and knew dirty limericks in all of them. His colleagues, famous thinkers in their own right, all agreed that the operations of Johnny's mind were too deep and far too fast to be entirely human. He was one of history's most brilliant physicists, logicians, and mathematicians, as well as the software genius who invented the first electronic digital computer. John von Neumann was the center of the group who created the "stored program" concept that made truly powerful computers possible, and he specified a template that is still used to design almost all computers--the "von Neumann architecture."

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Turing, "Computing Machinery and intelligence," Mind, vol. 59, no. 236 (1950). [7] Ibid. [8] Hodges, Turing, 488. Chapter Four: Johnny Builds Bombs and Johnny Builds Brains [1] Steve J. Heims, John von Neumann and Norbert Wiener (Cambridge, Mass.: MIT Press, 1980), 371. [2] C. Blair, "Passing of a great Mind," Life,, February 25, 1957, 96. [3] Stanislaw Ulam, "John von Neumann, 1903-1957," Bulletin of the American Mathematical Society, vol. 64, (1958), 4. [4] Goldstine, The Computer, 182. [5] Daniel Bell, The coming of Post-Industrial Society (New York: Basic Books. 1973), 31

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They built a tic-tac-toe machine, but gave up on it as a moneymaking venture when an adviser assured them that P. T. Barnum's General Tom Thumb had sewn up the market for traveling novelties. Ironically, although Babbage's game-playing machines were commercial failures, his theoretical approach created a foundation for the future science of game theory, scooping even that twentieth-century genius John von Neumann by about a hundred years. It was Charley and Ada's attempt to develop an infallible system for betting on the ponies that brought Ada to the sorry pass of twice pawning her husband's family jewels, without his knowledge, to pay off blackmailing bookies. At one point, Ada and Babbage--never one to turn down a crazy scheme--used the existing small scale working model of the Difference Engine to perform the calculations required by their complex handicapping scheme.

The Fractalist
by Benoit Mandelbrot
Published 30 Oct 2012

Am I describing a nightmare? No, but I wish I was. Having left MIT, I was spending the year 1953–54 at IAS as the last postdoctoral fellow that von Neumann sponsored. That lecture came about one day during a chat with Oppie on the commuter train. John von Neumann Many pure mathematicians I knew well—like Szolem or Paul Lévy—were not attuned to other fields. John von Neumann (1903–57) was a man of many trades—all sought after—and a known master of each. He continually stunned the mathematical sciences by zeroing in on problems acknowledged as the most challenging of the day, and with his speed, intellectual flexibility, and unsurpassed power, he arrived at solutions that encountered instant acclaim.

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French Air Force Engineers Reserve Officer in Training, 1949–50 12. Growing Addiction to Classical Music, Voice, and Opera 13. Life as a Grad Student and Philips Electronics Employee, 1950–52 14. First Kepler Moment: The Zipf-Mandelbrot Distribution of Word Frequencies, 1951 15. Postdoctoral Grand Tour Begins at MIT, 1953 16. Princeton: John von Neumann’s Last Postdoc, 1953–54 17. Paris, 1954–55 18. Wooing and Marrying Aliette, 1955 19. In Geneva with Jean Piaget, Mark Kac, and Willy Feller, 1955–57 20. An Underachieving and Restless Maverick Pulls Up Shallow Roots, 1957–58 Part Three: My Life’s Fruitful Third Stage 21. At IBM Research Through Its Golden Age in the Sciences, 1958–93 22.

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To help the biologist Jacques Monod decide between biology and music, his influential father appointed a committee. It reported that as a biologist he would match Pasteur and as a musician he would match Mozart. He chose biology and won a Nobel Prize. More important for me was the great mathematician John von Neumann, to be introduced later. Around 1920, Hungary, his motherland, was under a cloud of uncertainty far worse than Poland in 1920 and France in 1945. His rich father wanted him to play it safe and study chemical engineering, but agreed to hire a young Budapest professor named Michael Fekete to determine whether “Janos” should also be allowed to seek a Ph.D. in mathematics.

The Rise of the Quants: Marschak, Sharpe, Black, Scholes and Merton
by Colin Read
Published 16 Jul 2012

Nevertheless, the necessity for action and for decision compels us as practical men to do our best to overlook this awkward fact and to behave exactly as we should if we had behind us a good Benthamite calculation of a series of prospective advantages and disadvantages, each multiplied by its appropriate probability waiting to be summed.1 The finance literature further clarified that there are calculable risks and that there are uncertainties that cannot be quantified. In the 1930s, John von Neumann set about producing a model of expected utility that permitted the inclusion of risk. Then, Leonard Jimmie Savage described how our individual perceptions affect the probability of uncertainty, and Kenneth Arrow was able to include these probabilities of uncertainty in a model that established the existence of equilibrium in a market for financial securities.

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These are the questions that the pricing analysts sought to resolve. 2 A Roadmap to Resolve the Big Questions In the first half of the twentieth century, Irving Fischer described why people save. John Maynard Keynes then showed how individuals adjust their portfolios between cash and less liquid assets, while Franco Modigliani demonstrated how all these personal financial decisions evolve over one’s lifetime. John von Neumann, Leonard Jimmie Savage, and Kenneth Arrow then incorporated uncertainty into the mix, and Harry Markowitz packaged the state of financial science into Modern Portfolio Theory. However, none of these great minds provided a satisfactory explanation for how the price of individual securities evolve over time.

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This page intentionally left blank 3 The Early Years Jacob Marschak was not at all unusual among the cadre of great minds that formed the discipline of finance in the first half of the twentieth century. Like the families of Milton Friedman, Franco Modigliani, Leonard Jimmie Savage, Kenneth Arrow, John von Neumann, and Harry Markowitz, Marschak’s family tree was originally rooted in the Jewish culture and derived from the intellectually stimulating region of Eastern, Central and Southern Europe at the beginning of the twentieth century. This region, comprising what is now Ukraine, Hungary, Poland, Romania, and parts of Italy, was under the influence of the AustroHungarian Empire in the late nineteenth and early twentieth centuries.

The Innovators: How a Group of Inventors, Hackers, Geniuses and Geeks Created the Digital Revolution
by Walter Isaacson
Published 6 Oct 2014

In addition to specific notes below, this section draws on William Aspray, John von Neumann and the Origins of Modern Computing (MIT, 1990); Nancy Stern, “John von Neumann’s Influence on Electronic Digital Computing, 1944–1946,” IEEE Annals of the History of Computing, Oct.–Dec. 1980; Stanislaw Ulam, “John von Neumann,” Bulletin of the American Mathematical Society, Feb. 1958; George Dyson, Turing’s Cathedral (Random House, 2012; locations refer to Kindle edition); Herman Goldstine, The Computer from Pascal to von Neumann (Princeton, 1972; locations refer to Kindle edition). 41. Dyson, Turing’s Cathedral, 41. 42. Nicholas Vonneumann, “John von Neumann as Seen by His Brother” (Privately printed, 1987), 22, excerpted as “John von Neumann: Formative Years,” IEEE Annals, Fall 1989. 43.

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, 161; Norman Macrae, John von Neumann (American Mathematical Society, 1992), 281. 58. Ritchie, The Computer Pioneers, 178. 59. Presper Eckert oral history, conducted by Nancy Stern, Charles Babbage Institute, Oct. 28, 1977; Dyson, Turing’s Cathedral, 1952. 60. John von Neumann, “First Draft of a Report on the EDVAC,” U.S. Army Ordnance Department and the University of Pennsylvania, June 30, 1945. The report is available at http://www.virtualtravelog.net/wp/wp-content/media/2003-08-TheFirstDraft.pdf. 61. Dyson, Turing’s Cathedral, 1957. See also Aspray, John von Neumann and the Origins of Modern Computing. 62.

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Each tube could handle approximately a thousand bits of data at one-hundredth the cost of using a circuit of vacuum tubes. The next-generation ENIAC successor, Eckert and Mauchly wrote in a memo in the summer of 1944, should have racks of these mercury delay line tubes to store both data and rudimentary programming information in digital form. JOHN VON NEUMANN At this point, one of the most interesting characters in the history of computing reenters the tale: John von Neumann, the Hungarian-born mathematician who was a mentor to Turing in Princeton and offered him a job as an assistant. An enthusiastic polymath and urbane intellectual, he made major contributions to statistics, set theory, geometry, quantum mechanics, nuclear weapons design, fluid dynamics, game theory, and computer architecture.

A Fiery Peace in a Cold War: Bernard Schriever and the Ultimate Weapon
by Neil Sheehan
Published 21 Sep 2009

BOOK IV STARTING A RACE Chapters 29–31: Schriever interviews; also interviews with Marina von Neumann Whitman and Françoise Ulam and their reminiscences at Hofstra University conference on von Neumann, May 29-June 3, 1988; interviews with Foster Evans and Jacob Wechsler; also Evans’s lecture, “Early Super Work,” published in the Los Alamos Historical Society’s 1996 Behind Tall Fences; interview with Nicholas Vonneuman and his unpublished biography of his brother, “The Legacy of John von Neumann”; John von Neumann Papers in the Manuscript Division of the Library of Congress; Rhodes’s The Making of the Atomic Bomb and Dark Sun; Herman Goldstine’s 1972 The Computer from Pascal to von Neumann; Stanislaw Ulam’s 1976 Adventures of a Mathematician; William Poundstone’s 1992 Prisoner’s Dilemma; Norman Macrae’s 1992 John von Neumann; and Kati Marton’s 2006 The Great Escape: Nine Jews Who Fled Hitler and Changed the World. Chapter 32: Interviews with General Schriever, Col.

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Both Schriever and Gardner knew Ramo was indispensable for assembling the array of engineering and scientific talent needed to overcome the technological obstacles. COURTESY OF GENERAL BERNARD SCHRIEVER Cold War forgiveness: John von Neumann (right), a Jewish exile from Hitler’s Europe, conferring with Wernher von Braun, a former SS officer, Nazi Party member, and the führer’s V-2 missile man, during a visit to the Army’s Redstone Arsenal in Alabama. A mathematician and mathematical physicist with a mind second only to Albert Einstein’s, von Neumann headed the scientific advisory committee for the ICBM and lent the project his prestige. JOHN VON NEUMANN PAPERS, MANUSCRIPT DIVISION, LIBRARY OF CONGRESS The heartlessness of an early end: Seven months after immensely impressing Eisenhower at the July 28, 1955, White House briefing on the missile project, “Johnny” von Neumann had been driven to a wheelchair by the ravages of his cancer.

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Chapters 72–77: Neufeld, Ballistic Missiles in the United States Air Force, 1945–1960; Heppenheimer’s Countdown; Zubok and Pleshakov, Inside the Kremlin’s Cold War; Taubman’s Khrushchev; Robert Kennedy’s 1968 Thirteen Days: A Memoir of the Cuban Missile Crisis; Fred Kaplan’s 1983 The Wizards of Armageddon; Anatoly Dobrynin’s 1995 In Confidence; Aleksandr Fursenko and Timothy Naftali’s 1997 One Hell of a Gamble: Khrushchev, Castro, and Kennedy, 1958–1964; The Kennedy Tapes: Inside the White House During the Cuban Missile Crisis, Ernest May and Philip Zelikow’s 1997 editing of the tapes of the White House meetings during the crisis; Max Frankel’s 2004 High Noon in the Cold War: Kennedy, Khrushchev and the Cuban Missile Crisis; Fursenko and Naftali’s 2006 Khrushchev’s Cold War; Michael Dobbs’s 2008 One Minute to Midnight: Kennedy, Khrushchev, and Castro on the Brink of Nuclear War; the official SAC history, The Development of Strategic Air Command; Wynn’s RAF Nuclear Deterrent Forces. Chapter 78: Leonid Brezhnev’s cynical remark to his brother is recounted in the 1995 memoir by his niece, Luba Brezhneva’s The World I Left Behind: Pieces of a Past. EPILOGUE THE SCHRIEVER LUCK Chapter 79: The John von Neumann Papers, Manuscript Division of the Library of Congress; Col. Vincent Ford’s memoir; Macrae’s John von Neumann; Pound-stone’s Prisoner’s Dilemma. Chapter 80: Schriever interviews; Col. Vincent Ford’s memoir; interview with Trevor Gardner, Jr. Chapter 81: November 1, 1968, historical monograph on Army Ballistic Missile Agency; Edward Hall interview.

The Dream Machine: J.C.R. Licklider and the Revolution That Made Computing Personal
by M. Mitchell Waldrop
Published 14 Apr 2001

.: Research Laboratory for Electronics, MIT, 1966), 12. 4. Steve Helms, John von Neumann and Norbert Wiener: From Mathematics to the Technologies of Life and Death (Cambridge, Mass.: MIT Press, 1980), 206. 5. Norbert Wiener, Cybernetics, or Control and CommunicatiOn in the Animal and the Machine, 2d ed. (Cambndge, Mass.: MIT Press, 1961),23. 6. Heims, Von Neumann/Wiener, 189. 7. Norbert Wiener, "A Scientist Rebels," Atlantic Monthly, January 1947, and Bulletin of the Atomic Sci- entlSts, January 1947. 8. Helms, Von Neumann/Wiener, 334-35. 9. John von Neumann and Oskar Morgenstern, Theory of Games and Economic BehaviOr (Princeton, N.J.: Pnnceton University Press, 1944). 10.

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Winston, OH 196. 484 BIBLIOGRAPHY BOOKS AND ARTICLES Again, the written matenals listed below are only a tiny fraction of what's avaIlable on the history of computing, but they were partICularly helpful to me in telling the story of LICk and the ARPA com- munity. Aspray, Wilham. "The SCientific ConceptualIzation of Information: A Survey." Annals of the H15tory of Computing 7 (1985). -. "John von Neumann's Contributions to Computing and Computer Science." Annals of the H15- tory of ComputIng 11, no. 3 (1989). -.John von Neumann and the Orzglns of Modern ComputIng. Cambridge, Mass.: MIT Press, 1990. -. "The Intel 4004 MIcroprocessor: What Constituted Invention?" IEEE Annals of the H15tory of Computing 19, no. 3 (1997). Augarten, Stan. BIt by BIt: An Illustrated H15tory of Computers.

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Goldstine was awestruck. Before his current incarnation-he was liaison officer for the army's computing substation at the University of Pennsylvania's Moore School of Engineering-Goldstine had been a Ph.D. mathematics instructor at the University of Michigan. So he already knew the legends. At age forty, John von Neumann (pronounced fon NaY-man) held a place in mathematics that could be compared only to that of Albert Einstein in physics. In the single year of 1927, for example, while still a mere instructor at the University of Berlin, von Neumann had put the newly emerging theory of quantum mechanics on a rigorous mathematical footing; established new links between formal logical systems and the foundations of mathematics; and cre- ated a whole new branch of mathematics known as game theory, a way of ana- lyzing how people make decisions when they are competing with each other (among other things, this field gave us the term "zero-sum game").

Possible Minds: Twenty-Five Ways of Looking at AI
by John Brockman
Published 19 Feb 2019

The history of computing can be divided into an Old Testament and a New Testament: before and after electronic digital computers and the codes they spawned proliferated across the Earth. The Old Testament prophets, who delivered the underlying logic, included Thomas Hobbes and Gottfried Wilhelm Leibniz. The New Testament prophets included Alan Turing, John von Neumann, Claude Shannon, and Norbert Wiener. They delivered the machines. Alan Turing wondered what it would take for machines to become intelligent. John von Neumann wondered what it would take for machines to self-reproduce. Claude Shannon wondered what it would take for machines to communicate reliably, no matter how much noise intervened. Norbert Wiener wondered how long it would take for machines to assume control.

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Since the original of The Human Use of Human Beings is now out of print, lost to us is Wiener’s cri de coeur, more relevant today than when he wrote it sixty-eight years ago: “We must cease to kiss the whip that lashes us.” MIND, THINKING, INTELLIGENCE Among the reasons we don’t hear much about cybernetics today, two are central: First, although The Human Use of Human Beings was considered an important book in its time, it ran counter to the aspirations of many of Wiener’s colleagues, including John von Neumann and Claude Shannon, who were interested in the commercialization of the new technologies. Second, computer pioneer John McCarthy disliked Wiener and refused to use Wiener’s term “Cybernetics.” McCarthy, in turn, coined the term “artificial intelligence” and became a founding father of that field.

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In my own work with experimentalists on building quantum computers, I typically find that some of the technological steps I expect to be easy turn out to be impossible, whereas some of the tasks I imagine to be impossible turn out to be easy. You don’t know until you try. In the 1950s, partly inspired by conversations with Wiener, John von Neumann introduced the notion of the “technological singularity.” Technologies tend to improve exponentially, doubling in power or sensitivity over some interval of time. (For example, since 1950, computer technologies have been doubling in power roughly every two years, an observation enshrined as Moore’s Law.)

The Perfect Bet: How Science and Math Are Taking the Luck Out of Gambling
by Adam Kucharski
Published 23 Feb 2016

Review of Economic Studies 70 (2003): 395–415. 147Von Neumann completed his solution: Details of the dispute were given in: Kjedldsen, T. H. “John von Neumann’s Conception of the Minimax Theorem: A Journey Through Different Mathematical Contexts.” Archive for History of Exact Science 56 (2001). 149While earning his master’s degree in 2003: Follek, Robert. “Soar-Bot: A Rule-Based System for Playing Poker” (MSc diss., School of Computer Science and Information Systems, Pace University, 2003). 150Led by David Hilbert: O’Connor, J. J., and E. F. Robertson. “Biography of John von Neumann.” JOC/EFR, October 2003. http://www-history.mcs.st-and.ac.uk/Biographies/Von_Neumann.html. 150some inconsistencies in the US Constitution: “Kurt Gödel.”

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Faced with having to calculate a vast set of possibilities—the sort of monotonous work he usually tried to avoid—Ulam realized it might be quicker just to lay out the cards several times and watch what happened. If he repeated the experiment enough times, he would end up with a good idea of the answer without doing a single calculation. Wondering whether the same technique could also help with the neutron problem, Ulam took the idea to one of his closest colleagues, a mathematician by the name of John von Neumann. The two had known each other for over a decade. It was von Neumann who’d suggested Ulam leave Poland for America in the 1930s; he’d also been the one who invited Ulam to join Los Alamos in 1943. They made quite the pair, portly von Neumann in his immaculate suits—jacket always on—and Ulam with his absent-minded fashion sense and dazzling green eyes.

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Substitute talking and silence for advertising and cutting promotions, and it is the same problem the advertising firms faced. Nash received his PhD in 1950, for a twenty-seven-page thesis describing how his equilibrium can sometimes thwart seemingly beneficial outcomes. But Nash wasn’t the first person to take a mathematical hammer to the problem of competitive games. History has given that accolade to John von Neumann. Although later known for his time at Los Alamos and Princeton, in 1926 von Neumann was a young lecturer at the University of Berlin. In fact, he was the youngest in its history. Despite his prodigious academic record, however, there were still some things he wasn’t very good at. One of them was poker.

Thinking in Bets
by Annie Duke
Published 6 Feb 2018

The information about von Neumann, in addition to the sources mentioned in the section (which are cited in the Selected Bibliography and Recommendations for Further Reading), are from the following sources: Boston Public Library, “100 Most Influential Books of the Century,” posted on TheGreatestBooks.org; Tim Hartford, “A Beautiful Theory,” Forbes, December 10, 2006; Institute for Advanced Study, “John von Neumann’s Legacy,” IAS.edu; Alexander Leitch, “von Neumann, John,” A Princeton Companion (1978); Robert Leonard, “From Parlor Games to Social Science: von Neumann, Morgenstern, and the Creation of Game Theory 1928–1944,” Journal of Economic Literature (1995). The quotes from reviews that greeted Theory of Games are from Harold W. Kuhn’s introduction to the sixtieth anniversary edition. The influences behind the title character in Dr. Strangelove either are alluringly vague or differ based on who’s telling (or speculating). John von Neumann shared a number of physical characteristcs with the character and is usually cited as an influence.

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That makes poker a great place to find innovative approaches to overcoming this struggle. And the value of poker in understanding decision-making has been recognized in academics for a long time. Dr. Strangelove It’s hard for a scientist to become a household name. So it shouldn’t be surprising that for most people the name John von Neumann doesn’t ring a bell. That’s a shame because von Neumann is a hero of mine, and should be to anyone committed to making better decisions. His contributions to the science of decision-making were immense, and yet they were just a footnote in the short life of one of the greatest minds in the history of scientific thought.

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Strangelove: a heavily accented, crumpled, wheelchair-bound genius whose strategy of relying on mutually assured destruction goes awry when an insane general sends a single bomber on an unauthorized mission that could trigger the automated firing of all American and Soviet nuclear weapons. In addition to everything else he accomplished, John von Neumann is also the father of game theory. After finishing his day job on the Manhattan Project, he collaborated with Oskar Morgenstern to publish Theory of Games and Economic Behavior in 1944. The Boston Public Library’s list of the “100 Most Influential Books of the Century” includes Theory of Games.

How Not to Network a Nation: The Uneasy History of the Soviet Internet (Information Policy)
by Benjamin Peters
Published 2 Jun 2016

Wiener, Cybernetics, 1–25, 155–168. 8. Ibid., 16. 9. Dupuy, Mechanization of the Mind. See also John von Neumann, The Computer and the Brain, 2nd ed. (New Haven: Yale University Press, [1958] 2000). 10. Quoted in Claus Pias, “Analog, Digital, and the Cybernetic Illusion,” Kybernetes 34 (3–4) (2005): 544. 11. Claus Pias, ed., Cybernetics-Kybernetik 2: The Macy-Conferences 1946–1953 (Berlin: Diaphanes, 2004). 12. Steve J. Heims, The Cybernetics Group (Cambridge: MIT Press, 1991). 13. Ibid., 52–53, 207. 14. William Aspray, John von Neumann and the Origins of Modern Computing (Cambridge: MIT Press, 1990). 15. David Lipset, Gregory Bateson: The Legacy of a Scientist (New York: Prentice Hall, 1980).

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Borrowing from the language of Hannah Arendt, it recasts the Soviet network experience in light of other national network projects in the latter half of the twentieth century, suggesting the ways that the Soviet experience may appear uncomfortably close to our modern network situation. A few other summary observations for scholar and general-interest reader are offered in close. 1 A Global History of Cybernetics I am thinking about something much more important than bombs. I am thinking about computers. —John von Neumann, 1946 Cybernetics nursed early national computer network projects on both sides of the cold war. Cybernetics was a postwar systems science concerned with communication and control—and although its significance has been well documented in the history of science and technology, its implications as a carrier of early ideas about and language for computational communication have been largely neglected by communication and media scholars.1 This chapter discusses how cybernetics became global early in the cold war, coalescing first in postwar America before diffusing to other parts of the world, especially Soviet Union after Stalin’s death in 1953, as well as how Soviet cybernetics shaped the scientific regime for governing economics that eventually led to the nationwide network projects imagined in the late 1950s and early 1960s.

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Foundation in New York City. The Macy Conferences, as they were informally known, staked out a spacious interdisciplinary purview for cybernetic research.11 In addition to McCulloch, who directed the conferences, a few noted participants included Wiener himself, the mathematician and game theorist John von Neumann, leading anthropologist Margaret Mead and her then husband Gregory Bateson, founding information theorist and engineer Claude Shannon, sociologist-statistician and communication theorist Paul Lazarsfeld, psychologist and computer scientist J.C.R. Licklider, as well as influential psychiatrists, psychoanalysts, and philosophers such as Kurt Lewin, F.S.C.

Computer: A History of the Information Machine
by Martin Campbell-Kelly and Nathan Ensmenger
Published 29 Jul 2013

Licklider was a consummate political operator who motivated a generation of computer scientists and obtained government funding for them to work in the fields of human-computer interaction and networked computing. COURTESY OF MIT MUSEUM. Working at the Institute for Advanced Study, Princeton, Herman Goldstine and John von Neumann introduced the “flow diagram” (above) as a way of managing the complexity of programs and communicating them to others. COURTESY OF INSTITUTE FOR ADVANCED STUDY, PRINCETON: Herman H. Goldstine and John von Neumann, Planning and Coding of Problems for an Electronic Computing Instrument, Part II, Volume 2 (1948), p. 28. Programming languages, such as FORTRAN, COBOL, and BASIC, improved the productivity of programmers and enabled nonexperts to write programs.

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Other electrical manufacturers and business-machine companies, including IBM, also turned to this enterprise. The computer makers found a ready market in government agencies, insurance companies, and large manufacturers. The basic functional specifications of the computer were set out in a report written by John von Neumann in 1945, and these specifications are still largely followed today. However, decades of continuous innovation have followed the original conception. These innovations are of two types. One is the improvement in components, leading to faster processing speed, greater information-storage capacity, improved price/performance, better reliability, less required maintenance, and the like: today’s computers are literally millions of times better than the first computers on almost all measures of this kind.

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It was typical of Turing to be able to express a complex mathematical argument in terms that a nonmathematician could understand. The computability of mathematical functions would later become a cornerstone of computer science theory. Turing’s growing reputation earned him a research studentship at Princeton University to study under Alonzo Church in 1937. There he encountered John von Neumann, a founding professor of the Institute for Advanced Study at Princeton, who a few years later would play a pivotal role in the invention of the modern computer. Von Neumann was deeply interested in Turing’s work and invited him to stay on at the Institute. Turing, however, decided to return to Britain.

Fancy Bear Goes Phishing: The Dark History of the Information Age, in Five Extraordinary Hacks
by Scott J. Shapiro

Born in Budapest: Stanislaw Ulam, “John von Neumann, 1903–1957,” Bulletin of the American Mathematical Society 64, no. 3, pt. 2 (May 1958): 1; George Dyson, Turing’s Cathedral: The Origins of the Digital Universe (New York: Vintage, 2012), chap. 4; Herman Goldstine, The Computer: From Pascal to von Neumann (Princeton, NJ: Princeton University Press, 1980). In 1913, Emperor Franz Joseph ennobled John’s family for his father’s service to the Hapsburgs, adding the honorific Margittai to the family name. (Jonas Neumann de Margittai later Germanized his name to become John von Neumann.) both degrees simultaneously: Ulam, “John von Neumann,” 2.

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(it weighed thirty tons): Steven Levy, “A Brief History of the ENIAC,” Smithsonian Magazine, November 2013, https://www.smithsonianmag.com/history/the-brief-history-of-the-eniac-computer-3889120/. Levy claims that the ENIAC had 18,000 vacuum tubes, the figure used in the text, but other estimates range from 17,468 to 19,000. to study natural systems: John von Neumann, Theory of Self-Reproducing Automata, edited and completed by Arthur W. Burks (Champaign: University of Illinois Press, 1966), 64–73. Von Neumann is also credited: John von Neumann, “A First Draft of a Report on the EDVAC,” IEEE Annals of the History of Computing 15, no. 4 (1993). The credit to von Neumann has been much debated. See Dyson, Turing’s Cathedral, 77–80; B. J. Copeland and Giovanni Sommaruga, “Did Zuse Anticipate Turing and von Neumann?

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Descartes was summoned: “Go Forth and Replicate,” Scientific American 285, no. 2 (August 2001): 34–43. In 1949, von Neumann set out: Von Neumann completed two studies on self-replication. See “The General and Logical Theory of Automata,” in John von Neumann Collected Works, 5:288–328, and “Probabilistic Logics and the Synthesis of Reliable Organisms from Unreliable Components,” in John von Neumann Collected Works, 5:329–378. In 1957, von Neumann passed away, leaving two manuscripts on self-replicating automata unpublished: “Theory and Organization of Complicated Automata,” five lectures delivered at the University of Illinois, December 1949, and “The Theory of Automata: Construction, Reproduction, Homogeneity,” started in 1952 and worked on for a year.

The Information: A History, a Theory, a Flood
by James Gleick
Published 1 Mar 2011

♦ “CONTRARY TO APPEARANCES, SUCH A PROPOSITION”: Ibid., 151 n15. ♦ “AMAZING FACT”—“THAT OUR LOGICAL INTUITIONS”: Kurt Gödel, “Russell’s Mathematical Logic” (1944), 124. ♦ “A SUDDEN THUNDERBOLT FROM THE BLUEST OF SKIES”: Douglas R. Hofstadter, I Am a Strange Loop, 166. ♦ “THE IMPORTANT POINT”: John von Neumann, “Tribute to Dr. Gödel” (1951), quoted in Steve J. Heims, John von Neumann and Norbert Weiner (Cambridge, Mass.: MIT Press, 1980), 133. ♦ “IT MADE ME GLAD”: Russell to Leon Henkin, 1 April 1963. ♦ “MATHEMATICS CANNOT BE INCOMPLETE”: Ludwig Wittgenstein, Remarks on the Foundations of Mathematics (Cambridge, Mass.: MIT Press, 1967), 158

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Gödel’s first public mention of his discovery, on the third and last day of a philosophical conference in Königsberg in 1930, drew no response; only one person seems to have heard him at all, a Hungarian named Neumann János. This young mathematician was in the process of moving to the United States, where he would soon and for the rest of his life be called John von Neumann. He understood Gödel’s import at once; it stunned him, but he studied it and was persuaded. No sooner did Gödel’s paper appear than von Neumann was presenting it to the mathematics colloquium at Princeton. Incompleteness was real. It meant that mathematics could never be proved free of self-contradiction.

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.♦ Gödel’s retort took care of them both. “Russell evidently misinterprets my result; however, he does so in a very interesting manner,” he wrote. “In contradistinction Wittgenstein … advances a completely trivial and uninteresting misinterpretation.”♦ In 1933 the newly formed Institute for Advanced Study, with John von Neumann and Albert Einstein among its first faculty members, invited Gödel to Princeton for the year. He crossed the Atlantic several more times that decade, as fascism rose and the brief glory of Vienna began to fade. Gödel, ignorant of politics and naïve about history, suffered depressive breakdowns and bouts of hypochondria that forced him into sanatoria.

Theory of Games and Economic Behavior: 60th Anniversary Commemorative Edition (Princeton Classic Editions)
by John von Neumann and Oskar Morgenstern
Published 19 Mar 2007

ROSENBLITH Heads I Win, and Tails, You Lose, BY PAUL SAMUELSON Big D, BY PAUL CRUME Mathematics of Games and Economics, BY E. ROWLAND Theory of Games, BY CLAUDE CHEVALLEY Mathematical Theory of Poker Is Applied to Business Problems, BY WILL LISSNER A Theory of Strategy, BY JOHN MCDONALD The Collaboration between Oskar Morgenstern and John von Neumann on the Theory of Games, BY OSKAR MORGENSTERN Index Credits Introduction HAROLD W. KUHN Although John von Neumann was without doubt “the father of game theory,” the birth took place after a number of miscarriages. From an isolated and amazing minimax solution of a zero-sum two-person game in 1713 [1] to sporadic considerations by E. Zermelo [2], E.

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It was originated by one of the chief participants in the development of the bomb, the young and already great contemporary mathematician, John von Neumann, whose work in games was given a preliminary exploration in an essay on poker in Fortune (March, 1948). This story began more than a year ago when the author innocently looked into the game of poker with the idea of providing Fortune’s readers with some diverting comments on the national indoor game of strategy. When the first part of the story was published (March, 1948), however, it seemed that we had the bear by the tail. Madiematician John von Neumann, unknown to the poker-playing fraternity, had got there first and really made something out of it.

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Theory of Games and Economic Behavior Theory of Games and Economic Behavior John von Neumann and Oskar Morgenstern With an introduction by Harold W. Kuhn and an afterword by Ariel Rubinstein SIXTIETH-ANNIVERSARY EDITION Copyright © 1944 by Princeton University Press Copyright © renewed 1972 Princeton University Press Sixtieth-Anniversary Edition copyright © 2004 Princeton University Press Published by Princeton University Press, 41 William Street, Princeton, New Jersey 08540 In the United Kingdom: Princeton University Press, 3 Market Place, Woodstock, Oxfordshire 0X20 1SY All Rights Reserved Second Edition, 1947 Third Edition, 1953 Fourth printing, and first paperback printing, of Sixtieth Anniversary Edition, 2007 Paperback ISBN-13: 978-0-691-13061-3 Paperback ISBN-10: 0-691-13061-2 Library of Congress Control Number 2004100346 ISBN-13: 978-0-691-11993-9 ISBN-10: 0-691-11993-7 British Library Cataloging-in-Publication Data is available This book has been composed in Baskerville MT, Monotype Century, and Helvetica Condensed Printed on acid-free paper. ∞ press.princeton.edu Primed in the United States of America 9 10 Contents Introduction, BY HAROLD W.

Rise of the Machines: A Cybernetic History
by Thomas Rid
Published 27 Jun 2016

Woirol, The Technological Unemployment and Structural Unemployment Debates (Westport, CT: Greenwood, 1996), 78. 75.David Fouquet, “Automation Held Threat to US Value Code,” Washington Post, May 12, 1964, A24. 76.Diebold, Beyond Automation, 10. 77.Fouquet, “Automation Held Threat.” 78.Diebold, Automation, 170. 79.Diebold, Beyond Automation, 206. 80.Kahn left Rand before publishing The Year 2000. 81.Herman Kahn and Anthony Wiener, The Year 2000: A Framework for Speculation on the Next Thirty-Three Years (London: Macmillan, 1967), 350. 4. ORGANISMS 1.Pesi Masani, Norbert Wiener, 1894–1964 (Basel: Burkhäuser, 1990), 225. 2.Paul E. Ceruzzi, A History of Modern Computing (Cambridge, MA: MIT Press, 2003), 21. 3.Masani, Norbert Wiener, 184. 4.John von Neumann to Norbert Wiener, November 29, 1946, in ibid., 243. 5.John von Neumann, Theory of Self-Reproducing Automata, ed. Arthur W. Burks (Urbana: University of Illinois Press, 1966), fifth lecture, 78. 6.Ibid., 79. 7.Ibid., 86. 8.Ibid., 87. 9.Edward F. Moore, “Artificial Living Plants,” Scientific American, October 1956, 118–26. 10.Ibid., 118. 11.Ibid., 119. 12.Ibid., 121. 13.Ibid., 122. 14.Ibid., 126. 15.David R.

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Licklider, “Topics for Discussion at the Forthcoming Meeting,” Memorandum for Affiliates of the Intergalactic Computer Network, Advanced Research Projects Agency, Washington, DC, April 25, 1963. 86.Dan van der Vat, “Jack Good,” Guardian, April 29, 2009. 87.Irving J. Good, “Speculations concerning the First Ultraintelligent Machine,” Advances in Computers 6 (1965): 31–88. 88.John von Neumann discussed the effects of ever-accelerating technological progress with colleagues. In one such discussion, he allegedly said that humankind is approaching an essential “singularity” after which human affairs will be altered forever. See the recollection of Stanislaw Ulam: “Tribute to John von Neumann,” Bulletin of the American Mathematical Society 64, no. 3 (1958): 5. 89.Vernor Vinge, “First Word,” Omni 5, no. 1 (January 1983): 10. For Vinge’s weak scientific output, see his Google Scholar profile, http://bit.ly/vinge-scholar+. 90.Jürgen Kraus, “Selbstreproduktion bei Programmen” (master’s thesis, Universität Dortmund, Abteilung Informatik, 1980). 91.Ibid., 2. 92.Ibid., 154. 93.Ibid., 161. 94.Ibid., 160. 95.Ronald R.

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And there was no reason why the theory could not be applied to all complex systems. Many leading minds in engineering, mathematics, biology, and psychology, but also sociology, philosophy, anthropology, and political science, would initially be drawn to the new thinking of adaptive systems. The best-known early cyberneticists were the mathematician John von Neumann, a fellow polyglot, computer pioneer, and prominent professor at the Institute for Advanced Study in Princeton still in his early forties, nine years younger than Wiener; the American neurophysiologist and neural networks pioneer Warren McCulloch; the Austrian American physicist Heinz von Foerster; and the Mexican physician Arturo Rosenblueth, one of Wiener’s closest friends and collaborators.

On the Edge: The Art of Risking Everything
by Nate Silver
Published 12 Aug 2024

GO TO NOTE REFERENCE IN TEXT von Neumann’s estimated 190: “IQ Estimates of Geniuses,” Jan Bryxí (blog), janbryxi.com/iq-john-von-neumann-albert-einstein-mark-zuckerberg-elon-musk-stephen-hawking-kevin-mitnick-cardinal-richelieu-warren-buffett-george-soros-steve-jobs-isaac-new. GO TO NOTE REFERENCE IN TEXT “transformations are not”: John von Neumann, “Can We Survive Technology?,” sseh.uchicago.edu/doc/von_Neumann_1955.pdf. GO TO NOTE REFERENCE IN TEXT at Bikini Atoll: “John von Neumann,” Von Neumann and the Development of Game Theory, cs.stanford.edu/people/eroberts/courses/soco/projects/1998-99/game-theory/neumann.html.

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“CDC Releases First Estimates of the Number of Adults Living with ASD,” Centers for Disease Control and Prevention, April 27, 2020, cdc.gov/ncbddd/autism/features/adults-living-with-autism-spectrum-disorder.html. GO TO NOTE REFERENCE IN TEXT based on post-facto diagnoses: Michael Fitzgerald, “John von Neumann was on the autism spectrum,” ResearchGate, researchgate.net/publication/369141516_John_von_Neumann_was_on_the_autism_spectrum. GO TO NOTE REFERENCE IN TEXT classification scheme DSM-5: “What Is Asperger Syndrome?,” Autism Speaks, autismspeaks.org/types-autism-what-asperger-syndrome. GO TO NOTE REFERENCE IN TEXT moderately correlated personality: Lars-Olov Lundqvist and Helen Lindner, “Is the Autism-Spectrum Quotient a Valid Measure of Traits Associated with the Autism Spectrum?

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GO TO NOTE REFERENCE IN TEXT three times the population: “Largest Cities in Japan: Population from 1890,” Demographia, demographia.com/db-jp-city1940.htm. GO TO NOTE REFERENCE IN TEXT bomb’s horrifying effects: Bollard, Economists at War, 229. GO TO NOTE REFERENCE IN TEXT considered so valuable: Ashutosh Jogalekar, “What John von Neumann Really Did at Los Alamos,” 3 Quarks Daily, October 26, 2020, 3quarksdaily.com/3quarksdaily/2020/10/what-john-von-neumann-really-did-at-los-alamos.html. GO TO NOTE REFERENCE IN TEXT of nuclear deterrence: Andrew Brown and Lorna Arnold, “The Quirks of Nuclear Deterrence,” International Relations 24, no. 3 (September 2010): 293–312, doi.org/10.1177/0047117810377278.

Turing's Vision: The Birth of Computer Science
by Chris Bernhardt
Published 12 May 2016

We conclude by giving Turing’s proof that computable numbers are not effectively enumerable. Chapter 9 The final chapter describes both what happened to Turing and the computer in the years after his paper was published. It begins with Turing going to Princeton to obtain his Ph.D. under Church. This is where he gets to know John von Neumann. It then describes Turing’s move back to England and his work during the Second World War on code breaking. After this, we briefly look at how the modern computer came into existence during the forties. The procession from sophisticated calculator, to universal computer, to stored-program universal computer is outlined.

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McCulloch and Pitts realized that this was a simplified model of how brains actually worked, but studied neural nets to see how logic could be handled by them. Since their nets had basic features in common with neurons and the human brain, their work, they hoped, would shed some light on logical reasoning in people. Their paper caught the attention of both John von Neumann and Norbert Wiener. Both were very impressed. Wiener, the famous American mathematician and philosopher, saw the power of feedback loops. He realized that they were widely applicable and used this idea to develop the theory of cybernetics.1 Cybernetics naturally led to the idea of machines that could learn and, in turn, led to the birth of artificial intelligence.

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What is surprising is that we can design a Turing machine to simulate a modern computer, showing that Turing machines are equivalent in computing power to modern computers. We will sketch how this is done. The first step is to get a concrete description of the modern computer. Von Neumann Architecture Later we will talk more about John von Neumann, but it is important to know a few facts before we proceed. The First Draft of a Report on the EDVAC is probably the most important paper on the design of modern computers. It was written in 1945, as the first electronic computers were being built. It described the basic outline of how a computer should be designed, incorporating what had been learned from the design of earlier machines.

In Our Own Image: Savior or Destroyer? The History and Future of Artificial Intelligence
by George Zarkadakis
Published 7 Mar 2016

When Fremont-Smith became the Medical Director of the Macy Foundation, he set up a series of annual conferences that expanded the Man–Machine Project. Hosted by the Macy Foundation, these became known as the ‘Macy Conferences on Cybernetics’. Cybernetics as a field grew out of these interdisciplinary meetings, held from 1946 until 1953, which brought together a number of notable post-war intellectuals, including Norbert Wiener, John von Neumann, Warren McCulloch, Claude Shannon, Heinz von Foerster and W. Ross Ashby. From its original focus on machines and animals, cybernetics quickly broadened in scope to encompass the workings of the mind (e.g. in the work of Bateson and Ashby) as well as social systems (e.g. Stafford Beer’s management cybernetics), thus rediscovering Plato’s original focus on the control relations in society.

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I will return to the very interesting connection of cybernetics, Plato and global governance later in the book. For now, I want to focus on four individuals who took part in the Macy Conferences, and whose work laid the foundations for Artificial Intelligence: Norbert Wiener, Claude Shannon, Warren McCulloch and John von Neumann. We have already met the first two. Norbert Wiener was the grand visionary of cybernetics. Inspired by mechanical control systems, such as artillery targeting and servomechanisms, as well as Claude Shannon’s mathematical theory of communication and information, he articulated the theory of cybernetics in his landmark book, Cybernetics, of 1948.4 Godfather number two, Claude Shannon, was the genius who gave us information theory.

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Finally, there comes a tipping point, where global change happens and the artificial, agent-based system becomes intelligent, in a similar fashion to the neuron-based human brain.6 The fourth cybernetician godfather of Artificial Intelligence, who also took part in the Macy Conferences, was the legendary Hungarian-American mathematician John von Neumann (1903–1957). He was the modern equivalent of Gottfried Leibniz, a polymath who made fundamental contributions to several sciences including mathematics, computing, cybernetics, logic, economics and quantum physics – to name but a few! His last work, before his untimely death at the age of fifty-three, was an unfinished manuscript entitled ‘The Computer and the Brain’, which shows how deeply interested von Neumann had become in the nascent science of Artificial Intelligence.7 During the time he participated in the Macy Conferences, von Neumann expanded on his theory of self-replicating automata.

A Mind at Play: How Claude Shannon Invented the Information Age
by Jimmy Soni and Rob Goodman
Published 17 Jul 2017

That leap, as Walter Isaacson put it, “became the basic concept underlying all digital computers.” It was Shannon’s first great feat of abstraction. He was only twenty-one. A career that launched with “possibly the most important, and also the most famous, master’s thesis of the century” brought him into contact and collaboration with thinkers like Bush, Alan Turing, and John von Neumann: all, like Shannon, founders of our era. It brought him into often-reluctant cooperation with the American defense establishment and into arcane work on cryptography, computer-controlled gunnery, and the encrypted transatlantic phone line that connected Roosevelt and Churchill in the midst of world war.

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The last thing the functionary wants is to return home; for he has no home with any intrinsic value. And this: on his upward-spiraling road he encounters as eager fellow-pilgrims his functionary colleagues, from places and families he has scarcely heard of and surely hopes never to have to see. Who were Shannon’s new traveling companions in Princeton? Where did they come from? There was John von Neumann, a Jewish-Hungarian prodigy, who by the age of six could crack jokes in ancient Greek or give you the quotient of 93,726,784 divided by 64,733,647 (or any other eight-digit numbers) without pencil and paper. He was the kind of student who once literally brought a tutor to tears of awe, who spent a college lecture on “unsolved problems in mathematics” doodling the solutions in his notebook.

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And so hundreds of the world’s top mathematical minds put their personal research aside, swallowed various degrees of pride, and gathered at the outposts of Los Alamos, Bletchley Park, Tuxedo Park—and Bell Labs, where wartime contracts brought a fresh-from-fellowship Claude Shannon into contact with the latest in military technology and thought. * * * For men like Vannevar Bush, James Conant, John von Neumann, J. Robert Oppenheimer, and others, the war lifted the veil on their work. They were invited into the councils of power, asked to advise presidents, and tasked with steering millions of dollars of men and matériel. Many of these men had made modest names for themselves in the worlds of science and engineering, but in the arena of wartime politics, their work would receive wide public recognition.

Licence to be Bad
by Jonathan Aldred
Published 5 Jun 2019

Moggridge (Royal Economic Society), vol. 10, 173–4. 6 Letter to Morgenstern, 8 October 1947, explaining von Neumann’s refusal to review Paul Samuelson’s Foundations of Economic Analysis. Quoted in Morgenstern (1976), ‘The Collaboration between Oskar Morgenstern and John von Neumann on the Theory of Games’, Journal of Economic Literature, 14 (3), 810. 7 Morgenstern’s diary, April–May 1942. Quoted in Leonard, Robert J. (1995), ‘From Parlor Games to Social Science: Von Neumann, Morgenstern, and the Creation of Game Theory 1928–1944’, Journal of Economic Literature, 33 (2), 730. 8 Nasar, 94. 9 Ibid. 10 Quoted in Heims, S. (1980). John Von Neumann and Norbert Wiener: From Mathematics to the Technologies of Life and Death (Cambridge: MIT Press), 327. 11 Quoted in Poundstone, W. (1992), Prisoner’s Dilemma (New York: Anchor Books), 168. 12 Quoted in Ferguson, N. (2017), The Square and the Tower: Networks and Power, from the Freemasons to Facebook (London: Allen Lane), 260. 13 Hertzberg, H. (2001), ‘Comment: Tuesday, and After’, New Yorker, 24 September 2001, 27.

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The intellectual framework for all this nuclear strategizing was game theory. It was the perfect tool for the RAND style of military thinking. Game theory assumes that humans are purely selfish and hyper-rational, in possession not only of all the information relevant to making decisions but of perfect and exhaustive powers of computation and logical reasoning. John von Neumann is usually seen as the father of game theory. Nash may have been a genius, but he was almost a mathematical minnow in comparison to von Neumann. DR STRANGELOVE AND THE KAISER’S GRANDSON The 1964 film Dr Strangelove satirized the Cold War with its tale of impending Armageddon triggered by a crazy US air force general launching a nuclear first strike on the USSR.

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In 1956 President Eisenhower held regular secret meetings with a Hungarian mathematician confined to a wheelchair who would be taken back and forth by limousine to the White House from his bed at Walter Reed Hospital in Washington. The patient was under armed guard day and night because he would frequently descend into deranged babbling, so it was feared he might spill military secrets if an enemy agent could get to his bedside. The patient, in what was to be the last year of his life, was John von Neumann, undoubtedly one of the inspirations for Dr Strangelove. (At one point in the film, Strangelove refers to research by the ‘Bland Corporation’.) Before his tragic decline ‘Johnny’ von Neumann’s genius was so overwhelming that it is hard to summarize. He was a mathematical prodigy: at the age of eight, when given any two eight-digit numbers, he could divide one by the other in his head.

How to Create a Mind: The Secret of Human Thought Revealed
by Ray Kurzweil
Published 13 Nov 2012

Chapter 8: The Mind as Computer 1. Salomon Bochner, A Biographical Memoir of John von Neumann (Washington, DC: National Academy of Sciences, 1958). 2. A. M. Turing, “On Computable Numbers, with an Application to the Entscheidungsproblem,” Proceedings of the London Mathematical Society Series 2, vol. 42 (1936–37): 230–65, http://www.comlab.ox.ac.uk/activities/ieg/e-library/sources/tp2-ie.pdf. A. M. Turing, “On Computable Numbers, with an Application to the Entscheidungsproblem: A Correction,” Proceedings of the London Mathematical Society 43 (1938): 544–46. 3. John von Neumann, “First Draft of a Report on the EDVAC,” Moore School of Electrical Engineering, University of Pennsylvania, June 30, 1945.

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—Diane Ackerman Brains exist because the distribution of resources necessary for survival and the hazards that threaten survival vary in space and time. —John M. Allman The modern geography of the brain has a deliciously antiquated feel to it—rather like a medieval map with the known world encircled by terra incognita where monsters roam. —David Bainbridge In mathematics you don’t understand things. You just get used to them. —John von Neumann E ver since the emergence of the computer in the middle of the twentieth century, there has been ongoing debate not only about the ultimate extent of its abilities but about whether the human brain itself could be considered a form of computer. As far as the latter question was concerned, the consensus has veered from viewing these two kinds of information-processing entities as being essentially the same to their being fundamentally different.

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The basic idea is that the human brain is likewise subject to natural law, and thus its information-processing ability cannot exceed that of a machine (and therefore of a Turing machine). We can properly credit Turing with establishing the theoretical foundation of computation with his 1936 paper, but it is important to note that he was deeply influenced by a lecture that Hungarian American mathematician John von Neumann (1903–1957) gave in Cambridge in 1935 on his stored program concept, a concept enshrined in the Turing machine.1 In turn, von Neumann was influenced by Turing’s 1936 paper, which elegantly laid out the principles of computation, and made it required reading for his colleagues in the late 1930s and early 1940s.2 In the same paper Turing reports another unexpected discovery: that of unsolvable problems.

The Myth of the Rational Market: A History of Risk, Reward, and Delusion on Wall Street
by Justin Fox
Published 29 May 2009

The developments of the late 1930s, in which young Keynesians grafted a few kludgy imperfect-foresight formulas onto the body of perfect-foresight mathematical economics, aggravated him. He began consorting with the scientists and mathematicians of Vienna, one of whom steered him toward a 1928 paper about poker written by Hungarian mathematician John von Neumann.4 After emigrating to the United States in 1930, von Neumann became the brightest intellectual light at Princeton’s Institute for Advanced Study, a place that also employed Albert Einstein. He helped plan the Battle of the Atlantic, design the atomic bomb, and invent the computer. In the late 1950s, dying of bone cancer likely brought on by witnessing one too many atomic test blasts, he peddled his doctrine of nuclear brinksmanship while rolling his wheelchair down the halls of power in Washington—providing at least part of the inspiration for Stanley Kubrick’s Dr.

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At the University of Chicago, he found an enthusiastic follower in Eugene Fama, another student of Harry Roberts studying market movements. Holbrook Working rejoined the fray with a paper showing that Alfred Cowles’s 1937 finding of patterns in stock movements was largely the result of a statistical error.12 Oskar Morgenstern chipped in, too. His friend John von Neumann had suggested before he died in 1957 that Morgenstern use a statistical technique called spectral analysis, helpful in distinguishing between true cycles and randomly generated ones, to examine economic data. Morgenstern wasn’t enough of a mathematician to do this himself, but he hired young British statistician Clive Granger and put him to work examining stock prices.

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Even before that happened, scholars on multiple campuses were making it clear that, in theory, it would be awfully convenient if speculative markets functioned perfectly. CHAPTER 5 MODIGLIANI AND MILLER ARRIVE AT A SIMPLIFYING ASSUMPTION Finance, the business school version of economics, is transformed from a field of empirical research and rules of thumb to one ruled by theory. FOUR YEARS AFTER JOHN VON Neumann and Oskar Morgenstern published their equation-filled guide to weighing potential rewards and losses in an uncertain future, economist Milton Friedman and statistician Jimmie Savage made a startling proposal. With just a few tweaks, they wrote, the von Neumann-Morgenstern utility theory could describe the way real people made economic decisions.

The Computer Boys Take Over: Computers, Programmers, and the Politics of Technical Expertise
by Nathan L. Ensmenger
Published 31 Jul 2010

The Computer Boys Take Over History of Computing William Aspray, editor The Government Machine: A Revolutionary History of the Computer William Aspray John von Neumann and the Origins of Modern Computing William Aspray and Paul E. Ceruzzi, editors The Internet and American Business Charles J. Bashe, Lyle R. Johnson, John H. Palmer, and Emerson W. Pugh IBM’s Early Computers Martin Campbell-Kelly From Airline Reservations to Sonic the Hedgehog: A History of the Software Industry Paul E. Ceruzzi A History of Modern Computing I. Bernard Cohen Howard Aiken: Portrait of a Computer Pioneer I.

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The ENIAC women would simply set up the machine to perform these predetermined plans; that this work would turn out to be difficult and require radically innovative thinking was completely unanticipated.32 The telephone switchboardlike appearance of the ENIAC programming cable-and-plug panels reinforced the notion that programmers were mere machine operators, that programming was more handicraft than science, more feminine than masculine, more mechanical than intellectual. The idea that the development of hardware was the real business of computing, and that software was at best secondary, persisted throughout the 1940s and early 1950s. In the first textbooks on computing published in the United States, for example, John von Neumann and Herman Goldstine outlined a clear division of labor in computing—presumably based on their experience with the ENIAC project—that clearly distinguished between the headwork of the (male) scientist or “planner,” and the handwork of the (largely female) “coder.” In the von Neumann and Goldstine schema, the planner did the intellectual work of analysis and the coder simply translated this work into a form that a computer could understand.

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Conventional histories of computer programming tend to conflate programming as a vocational activity with computer science as an academic discipline. In many of these accounts, programming is represented as a subdiscipline of formal logic and mathematics, and its origins are identified in the writings of early computer theorists Alan Turing and John von Neumann. The development of the discipline is evaluated in terms of advances in programming languages, formal methods, and generally applicable theoretical research. This purely intellectual approach to the history of programming, however, conceals the essentially craftlike nature of early programming practice.

From Counterculture to Cyberculture: Stewart Brand, the Whole Earth Network, and the Rise of Digital Utopianism
by Fred Turner
Published 31 Aug 2006

On the laboratory floor, this led to an egalitarian ethic of collaboration and a “hybrid of practices” in Galison’s terms, known as “Radar Philosophy” (152). 28. As several historians have pointed out, the “systems” approach taken by cybernetics predated the invention of the term itself by a little more than a decade. In 1928, for instance, John Von Neumann published his “Theory of Parlor Games,” thus inventing game theory. Heims, John Von Neumann and Norbert Wiener, 84. In the 1930s in England, Robert Lilienfeld has argued, the invention of radar led to the need for the coordination of machines and thus the invention of the “total point of view” characteristic of systems thinking. Lilienfeld, Rise of Systems Theory, 103.

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As in its profile of the Electronic Frontier Foundation, Wired had offered the freelance lifestyle of a high-profile consultant as a model of the independent lifestyle ostensibly becoming available to the digital generation as a whole. A month after Kelly’s first interview with Gilder appeared in Wired, Paulina Borsook published a similar profile of Dyson. The story moved swiftly through Dyson’s biography— child of physicist Freeman Dyson, childhood friend of Alice Bigelow (daughter of Julian Bigelow, John Von Neumann’s engineer), former Forbes reporter and Wall Street stock analyst, later editor of the newsletter Release 1.0 and hostess of the annual PC Forum conference. When it came to Dyson’s present career, however, the story slipped into information-system metaphors like those that appeared in Bronson’s profile of Gilder.

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Cybernetics emerged as a self-consciously comprehensive field of thought, however, with the work of Norbert Wiener. For a fuller account of Wiener’s career and the emergence of his cybernetics, see also Galison, “Ontology of the Enemy”; and Hayles, How We Became Posthuman. 29. Wiener, Cybernetics, 8. 30. Ibid., 9. 31. Heims, John Von Neumann and Norbert Wiener, 182 – 88. For a chronicle of Wiener’s shifting relationship to the Rad Lab, see Conway and Siegelman, Dark Hero of the Information Age, 115 –25. 32. Wiener, I Am a Mathematician, 251–52. N o t e s t o Pa g e s 2 1 _ 2 6 [ 265 ] 33. For a critical analysis of this choice, and especially its relationship to conceptions of the Other in contemporary cultural theory, see Galison, “Ontology of the Enemy.” 34.

New Dark Age: Technology and the End of the Future
by James Bridle
Published 18 Jun 2018

He may perish in conflict before he learns to wield that record for his true good. Yet, in the application of science to the needs and desires of man, it would seem to be a singularly unfortunate stage at which to terminate the process, or to lose hope as to the outcome.12 One of Bush’s colleagues at the Manhattan Project was another scientist, John von Neumann, who shared similar concerns about the overwhelming volumes of information being produced – and required – by the scientific endeavours of the day. He was also captivated by the idea of predicting, and even controlling, the weather. In 1945, he came across a mimeograph entitled ‘Outline of Weather Proposal’, written by a researcher at RCA Laboratories named Vladimir Zworykin.

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It consumed 140 kilowatts of power, and pumped out so much heat that special ceiling fans had to be installed. To reprogram it, it was necessary to turn hundreds of ten-pole rotary switches by hand, the operators moving between the stacks of equipment, connecting cables and checking hundreds of thousands of hand-soldered joints. Among the operators was Klára Dán von Neumann, John von Neumann’s wife, who wrote most of the meteorological code and checked the work of the others. In 1950, a team of meteorologists assembled at Aberdeen in order to perform the first automated twenty-four-hour weather forecast, along exactly the same lines as Richardson had proposed. For this project, the boundaries of the world were the edges of the continental United States; a grid separated it into fifteen rows and eighteen columns.

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The ENIAC was actually one that you kind of lived inside.’18 But in fact, today, we all live inside a version of the ENIAC: a vast machinery of computation that encircles the entirety of the globe and extends into outer space on a network of satellites. It is this machine, imagined by Lewis Fry Richardson and actualised by John von Neumann, that governs in one way or another every aspect of life today. And it is one of the most striking conditions of this computational regime that it has rendered itself almost invisible to us. It is almost possible to pinpoint the exact moment when militarised computation, and the belief in prediction and control that it embodies and produces, slid out of view.

Nobody's Fool: Why We Get Taken in and What We Can Do About It
by Daniel Simons and Christopher Chabris
Published 10 Jul 2023

Goichberg knew that anyone capable of drawing a grandmaster and beating a master could solve the chess problem in one second. But when asked to prove himself, von Neumann refused to even try. He left the playing area in a huff and never received a prize or a refund. Indeed, he never entered another rated chess tournament—at least not under the name of John von Neumann—and neither he nor his accomplice has been seen in the chess world again. The case of John von Neumann remains one of the great unsolved mysteries of chess.4 Since 1993, there have been many similar incidents of computer-assisted cheating, a form of “intellectual doping,” in chess tournaments. Now that the processing power of a smartphone is sufficient to outplay the human world champion, it has become easier than ever to gain an unfair advantage.

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But no one was expecting what happened in 1993.1 It began with a minor sensation. In the second round, when the top seeds are normally still mowing down weaker opponents on their way to eventually playing against one another, grandmaster Helgi Ólafsson of Iceland was held to a draw. His opponent, a player from California named John von Neumann, was unrated and playing in his first official tournament—or so he said when he registered for the event and joined the US Chess Federation.2 It wasn’t unheard of for players to make the World Open their first US tournament. The large prize fund attracted many players from the former Soviet Union who had not stood out in their home country, where chess was practically a national sport, but who were good enough to compete for prizes in the United States.

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For the entire game, lazzir had never made a move in fewer than five seconds or more than twelve seconds. Chris, on the other hand, made his opening moves in just one or two seconds each but took over thirty seconds on several moves and almost two full minutes on one of them. Recall that odd timing between moves was one of the tells that revealed John von Neumann’s chess cheating. Master-level players often rely on memory for the initial moves in a game, which tend to follow well-established plans. Later parts of the game require more thought and decision-making, and at some point, taking extra time to find the best move—or at least avoid a losing one—can be critical.

I, Warbot: The Dawn of Artificially Intelligent Conflict
by Kenneth Payne
Published 16 Jun 2021

He demonstrated that logical propositions, like AND/OR and IF/THEN, and binary numbers could be implemented by switches in a telephone circuit.13 Switches, binary code and Boolean logic soon became part of the basic architecture of modern digital computing. Another significant figure early in the history of computing was the charismatic American, John von Neumann.14 His contribution is rather less clear-cut; others can also lay claim to what has become known as the von Neumann architecture for modern computers. By this stage, spurred by the war and the problems of fire control, communications and code-breaking, computing was a large and rapidly expanding field.

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The long-lived Schelling wrote his classic works on strategy in the 1950s and 60s, an era when nuclear weapons were a new and terrifying weapon.21 And also an era when computers and quantitative thinking seemed to offer a new, more rigorous approach to thinking about all manner of social questions, including nuclear war. One exciting branch of maths was ‘game theory’, which considered the way in which rational agents interacted. Pioneered by the brilliant John von Neumann, co-inventor of the modern computer, game theory looked like a good way of modelling the sorts of adversarial behaviours that took place in international relations. It was enthusiastically embraced by a group of quantitative theorists then growing in prominence, some of whom worked for the US Air Force’s inhouse thinktank, the RAND Corporation.22 Schelling himself was a mathematically trained economist, and so was well placed to take advantage of the new technique.

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‘Solving’ real strategy problems will require much more than solving poker. In the real world, decisions are altogether more complex than card games, whatever Clausewitz wrote. Something else is needed—but what? THE ART OF i-WAR Libratus, the poker playing AI, isn’t a genius. The geniuses are the mathematicians who built it—John von Neumann, originator of both the computer and game theory; John Nash, discoverer of the optimum strategy for two-player games; and the Libratus team at Carnegie Mellon University, who harnessed some abstract maths to a powerful deep learning architecture. To see the difference and appreciate why it will be difficult to find a genius warbot general, we need to think about creativity in humans and machines.

The Logician and the Engineer: How George Boole and Claude Shannon Created the Information Age
by Paul J. Nahin
Published 27 Oct 2012

With his PhD in hand, and after spending the summer of 1940 back at Bell Labs, Shannon used a National Research Council Fellowship for a year’s stay at the Institute for Advanced Study in Princeton, New Jersey, where he worked under the great mathematician Hermann Weyl. Also there were such luminaries as John von Neumann and Albert Einstein. He might even have bumped into Richard Feynman, who was working on his PhD in physics at Princeton. Also there with Shannon was his first wife, Norma Levor (born 1920), whom he had married in 1939. Theirs was an intense, passionate, but ultimately doomed brief marriage, and Norma left him in June 1941.

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So, the answer is that the additional relay E does not result in improved reliability.8 6.5 MAJORITY LOGIC In this final section I’ll comment just a bit on what sparked Shannon’s interest in building more reliable circuits out of less reliable components. In 1956 Shannon was coeditor of an anthology of technical papers, one of which was authored by the great Hungarian-born American mathematician John von Neumann (1903–1957). Titled “Probabilistic Logics and the Synthesis of Reliable Organisms from Unreliable Components,” Shannon read that paper as an editor long before the anthology appeared, and in his 1956 “crummy relay” paper specifically cited von Neumann’s earlier work.9 Von Neumann’s paper is heavily oriented toward mimicking the fundamental component of the human brain, the neuron cell that “fires” (that is, produces an output) when its multiple inputs (the outputs of other neurons) satisfy certain requirements.

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An implicit recognition of this can be found as long ago as 1929, in an important thermodynamics paper by the Hungarian physicist Leo Szilard (1898–1964).7 The explicit tying together of information, energy, and computation in analysts’ minds is, however, almost certainly due to a remark made by the Institute for Advanced Study mathematician John von Neumann (1903–1957) in a December 1949 lecture at the University of Illinois.8 In that lecture he asserted that the minimum energy Emin associated with manipulating a bit to be kT ln(2) joules (J), where T is the temperature on the Kelvin scale and k is Boltzmann’s constant (k = 1.38. 10−23).9 (Power is energy per unit time and so, just to keep the scale of this in mind, 1 = 1 = 1 watt) At “room temperature,” that is, at T = 300 K, this minimum energy is 2.87 · 10−21 J, a very tiny amount of energy.

In Pursuit of the Traveling Salesman: Mathematics at the Limits of Computation
by William J. Cook
Published 1 Jan 2011

Nonetheless, plots such as the one displayed in figure 2.21 strongly suggest a steady decrease √ in the average tour length divided by n as n increases, pointing toward an ultimate value of approximately 0.712 for b.29 43 3: The Salesman in Action Because my mathematics has its origin in a real problem doesn’t make it less interesting to me—just the other way around. —George Dantzig, 1986.1 he name itself announces the applied nature of the traveling salesman problem. This has surely contributed to a focus on computational issues, keeping the research topic well away from perils famously described in John von Neumann’s essay “The Mathematician”. “In other words, at a great distance from its empirical source, or after much ‘abstract’ inbreeding, a mathematical subject is in danger of degeneration”. Indeed, a strength of TSP research is the steady stream of practical applications that breathe new life into the area.

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Naturally I agreed. von Neumann said: “The speaker titled his talk ‘linear programming’ and carefully stated his axioms. If you have an application that satisfies the axioms, well use it. If it does not, then don’t.” Fortunately for the world, many of its complexities can in fact be described in sufficient detail by linear models. The episode with Dantzig, Hotelling, and John von Neumann is summed up nicely by a cartoon Dantzig’s Stanford colleagues reported as hanging outside his office.9 It featured the Peanuts character Linus in his traditional pose, sucking his thumb and Linear Programming holding a blanket. The caption read, “Happiness is assuming the world is linear.”

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It is remarkable that there always exists such a simple and elegant proof of optimality: the simplex algorithm constructs multipliers that can be used to combine the primal LP constraints into a convincing statement that no solution gives an objective value greater than that supplied by the final dictionary. Moreover, the multipliers are themselves an optimal solution to the dual LP problem and the optimal primal and dual objective values 107 108 Chapter 5 are equal. This beautiful result is known as the strong duality theorem, first stated and proved by John von Neumann.22 Strong duality gets top billing in LP theory, but in our TSP discussion we really only need the much easier statement that any dual LP solution provides a bound on the primal objective; this is called the weak duality theorem. And don’t worry if you missed a few details in our rush through material in the past few pages: in the special case of the TSP we provide an intuitive explanation of duality, showing how to trap the salesman with linear inequalities.

Algorithms to Live By: The Computer Science of Human Decisions
by Brian Christian and Tom Griffiths
Published 4 Apr 2016

As long as the two stacks were themselves sorted, the procedure of merging them into a single sorted stack was incredibly straightforward and took linear time: simply compare the two top cards to each other, move the smaller of them to the new stack you’re creating, and repeat until finished. The program that John von Neumann wrote in 1945 to demonstrate the power of the stored-program computer took the idea of collating to its beautiful and ultimate conclusion. Sorting two cards is simple: just put the smaller one on top. And given a pair of two-card stacks, both of them sorted, you can easily collate them into an ordered stack of four.

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The computer industry is currently in transition from hard disk drives to solid-state drives; at the same price point, a hard disk will offer dramatically greater capacity, but a solid-state drive will offer dramatically better performance—as most consumers now know, or soon discover when they begin to shop. What casual consumers may not know is that this exact tradeoff is being made within the machine itself at a number of scales—to the point where it’s considered one of the fundamental principles of computing. In 1946, Arthur Burks, Herman Goldstine, and John von Neumann, working at the Institute for Advanced Study in Princeton, laid out a design proposal for what they called an electrical “memory organ.” In an ideal world, they wrote, the machine would of course have limitless quantities of lightning-fast storage, but in practice this wasn’t possible. (It still isn’t.)

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Exactly calculating the chances of some particular outcome of that process, with many, many particles interacting, is hard to the point of impossibility. But simulating it, with each interaction being like turning over a new card, provides an alternative. Ulam developed the idea further with John von Neumann, and worked with Nicholas Metropolis, another of the physicists from the Manhattan Project, on implementing the method on the Los Alamos computer. Metropolis named this approach—replacing exhaustive probability calculations with sample simulations—the Monte Carlo Method, after the Monte Carlo casino in Monaco, a place equally dependent on the vagaries of chance.

When Einstein Walked With Gödel: Excursions to the Edge of Thought
by Jim Holt
Published 14 May 2018

Having launched himself with his offbeat thesis as a “solo scientist,” Mandelbrot sought out other similarly innovative mathematicians. One such was Norbert Wiener, the founder (and coiner) of “cybernetics,” the science of how systems ranging from telephone switchboards to the human brain are controlled by feedback loops. Another was John von Neumann, the creator of game theory (and much else). To Mandelbrot, these two men were “made of stardust.” He served as postdoc to both: first to Wiener at MIT, and then to von Neumann at the Institute for Advanced Study in Princeton, where he had a nightmarish experience. Delivering a lecture on the deep links between physics and linguistics, he watched as one after another of the famous figures in the audience nodded off and snored.

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At Princeton, Turing took the first steps toward building a working model of his imaginary computer, pondering how to realize its logical design in a network of relay-operated switches; he even managed to get into a machine shop in the physics department and construct some of the relays himself. In addition to his studies with Church, he had dealings with the formidable John von Neumann, who would later be credited with innovations in computer architecture that Turing himself had pioneered. On the social side, he found the straightforward manners of Americans congenial, with certain exceptions: “Whenever you thank them for anything, they say ‘You’re welcome.’ I rather liked it at first, thinking I was welcome, but now I find it comes back like a ball thrown against a wall, and become positively apprehensive.

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A marine biologist on the scene recalled that a week after the H-bomb test he was still finding terns with their feathers blackened and scorched and fish whose “skin was missing from a side as if they had been dropped in a hot pan.” The computer, one might well conclude, was conceived in sin. Its birth helped ratchet up, by several orders of magnitude, the destructive force available to the superpowers during the cold war. And the man most responsible for the creation of that first computer, John von Neumann, was himself among the most ardent of the cold warriors, an advocate of a preemptive military attack on the Soviet Union, and one of the models for the film character Dr. Strangelove. “The digital universe and the hydrogen bomb were brought into existence at the same time,” the historian of science George Dyson has observed.

Gaming the Vote: Why Elections Aren't Fair (And What We Can Do About It)
by William Poundstone
Published 5 Feb 2008

(U.S. Senate Collection. Center for Legislative Archives) To Scott Contents Prologue: The Wizard and the Lizard 3 THE PROBLEM 25 I. Game Theory Kurt Code! • Adolf Hitler· Albert Einstein· Oskar Morgenstern· Bambi· the u.s. Constitution· Joseph Goebbels • God· Kaiser Wilhelm II • John von Neumann" Kenneth Arrow" J\'larxism • Alfred Tarski • intransitivity· Harold Hotelling· ice cream· John Hicks· "Scissors, Paper. Stone" • Duncan Black· the "forty-seven-year-old wife of a machinist liVing in Dayton. Ohio" • the RAND Corporation· Condoleezzrl Rice· Olaf Helmer· Harry Truman· Joseph Stalin· Abram Bergson 2.

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Had the muse of genius allotted Morgenstern a steadier flow of great ideas, he might have lacked the time to play this role. A man who cared more about being liked could not have deployed sharp elbows as effectively as he did. The most impressive of Morgenstern's projects was game theory, the creation mainly of Hungarian-born mathematician John von Neumann. Despite the name, game theory is not primarily about games such as chess or Monopoly or Halo. It is more an exact science of strategy. It explores how rational adversaries make decisions, knowing that their opponents are trying to second-guess or double-cross them. In 1928 von Neumann published the paper inaugurating this field.

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, and Einstein, Morgenstern was emphatically not on their level. 32 Game Theory He would sometimes sit in on mathematical seminars and ask questions that appeared to confirm this assessment. Shubik tells of an excruciating lecture in which Morgenstern spent three hours trying and failing to reproduce a result from "his" game theory book. "We would have all been happier," Shubik said, "if Oskar had not attempted to go through formal proofs." John von Neumann had his talking point down pat. a. Johnny, what did Morgenstern really contribute? Come on. You can tell. A. "Without Oskar, I would have never written the Theory of Games and Economic BehmJior." No politician could have handled the question better. I met Kenneth Arrow on a sunny afternoon at the Stanford Faculty Club.

Blindside: How to Anticipate Forcing Events and Wild Cards in Global Politics
by Francis Fukuyama
Published 27 Aug 2007

Presper Eckert Jr., “The ENIAC,” A History of Computing in the Twentieth Century, edited by Metropolis, Howlett, and Rota, p. 525; and John W. Mauchly, “The ENIAC,” A History of Computing in the Twentieth Century, edited by Metropolis, Howlett, and Rota, p. 541. 7. William Aspray, John von Neumann and the Origins of Modern Computing (MIT Press, 1990); William Aspray, “John von Neumann’s Contributions to Computing and Computer Science,” Annals of the History of Computing 11, no. 3: 189–95 (1989). 8. Paul E. Ceruzzi, A History of Modern Computing (MIT Press, 1998), chap. 7; Martin Campbell-Kelly and William Aspray, Computer: A History of the Information Machine (New York: Basic Books, 1996), chap. 10. 9.

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The war, of course, created any number of desperate demands for computation, which in turn led to two of the most famous of the pioneering computers: the digital, all-electronic Colossus, which was actually a series of machines created at the British code-breaking center, Bletchley Park, as a tool for cracking the most difficult German ciphers;5 and the digital, all-electronic ENIAC, which was constructed by engineers at the University of Pennsylvania to calculate artillery trajectories.6 Starting in mid-1944, moreover, the ENIAC team was joined by the world-renowned, Hungarian-born mathematician John von Neumann, who was also a participant in the super-secret Manhattan Project—and who was looking for computing machines that could help out with the horrendous calculations needed in that effort. Although ENIAC was too late to help in designing the atomic bomb—the machine did not become operational until 1946—von Neumann was inspired nonetheless.

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The history of information technology offers many other examples of invention-by-convergence. Among them: —The modern concept of information and information processing was a synthesis of insights developed in the 1930s and 1940s by Alan Turing, Claude Shannon, Norbert Wiener, Warren McCulloch, Walter Pitts, and John von Neumann.12 —The hobbyists who sparked the personal computer revolution in the late 1970s were operating (consciously or not) in the context of ideas that had been around for a decade or more. There was the notion of interactive comput- 2990-7 ch11 waldrop 7/23/07 12:13 PM innovation and adaptation Page 125 125 ing, for example, in which a computer would respond to the user’s input immediately (as opposed to generating a stack of fanfold printout hours later); this idea dated back to the Whirlwind project, an experiment in real-time computing that began at MIT in the 1940s.13 There were the twin notions of individually controlled computing (having a computer apparently under the control of a single user) and home computing (having a computer in your own house); both emerged in the 1960s from MIT’s Project MAC, an early experiment in time-sharing.14 And then there was the notion of a computer as an open system, meaning that a user could modify it, add to it, and upgrade it however he or she wanted; that practice was already standard in the minicomputer market, which was pioneered by the Digital Equipment Corporation in the 1960s.15 —The Internet as we know it today represents the convergence of (among other ideas) the notion of packet-switched networking from the 1960s;16 the notion of internetworking (as embodied in the TCP/IP protocol), which was developed in the 1970s to allow packets to pass between different networks;17 and the notion of hypertext—which, of course, goes back to Vannevar Bush’s article on the memex in 1945. 2990-7 ch11 waldrop 7/23/07 12:13 PM Page 126 2990-7 ch12 kurth 7/23/07 12:14 PM Page 127 Part IV What Could Be 2990-7 ch12 kurth 7/23/07 12:14 PM Page 128 2990-7 ch12 kurth 7/23/07 12:14 PM Page 129 12 Cassandra versus Pollyanna A Debate between James Kurth and Gregg Easterbrook James Kurth: I am an optimist about the current pessimism, but a pessimist overall.

Thinking Machines: The Inside Story of Artificial Intelligence and Our Race to Build the Future
by Luke Dormehl
Published 10 Aug 2016

Compared with the unreliable memory of humans, a machine capable of accessing thousands of items in the span of microseconds had a clear advantage. There are entire books written about the birth of modern computing, but three men stand out as laying the philosophical and technical groundwork for the field that became known as Artificial Intelligence: John von Neumann, Alan Turing and Claude Shannon. A native of Hungary, von Neumann was born in 1903 into a Jewish banking family in Budapest. In 1930, he arrived at Princeton University as a maths teacher and, by 1933, had established himself as one of six professors in the new Institute for Advanced Study in Princeton: a position he stayed in until the day he died.

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By trying to find and isolate malicious behaviour online, usually based on the language involved, he came up with what is possibly the most advanced real-world version of that ambition. Negobot is programmed to operate according to the rules of game theory. Game theory was a concept first suggested by the maths pioneer John von Neumann, whose work I briefly described in chapter one. It is the study of strategic decision-making, in which there are multiple players all with their own motives. The payoff depends on the behaviour of these different players. Not everyone can get what they want – and the aim is to predict how people will act and hopefully to turn this to your advantage.

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‘Shortly after, the human era will be ended.’ This term, ‘the Singularity’, referring to the point at which machines overtake humans on the intelligence scale, has become an AI reference as widely cited as the Turing Test. It is often credited to Vinge, although in reality the first computer scientist to use it was John von Neumann. In the last decade of von Neumann’s life, he had a conversation with Stan Ulam, a Polish-American mathematician with whom he had collaborated on the Manhattan Project. Recalling the conversation later, Ulam noted that von Neumann was fascinated – and perhaps alarmed – by ‘the ever-accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue’.

How Markets Fail: The Logic of Economic Calamities
by John Cassidy
Published 10 Nov 2009

To do this, though, he was forced to introduce some simplifications, such as assuming that the price of each commodity depended only on the quantities it was produced in, and not on the quantities of competing goods. Since the main point of Walrasian economics was to explore the connections between different markets, this wasn’t entirely satisfactory, but it was the best Wald could do. There things rested until 1937, when John von Neumann, a Princeton mathematician who had taught in Berlin before moving to the United States, visited Vienna and presented a paper to Menger’s Mathematical Colloquium that one leading historian of economic ideas has called “the single most important article in mathematical economics.” That judgment is debatable, but von Neumann, who was born in Budapest in 1903, was undoubtedly some sort of genius.

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In many cases, it is the basis of what I call rational irrationality, by which I mean a situation in which the application of rational self-interest in the marketplace leads to an inferior and socially irrational outcome. When the prisoner’s dilemma was first introduced, back in the early 1950s, many people refused to accept that the two firms wouldn’t be able to reach the cooperative solution. Economists and mathematicians were excitedly exploring the new science of game theory that John von Neumann and Oskar Morgenstern had invented in their 1944 treatise Theory of Games and Economic Behavior. Many smart people held out great hope for game theory, imagining it could solve many of the outstanding problems in the social sciences. The key to this process was thought to lie in extending the solution methods that von Neumann and Morgenstern had introduced, most of which applied to zero-sum games, such as coin-tossing and poker.

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“They are concerned, not with what an investment is really worth to a man who buys it ‘for keeps,’ but with what the market will value it at, under the influence of mass psychology, three months or a year hence.” (If he had been writing in today’s world of day traders and momentum funds, Keynes might well have written “three hours or a day hence.”) Like John von Neumann, the Hungarian genius who invented game theory, Keynes believed that simple parlor games have much to teach economists: they feature the sort of strategic interactions that are largely absent from orthodox economics, but that play such an important role in reality. On Wall Street, Keynes pointed out, investing is a “battle of wits,” the primary aim being “to outwit the crowd, and to pass the bad, or depreciating, half-crown to the other fellow.”

Our Final Invention: Artificial Intelligence and the End of the Human Era
by James Barrat
Published 30 Sep 2013

It means that an individual or “agent” will have goals and also preferences (called a utility function in economics). He will have beliefs about the world and the best way to achieve his goals and preferences. As conditions change, he will update his beliefs. He is a rational economic agent when he pursues his goals with actions based on up-to-date beliefs about the world. Mathematician John von Neumann (1903–1957) codeveloped the idea connecting rationality and utility functions. As we’ll see, von Neumann laid the groundwork for many ideas in computer science, AI, and economics. Yet social scientists argue that a “rational economic agent” is a load of hogwash. Humans are not rational—we don’t specify our goals or our beliefs, and we don’t always update our beliefs as conditions change.

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Probably only Good, and Leslie Pendleton, knew about it. Vernor Vinge was the first person to formally use the word “singularity” when describing the technological future—he did it in a 1993 address to NASA, entitled “The Coming Technological Singularity.” Mathematician Stanislaw Ulam reported that he and polymath John von Neumann had used “singularity” in a conversation about technological change thirty-five years earlier, in 1958. But Vinge’s coinage was public, deliberate, and set the singularity ball rolling into the hands of Ray Kurzweil and what is today a Singularity movement. With that street cred, why doesn’t Vinge work the lecture and conference circuits as the ultimate Singularity pundit?

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So, there’s hundreds of thousands of people in the world, very smart people, who are working on things that lead to superhuman intelligence. And probably most of them don’t even look at it that way. They look at it as faster, cheaper, better, more profitable.” Vinge compares it to the Cold War strategy called MAD—mutually assured destruction. Coined by acronym-loving John von Neumann (also the creator of an early computer with the winning initials, MANIAC), MAD maintained Cold War peace through the promise of mutual obliteration. Like MAD, superintelligence boasts a lot of researchers secretly working to develop technologies with catastrophic potential. But it’s like mutually assured destruction without any commonsense brakes.

The Doomsday Calculation: How an Equation That Predicts the Future Is Transforming Everything We Know About Life and the Universe
by William Poundstone
Published 3 Jun 2019

As philosopher Daniel Hill said of Bostrom, “His interest in science was a natural outgrowing of his understandable desire to live forever, basically.” Another transhumanist tenet is the singularity. The term was first used by mathematician Stanislaw Ulam in 1958, recalling a conversation with John von Neumann (who died in 1957). In math, dividing by zero creates a singularity—a point where a function is undefined. Ulam and von Neumann used the jargon metaphorically, speaking of “the ever accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue.”

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They annihilate themselves in global war before they get around to exploring the galaxy. “What we all fervently hope,” Fermi once said, “is that man will soon grow sufficiently adult to make good use of the powers that he acquires over nature.” Privately Fermi believed atomic weapons would lead to war. His Manhattan Project colleague, mathematician John von Neumann, minced no words. He rated it “absolutely certain (1) that there would be a nuclear war; and (2) that everyone would die in it.” Drake Equation The consensus of biologists and screenwriters is that we are not alone in the universe. This is not a new idea. Dominican friar Giordano Bruno, a supporter of Copernicus, asserted that stars are suns, circled by planets harboring intelligent beings.

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(We have little interest in communicating with ibexes, much less in establishing diplomatic relations with every ibex herd or zoo specimen.) It could be that contact with a more advanced civilization is known to be devastating to the less advanced civilization. ETs might be avoiding us for our own protection. Even in Fermi’s time, there was a comeback for these ideas: von Neumann probes, described by John von Neumann. The best-known examples, fictional of course, are the black monoliths in Stanley Kubrick’s 2001: A Space Odyssey. An early cut of the film had a segment explaining exactly what the monoliths were. They were identified as self-reproducing machines, designed to explore space. Kubrick decided to cut the exposition, leaving the monoliths enigmatic and symbolic.

AIQ: How People and Machines Are Smarter Together
by Nick Polson and James Scott
Published 14 May 2018

It took the bombing of Pearl Harbor to rouse the American people from their torpor, but roused they were at last. Young men surged forward to enlist. Women joined factories and nursing units. And scientists rushed to their labs and chalkboards, especially the many émigrés who’d fled the Nazis in terror: Albert Einstein, John von Neumann, Edward Teller, Stanislaw Ulam, and hundreds of other brilliant refugees who gave American science a decisive boost during the war. Abraham Wald, too, was eager to answer the call. He was soon given the chance, when his colleague W. Allen Wallis invited him to join Columbia’s Statistical Research Group.

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It’s a good thing that an Nvidia graphics card in 2018 can do 1.5 billion calculations in less than 0.0001 seconds. Factor 2: Massive Data But there’s a caveat: to fit a massive model, you need a massive data set. A model like Google’s Inception, with 388,736 parameters, tends to blow the minds of old-school scientists and engineers, who regard such massive models with contempt. The great mathematician John von Neumann, for example, once famously criticized a complicated model with the following enigmatic quip: “With four parameters I can fit an elephant, and with five I can make him wiggle his trunk.” Von Neumann meant that a model with lots of parameters is in danger of “overfitting,” which happens when a model just memorizes the random noise in the training data rather than learns the underlying pattern.

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This solution doesn’t work for presidential elections; there have only been 56 of them, so there is basically no way to tell from the data alone whether a complicated post-hoc explanation of who wins the presidency has any value in predicting the future. But it works brilliantly for models that extract patterns from images, texts, and videos, which we have in abundance. John von Neumann would surely be amazed at the result. He thought that you could “fit an elephant” with only four parameters, but it turns out you need 388,736 of them—or at least you need that many parameters to identify an elephant in the photos from your African safari. There’s no magic here, just massive data sets with millions or billions of data points.

The Demon in the Machine: How Hidden Webs of Information Are Finally Solving the Mystery of Life
by Paul Davies
Published 31 Jan 2019

Many of the great scientists of the twentieth century spotted the connection between Turing’s ideas and biology. What was needed to cement the link with biology was the transformation of a purely computational process into a physical construction process. A MACHINE THAT COPIES ITSELF Across the other side of the Atlantic from Alan Turing, the Hungarian émigré John von Neumann was similarly preoccupied with designing an electronic computer for military application, in his case in connection with the Manhattan Project (the atomic bomb). He used the same basic idea as Turing – a universal programmable machine that could compute anything that is computable. But von Neumann also had an interest in biology.

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And there is nothing in the rules of quantum mechanics as formulated by Schrödinger and others to project out a particular, single, concrete reality from the legion of ghostly overlapping pseudo-realities characteristic of the quantum micro-world. So vexatious is this problem that a handful of physicists, including John von Neumann of universal constructor fame, suggested that the ‘concretizing factor’ (often called ‘the collapse of the wave function’) might be the mind of the experimenter. In other words, when the result of the measurement enters the consciousness of the measurer – wham! – the nebulous quantum world out there abruptly gels into commonsense reality.

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Harmer et al., ‘Brownian ratchets and Parrondo’s games’, Chaos, 11, 705 (2001); doi: 10.1063/1.1395623 Peter Hoffman, Life’s Ratchet (Basic Books, 2012) —, ‘How molecular motors extract order from chaos’, Reports on Progress in Physics, vol. 79, 032601 (2016) William Lanouette and Bela Silard, Genius in the Shadows: A Biography of Leo Szilárd, the Man behind the Bomb (University of Chicago Press, 1994) C. H. Lineweaver, P. C. W. Davies and M. Ruse (eds.), Complexity and the Arrow of Time (Cambridge University Press, 2013) Norman MacRae, John von Neumann: The Scientific Genius Who Pioneered the Modern Computer, Game Theory, Nuclear Deterrence, and Much More (American Mathematical Society; 2nd edn, 1999) J. P. S. Peterson et al., ‘Experimental demonstration of information to energy conversion in a quantum system at the Landauer limit’, Proceedings of The Royal Society A, vol. 472, issue 2188 (2016): 20150813 Takahiro Sagawa, ‘Thermodynamic and logical reversibilities revisited’, Journal of Statistical Mechanics (2014); doi: 10.1088/1742-5468/2014/03/P03025 Jimmy Soni and Rob Goodman, A Mind at Play: How Claude Shannon Invented the Information Age (Simon and Schuster, 2017) 3.

The Quest: Energy, Security, and the Remaking of the Modern World
by Daniel Yergin
Published 14 May 2011

Pollack, and Carl Sagan, “Nuclear Winter: Global Consequences of Multiple Nuclear Explosions,” Science 222, no. 4630 (1983), pp. 1283–92. 25 Hart and Victor, “Scientific Elites,” pp. 657–61 (“advertant”); Weart, The Discovery of Global Warming, p. 5 (Kennedy); Martin Campbell-Kelly and William Aspray, Computer: A History of the Information Machine (Boulder, CO: Westview Press, 2004), p. 79 (“considerable temerity”). 26 Norman Macrae, John von Neumann: The Scientific Genius Who Pioneered the Modern Computer, Game Theory, Nuclear Deterrence, and Much More (American Mathematical Society, 2008), pp. 5, 248 (“last words”). 27 Macrae, John von Neumann, pp. 52, 250, 266, 325, 369; Stanislaw M. Ulam, Adventures of a Mathematician (Berkeley: University of California Press, 1991), pp. 4, 203, 245. 28 Campbell-Kelly and Aspray, Computer, pp. 3–4 (“computers”); Macrae, John von Neumann, p. 234 (“modern mathematical modeling”). 29 Macrae, John von Neumann, pp. 298, 302 (“phenomena”). 30 Spencer Weart, “Government: The View from Washington, DC,” The Discovery of Global Warming, at http://www.aip.org/history/climate/Govt.htm (“warfare”); Macrae, John von Neumann, pp. 298, 316 (“jiggle,” “weather predictions”); New York Times, February 9, 1957 (“electronic brain”). 31 Norman Phillips, “Jule Charney, 1917–1981,” Annals of the History of Computing 3, no. 4 (1981), pp. 318–19; Norman Phillips, “Jule Charney’s Influence on Meteorology,” Bulletin of the American Meteorological Society 63, no. 5 (1982), pp. 492–98; John M.

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A young mathematician caught sight of a world-famous figure—at least world famous in the worlds of science and mathematics. His name was John von Neumann. “With considerable temerity” the mathematician, Herman Goldfine, started a conversation. To Goldfine’s surprise, von Neumann, despite his towering reputation, was quite friendly. But when Goldfine told von Neumann that he was helping develop “an electronic computer capable of 333 multiplications per second,” the conversation abruptly changed “from one of relaxed good humor to one more like the oral examination for the doctor’s degree in mathematics.”25 John von Neumann—born János Neumann in Budapest—had emigrated to the United States in 1930 to become, along with Albert Einstein, one of the first faculty members at Princeton’s Institute for Advanced Study.

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Ulam, Adventures of a Mathematician (Berkeley: University of California Press, 1991), pp. 4, 203, 245. 28 Campbell-Kelly and Aspray, Computer, pp. 3–4 (“computers”); Macrae, John von Neumann, p. 234 (“modern mathematical modeling”). 29 Macrae, John von Neumann, pp. 298, 302 (“phenomena”). 30 Spencer Weart, “Government: The View from Washington, DC,” The Discovery of Global Warming, at http://www.aip.org/history/climate/Govt.htm (“warfare”); Macrae, John von Neumann, pp. 298, 316 (“jiggle,” “weather predictions”); New York Times, February 9, 1957 (“electronic brain”). 31 Norman Phillips, “Jule Charney, 1917–1981,” Annals of the History of Computing 3, no. 4 (1981), pp. 318–19; Norman Phillips, “Jule Charney’s Influence on Meteorology,” Bulletin of the American Meteorological Society 63, no. 5 (1982), pp. 492–98; John M. Lewis, “Smagorinsky’s GFDL: Building the Team,” Bulletin of the American Meteorological Society 89, no. 9 (2008), pp. 1339–53; Macrae, John von Neumann, pp. 316–20. 32 “ ‘Suki’ Manabe: Pioneer of Climate Modeling,” IPRC Climate 5, no. 2 (2005), pp. 11–15; Syukuro Manabe and Richard Wetherald, “Thermal Equilibrium of the Atmosphere with a Given Distribution of Relative Humidity,” Journal of Atmospheric Sciences 24, no. 3 (1967), pp. 241–59; Spencer Weart, “General Circulation Models of Climate,” The Discovery of Global Warming, at http://www.aip.org/history/climate/GCM.htm. 33 Interview with Fred Krupp. 34 Macrae, John von Neumann, p. 3245–326 (most prominent meteorologist); James G.

The Making of the Atomic Bomb
by Richard Rhodes
Published 17 Sep 2012

At the end of March 1933 the most original physicist of the twentieth century once again renounced his German citizenship. Princeton University acquired John von Neumann and Eugene Wigner in 1930, in Wigner’s puckish recollection, as a package deal. The university sought advice on improving its science from Paul Ehrenfest, who “recommended to them not to invite a single person but at least two . . . who already knew each other, who wouldn’t feel suddenly put on an island where they have no intimate contact with anybody. Johnny’s name was of course well known by that time the world over, so they decided to invite Johnny von Neumann. They looked: who wrote articles with John von Neumann? They found: Mr. Wigner. So they sent a telegram to me also.”683 In fact, Wigner had already earned a high reputation in a recondite area of physics known as group theory, about which he published a book in 1931.

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Caught between predominantly Jewish socialists and radicals on one side and the entrenched bureaucracy on the other, both sides hostile, the Jewish commercial elite allied itself for survival with the old nobility and the monarchy; one measure of that conservative alliance was the dramatic increase in the early twentieth century of ennobled Jews. George de Hevesy’s prosperous maternal grandfather, S. V. Schossberger, became in 1863 the first unconverted Jew ennobled since the Middle Ages, and in 1895 de Hevesy’s entire family was ennobled.387 Max Neumann, the banker father of the brilliant mathematician John von Neumann, was elevated in 1913. Von Kármán’s father’s case was exceptional. Mór Kármán, the founder of the celebrated Minta school, was an educator rather than a wealthy businessman. In the last decades of the nineteenth century he reorganized the haphazard Hungarian school system along German lines, to its great improvement—and not incidentally wrested control of education from the religious institutions that dominated it and passed that control to the state.

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They were thus brought into political connection, their power of independent action siphoned away. Out of the prospering but vulnerable Hungarian Jewish middle class came no fewer than seven of the twentieth century’s most exceptional scientists: in order of birth, Theodor von Kármán, George de Hevesy, Michael Polanyi, Leo Szilard, Eugene Wigner, John von Neumann and Edward Teller. All seven left Hungary as young men; all seven proved unusually versatile as well as talented and made major contributions to science and technology; two among them, de Hevesy and Wigner, eventually won Nobel Prizes. The mystery of such a concentration of ability from so remote and provincial a place has fascinated the community of science.

The Logic of Life: The Rational Economics of an Irrational World
by Tim Harford
Published 1 Jan 2008

The Vegas lobby, with poker on one side and slot machines on the other, is a visual metaphor for how game theory has matured—a story that can best be told by contrasting two of the most famous game theorists. Both were cold war intellectuals, advising the U.S. government at the highest levels and using game theory to try to understand the riskiest of all games, nuclear war. Game theory emerged from the sparkling mind of John von Neumann, a celebrated mathematical prodigy, when he decided to create a theory of poker. Von Neumann’s academic brilliance offered penetrating insights but the cold force of his logic could have led us to Armageddon. It was tempered by the earthier wisdom—usually expressed in witty prose rather than equations—of Thomas Schelling.

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Tormented by a tobacco addiction he could not kick, Schelling nudged game theory into a direction that now offers us surprising insights into the minds of hapless slot machine addicts. LATE IN THE 1920s, the most ostentatiously brilliant man in the world decided to work out the correct way to play poker. John von Neumann, a mathematician who helped to develop both the computer and the atomic bomb, had been struck by an engaging new conceit. Could his beloved mathematics uncover the secrets of poker, which seemed to be a quintessentially human game of secrets and lies? Von Neumann believed that if you wanted a theory—he called it “game theory”—that could explain life, you should start with a theory that could explain poker.

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It was not a high spot for Andy, nor for my project of using economics as a tool for self-improvement. You might think that was the first and last time any economist has dared to show his face at a speed date, but not at all. We can’t get enough of them. Economists at Columbia University even went to the trouble of organizing one. Ever since John von Neumann’s game theory promised to help us understand love and marriage, economists have been interested in how people choose their partners and how relationships work. And if you want to understand the way people choose their partners, a speed date is a great place to start. At a speed date you can get information about how each person responded to dozens of potential partners, something that would be impossible to collect in more traditional dating situations without binoculars, snooping devices, and a good private investigator.

The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do
by Erik J. Larson
Published 5 Apr 2021

Turing, for instance, disliked viewing thinking or intelligence as something social or situational.8 Yet the Bletchley success was in fact part of a vast system that extended far outside its cloistered walls. A massive effort was underway. It would soon pull in the United States and the work of scientists like Shannon at Bell Labs, as well as scientists at Princeton’s celebrated Institute for Advanced Studies—where Einstein, Gödel, and John Von Neumann all had appointments. The expanded, human-machine system is actually much more realistic as a model of how actual real-world problems get solved—of which, world war must certainly count among the most complex and important. AI’s tone-deafness on social or situational intelligence has been noted before, more recently by machine learning scientist François Chollet, who summarizes his critique of Turing’s (and, more broadly, the AI field's) view of intelligence nicely.

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One major problem with assumptions about increases in intelligence in AI circles is the problem of circularity: it takes (seemingly general) intelligence to increase general intelligence. A closer look reveals no linear progression, but only mystery. VON NEUMANN AND SELF-REPRODUCING MACHINES Good introduced the idea of self-improving AIs leading to ultraintelligence in the mid-1960s, but nearly two decades earlier John Von Neumann had considered the idea and rejected it. In a 1948 talk at the Institute for Advanced Studies at Princeton, Von Neumann explained that, while human reproduction often improves on prior “designs,” it’s clear that machines tasked with designing new and better machines face a fundamental stumbling block, since any design for a new machine must be specified in the parent machine.

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The idea of superintelligence is in reality a multiplication of errors, and it represents in barest form the extension of the fantasy about the rise of AI. To dig deeper into all of this, we should push further into this fantasy. It’s called the Singularity, and we turn to it next. Chapter 4 THE SINGULARITY, THEN AND NOW In the 1950s, the mathematician Stanislaw Ulam recalled an old conversation with John Von Neumann, in which Von Neumann discussed the possibility of a technological turning point for humanity: “the ever accelerating progress of technology … gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue.”1 Von Neumann likely made this comment as digital computers were arriving on the technological scene.

The Chip: How Two Americans Invented the Microchip and Launched a Revolution
by T. R. Reid
Published 18 Dec 2007

The ingenious, indeed breathtaking, insight that binary mathematics was perfectly suited to electronic computers occurred more or less simultaneously on both sides of the Atlantic to a pair of ingenious, indeed breathtaking, visionaries who had scoped out, by the late 1940s, remarkably accurate forecasts of the development of digital computers over the ensuing half century. These two cybernetic pioneers were John von Neumann and Alan M. Turing. Von Neumann was born in Budapest, the son of a wealthy banker, in 1903. He was recognized almost immediately as a prodigious mathematical talent, and spent his youth shuttling from one great university to another: Berlin, Zurich, Budapest, Göttingen, Hamburg. He published his first scholarly monograph at the age of eighteen and thereafter turned out key papers in a wide variety of fields.

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One of the first things Kilby realized was that tearing apart existing adding machines to see how they worked—a process known as reverse engineering—would offer little, if any, help, because the basic architecture of this pocket-size device would have to be completely new. And so the team started at ground zero, setting down the fundamental elements that their calculator would require. In accordance with the architecture worked out by Alan Turing and John von Neumann, all digital devices, from the most powerful mainframe supercomputer to the simplest handheld electronic game, can be divided into four basic parts serving four essential functions: Input: The unit that receives information from a human operator, a sensory device, or another computer and delivers it to the processing unit.

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An important contribution to this literature is Herman Goldstine’s The Computer from Pascal to von Neumann (Princeton, N.J.: Princeton University Press, 1972), which is strangely organized but has the immediacy that could be conveyed only by one who was present at the creation of the modem electronic computer. Andrew Hodges, Alan Turing: The Enigma (New York: Simon & Schuster, 1983), and Steve J. Heims, John von Neumann and Norbert Wiener (Cambridge, Mass.: MIT Press, 1980), are the first complete biographies. Von Neumann’s seminal paper “Preliminary Discussion of the Logical Design of an Electronic Computing Instrument” is reprinted in John Diebold, ed., The World of the Computer (New York: Random House, 1973).

Code: The Hidden Language of Computer Hardware and Software
by Charles Petzold
Published 28 Sep 1999

Atanasoff (1903–1995), who earlier designed an electronic computer that never worked quite right. The ENIAC attracted the interest of mathematician John von Neumann (1903–1957). Since 1930, the Hungarian-born von Neumann (whose last name is pronounced noy mahn) had been living in the United States. A flamboyant man who had a reputation for doing complex arithmetic in his head, von Neumann was a mathematics professor at the Princeton Institute for Advanced Study, and he did research in everything from quantum mechanics to the application of game theory to economics. John von Neumann helped design the successor to the ENIAC, the EDVAC (Electronic Discrete Variable Automatic Computer).

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Microsoft, MS-DOS, and Windows are either registered trademarks or trademarks of Microsoft Corporation in the United States and/or other countries. Other product and company names mentioned herein may be the trademarks of their respective owners. Images of Charles Babbage, George Boole, Louis Braille, Herman Hollerith, Samuel Morse, and John von Neumann appear courtesy of Corbis Images and were modified for this book by Joel Panchot. The January 1975 cover of Popular Electronics is reprinted by permission of Ziff-Davis and the Ziff family. All other illustrations in the book were produced by Joel Panchot. Unless otherwise noted, the example companies, organizations, products, people, and events depicted herein are fictitious.

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Such memory consisted of large arrays of little magnetized metal rings strung with wires. Each little ring could store a bit of information. Long after core memory had been replaced by other technologies, it was common to hear older programmers refer to the memory that the processor accessed as core. John von Neumann wasn't the only person doing some major conceptual thinking about the nature of computers in the 1940s. Claude Shannon (born 1916) was another influential thinker. In Chapter 11, I discussed his 1938 master's thesis, which established the relationship between switches, relays, and Boolean algebra.

Against the Gods: The Remarkable Story of Risk
by Peter L. Bernstein
Published 23 Aug 1996

So we usually settle for compromise alternatives, which may require us to make the best of a bad bargain; game theory uses terms like "maximin" and "minimax" to describe such decisions. Think of seller-buyer, landlord-tenant, husband-wife, lender-borrower, GM-Ford, parentchild, President-Congress, driver-pedestrian, boss-employee, pitcherbatter, soloist-accompanist. Game theory was invented by John von Neumann (1903-1957), a physicist of immense intellectual accomplishment.' Von Neumann was instrumental in the discovery of quantum mechanics in Berlin during the 1920s, and he played a major role in the creation of the first American atomic bomb and, later, the hydrogen bomb. He also invented the digital computer, was an accomplished meteorologist and mathematician, could multiply eight digits by eight digits in his head, and loved telling ribald jokes and reciting off-color limericks.

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Von Neumann was born in Budapest to a well-to-do, cultured, jolly family. Budapest at the time was the sixth-largest city in Europe, prosperous and growing, with the world's first underground subway. Its literacy rate was over 90%. More than 25% of the population was Jewish, including the von Neumanns, although John von Neumann paid little attention to his Jewishness except as a source of jokes. He was by no means the only famous product of pre-World War I Budapest. Among his contemporaries were famous physicists like himself-Leo Szilard and Edward Teller-as well as celebrities from the world of entertainment-George Solti, Paul Lukas, Leslie Howard (born Lazlo Steiner), Adolph Zukor, Alexander Korda, and, perhaps most famous of all, ZsaZsa Gabor.

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Strangely, Markowitz had no interest in equity investment when he first turned his attention to the ideas dealt with in "Portfolio Selection." He knew nothing about the stock market. A self-styled "nerd" as a student, he was working in what was then the relatively young field of linear programming. Linear programming, which happened to be an innovation to which John von Neumann had made significant contributions, is a means of developing mathematical models for minimizing costs while holding outputs constant, or for maximizing outputs while holding costs constant. The technique is essential for dealing with problems like those faced by an airline that aims to keep a limited number of aircraft as busy as possible while flying to as many destinations as possible.

The Singularity Is Near: When Humans Transcend Biology
by Ray Kurzweil
Published 14 Jul 2005

Although the number of transistors per unit cost has doubled every two years, transistors have been getting progressively faster, and there have been many other levels of innovation and improvement. The overall power of computation per unit cost has recently been doubling every year. In particular, the amount of computation (in computations per second) that can be brought to bear to a computer chess machine doubled every year during the 1990s. 3. John von Neumann, paraphrased by Stanislaw Ulam, "Tribute to John von Neumann," Bulletin of the American Mathematical Society 64.3, pt. 2 (May 1958): 1–49. Von Neumann (1903–1957) was born in Budapest into a Jewish banking family and came to Princeton University to teach mathematics in 1930. In 1933 he became one of the six original professors in the new Institute for Advanced Study in Princeton, where he stayed until the end of his life.

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The Intuitive Linear View Versus the Historical Exponential View When the first transhuman intelligence is created and launches itself into recursive self-improvement, a fundamental discontinuity is likely to occur, the likes of which I can't even begin to predict. —MICHAEL ANISSIMOV In the 1950s John von Neumann, the legendary information theorist, was quoted as saying that "the ever-accelerating progress of technology ... gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue."3 Von Neumann makes two important observations here: acceleration and singularity.

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One theory speculates that the universe itself began with such a Singularity.18 Interestingly, however, the event horizon (surface) of a black hole is of J finite size, and gravitational force is only theoretically infinite at the zero-size center of the black hole. At any location that could actually be measured, the forces are finite, although extremely large. The first reference to the Singularity as an event capable of rupturing the fabric of human history is John von Neumann's statement quoted above. In the 1960s, I. J. Good wrote of an "intelligence explosion" resulting from intelligent machines' designing their next generation without human intervention. Vernor Vinge, a mathematician and computer scientist at San Diego State University, wrote about a rapidly approaching "technological singularity" in an article for Omni magazine in 1983 and in a science-fiction novel, Marooned in Realtime, in 1986.19 My 1989 book, The Age of Intelligent Machines, presented a future headed inevitably toward machines greatly exceeding human intelligence in the first half of the twenty-first century.20 Hans Moravec's 1988 book Mind Children came to a similar conclusion by analyzing the progression of robotics.21 In 1993 Vinge presented a paper to a NASA-organized symposium that described the Singularity as an impending event resulting primarily from the advent of "entities with greater than human intelligence," which Vinge saw as the harbinger of a runaway phenomenon.22 My 1999 book, The Age of Spiritual Machines: When Computers Exceed Human Intelligence, described the increasingly intimate connection between our biological intelligence and the artificial intelligence we are creating.23 Hans Moravec's book Robot: Mere Machine to Transcendent Mind, also published in 1999, described the robots of the 2040s as our "evolutionary heirs," machines that will "grow from us, learn our skills, and share our goals and values, ... children of our minds."24 Australian scholar Damien Broderick's 1997 and 2001 books, both titled The Spike, analyzed the pervasive impact of the extreme phase of technology acceleration anticipated within several decades.25 In an extensive series of writings, John Smart has described the Singularity as the inevitable result of what he calls "MEST" (matter, energy, space, and time) compression.26 From my perspective, the Singularity has many faces.

Superintelligence: Paths, Dangers, Strategies
by Nick Bostrom
Published 3 Jun 2014

In particular, cognitive enhancement could accelerate science and technology, including progress toward more potent forms of biological intelligence amplification and machine intelligence. Consider how the rate of progress in the field of artificial intelligence would change in a world where Average Joe is an intellectual peer of Alan Turing or John von Neumann, and where millions of people tower far above any intellectual giant of the past.63 A discussion of the strategic implications of cognitive enhancement will have to await a later chapter. But we can summarize this section by noting three conclusions: (1) at least weak forms of superintelligence are achievable by means of biotechnological enhancements; (2) the feasibility of cognitively enhanced humans adds to the plausibility that advanced forms of machine intelligence are feasible—because even if we were fundamentally unable to create machine intelligence (which there is no reason to suppose), machine intelligence might still be within reach of cognitively enhanced humans; and (3) when we consider scenarios stretching significantly into the second half of this century and beyond, we must take into account the probable emergence of a generation of genetically enhanced populations—voters, inventors, scientists—with the magnitude of enhancement escalating rapidly over subsequent decades.

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Both of these approaches were proposed at the time. The hardline approach of launching or threatening a first strike was advocated by some prominent intellectuals such as Bertrand Russell (who had long been active in anti-war movements and who would later spend decades campaigning against nuclear weapons) and John von Neumann (co-creator of game theory and one of the architects of US nuclear strategy).34 Perhaps it is a sign of civilizational progress that the very idea of threatening a nuclear first strike today seems borderline silly or morally obscene. A version of the benign approach was tried in 1946 by the United States in the form of the Baruch plan.

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Still, it is impressive that an amount of economic growth that took 200 years seven thousand years ago takes just ninety minutes now, and that the world population growth that took two centuries then takes one and a half weeks now. See also Maddison (2005). 2. Such dramatic growth and acceleration might suggest one notion of a possible coming “singularity,” as adumbrated by John von Neumann in a conversation with the mathematician Stanislaw Ulam: Our conversation centred on the ever accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue.

How We Got Here: A Slightly Irreverent History of Technology and Markets
by Andy Kessler
Published 13 Jun 2005

The need for precision weapons would both directly and indirectly launch the digital revolution: Transistors in 1948, lasers and integrated circuits in 1958, packet switching in 1964 and microprocessors in 1970, and that was just the easy stuff. Using Edison effect tubes and relays and other forms of logic and memory, scientists and engineers invented electronic computers to help win World War II. John von Neumann at the Moore School at the University of Pennsylvania designed the ENIAC digital computer, the birth mother of the U.S. computer industry, to speed up calculations for artillery firing tables for Navy guns. At the same time, Alan Turing and the British at Bletchley Park designed the Colossus computer to decipher Enigma codes.

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It also might take hours or even several days to change the algorithm or program that the ENIAC 116 HOW WE GOT HERE worked on. It had very little internal memory. Of course, the biggest problem with the ENIAC was that it was still a decimal machine working with 10 digits instead of the two of Boolean binary math. That increased its complexity, probably 100-fold. One of the folks working on ENIAC was John von Neumann, who had come over in June 1944 from Princeton’s Institute of Advanced Study, where Turing had studied. Von Neumann reengineered the ENIAC to store the algorithm/program inside it along with the data to be processed, and also added a “conditional control transfer.” For memory, von Neumann noticed that mercury delay lines, used in radar systems to store aircraft location information, stored a pulse or wave in a vial of slow moving mercury.

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. *** The need for precision weapons would both directly and indirectly launch the digital revolution: transistors in 1948, lasers and integrated circuits in 1958, packet switching in 1964 and microprocessors in 1970, and that was just the easy stuff. Computers were invented to help win World War II. John von Neumann and the Moore School at the University of Pennsylvania designed the ENIAC digital computer, the birth mother of the U.S. computer industry, to speed up calculations for artillery firing tables for Navy guns. At the same time, Alan Turing and the British at Bletchley Park designed the Colossus computer to decipher Enigma codes.

The Scientist as Rebel
by Freeman Dyson
Published 1 Jan 2006

The day after his arrival, he died suddenly of a pulmonary embolism on the steps of the Royal Institute of Technology in Stockholm. Dark Hero of the Information Age5 is the third biography of Norbert Wiener, unless there are others of which I am ignorant. First came a joint biography of Wiener and the mathematician John von Neumann, John von Neumann and Norbert Wiener: From Mathematics to the Technologies of Life and Death, by Steve Heims in 1980.6 Then came Norbert Wiener, 1894–1964, by Pesi Masani in 1990.7 The main justification for a new biography is that the three biographies emphasize different aspects of Wiener’s life and character.

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But here too, even when Teller is most heavily engaged in political battles, he portrays his opponents as human beings and describes their concerns fairly. There is sadness in his account but no bitterness. The greatest sadness is the personal sadness, when three of his close friends and allies, Enrico Fermi, John von Neumann, and Ernest Lawrence, die untimely deaths before their work is done. Throughout his struggles he maintains his talent for friendship. Szilard, who disagreed violently with Teller about almost everything, remained one of his closest friends. The worst period of Teller’s life began in 1954 when he testified against J.

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In parallel with our exploitation of biological engineering, we may achieve an equally profound industrial revolution by following the alternative route of self-reproducing machinery. Self-reproducing machines are devices which have the multiplying and self-organizing capabilities of living organisms but are built of metal and computers instead of protoplasm and brains. It was the mathematician John von Neumann who first demonstrated that self-reproducing machines are theoretically possible and sketched the logical principles underlying their construction. The basic components of a self-reproducing machine are precisely analogous to those of a living cell. The separation of function between genetic material (DNA) and enzymatic machinery (protein) in a cell corresponds exactly to the separation between software (computer programs) and hardware (machine tools) in a self-reproducing machine.

Smart Mobs: The Next Social Revolution
by Howard Rheingold
Published 24 Dec 2011

Those “covenants” mentioned by Hobbes turn out to be tricky because humans play elaborate games of trust and deception. Economists have long sought the mathematical grail that could predict the behavior of markets. In 1944, John von Neumann and Oskar Morgenstern’s Theory of Games and Economic Behavior provided, if not a grail, a means of looking at the way people compete and collude, cooperate and defect, in competitive situations.28 John von Neumann was arguably the most influential but least-famous scientist in history, considering his fundamental contributions to mathematics, quantum physics, game theory, and the development of the atomic bomb, digital computer, and intercontinental ballistic missile.29 Von Neumann was a prodigy who joked with his father in classical Latin and Greek at the age of six, was a colleague of Einstein at Princeton’s Institute for Advanced Study, and was perhaps the most brilliant of the stellar collection of scientists gathered at Los Alamos to undertake the Manhattan Project.

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Hamilton, “The Genetical Evolution of Social Behavior,” Journal of Theoretical Biology 7 (1964): 152. 26. Richard Dawkins, The Selfish Gene (Oxford: Oxford University Press, 1976). 27. Hobbes, Leviathan, 95. 28. John von Neumann and Oskar Morgenstern, Theory of Games and Economic Behavior (Princeton: Princeton University Press, 1944). 29. William Poundstone, Prisoner’s Dilemma: John von Neumann, Game Theory, and the Puzzle of the Bomb (New York: Doubleday, 1992). 30. J. Bronowski, The Ascent of Man (Toronto: Little, Brown, 1973). 31. Herman Kahn, On Thermonuclear War (Princeton: Princeton University Press, 1960). 32.

The Deep Learning Revolution (The MIT Press)
by Terrence J. Sejnowski
Published 27 Sep 2018

The world of law is well on its way to becoming “Legally Deep.”25 Learning How to Play Poker Heads-up no-limit Texas hold ’em is one of the most popular versions of poker, commonly played in casinos, and the no-limit betting form is played at the main event of the World Series of Poker (figure 1.7). Poker is challenging because, unlike chess, where both players have access to the same information, poker players have imperfect information, and, at the highest levels of play, skills in bluffing and deception are as important as the cards that are dealt. The mathematician John von Neumann, who founded mathematical game theory and pioneered digital computers, was particularly fascinated with poker. As he put it: “Real life consists of bluffing, of little tactics of deception, of asking yourself what is the other man going to think I mean to do. And that is what games are about in my theory.”26 Poker is a game that reflects parts of human intelligence that were refined by evolution.

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Cellular automata typically have only a few discrete values that evolve in time, depending on the states of the other cells. One of the simplest cellular automata is a one-dimensional array of cells, each with value of 0 or 1 (box 13.1). Perhaps the most famous cellular automaton is called the “Game of Life,” which was invented by John Conway, the John von Neumann professor of mathematics at Princeton, in 1968, popularized by Martin Gardner in his “Mathematical Games” column in Scientific American, and is illustrated in figure 13.2. The board is a two-dimensional array of cells that can only be “on” or “off” and the update rule only depends on the four nearest neighbors.

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In 1943, Warren McCulloch and Walter Pitts showed that it was possible to build a digital computer out of simple binary threshold units like the perceptron, which could be wired up to make the elementary logical gates in a computer.11 We now know that brains have mixed analog and digital properties and that their neural circuits generally do not compute logical functions. But McCulloch and Pitts’s 1943 paper received a lot of attention at the time and, in particular, inspired John von Neumann to think about computers. He built one of the first digital computers that had stored programs, an unusual project for a mathematician at that time, although when von Neumann died in 1957, the Institute for Advanced Study did not continue his line of research and scrapped his computer.12 Von Neumann also was interested in the brain.

Life's Greatest Secret: The Race to Crack the Genetic Code
by Matthew Cobb
Published 6 Jul 2015

If the demon and the chamber were taken as a whole, the entropy of the system would not decline, and the second law remained intact. Although Szilárd did not use the term information, his theoretical discussion linked entropy and measures of knowledge in a way that proved significant. * At the beginning of 1945, Wiener and fellow mathematician John von Neumann organised a meeting of the newly formed Teleological Society. The aim of the society was to study ‘how purpose is realised in human and animal conduct and on the other hand how purpose can be imitated by mechanical and electrical means.’21 Von Neumann was a mathematician and a pioneer of game theory – mathematical models that describe and predict simple behaviours.

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The backdrop to the developments in cybernetics, and indeed the source of much of its funding, was the Cold War. In February 1949, the US lost its monopoly on nuclear weapons when the USSR exploded its first atom bomb. In 1950, the Cold War began to heat up as the Korean War broke out and the US fought a proxy war against the Russians and the Chinese. Shocked by these developments, the anti-communist John von Neumann pressed the US government to focus all its research effort on building a hydrogen bomb. Thanks in part to his lobbying, a major development programme began in which he was heavily involved, leaving little time for his other interests. The project culminated in the explosion of the first H-bomb in November 1952, with a yield that was nearly 1,000 times more destructive than that of Hiroshima.

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M., ‘Alphonse Raymond Dochez, 1882–1964’, Biographical Memoir, Washington DC, National Academy of Sciences, 1971. Heijmans, B. T., Tobi, E. W., Stein, A. D. et al., ‘Persistent epigenetic differences associated with prenatal exposure to famine in humans’, Proceedings of the National Academy of Sciences USA, vol. 105, 2008, pp. 17046–9. Heims, S. J., John von Neumann & Norbert Weiner: From Mathematics to the Technologies of Life and Death, London, MIT Press, 1980. Heims, S. J., The Cybernetics Group, London, MIT Press, 1991. Henikoff, S., Keene, M. A., Fechtel, K. and Fristrom, J. W., ‘Gene within a gene: nested Drosophila genes encode unrelated proteins on opposite DNA strands’, Cell, vol. 44, 1986, pp. 33–42.

Chaos: Making a New Science
by James Gleick
Published 18 Oct 2011

Implicitly, the mission of many twentieth-century scientists—biologists, neurologists, economists—has been to break their universes down into the simplest atoms that will obey scientific rules. In all these sciences, a kind of Newtonian determinism has been brought to bear. The fathers of modern computing always had Laplace in mind, and the history of computing and the history of forecasting were intermingled ever since John von Neumann designed his first machines at the Institute for Advanced Study in Princeton, New Jersey, in the 1950s. Von Neumann recognized that weather modeling could be an ideal task for a computer. There was always one small compromise, so small that working scientists usually forgot it was there, lurking in a corner of their philosophies like an unpaid bill.

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Inner Rhythms The sciences do not try to explain, they hardly even try to interpret, they mainly make models. By a model is meant a mathematical construct which, with the addition of certain verbal interpretations, describes observed phenomena. The justification of such a mathematical construct is solely and precisely that it is expected to work. —JOHN VON NEUMANN BERNARDO HUBERMAN LOOKED OUT over his audience of assorted theoretical and experimental biologists, pure mathematicians and physicians and psychiatrists, and he realized that he had a communication problem. He had just finished an unusual talk at an unusual gathering in 1986, the first major conference on chaos in biology and medicine, under the various auspices of the New York Academy of Sciences, the National Institute of Mental Health, and the Office of Naval Research.

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Press, 1981), 3:371. Wiener anticipated Lorenz in seeing at least the possibility of “self-amplitude of small details of the weather map.” He noted, “A tornado is a highly local phenomenon, and apparent trifles of no great extent may determine its exact track.” “THE CHARACTER OF THE EQUATION” John von Neumann, “Recent Theories of Turbulence” (1949), in Collected Works, ed. A. H. Taub (Oxford: Pergamon Press, 1963), 6:437. CUP OF HOT COFFEE “The predictability of hydrodynamic flow,” in Transactions of the New York Academy of Sciences II:25:4 (1963), pp. 409–32. “WE MIGHT HAVE TROUBLE” Ibid., p. 410.

The Blockchain Alternative: Rethinking Macroeconomic Policy and Economic Theory
by Kariappa Bheemaiah
Published 26 Feb 2017

To combat this, pioneers such as Alan Turing and his mentor Max Newman, set about designing and building automated machines (Turing Machines) that could decrypt these camouflaged communiqués. This effectively changed the use of the computer and increased the diversity of the kinds of computers. After the war, advances by notable inventors such as John Mauchly, Presper Eckert and John von Neumann (a veritable polymath) led to the creation of the EDVAC (Electronic Discrete Variable Automatic Computer) , the first binary computer. With binary computers coming of age, there was an increasing need to develop software to give instructions to computers. Punch cards were soon replaced by logic gates (from Boolean algebra) and languages such as COBOL and FORTRAN (FORmula TRANslation), helped in the creation of early operating systems.

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The mapping of macroeconoic movements to the flow of fluids was representative that these thinkers looked at the economy as a subject of physical inquiry. Figure 4-5.Professor A.W.H (Bill) Phillips with the Phillips Machine (MONIAC) Source: The Phillips Machine Project’ by Nicholas Bar, LSE Magazine, June 1988, No 75. However, the introduction of mathematical game theory in the 1950s by John von Neumann, threw a monkey wrench into this link between economic and physics. When game theory was introduced (See ‘Theory of Games and Economic Behaviour’, von Neumann and Morgenstern), economics immediately realised that the maths of this field could be used to study the behaviour of selfish agents to get the better of other agents in an economy.

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Mauchly, a physicist, who was interested in meteorology tried to develop a weather prediction model. But he soon realized that this would not be possible without some kind of automatic calculating machine. As a result, he developed the concept of an electronic computer using vacuum tubes. It was during the time of developing ENIAC that he met the renowned polymath, John von Neumann, and with his help went on to design a stored-program computer, the EDVAC (Electronic Discrete Variable Automatic Computer), the first binary computer (ENIAC was decimal). See Figure 4-11. Figure 4-11.General design of the Electronic Discrete Variable Automatic Computer. Reference Source: ‘The von Neumann Architecture’, The Computing Universe, 2014 From an abstract architecture perspective, von Neumann’s design is logically equivalent to Turing’s Universal Turing Machine.

Beyond Weird
by Philip Ball
Published 22 Mar 2018

How the founders puzzled, argued, improvised, guessed, in their efforts to come up with a theory to explain it all. How knowledge once deemed precise and objective now seemed uncertain, contingent and observer-dependent. And the cast! Albert Einstein, Niels Bohr, Werner Heisenberg, Erwin Schrödinger, and other colourful intellectual giants like John von Neumann, Richard Feynman and John Wheeler. Best of all for its narrative value is the largely good-natured but trenchant dispute that rumbled on for decades between Einstein and Bohr about what it all meant – about the nature of reality. This is indeed a superb story, and if you haven’t heard it before then you should.fn1 Yet most popular descriptions of quantum theory have been too wedded to its historical evolution.

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We don’t ban some questions simply because we don’t know what to say about them, but instead recognize that quantum mechanics has no maths that can provide an answer: it’s rather like expecting simple arithmetic to tell us what an apple tastes like. In that much, Consistent Histories offers a valuable tool. But it stops short of supplying a physical picture that improves on other interpretations – which is why it is not exactly inconsistent with some of them. The Hungarian mathematical physicist John von Neumann was one of the first to make wavefunction collapse an ‘official’ component of quantum mechanics, incorporating it into his 1932 textbook on the subject. He pointed out that the collapse happens through the intervention of an observer, and so figured that it must have something to do with the act of observation itself.

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Many of the pioneers of quantum computing are the same folk who think most profoundly about what quantum mechanics means. Had these machines and related quantum information technologies been invented sooner – and really there is no clear reason why they should not have been – we can be sure that the likes of Bohr, Einstein, John von Neumann and John Wheeler would have had plenty to say about them. After all, one of those quantum pioneers is credited with the initial concept. In 1982 Richard Feynman wondered about the best way of ‘simulating physics with computers’. Computer simulation is now a mature discipline: a way of predicting how things behave by representing them as a kind of computer model governed by physical laws, and letting the laws unfold to see what emerges.

The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant From Two Centuries of Controversy
by Sharon Bertsch McGrayne
Published 16 May 2011

Turing was given an Order of the British Empire (OBE), a routine award given to high civil servants. Newman was so angry at the government’s “derisory” lack of gratitude to Turing that he refused his own OBE. Britain’s science, technology, and economy were losers, too. The Colossi were built and operational years before the ENIAC in Pennsylvania and before John von Neumann’s computer at the Institute for Advance Study in Princeton, but for the next half century the world assumed that U.S. computers had come first. Obliterating all information about the decryption campaign distorted Cold War attitudes about the value of cryptanalysis and about antisubmarine warfare.

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In that 80% of aircraft crashes occurred within 3 miles of an air force base, the likelihood of public exposure was growing. And so it went. None of these studies involved a nuclear explosion, but to a Bayesian they suggested ominous possibilities. Computationally, Madansky was confident that RAND’s two powerful computers, a 700 series IBM and the Johnniac, designed by and named for John von Neumann, could handle the job. But he hoped to avoid using them by solving the problem with pencil and paper. Given the power and availability of computers in the 1950s, many Bayesians were searching for ways to make calculations manageable. Madansky latched onto the fact that many types of priors and posteriors share the same probability curves.

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“In the year I studied statistics, I don’t think I heard the word ‘Bayes.’ As a way of inference, it was nonexistent. It was all strictly Neyman-Pearson, classical, objectivistic (frequency-based) statistics.”10 Although Schlaifer had embraced Bayes in one fell swoop, Raiffa inched grudgingly toward its subjectivity. But reading John von Neumann and Oskar Morgenstern’s book Game Theory (1944), he instinctively assessed how others would play in order to determine how he himself should compete: “In my naiveté, without any theory or anything like that. . . . [I began] assessing judgmental probability distributions. I slipped into being a subjectivist without realizing how radically I was behaving.

Genius: The Life and Science of Richard Feynman
by James Gleick
Published 1 Jan 1992

He was nervous about it. As the day approached, Wigner, who ran the colloquiums, stopped Feynman in the hall. Wigner said he had heard enough from Wheeler about the absorber theory to think it was important. Because of its implications for cosmology he had invited the great astrophysicist Henry Norris Russell. John von Neumann, the mathematician, was also going to come. The formidable Wolfgang Pauli happened to be visiting from Zurich; he would be there. And though Albert Einstein rarely bestirred himself to the colloquiums, he had expressed interest in attending this one. Wheeler tried to calm Feynman by promising to field questions from the audience.

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Computing by Brain Walking around the hastily built wooden barracks that housed the soul of the atomic bomb project in 1943 and 1944, a scientist would see dozens of men laboring over computation. Everyone calculated. The theoretical department was home to some of the world’s masters of mental arithmetic, a martial art shortly to go the way of jiujitsu. Any morning might find men such as Bethe, Fermi, and John von Neumann together in a single small room where they would spit out numbers in a rapid-fire calculation of pressure waves. Bethe’s deputy, Weisskopf, specialized in a particularly oracular sort of guesswork; his office became known as the Cave of the Hot Winds, producing, on demand, unjustifiably accurate cross sections (shorthand for the characteristic probabilities of particle collisions in various substances and circumstances).

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Such questions required a workable formula for the propagation of a spherical detonation wave in a compressible fluid, the “compressible fluid” in this case being the shotput-size piece of plutonium liquefied in the microseconds before it became a nuclear blast. The pressure would be more intense than at the earth’s center. The temperature would reach 50 million degrees Centigrade. The theorists were on their own here; experimentalists could offer little more than good wishes. All during 1944 the computation effort grew. John von Neumann served as a traveling consultant with an eye on the postwar future. Von Neumann—mathematician, logician, game theorist (he was more and more a fixture in the extraordinary Los Alamos poker game), and one of the fathers of modern computing—talked with Feynman while they worked on the IBM machines or walked though the canyons.

The End of Theory: Financial Crises, the Failure of Economics, and the Sweep of Human Interaction
by Richard Bookstaber
Published 1 May 2017

As the mathematician and economist Donald Saari puts it, “Economics so effortlessly offers the needed ingredients for chaos that, rather than being surprised about exotic dynamics, we should be suspicious about models which always are stable.”7 And just as it is for the three-body problem of astronomy, Saari notes that there are examples of three-person, three-commodity economies with permanently unstable price dynamics, showing that we cannot hope to prove the stability of general equilibrium in all cases.8 CALL ME IRREDUCIBLE: THE ROCKET MAN AND CONWAY’S GAME OF LIFE In the 1940s, the famed Princeton polymath John von Neumann developed an abstract template for self-replicating machines, which he called a universal constructor. He simulated it, not on a computer, but using the cells on a sheet of graph paper, where each cell could take on any of twenty-nine states. His universal constructor gave rise to the concept of a von Neumann probe, a spacecraft capable of replicating itself, which could land on one galactic outpost, build a hundred copies of itself, each traveling off in one of a hundred different directions, discover other worlds, and replicate again, thereby exploring the universe—and, depending on the design of the machines, conquering the universe—with exponential efficiency.

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His universal constructor gave rise to the concept of a von Neumann probe, a spacecraft capable of replicating itself, which could land on one galactic outpost, build a hundred copies of itself, each traveling off in one of a hundred different directions, discover other worlds, and replicate again, thereby exploring the universe—and, depending on the design of the machines, conquering the universe—with exponential efficiency. The universal constructor caught the interest of John Conway, a British mathematician who would later hold the John von Neumann Chair of Mathematics at Princeton, and over “eighteen months of coffee times,” as he describes it, he began tinkering to simplify its set of rules. The result was what became known as Conway’s Game of Life.9 The “game” really isn’t one—it is a zero-player game, because once the initial conditions of the cells are set, there is no further interaction or input as the process evolves.

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Others have called them explanatory inductions, theoretical inductions, or theoretical inferences. More recently, many philosophers have used the term inference to the best explanation (Harman 1965; Lipton 2004). Chapter 15: Conclusion 1. Lynch (2008) provides a history of the development of modern weather forecasting. John von Neumann, in addition to the roles I have already mentioned in developing game theory and conceptualizing replicating machines, and in addition to his foundational work in mathematics, physics, computer science, and economics, also was central in this effort. 2. This fits within an emerging interest among the socially minded in the financial community called impact investing, in which investments are made with an eye toward profits but also with an objective of social returns.

The Simulation Hypothesis
by Rizwan Virk
Published 31 Mar 2019

It was then adopted by physicists as a more technical term for black holes—again using the idea of approaching infinity, in this case infinite gravity. More recently, the term has entered popular usage around the idea of artificial intelligence reaching, or even exceeding, human intelligence, resulting in an “intelligence explosion.” The origins of this use of the term singularity date back to the 1950s, at which time mathematician John von Neumann supposedly coined the term when he said, “The ever-accelerating progress of technology ... gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue.”17 Irving John Good, another mathematician, was one of the first to call super-intelligent AI the “last invention that man need make.”

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This means that the observer, who is a conscious entity, is participating in the outcome of the results of physical phenomena, at least at the subatomic level. This led to an uncomfortable idea for many scientists that consciousness was somehow involved in the physical universe, an idea first proposed by famous mathematician John von Neumann in the 1930s and has been a source of debate ever since. There are other interpretations of the basic findings of quantum physics (we’ll look at one alternative interpretation, the many-worlds interpretation, in the next chapter), but the most prominent, called the Copenhagen interpretation, put forth by Max Born, Heisenberg, and Bohr, is consistent with this worldview: that probabilities are collapsed by observation.

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Knowing that several particles (or qubits) are entangled, it’s possible to come up with error-correction code such that if one qubit flips unexpectedly, this can be found out and reversed. Without going too far into the computer science or the physics, if evidence for error-correcting codes is found in models of the universe, it becomes ever more likely that the universe is some kind of simulation running on a computer. Researchers from Einstein and John von Neumann’s research center, the Institute for Advanced Study in Princeton, New Jersey, Ahmed Almheiri, Xi Dong, and Daniel Harlow, have discovered that these quantum error-correction codes not only exist but, at least in their simulated worlds, the error codes may define the fabric of space-time itself.

The Golden Ticket: P, NP, and the Search for the Impossible
by Lance Fortnow
Published 30 Mar 2013

P versus NP P versus NP is about all the problems described above and thousands more of a similar flavor: How fast can we search through a huge number of possibilities? How easily can we find that “golden ticket,” that one best answer? The P versus NP problem was first mentioned in a 1956 letter from Kurt Gödel to John von Neumann, two of the greatest mathematical minds of the twentieth century. That letter was unfortunately lost until the 1980s. The P versus NP problem was first publicly announced in the early 1970s by Steve Cook and Leonid Levin, working separately in countries on opposite sides of the Cold War. Richard Karp followed up with a list of twenty-one important problems that capture P versus NP, including the traveling salesman problem and the partition puzzle mentioned earlier.

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Alexander Razborov, a Russian student, played a major role in the development of circuit complexity as an approach to proving P ≠ NP, a story we tell in chapter 7. After the collapse of the Soviet Union and the rise of the Internet, Russian mathematical researchers no longer worked in isolation. The world is now a truly global research environment. The Gödel Letter In 1956 Kurt Gödel wrote a letter to John von Neumann, one of the pioneers of computer science and many other fields. In this letter (written in German), Gödel talks about the satisfiability problem and formulates the P versus NP question in different terminology. He suggests that if we lived in a world where P = NP, “the mental work of a mathematician concerning Yes-or-No questions could be completely replaced by a machine. … Now it seems to me, however, to be completely within the realm of possibility.”

Darwin's Dangerous Idea: Evolution and the Meanings of Life
by Daniel C. Dennett
Published 15 Jan 1995

If it exhibits peculiarities that could only have arisen in the course of solving the subproblems in some apparently remote branch of the Tree that grows in Design Space, then barring a miracle or a coincidence too Cosmic to credit, there must be a copying event of some kind that moved that completed design work to its new location. {134} There is no single summit in Design Space, nor a single staircase or ladder with calibrated steps, so we cannot expect to find a scale for comparing amounts of design work across distant developing branches. Thanks to the vagaries and digressions of different "methods adopted," something that is in some sense just one problem can have both hard and easy solutions, requiring more or less work. There is a famous story about the mathematician and physicist (and coinventor of the computer) John von Neumann, who was legendary for his lightning capacity to do prodigious calculations in his head. (Like most famous stories, this one has many versions, of which I choose the one that best makes the point I am pursuing.) One day a colleague approached him with a puzzle that had two paths to solution, a laborious, complicated calculation and an elegant, Aha!

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What on Earth inspired Conway and his students to create first this world and then this amazing denizen of that world? They were trying to answer at a very abstract level one of the central questions we have been considering in this chapter: what is the minimal complexity required for a self-reproducing thing? They were following up the brilliant early speculations of John von Neumann, who had been working on the question at the time of his death {172} in 1957. Francis Crick and James Watson had discovered DNA in 1953, but how it worked was a mystery for many years. Von Neumann had imagined in some detail a sort of floating robot that picked up pieces of flotsam and jetsam that could be used to build a duplicate of itself that would then be able to repeat the process.

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(See Mazlish 1993) It is a long and winding road from molecules to minds, with many diverting spectacles along the way — and we will tarry over the most interesting of these in subsequent chapters — but now is the time to look more closely than usual at the Darwinian beginnings of Artificial Intelligence. 5. THE COMPUTER THAT LEARNED TO PLAY CHECKERS Alan Turing and John von Neumann were two of the greatest scientists of the century. If anybody could be said to have invented the computer, they did, and their brainchild has come to be recognized as both a triumph of engineering and an intellectual vehicle for exploring the most abstract realms of pure science. Both thinkers were at one and the same time awesome theorists and deeply practical, epitomizing an intellectual style that has been playing a growing role in science since the Second World War.

The Transhumanist Reader
by Max More and Natasha Vita-More
Published 4 Mar 2013

In 1970, Marvin Minsky made what turned out to be highly optimistic forecasts of the advent of super-intelligent artificial intelligence (AI), then in a 1994 Scientific American article explained why vastly extended lives will require replacing our biological brains with superior computational devices. The idea of accelerating technological progress driven by machine super-intelligence dates back several decades. This idea, now frequently referred to as “the singularity,” was explicitly pondered in a 1958 conversation between Stanislaw Ulam and John von Neumann during which they discussed “the ever accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue” (Ulam 1958). In 1965, I.J.

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Sandberg, Anders (2001) “Morphological Freedom – Why We Not Just Want It, but Need It.” http://www.nada.kth.se/~asa/Texts/MorphologicalFreedom.htm. Retrieved November 21, 2011. Stambler, Ilia (2010) “Life Extension: A Conservative Enterprise? Some Fin-de-Siècle and Early Twentieth-Century Precursors of Transhumanism.” Journal of Evolution and Technology 21/1 (March), pp. 13–26. Ulam, Stanislaw (1958) “John von Neumann 1903–1957.” Bulletin of the American Mathematical Society (May), part 2, pp. 1–49. Various (2002) “The Transhumanist Declaration.” http://humanityplus.org/philosophy/transhumanist-declaration/. Various (2003) “The Transhumanist FAQ: v 2.1.” World Transhumanist Association. http://humanityplus.org/philosophy/transhumanist-faq/.

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The problem is that while we can imagine, for example, a robot arm that can screw, bolt, solder, and weld enough to assemble a robot arm from parts, it needs a sequence of instructions to obey in this process. And there is more than one instruction per part. But the instructions must be embodied in some physical form, so to finish the process we need instructions to build the instructions, and so on, in an infinite regress. The answer to this seeming conundrum was given mathematically by John von Neumann, and at roughly the same time (the 1950s) was teased out of the naturally occurring self-reproducing machines we find all around us, living cells. It turns out to be the same answer in both cases. First, design a machine that can build machines, like the robot arm above. (In a cell, there is such a thing, called a ribosome.)

The Undercover Economist: Exposing Why the Rich Are Rich, the Poor Are Poor, and Why You Can Never Buy a Decent Used Car
by Tim Harford
Published 15 Mar 2006

One auction really did raise less than 1 percent of what was hoped for, while another raised ten times as much as expected. This wasn’t down to luck but to cleverness in some cases and blundering in others. Auctioning air, like playing poker, is a game of great skill—and one that was played for very high stakes indeed. Love, war, and poker Many of those who knew the mathematician John von Neumann regarded him as the “best brain in the world,” and they had a chance to compare him with some stiff competition, given that one of von Neumann’s colleagues at Princeton was Albert Einstein. Von Neumann was a genius around whom grew a my-thology of almost superhuman intelligence. According to one story, Von Neumann was asked to assist with the design of a new supercomputer, required to solve a new and important mathematical problem, which was beyond the capacities of existing supercomputers.

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But poker with your buddies in the garage is not the World Series; what can game theory say about players who get drunk and bluff badly? This is not a knockdown objection to game theory. It is possible to model mistakes, forgetfulness, misinformation, and any other kind of failure on the part of the players to live up to the impossibly high standards of John Von Neumann. The trouble is that the more mistakes that need to be taken into account, the more complicated and the less useful game theory becomes. It is always useful for the game theorist to draw on experience as well as pure theory, because if the game becomes too complex for the players to understand, then the theory becomes nearly useless for practical purposes since it tells us nothing about what they will actually do.

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Telecom executives may curse the British auctions since 3G remains commercially unproven and threatened by competitors like Wi-Fi, but the public should celebrate them. All the compa- • 174 • T H E M E N W H O K N E W T H E V A L U E O F N O T H I N G nies involved were convinced that the 3G licenses offered tremendous scarcity value, and these auctions successfully secured a fair price for that apparent value. John von Neumann’s successors used game theory to achieve one of the most spectacular, if controversial, policy triumphs that economics had ever seen. The men who knew the “value of nothing” had shown that economists, like dentists, could finally earn their keep. • 175 • This page intentionally left blank W H Y P O O R C O U N T R I E S A R E P O O R E I G H T Why Poor Countries Are Poor They call Douala “the Armpit of Africa.”

Beyond: Our Future in Space
by Chris Impey
Published 12 Apr 2015

Using fairly conventional forms of propulsion, these probes could spread through a galaxy the size of the Milky Way in less than a few million years. The probes could investigate planetary systems and send information back to us on the home planet.23 The concept is named after the Hungarian mathematician and physicist John von Neumann. He was one of the major intellectual figures of the twentieth century, making important contributions to mathematics, physics, computer science, and economics. Noted physicist Eugene Wigner recalled that von Neumann’s unusual mind was like a “. . . perfect instrument whose gears were machined to mesh accurately within a thousandth of an inch.”

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This is the time, projected to be in the middle of the twenty-first century, when civilization and human nature itself are fundamentally transformed. One variant of the singularity is when artificial intelligence surpasses human intelligence. Software-based synthetic minds begin to program themselves and a runaway reaction of self-improvement occurs. This event was foreshadowed by John von Neumann and Alan Turing in the 1950s. Turing wrote that “. . . at some stage therefore we should have to expect the machines to take control . . . ,” and von Neumann described “. . . an ever-accelerating progress and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue.”17 A dystopian version of this event permeates the popular culture, from science fiction novels to movies such as Blade Runner and The Terminator.

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“X-Tech and the Search for Infra Particle Intelligence” by H. de Garis 2014, from Best of H+, online at http://hplusmagazine.com/2014/02/20/x-tech-and-the-search-for-infra-particle-intelligence/. 17. Intelligent Machinery, A Heretical Theory by A. Turing 1951, reprinted in Philosophia Mathematica 1996, vol. 4, no. 3, pp. 256–60. The von Neumann quote comes from Stanislaw Ulam’s “Tribute to John von Neumann” in the May 1958 Bulletin of the American Mathematical Society, p. 5. 18. “Are You Living in a Computer Simulation?” by N. Bostrom 2003. Philosophical Quarterly, vol. 53, no. 211, pp. 243–55. The views of Kurzweil and Moravec are represented in their popular books, in particular The Singularity Is Near: When Humans Transcend Biology by R.

The Road to Conscious Machines
by Michael Wooldridge
Published 2 Nov 2018

Surely what we really want is for them to make good choices – the best choices possible. The goal of AI thus began to shift from building agents that make human choices to agents that make optimal choices. The theory of optimal decision-making that underpins most work in AI goes back to the 1940s, and the work of John von Neumann – the same John von Neumann whom we met in Chapter 1, who did seminal work on the design of the earliest computers. Along with his colleague Oskar Morgenstern, he developed a mathematical theory of rational decision-making. This theory showed how the problem of making a rational choice could be reduced to a mathematical calculation.12 In agent-based AI, the idea was that the agent would use their theory to make optimal decisions on your behalf.

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In wartime Munich, Konrad Zuse designed a computer called the Z3 for the German Air Ministry – although it was not quite a modern computer, it introduced many of the key ingredients of one. Across the Atlantic in Pennsylvania, a team led by John Mauchly and J. Presper Eckert developed a machine called ENIAC to compute artillery tables. With some tweaks by the brilliant Hungarian mathematician John von Neumann, ENIAC established the fundamental architecture of the modern computer (the architecture of conventional computers is called the Von Neumann architecture, in his honour). Over in post-war England, Fred Williams and Tom Kilburn built the Manchester Baby, which led directly to the world’s first commercial computer, the Ferranti Mark 1 – Turing himself joined the staff of Manchester University in 1948, and wrote some of the first programs to run on it.

The Scandal of Money
by George Gilder
Published 23 Feb 2016

Like the electromagnetic spectrum, which bears all the messages of the Internet to and from your smartphone or computer, it must be rooted in the absolute speed of light, the ultimate guarantor of the integrity of time. Dominating our own era and revealing in fundamental ways the nature of money is the information theory of Kurt Gödel, John von Neumann, Alan Turing, and Claude Shannon. Information theory tells us that information is not order but disorder, not the predictable regularity that contains no news, but the unexpected modulation, the surprising bits. But human creativity and surprise depend upon a matrix of regularities, from the laws of physics to the stability of money.4 Information theory has impelled the global ascendancy of information technology.

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Gödel’s incompleteness theorem: Every logical system depends on propositions outside the system that are unprovable within the system. The first person to appreciate and publicize the importance of Kurt Gödel’s demonstration in 1931 that mathematical statements can be true but unprovable was John von Neumann. As von Neumann saw, Gödel’s proof depended on his invention of a mathematical “machine” that used numbers to encode and prove algorithms also expressed in numbers. This invention, absorbed by von Neumann and Alan Turing, launched computer science and INFORMATION THEORY and enabled the development of the Internet and the BLOCKCHAIN.

In Pursuit of the Perfect Portfolio: The Stories, Voices, and Key Insights of the Pioneers Who Shaped the Way We Invest
by Andrew W. Lo and Stephen R. Foerster
Published 16 Aug 2021

His reading of Hume had piqued his interest in philosophical questions such as “What do we know?” and “How do we know it?” and the uncertainty surrounding those questions. Consequently, Markowitz was drawn to the economics of uncertainty, particularly the theory of games and utility theory developed by John von Neumann and Oskar Morgenstern, and soon to the work on subjective probability by the University of Chicago’s own Leonard Jimmie Savage. Expected utility theory is the framework in economics for understanding how people make decisions over their lifetimes based on their preferences in consumption and savings—how much and when they want to consume or save.

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For background related to Markowitz’s early years and for events surrounding the serendipitous moment described in this chapter, see Markowitz (1991), Bernstein (1992), Markowitz (1993), Yost (2002), Buser (2004a), Fox (2009), and Markowitz (2010). 3. Interview with authors. 4. Interview with authors. 5. Interview with authors. 6. Interview with authors. Markowitz was particularly proud to be a recipient of the John von Neumann Theory Prize, awarded to an individual or group that has made fundamental and sustained contributions to theory in operations research and the management sciences. “I have von Neumann’s picture with the first computer posted on the cork board in one of my rooms.” 7. See Friedman (1976). First awarded in 1969 and commonly known as the Nobel Prize in Economics (as we will typically refer to it), but not one of the original categories, the formal name of the prize is the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel. 8.

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Vertin Award, received by Leibowitz, 200 Janus Henderson Investors, 169, 352–53n79 Jarrow, Robert, 180, 190–91 Jefferson, Thomas, 333 Jenrette, Richard, 260 Jensen, Michael, 90, 96, 109, 146, 218, 347n46; agency problem and, 108–9; collaboration with Scholes and Black, 146, 148; education of, 143; on efficient market hypothesis, 81; publications of, 148 Johnson, Craig, 75 John von Neumann Theory Prize, Markowitz as winner of, 336n6 joint-stock companies, modern, first, 8–9 Journal of Finance, 27 JP Morgan, Merton Model and, 185 Kahneman, Daniel, 42, 83–84 Kamstra, Mark, 253, 254 Kaplan, Paul, on Markowitz’s contribution to portfolio construction, 44 Katona, George, 228 Ketchum, Marshall, 23, 338n40 Keynes, John Maynard, 283, 320; Cambridge University endowment managed by, 16–17; lack of impact on investing, 17; publications of, 15, 16, 17, 132; on sources of return, 132 Kindleberger, Charles, 240–41 Klein, Lawrence, as Nobel Prize winner, 22 Klingenstein, J.

American Prometheus: The Triumph and Tragedy of J. Robert Oppenheimer
by Kai Bird and Martin J. Sherwin
Published 18 Dec 2007

Another German physicist, Ernst Pascual Jordan, was collaborating with Born and Heisenberg to formulate the matrix mechanics version of quantum theory. The young English physicist Paul Dirac, whom Oppenheimer had met at Cambridge, was then working on early quantum field theory, and in 1933 he would share a Nobel Prize with Erwin Schrödinger. The Hungarian-born mathematician John Von Neumann would later work for Oppenheimer on the Manhattan Project. George Eugene Uhlenbeck was an Indonesian-born Dutchman who, together with Samuel Abraham Goudsmit, discovered the concept of electron spin in late 1925. Robert quickly drew the attention of these men. He had met Uhlenbeck the previous spring during his weeklong visit to the University of Leiden.

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Despite having been skeptical about the implosion idea when it was first broached by Serber, Oppenheimer now marshaled all his persuasive powers to argue that they gamble everything on an implosion-design plutonium bomb. It was an audacious and brilliant gamble. Since the spring of 1943, when Seth Neddermeyer had volunteered to experiment with the concept, little progress had been made. But in the autumn of 1943, Oppenheimer brought the Princeton mathematician John von Neumann to Los Alamos, and von Neumann calculated that implosion was possible, at least theoretically. Oppenheimer was willing to bet on it. The next day, July 18, Oppenheimer summarized his conclusions for Groves: “We have investigated briefly the possibility of an electromagnetic separation. . . .

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Flexner wanted the very best people, and he wanted to ensure that none of his scholars would ever feel compelled to supplement their income by “writing unnecessary textbooks or engaging in other forms of hack work.” There would be “no duties, only opportunities.” Throughout the 1930s, Flexner recruited brilliant minds, mostly mathematicians like John von Neumann, Kurt Gödel, Hermann Weyl, Deane Montgomery, Boris Podolsky, Oswald Veblen, James Alexander and Nathan Rosen. Flexner hailed the “usefulness of useless knowledge.” But by the 1940s, the Institute was in danger of acquiring a reputation for coddling brilliant minds with forever unfulfilled potential.

Priceless: The Myth of Fair Value (And How to Take Advantage of It)
by William Poundstone
Published 1 Jan 2010

Born in Morristown, New Jersey, he was the son of an economist and grew up hearing the table talk of his father’s colleagues. This instilled in him a rebellious skepticism toward economics. Ward decided on psychology as a career, studying at Swarthmore and Harvard. It was at Harvard that he read the work of John von Neumann and Oskar Morgenstern, and he wasn’t crazy about all he read. Hungarian-born John von Neumann was one of the great mathematicians of the twentieth century. At the urging of Princeton economist Oskar Morgenstern, von Neumann turned his brilliant mind to the problems of economics. The result was a 1944 book, Theory of Games and Economic Behavior.

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Between the trick haircut and the tight smile that might be a frown, Allais’ face evoked one of those odd pictures that becomes a different face when turned upside down. Allais had told Savage he had something to show him. It was a little test he wanted him to take. The important thing is that Savage failed the test. Savage was a brash statistician, then at the University of Chicago. He had gone into statistics on the advice of John von Neumann himself. Visually, the most remarkable thing about him was his eyeglasses. Their lenses packed enough diopters to reveal the space behind his head. At Chicago, Savage had acquired a second mentor, Milton Friedman—founding father of the Chicago school of economics, future Nobel laureate, and veritable saint to Reagan-era capitalists.

Einstein's Fridge: How the Difference Between Hot and Cold Explains the Universe
by Paul Sen
Published 16 Mar 2021

A few months later, in January of 1940, the couple married and honeymooned in New Hampshire, an event spoiled by an anti-Semitic hotelier who wouldn’t let them have a room because Levor was Jewish. Later that year, the couple moved to Princeton, where Shannon had been granted a fellowship at the Institute for Advanced Study. Here the pair rubbed shoulders with some of the world’s greatest mathematicians and physicists, Hermann Weyl, John von Neumann, and Einstein, who had made Princeton his home after having been driven out of Germany in 1933. For the Shannons, however, life took an unhappy turn. Their affair, which had blossomed so quickly, self-destructed just as fast. To Levor, Shannon changed. He found the atmosphere at Princeton alienating and stifling and his natural joie de vivre evaporated.

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Here’s Shannon’s equation for calculating the size of any given piece of information: H = –Σi pi logb pi And here’s one way of stating Boltzmann’s equation for calculating the entropy of any given system: S = –kB Σi pi ln pi These two equations don’t just look similar; they’re effectively the same. Shortly after deriving his equation, Shannon pointed the similarity out to John von Neumann, then widely considered the world’s best mathematician. Von Neumann shrugged, suggesting that Shannon call his measure of the number of bits needed to carry a piece of information information entropy on the grounds that no one really understood thermodynamic entropy either. The similarity occurs because Shannon considered a system of communication like written English in much the same way that Boltzmann had thought about a gas.

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“I had talked to him several times”: From “Shannon: An Interview by Price.” “They never told me”: As quoted in Mind at Play by Soni and Goodman. “A Mathematical Theory of Communication”: From Bell System Technical Journal 27 (1948). “reproducing at one point”: From the above paper. Shannon pointed the similarity out to John von Neumann: This anecdote originates in a 1971 article, “Energy and Information,” Scientific American, by Myron Tribus and Edward C. McIrvine. But in 1982, in a taped interview Shannon is rather hazy about why he chose the term entropy. MST PPL HV: From “Information Theory” by Claude E. Shannon, Encyclopaedia Britannica, 14th ed.

The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma
by Mustafa Suleyman
Published 4 Sep 2023

Consider these words, in their own way as chilling as his famous Bhagavad Gita quotation (on seeing the first nuclear test, he recalled some lines from Hindu scripture: “Now I am become Death, the destroyer of worlds”): “When you see something that is technically sweet, you go ahead and do it, and you argue about what to do about it only after you have had your technical success.” It was an attitude shared by his colleague on the Manhattan Project, the brilliant, polymathic Hungarian American John von Neumann. “What we are creating now,” he said, “is a monster whose influence is going to change history, provided there is any history left, yet it would be impossible not to see it through, not only for military reasons, but it would also be unethical from the point of view of the scientists not to do what they know is feasible, no matter what terrible consequences it may have.”

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The costs of saying no are existential. And yet every path from here brings grave risks and downsides. This is the great dilemma. WHERE NEXT? From the start of the nuclear and digital age, this dilemma has been growing clearer. In 1955, toward the end of his life, the mathematician John von Neumann wrote an essay called “Can We Survive Technology?” Foreshadowing the argument here, he believed that global society was “in a rapidly maturing crisis—a crisis attributable to the fact that the environment in which technological progress must occur has become both undersized and underorganized.”

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GO TO NOTE REFERENCE IN TEXT Demand for lithium, cobalt “Climate-Smart Mining: Minerals for Climate Action,” World Bank, www.worldbank.org/​en/​topic/​extractiveindustries/​brief/​climate-smart-mining-minerals-for-climate-action. GO TO NOTE REFERENCE IN TEXT Given the population and resource constraints Galor, The Journey of Humanity, 130. GO TO NOTE REFERENCE IN TEXT In 1955, toward the end of his life John von Neumann, “Can We Survive Technology?,” in The Neumann Compendium (River Edge, N.J.: World Scientific, 1995), geosci.uchicago.edu/​~kite/​doc/​von_Neumann_1955.pdf. GO TO NOTE REFERENCE IN TEXT Chapter 13: Containment Must Be Possible How do they account for an age David Cahn et al., “AI 2022: The Explosion,” Coatue Venture, coatue-external.notion.site/​AI-2022-The-Explosion-e76afd140f824f2eb6b049c5b85a7877.

When Things Start to Think
by Neil A. Gershenfeld
Published 15 Feb 1999

There is a disconnect between the breathless pronouncements of cyber gurus and the experience of ordinary people left perpetually upgrading hardware to meet the demands of new software, or wondering where their files have gone, or trying to understand why they can't connect to the network. The revolution so far has been for the computers, not the people. Digital data of all kinds, whether an e-mail message or a movie, is encoded as a string of O's and 1's because of a remarkable discovery by Claude Shannon and John von Neumann in the 1940s. Prior to their work, it was obvious that engineered systems degraded with time and use. A tape recording sounds worse after it is duplicated, a photocopy is less satisfactory than an original, a telephone call becomes more garbled the farther it has to travel. They showed that this is not so for a digital representation.

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When Babbage started building machines to evaluate not just arithmetic but more complex functions he likewise used discrete values. This required approximating the continuous changes by small differences, hence the name of the Difference Engine. These approximations have been used ever since in electronic digital computers to allow them to manipulate models of the continuous world. Starting in the 1940s with John von Neumann, people realized that this practice was needlessly circular. Most physical phenomena start out discrete at some level. A fluid is not actually continuous; it is just made up of so many molecules that it appears to be continuous. The equations of calculus for a fluid are themselves an approximation of the rules for how the molecules behave.

Why Information Grows: The Evolution of Order, From Atoms to Economies
by Cesar Hidalgo
Published 1 Jun 2015

By quantifying the number of bits we need to encode messages, he helped develop digital communication technologies. Yet what Shannon did not know when he developed his formula was that it was identical to the formula discovered by Boltzmann nearly half a century earlier. At the suggestion of John von Neumann, the famous Hungarian mathematician, Shannon decided to call his measure “entropy,” since Shannon’s formula was equivalent to the formula for entropy used by statistical physicists. (Also—as the legend goes—von Neumann told Shannon that calling his measure entropy would guarantee Shannon’s victory in every argument, since nobody really knew what entropy was.)

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The fact that the genetic variation between individuals is much larger than the genetic variation between groups is a key argument to fend off racist and eugenic arguments. This explanation is key to the line of argumentation advanced in Pinker, The Blank Slate. 14. Speculating about the knowledge- and information-carrying capacity of the human brain is an interesting exercise. Among the first ones to perform this exercise was John von Neumann, the Hungarian polymath who became interested in computers while working on the Manhattan Project. Some of his speculations on the topic are presented in his The Computer and the Brain (New Haven, CT: Yale University Press, 1958). There, Neumann notes that the architecture of the brain is fundamentally different from that of computers.

Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots
by John Markoff
Published 24 Aug 2015

In his final book, God & Golem, Inc., he explored the future human relationship with machines through the prism of religion. Invoking the parable of the golem, he pointed out that despite best intentions, humans are incapable of understanding the ultimate consequences of their inventions.16 In his 1980 dual biography of John von Neumann and Wiener, Steven Heims notes that in the late 1960s he had asked a range of mathematicians and scientists about Wiener’s philosophy of technology. The general reaction of the scientists was as follows: “Wiener was a great mathematician, but he was also eccentric. When he began talking about society and the responsibility of scientists, a topic outside of his area of expertise, well, I just couldn’t take him seriously.”17 Heims concludes that Wiener’s social philosophy hit a nerve with the scientific community.

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He was drafted relatively late in the war, so his army career was more about serving as a cog in the bureaucracy than combat. Stationed close to home at Fort MacArthur in the port city of San Pedro, California, he began as a clerk, preparing discharges, then promotions for soldiers leaving the military. He made his way to Princeton for graduate school and promptly paid a visit to John von Neumann, the applied mathematician and physicist who would become instrumental in defining the basic design of the modern computer. At this point the notion of “artificial intelligence” was fermenting in McCarthy’s mind, but the coinage had not yet come to him. That wouldn’t happen for another half decade in conjunction with the summer 1956 Dartmouth conference.

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pagewanted=all. 12.Ibid. 13.Ibid. 14.Carew, Walter Reuther, 144. 15.The Ad Hoc Committee on the Triple Revolution, “The Triple Revolution,” Liberation, April 1964, http://www.educationanddemocracy.org/FSCfiles/C_CC2a_TripleRevolution.htm. 16.Mark D. Stahlman, “Wiener’s Genius Project” (invited paper, IEEE 2014 Conference on Norbert Wiener in the 21st Century, 2014). 17.Steve J. Heims, John von Neumann and Norbert Wiener: From Mathematics to the Technologies of Life and Death (Cambridge, MA: MIT Press, 1980), 343. 18.Norbert Wiener, God and Golem, Inc.: A Comment on Certain Points where Cybernetics Impinges on Religion (Cambridge, MA: MIT Press, 1964), 29. 19.“Machines Smarter Than Men? Interview with Dr.

The Missing Billionaires: A Guide to Better Financial Decisions
by Victor Haghani and James White
Published 27 Aug 2023

So he should not be willing to pay more than $18 to play, resolving the “paradox.”d The reverberations from this change in perspective from expected wealth to Expected Utility are still being felt nearly 300 years later. Expected Utility and Choice Theory If people do not believe that mathematics is simple, it is only because they do not realize how complicated life is. —John von Neumann Bernoulli's ruminations on the St. Petersburg paradox, utility and Expected Utility took place in the early 1700s. For the next two centuries, economists and philosophers appropriated and expanded Bernoulli's concept of utility, applying it in wide‐ranging economic, political, and moral models, sometimes far removed from the original application to individual financial decision‐making.

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For the next two centuries, economists and philosophers appropriated and expanded Bernoulli's concept of utility, applying it in wide‐ranging economic, political, and moral models, sometimes far removed from the original application to individual financial decision‐making. The embers of Bernoulli's original idea were kept burning by a handful of economists and mathematicians throughout the years. The flame burned brightly again in 1944 with the publication of The Theory of Games and Economic Behavior by polymath John von Neumann and economist Oskar Morgenstern, a work which gave birth to two new fields of study: choice theory and game theory. They took Bernoulli's concept of subjective utility and extended it into a logical, axiomatic framework for rational decision‐making under uncertainty. In a nutshell, they proved that any individual whose preferences satisfy four “axioms of rational choice” would prefer actions that maximize Expected Utility.

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Do stocks outperform Treasury bills? Journal of Financial Economics, 129(3), 440–457. Bessembinder, H., Chen, T., Choi, G., and John Wei, K.C. (2019). Do global stocks outperform US Treasury bills? SSRN Electronic Journal. Bhattacharya, A. (2022). The man from the future: The visionary ideas of John von Neumann. New York: W.W. Norton Publishing. Black, F. (1976). The investment policy spectrum: Individuals, endowment funds and pension funds. Financial Analysts Journal, 32(1), 23–31. Black, F. (1986). Noise. Journal of Finance, 41(3), 528–543. Black, F. (1993). Estimating expected returns. Financial Analysts Journal, 49(5), 36–38.

Rationality: What It Is, Why It Seems Scarce, Why It Matters
by Steven Pinker
Published 14 Oct 2021

Three quarters of Americans believe in at least one phenomenon that defies the laws of science, including psychic healing (55 percent), extrasensory perception (41 percent), haunted houses (37 percent), and ghosts (32 percent)—which also means that some people believe in houses haunted by ghosts without believing in ghosts.12 In social media, fake news (such as Joe Biden Calls Trump Supporters “Dregs of Society” and Florida Man Arrested for Tranquilizing and Raping Alligators in the Everglades) is diffused farther and faster than the truth, and humans are more likely to spread it than bots.13 It has become commonplace to conclude that humans are simply irrational—more Homer Simpson than Mr. Spock, more Alfred E. Neuman than John von Neumann. And, the cynics continue, what else would you expect from descendants of hunter-gatherers whose minds were selected to avoid becoming lunch for leopards? But evolutionary psychologists, mindful of the ingenuity of foraging peoples, insist that humans evolved to occupy the “cognitive niche”: the ability to outsmart nature with language, sociality, and know-how.14 If contemporary humans seem irrational, don’t blame the hunter-gatherers.

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Either way, the theory shines a light on perplexing conundrums of rationality, and despite its provenance in pure math, it can be a source of profound life lessons.4 The theory of rational choice goes back to the dawn of probability theory and the famous argument by Blaise Pascal (1623–1662) on why you should believe in God: if you did and he doesn’t exist, you would just have wasted some prayers, whereas if you didn’t and he does exist, you would incur his eternal wrath. It was formalized in 1944 by the mathematician John von Neumann and the economist Oskar Morgenstern. Unlike the pope, von Neumann really might have been a space alien—his colleagues wondered about it because of his otherworldly intelligence. He also invented game theory (chapter 8), the digital computer, self-replicating machines, quantum logic, and key components of nuclear weapons, while making dozens of other breakthroughs in math, physics, and computer science.

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F., van Bochoven, A., et al. 2015. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nature Genetics, 47, 702–9. https://doi.org/10.1038/ng.3285. Popper, K. R. 1983. Realism and the aim of science. London: Routledge. Poundstone, W. 1992. Prisoner’s dilemma: John von Neumann, game theory, and the puzzle of the bomb. New York: Anchor. President’s Council of Advisors on Science and Technology. 2016. Report to the President: Forensic science in criminal courts: ensuring scientific validity of feature-comparison methods. https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/PCAST/pcast_forensic_science_report_final.pdf.

I Am a Strange Loop
by Douglas R. Hofstadter
Published 21 Feb 2011

They envisioned their computers as being specialized, single-purpose machines — a little like wind-up music boxes that could play just one tune each. But at some point, when Alan Turing’s abstract theory of computation, based in large part on Gödel’s 1931 paper, collided with the concrete engineering realities, some of the more perceptive people (Turing himself and John von Neumann especially) put two and two together and realized that their machines, incorporating the richness of integer arithmetic that Gödel had shown was so potent, were thereby universal. All at once, these machines were like music boxes that could read arbitrary paper scrolls with holes in them, and thus could play any tune.

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SL #641: When we do look down at our fine-grained substrates through scientific experiments, we find small miracles just as Gödelian as is “I”. SL #642: Ah, yes, to be sure — little microgödelinos! But… such as? SL #641: I mean the self-reproduction of the double helix of DNA. The mechanism behind it all involves just the same abstract ideas as are implicated in Gödel’s type of self-reference. This is what John von Neumann unwittingly revealed when he designed a self-reproducing machine in the early 1950’s, and it had exactly the same abstract structure as Gödel’s self-referential trick did. SL #642: Are you saying microgödelinos are self-replicating machines? SL #641: Yes! It’s a subtle but beautiful analogy.

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Page 139 an elegant linguistic analogy… See [Quine] for the original idea (which is actually a variation of Gödel’s idea (which is itself a variation of an idea of Jules Richard (which is a variation of an idea of Georg Cantor (which is a variation of an idea of Euclid (with help from Epimenides))))), and [Hofstadter 1979] for a variation on Quine’s theme. Page 147 “…and Related Systems (I)”… Gödel put a roman numeral at the end of the title of his article because he feared he had not spelled out sufficiently clearly some of his ideas, and expected he would have to produce a sequel. However, his paper quickly received high praise from John von Neumann and other respected figures, catapulting the unknown Gödel to a position of great fame in a short time, even though it took most of the mathematical community decades to absorb the meaning of his results. Page 150 respect for …the most mundane of analogies… See [Hofstadter 2001] and [Sander], as well as Chapter 24 in [Hofstadter 1985] and [Hofstadter and FARG].

The Age of Radiance: The Epic Rise and Dramatic Fall of the Atomic Era
by Craig Nelson
Published 25 Mar 2014

To keep Fermi from talking about his work to others, Baudino got him to talk about it to him. So Fermi started saying that, “Soon Johnny will know so much about the project he will need a bodyguard, too.” When agent Charles Campbell, who hated physics but pretended to like it as part of his job, mentioned to John von Neumann that he was too busy to study, von Neumann got upset: “It is my fault! You will come with me and together we will study theoretical physics in New Mexico!” FBI surveillance teams used walkie-talkies disguised as hearing aids, and any assembled together looked conspicuously like an outing of deaf people.

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By the summer of 1948, Edward Teller’s Chicago idyll was upended by news of the Soviet invasions of Hungary, his birthplace, and Czechoslovakia, with its uranium mother lode at St. Joachimsthal. Communists were victorious in China, and soon enough, they would successfully blockade Berlin. It appeared to many that America’s foes were taking over the world, that the United States was in real danger. “Russia was traditionally the enemy,” John von Neumann said of his countrymen. “I think you will find, generally speaking, among Hungarians an emotional fear and dislike of Russia.” Had Edward Teller been certain that a hydrogen bomb was impossible, that nobody could make it, he would have set his sights elsewhere. But like Leo Szilard’s thinking of Hitler, Ed was tormented by what might happen if the Americans failed to create such a mighty weapon, and the totalitarians succeeded.

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The very least we can conclude is that our twenty-thousandth bomb, useful as it may be in filling the vast munitions pipelines of a great war, will not in any deep strategic sense offset their two-thousandth.” Hearing this, physicist John Wheeler complained to a congressman, “Anybody who says twenty thousand weapons are no better than two thousand ought to read the history of wars.” Sharing Wheeler’s perspective was Hungarian mathematician John von Neumann, who announced in 1950, “If you say why not bomb them tomorrow, I say why not today? If you say today at five o’clock, I say why not one o’clock?” Von Neumann’s promotion of a preemptory nuclear strike was one of the many oddities that inspired Einstein to nickname his Princeton colleague Denktier, “think animal.”

To Be a Machine: Adventures Among Cyborgs, Utopians, Hackers, and the Futurists Solving the Modest Problem of Death
by Mark O'Connell
Published 28 Feb 2017

With these invocations, he moves his arms downward, then outward to either side, before clasping his hands to his chest. He turns about the room, bestowing a gesture of esoteric benediction on the four points of the compass, speaking in each of these positions the hallowed name of a prophet of the computer age: Alan Turing, John von Neumann, Charles Babbage, Ada Lovelace. Then he stands perfectly still, this priestly young man, arms outspread in a cruciform posture. “Around me shines the bits,” he says, “and in me is the bytes. The data, the code, the communications. Forever, amen.” This young man, I learned, was a Swedish academic named Anders Sandberg.

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In the broadest sense, the term refers to a time to come in which machine intelligence greatly surpasses that of its human originators, and biological life is subsumed by technology. It is, in its way, an extreme expression of techno-progressivism, the belief that the universal application of technology will solve the world’s most intractable problems. The idea has been around in some form for at least half a century. In his 1958 obituary for the physicist John von Neumann, with whom he had worked on the Manhattan Project, Stanislaw Ulam wrote about a conversation they once had about “the ever accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue.”

Bootstrapping: Douglas Engelbart, Coevolution, and the Origins of Personal Computing (Writing Science)
by Thierry Bardini
Published 1 Dec 2000

(Licklider I9 6 5, 94 -9 5) Efforts to develop "equipment in which the user and the computer make their marks on different screens," that is, equipment in which, as on a type- writer keyboard, what the hand does and what the eye sees were again un- linked, in fact had started in the mid-1950'S, with the design and operation of JOHNNIAC, a Princeton-class computer built at RAND between 1950 and 1953 and named after John von Neumann, and the work of Allen Newell, Herbert Simon, and Cliff Shaw at RAND on JOSS, the JOHNNIAC Open Shop System, between 1960 and 1964. JOSS's main application was a "help- ful assistant" in the Artificial Intelligence tradition designed for mathemati- cians, an "open-shop" experiment in on-line communication. 8 "Open shop" in this context meant that JOSS was directly available to its users, who for the first time in computing history were not programmers or computer scientists: they were mathematicians at RAND.

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The excerpt of Engelbart's 1962 report surely conveys the impression that Engelbart directly quotes Whorf. It IS obvious that It IS not the case, but that Engel- bart IS gIving here hIs own translation of the hypothesis. 13. During their training years as young mathematicians, from 1924 to 1926, both Norbert Wiener and John von Neumann spent some time in Gottingen, Ger- many, where their paths first crossed when they attended lectures by Heisenberg (Heims 19 8o , 5 1 - 52). 14. Korzybski summarized these three Aristotelian laws of thought as follows: "( I) The law of identity (Whatever is, is. (A thing is what it is}); (2) The law of con- tradiction.

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AvaIl- able on-line at http://picasso.dei.isep.ipp.pt/docs/arpa.html. Hayles, N. K. 1999. How We Became Post-Human: VIrtual Bod,es In CybernetIcs, LIterature, and Informatics. Chicago: UniversIty of ChIcago Press. Helm, M. 1993. MetaphysIcs of Virtual Reality. Oxford: Oxford University Press. Heims, S. J. 1980. John von Neumann and Norbert WIener: From Mathematics to TechnologIes of Life and Death. Cambridge, Mass.: MIT Press. . 1991. The CybernetIcs Group. Cambridge, Mass.: MIT Press. HerkImer County HIstorical Society. 1923. The Story of the TypewrIter, 1873- 1923. HerkImer, N.Y. Hodges, A. 1992 [1983]. Alan TurIng: The EnIgma.

Fire in the Valley: The Birth and Death of the Personal Computer
by Michael Swaine and Paul Freiberger
Published 19 Oct 2014

Because it could perform any task described in the instructions, such a machine would be a true general-purpose device. Perhaps no one before Turing had ever entertained an idea this large. But within a decade, Turing’s visionary idea became reality. The instructions became programs, and his concept, in the hands of another mathematician, John von Neumann, became the general-purpose computer. Most of the work that brought the computer into existence happened in secret laboratories during World War II. That’s where Turing was working. In the US in 1943, at the Moore School of Electrical Engineering in Philadelphia, John Mauchly and J. Presper Eckert proposed the idea for a computer.

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(Courtesy of IBM Archives) Credit for inventing the electronic digital computer is disputed, and perhaps ENIAC was based in part on ideas Mauchly hatched during a visit to John Atanasoff. But ENIAC was real. Mauchly and Eckert attracted a number of bright mathematicians to the ENIAC project, including the brilliant John von Neumann. Von Neumann became involved with the project and made various–and variously reported–contributions to building ENIAC, and in addition offered an outline for a more sophisticated machine called EDVAC (Electronic Discrete Variable Automatic Computer). Because of von Neumann, the emphasis at the Moore School swung from technology to logic.

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Despite all that electrical power, at any given time ENIAC could handle only 20 numbers of 10 decimal digits each. But even before construction was completed on ENIAC, it was put to significant use. In 1945, it performed calculations used in the atomic-bomb testing at Los Alamos, New Mexico. * * * Figure 9. John von Neumann Von Neumann was a brilliant polymath who made foundational contributions to programming and the ENIAC and EDVAC computers. (Courtesy of The Computer Museum History Center, San Jose) A new industry emerged after World War II when the secret labs began to disclose their discoveries and creations.

Between Human and Machine: Feedback, Control, and Computing Before Cybernetics
by David A. Mindell
Published 10 Oct 2002

Presper Eckert, John Mauchly, Claude Shannon, and Jay Forrester, among others, participated in the NDRC’s research program on control systems. This is more than coincidence, for these men did not build electronic digital computers simply as calculators. Nor were they generally concerned with the questions of computability and logic that occupied mathematicians like Alan Turing and John von Neumann. Rather, they drew on longstanding traditions of control engineering, especially the technologies of fire control. My point is not to rewrite the history of computing—mathematicians of course played critical roles, as did the business machine industry—but rather to establish how the era of cyberspace and the Internet, with its emphasis on the computer as a communications device and as a vehicle for human interaction, connects to a longer history of control systems that generated computers as networked communications devices.

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The Penn group’s ENIAC sought to innovate simultaneously in components and architecture, but this meant that they had difficulty selling the project and that their architecture was not as innovative as it might have been. After all, the ENIAC project’s major contribution to computing theory, John von Neumann’s landmark treatise on the stored program architecture, described not the Penn machine but its proposed successor, the EDVAC, which was never built. 94 George Stibitz succeeded in building wartime computers because he was able to base his architectural innovations on material practice, relying on an established and stable set of components, workers, and procedures to build the machines.

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Journal of the AIEE 44 (1925). Hecht, Gabrielle. The Radiance of France: Nuclear Power and National Identity after World War II . Cambridge: MIT Press, 1998. Heims, Steve J. Constructing a Social Science for Postwar America: The Cybernetics Group, 1946–1953 . Cambridge: MIT Press, 1993. ———. John von Neumann and Norbert Wiener: From Mathematics to the Technologies of Life and Death . Cambridge: MIT Press, 1980 . Hewlett, E. M. “The Selsyn System of Position Indication.” General Electric Review 24 (March 1921): 210–18. Hochheiser, Sheldon. “What Makes the Picture Talk: AT&T and the Development of Sound Motion Picture Technology.”

The Marginal Revolutionaries: How Austrian Economists Fought the War of Ideas
by Janek Wasserman
Published 23 Sep 2019

The Austrians embarked on a second act in the Anglophone world, reinventing themselves as intellectual entrepreneurs who transplanted their ideas into related disciplines while simultaneously drawing inspiration from novel sources.25 Schumpeter’s “Literary” Turn If the early 1940s represented a low point for the popularity of the Austrian economists, then the mid- and late-1940s resurgence was a vindication of Austrian and central European thought styles. The list of impactful titles produced by the Austrians between 1942 and 1945 is staggering: Schumpeter, Capitalism, Socialism, and Democracy (1942); Hayek, Road to Serfdom (1944); Mises, Bureaucracy (1944); John von Neumann and Morgenstern, The Theory of Games and Economic Behavior (1944). If you include the works of Karl Polanyi and Karl Popper—The Great Transformation (1944) and The Open Society and Its Enemies (1945)—the Austrians may have produced more important texts in social and political theory than any other midcentury group.

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Morgenstern pivoted away from his earliest work on time and economic methodology under the influence of Karl Menger and through collaboration with Abraham Wald. He became convinced that advanced mathematics provided the way forward for economics. Upon arrival at Princeton, he struck up a fast friendship with the Hungarian émigré mathematician John von Neumann, who had been at the Institute for Advanced Studies since fleeing Germany in 1932. This friendship became the most meaningful of Morgenstern’s life. Its culmination was the 1944 book The Theory of Games and Economic Behavior, the foundational work of game theory. Morgenstern believed that the work continued the revolution that the marginalists had initiated.

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Morgenstern got on famously with the erstwhile heir to the Habsburg crown, Otto Habsburg; he enjoyed hobnobbing with the Rockefellers, David and Nelson. Morgenstern also had an uncanny eye for talent, hitching his star to brilliant thinkers. Theory of Games represented a successful accommodation by Morgenstern to the US academy, and it was a harbinger of the collaborations that sustained his intellectual path going forward. After John von Neumann’s tragic death in 1957, Morgenstern grew close with another Viennese, the mathematician Kurt Gödel. He mentored the Nobel laureate Martin Shubik and produced late-career work on the unpredictability of the stock market with Clive Granger, another Nobelist. The latter work, developed with Burton Malkiel, inspired Malkiel’s well-known random walk theory concerning the stock market.

Ways of Being: Beyond Human Intelligence
by James Bridle
Published 6 Apr 2022

Once again, we come to the only conclusion available: the oracle is the world. It’s another quirk of computational history that one of the people in part responsible for the fixed and inflexible nature of most modern computers is also partly responsible for one of the most powerful applications of randomness. John von Neumann, a Hungarian-American physicist best known for his role in the development of the atomic bomb, was closely involved in the development of the first computers, which were based on Turing’s designs. These machines were initially developed to assist in the design of the bomb, which required complex calculations beyond the reach of existing calculating machines.

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And it was a direct result of trying to get machines to implement a randomized approach to complex problem-solving. In order to fully implement the Monte Carlo method, there was one further, crucial requirement: a source of random numbers, which could not be generated by the computer itself. John von Neumann was all too aware of the failure of machines in this regard. In a paper written on the subject in 1949, he warned that ‘anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin’.8 In response to this need, the RAND Corporation – an offshoot of the US armed forces, which employed von Neumann as a consultant – built an ‘electronic roulette wheel’, which consisted of a pulse generator and a noise source, most likely a small gas-filled transistor valve similar to the kind used in ERNIE.

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He too had a ‘system’ which he employed at the casino, which involved throwing dice to decide where to bet at roulette. He claimed it was successful – although excruciatingly slow – and in 1924 produced a series of prints, called the Monte Carlo Bonds, which were simultaneously conceptual artworks and legal documents, bearing the value of his winnings. 8. John von Neumann, ‘Various Techniques Used in Connection with Random Digits’ in Proceedings of a Symposium held 29, 30 June and 1 July 1949, in Los Angeles, California, under the sponsorship of the RAND Corporation and the National Bureau of Standards, with the cooperation of the Oak Ridge National Laboratory; also published in A.

Fermat’s Last Theorem
by Simon Singh
Published 1 Jan 1997

In 1931 Gödel published his book Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme (On Formally Undecidable Propositions in Principia Mathematica and Related Systems), which contained his so-called theorems of undecidability. When news of the theorems reached America the great mathematician John von Neumann immediately cancelled a lecture series he was giving on Hilbert’s programme and replaced the remainder of the course with a discussion of Gödel’s revolutionary work. Gödel had proved that trying to create a complete and consistent mathematical system was an impossible task. His ideas could be encapsulated in two statements.

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Hardy declared that the best mathematics is largely useless, he was quick to add that this was not necessarily a bad thing: ‘Real mathematics has no effects on war. No one has yet discovered any warlike purpose to be served by the theory of numbers.’ Hardy was soon to be proved wrong. In 1944 John von Neumann co-wrote the book The Theory of Games and Economic Behavior, in which he coined the term game theory. Game theory was von Neumann’s attempt to use mathematics to describe the structure of games and how humans play them. He began by studying chess and poker, and then went on to try and model more sophisticated games such as economics.

Is God a Mathematician?
by Mario Livio
Published 6 Jan 2009

Russell’s paradox was bypassed in this theory by a careful choice of construction principles that eliminated contradictory ideas such as “the set of all sets.” Zermelo’s scheme was further augmented in 1922 by the Israeli mathematician Abraham Fraenkel (1891–1965) to form what has become known as the Zermelo-Fraenkel set theory (other important changes were added by John von Neumann in 1925). Things would have been nearly perfect (consistency was yet to be demonstrated) were it not for some nagging suspicions. There was one axiom—the axiom of choice—that just like Euclid’s famous “fifth” was causing mathematicians serious heartburn. Put simply, the axiom of choice states: If X is a collection (set) of nonempty sets, then we can choose a single member from each and every set in X to form a new set Y.

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I don’t see any reason why we should have less confidence in this kind of perception, i.e., in mathematical intuition, than in sense perception. By an ironic twist of fate, just as the formalists were getting ready for their victory march, Kurt Gödel—an avowed Platonist—came and rained on the parade of the formalist program. The famous mathematician John von Neumann (1903–57), who was lecturing on Hilbert’s work at the time, canceled the rest of his planned course and devoted the remaining time to Gödel’s findings. Gödel the man was every bit as complex as his theorems. In 1940, he and his wife Adele fled Nazi Austria so he could take up a position at the Institute for Advanced Study in Princeton, New Jersey.

Richard Dawkins: How a Scientist Changed the Way We Think
by Alan Grafen; Mark Ridley
Published 1 Jan 2006

I am not concerned here with the psychology of motives.’14 Dawkins’ brilliant application of mentalistic behaviorism—what I call the intentional stance—to evolutionary biology was, like my own coinage, an articulation of ideas that were already proving themselves in the work of many other theorists. We are both clari-fiers and unifiers of practices and attitudes pioneered by others, and we share a pantheon: Alan Turing and John von Neumann on the one hand, and Bill Hamilton, John Maynard Smith, George Williams, and Bob Trivers on the other. We see computer science and evolutionary theory fitting together in excellent harmony; it’s algorithms all the way down. Dawkins and I have both had to defend our perspective against those who cannot fathom—or abide—this strategic approach to such deep matters.

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He was particularly interested in accounting for the tendency of spiral patterns in many plant structures to obey the Fibonacci sequence (e.g. if you count the number of whirls running clockwise on a pine cone and the number running anticlockwise, the two numbers will be consecutive terms in Fibonacci’s famous sequence of integers: 0, 1, 1, 2, 3, 5, 8, 12, ...). At the same time, John von Neumann, one of history’s great polymaths and the man responsible for game theory and the architecture of the modern computer among many other things typically considered to lie far from the muddy field of biology, worked on the problem of selfreplication:2 over evolutionary time, simple life-forms have given rise to more complicated creatures, but how, von Neumann asked, could a machine (like a dog or an amoeba or a robot) make a more complex version of itself?

Sun in a Bottle: The Strange History of Fusion and the Science of Wishful Thinking
by Charles Seife
Published 27 Oct 2009

Other facilities, such as one at Oak Ridge in Tennessee and another at Hanford in Washington, were crucial to figuring out the best way to separate bombworthy uranium-235 from the much more common uranium-238 and how to manufacture plutonium-239.2 However, the big minds roamed at Los Alamos: Oppenheimer, Hans Bethe, Richard Feynman, Stanislaw Ulam, John von Neumann, Enrico Fermi, and Edward Teller. Teller, a Hungarian émigré and, arguably, a better theoretician than Oppenheimer, was brought to the University of Chicago in mid-1942 by the Manhattan Project just as it was getting under way. When Teller arrived, nobody assigned him a task, so he set to work trying to design the ultimate weapon, more powerful even than the one the project’s scientists were trying to build.

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“British-U.S. Data on Hydrogen Due.” New York Times, 13 January 1958. ———. “Briton 90% Sure Fusion Occurred.” New York Times, 25 January 1958. ———. “Butler Affirms Atom Fusion Lead.” New York Times, 31 January 1958. ———. “H-Bomb Untamed, Britain Admits.” New York Times, 17 May 1958. Macrae, Norman. John von Neumann. New York: Pantheon, 1992. Maddox, John. “What to Say about Cold Fusion.” Nature 338 (27 April 1989): 701. Magnetic Fusion Energy Engineering Act of 1980. Public Law 96-386 (7 October 1980). Malakoff, David. “DOE Slams Livermore for Hiding NIF Problems.” Science 285 (10 September 1999): 1647.

Massive: The Missing Particle That Sparked the Greatest Hunt in Science
by Ian Sample
Published 1 Jan 2010

Its most famous resident, Albert Einstein, who had died in 1955, had spent the last twenty-five years of his life there, trying to explain how the forces of nature were born. The Austrian-American logician Kurt Gödel was still there, redefining the limits of human knowledge. He and Einstein had been friends, though he had vexed Einstein by pointing out that his famous theories allowed time travel to be possible.21 The father of modern computing, John von Neumann, was also at the institute, turning the mathematics of poker into a political strategy to win the Cold War.22 Robert Oppenheimer, the towering figure who had led the Manhattan Project to build the atomic bomb, had become head of the institute in 1946, only adding to the intimidating aura of the place.

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The Higgs boson is the quantum of the remaining neutral component field. 21 For more on Gödel’s work, see Thinking about Gödel and Turing: Essays on Complexity, 1970-2007, by Gregory J. Chaitin, World Scientific, 2007. 22 For more on von Neumann’s work on game theory, see Prisoner’s Dilemma: John von Neumann, Game Theory and the Puzzle of the Bomb, by William Poundstone, Anchor Books, 1993. 23 Interview with the author, August 2008. 24 Interview with the author, August 2007. 25 As recalled in an interview with the author, August 2008. 26 See “Conserved Currents and Associated Symmetries: Goldstone’s Theorem,” by Daniel Kastler, Derek W.

E=mc2: A Biography of the World's Most Famous Equation
by David Bodanis
Published 25 May 2009

He nurtured the first theorists who proposed implosion; he assembled the right explosives experts; as the project grew to a level that under anyone else’s supervision it might have fallen apart in a mess of squabbling egos, he deftly manipulated the participants so that all the different groups involved worked together in parallel. At one point he had the top U.S. explosives expert, and the top UK explosives expert, and the Hungarian John von Neumann—the quickest mathematician anyone had met, who would also help create the computer in his long career—and a host of other nationalities all working on it. He even had Feynman joining in! The one prima donna who might have destroyed the effort was the embarrassingly egocentric Hungarian physicist Edward Teller.

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Sticking out of the bomb’s back, near the spinning fins, were a number of whiplike thin radio antennae. Those collected the returning radio signals, and used the time lag each took 163 2 adulthood to return as a way of measuring the height remaining to the ground. At 1,900 feet the last rebounded radio signal arrived. John von Neumann and others had calculated that a bomb exploding much higher would dissipate much of its heat in the open air; exploding much lower, it would dig a huge crater in the ground. At just under 2,000 feet the height would be ideal. An electric impulse lit cordite sacs, producing a conventional artillery blast.

Hacker's Delight
by Henry S. Warren
Published 26 Jul 2002

[Bern] Bernstein, Robert. "Multiplication by Integer Constants." Software—Practice and Experience 16, 7 (July 1986), 641-652. [BGN] Burks, Arthur W., Goldstine, Herman H., and von Neumann, John. "Preliminary Discussion of the Logical Design of an Electronic Computing Instrument, Second Edition" (1947). In Papers of John von Neumann on Computing and Computing Theory, Volume 12 in the Charles Babbage Institute Reprint Series for the History of Computing, MIT Press, 1987. [CJS] Stephenson, Christopher J. Private communication. [Cohen] These rules were pointed out by Norman H. Cohen. [Cut] Cutland, Nigel J. Computability: An Introduction to Recursive Function Theory.

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Oxford University Press, 1960. [IBM] From an IBM programming course, 1961. [Irvine] Irvine, M. M. "Early Digital Computers at Bell Telephone Laboratories." IEEE Annals of the History of Computing 23, 3 (July-September 2001), 22-42. [JVN] von Neumann, John. "First Draft of a Report on the EDVAC." In Papers of John von Neumann on Computing and Computing Theory, Volume 12 in the Charles Babbage Institute Reprint Series for the History of Computing, MIT Press, 1987. [Ken] Found in a GNU C compiler for the RS/6000 that was ported by Richard Kenner. He attributes this to a 1992 PLDI conference paper by him and Torbjörn Granlund.

Ten Billion Tomorrows: How Science Fiction Technology Became Reality and Shapes the Future
by Brian Clegg
Published 8 Dec 2015

To have spread well across the galaxy, probes of this kind would have to have started out many thousands of years ago, and it’s entirely possible that if one did visit the Earth, it wouldn’t arrive during the few thousand years so far when human beings would have been able to record its visit. Such devices, sometimes called von Neumann probes after the mathematician John von Neumann who worked on the concept, do also have a practical problem that to make an easily replicable device mechanically would require it to be simple—and yet the task it has to perform, refining ores, producing complex machinery and electronics, is very complex. We simply couldn’t build a device that could replicate itself from raw materials like this at all, let alone one that then had the ability to power itself out of the Earth’s gravity well and navigate between the stars.

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Babbage speculated that his mechanical programmable computer, the Analytical Engine, which was designed but never built, would be able to play chess, while Turing wrote a simple program for chess playing that was only ever executed by hand. More impetus was given by information theorist Claude Shannon in the 1950s. Shannon made use of John von Neumann’s minimax algorithm, which would give a score to different possible moves and used it to calculate what was thought to be the optimum strategy. Shannon never produced a workable program, but by the time 2001 was filmed, there were crude chess-playing programs running on mainframe computers, and in 1973, David Slate and Larry Atkin wrote Chess 4.0, the first truly effective software that was able to make use of a computer’s strengths and play a game that could beat most everyday players.

Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy
by George Gilder
Published 16 Jul 2018

In opposition to the foolish ignorabimus our slogan shall be: ‘We must know, we will know’ ”—Wir müssen wissen, wir werden wissen—a declaration that was inscribed on his tombstone.7 Preceding the conference was a smaller three-day meeting on the “Epistemology of the Exact Sciences” addressed by the rising mathematical stars Rudolf Carnap, a set theorist; Arend Heyting, a mathematical philosopher; and John von Neumann, a polymathic prodigy and Hilbert’s assistant. All were soldiers in Hilbert’s epistemological campaign, and all, like Hilbert, expected the pre-conference to be a warmup for the triumphalist celebration of the main conference. After the pre-conference ended, however, everyone might as well have gone home.

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In developing his proof, Gödel (1906–1978) invented a mathematical machine that used numbers to embody axioms and thus anticipated the discoveries of computer science. By showing that mathematics could not be hermetically sealed or physically determinist, Gödel opened the way to postmodern mathematics: a mathematics of software and creativity. John von Neumann (1903–1957) was the first person to appreciate and publicize the importance of Gödel’s demonstration in 1931 that mathematical statements can be true but unprovable. As von Neumann saw, Gödel’s proof depended on his invention of a mathematical “machine” that used numbers to encode and prove algorithms also expressed in numbers.

The Alignment Problem: Machine Learning and Human Values
by Brian Christian
Published 5 Oct 2020

Other material is drawn from oral histories of Pitts’s contemporaries, particularly Jerome (Jerry) Lettvin in Anderson and Rosenfeld, Talking Nets, as well as the essays and recollections in McCulloch, The Collected Works of Warren S. McCulloch. For other accounts of Pitts’s life, see, e.g., Smalheiser, “Walter Pitts”; Easterling, “Walter Pitts”; and Gefter, “The Man Who Tried to Redeem the World with Logic.” Further details exist in biographies of McCulloch, Norbert Wiener, and the cybernetics group—e.g., Heims, John von Neumann and Norbert Wiener and The Cybernetics Group, and Conway and Siegelman, Dark Hero of the Information Age. 2. Whitehead and Russell, Principia Mathematica. 3. Thanks to the staff at the Bertrand Russell Archives at McMaster University for their help in attempting to locate a copy of this letter; unfortunately, no extant copy is known. 4.

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Some of the roots of this thinking predate McCulloch’s work with Pitts; see, e.g., McCulloch, “Recollections of the Many Sources of Cybernetics.” 8. See Piccinini, “The First Computational Theory of Mind and Brain,” and Lettvin, Introduction to McCulloch, The Collected Works of Warren S. McCulloch. 9. John von Neumann’s 1945 EDVAC report, the first description ever written of a stored-program computer, will contain—for all its 101 pages—a single citation: McCulloch and Pitts, 1943. (See Neumann, “First Draft of a Report on the EDVAC.” Von Neumann actually misspells it in the original text: “Following W. S.

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Hastie, Trevor, and Robert Tibshirani. “Generalized Additive Models.” Statistical Science 1, no. 3 (1986): 297–318. Hebb, Donald Olding. The Organization of Behavior: A Neuropsychological Theory. New York: John Wiley & Sons, 1949. Heims, Steve Joshua. The Cybernetics Group. MIT Press, 1991. ———. John von Neumann and Norbert Wiener. MIT Press, 1980. Heine, Steven J., Timothy Takemoto, Sophia Moskalenko, Jannine Lasaleta, and Joseph Henrich. “Mirrors in the Head: Cultural Variation in Objective Self-Awareness.” Personality and Social Psychology Bulletin 34, no. 7 (2008): 879–87. Hendrycks, Dan, Mantas Mazeika, and Thomas Dietterich.

The Misbehavior of Markets: A Fractal View of Financial Turbulence
by Benoit Mandelbrot and Richard L. Hudson
Published 7 Mar 2006

In 1945, he dropped out of France’s most prestigious school, the École Normale Supérieure, on the second day, to enroll at the less-exalted but more appropriate École Polytechnique. He proceeded to Caltech; then—after a Ph.D. in Paris—to MIT; then to the Institute for Advanced Study in Princeton, as the last post-doc to study with the great Hungarian-born mathematician, John von Neumann; then to Geneva and back to Paris for a time. Atypically for a scientist in those days, Mandelbrot ended up working, not in a university lecture hall, but in an industrial laboratory, IBM Research, up the Hudson River from Manhattan. At that time IBM’s bosses were drawing into that lab and its branches a number of brainy, unpredictable people, not doubting they would do something brilliant for the company.

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I recall that by the end of one such series, I was his sole auditor; we could as easily have quit the auditorium and adjourned to his office for a chat. At seventy-eight, he received belated recognition by election to France’s Académie des Sciences. But he was ever an anomaly. As a later teacher of mine, John von Neumann, told me: “I think I understand how every other mathematician operates, but Lévy is like a visitor from a strange planet. He seems to have his own private methods of arriving at the truth, which leave me ill at ease.” Lévy did not “arrive at” probability theory until he was nearly forty, when he was asked shortly after World War I to lecture on targeting errors in gunnery.

The Road Ahead
by Bill Gates , Nathan Myhrvold and Peter Rinearson
Published 15 Nov 1995

For the next century mathematicians worked with the ideas Babbage had outlined and finally, by the mid-1940s, an electronic computer was built based on the principles of his Analytical Engine. It is hard to sort out the paternity of the modern computer, because much of the thinking and work was done in the United States and Britain during World War II under the cloak of wartime secrecy. Three major contributors were Alan Turing, Claude Shannon, and John von Neumann. In the mid-1930s, Alan Turing, like Babbage a superlative Cambridge-trained British mathematician, proposed what is known today as a Turing machine. It was his version of a completely general-purpose calculating machine that could be instructed to work with almost any kind of information. In the late 1930s, when Claude Shannon was still a student, he demonstrated that a machine executing logical instructions could manipulate information.

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If this is true, it gives new meaning to the term "bugs" for the little glitches that can plague computer hardware or software. When all the tubes were working, a staff of engineers could set up ENIAC to solve a problem by laboriously plugging in 6,000 cables by hand. To make it perform another function, the staff had to reconfigure the cabling—every time. John von Neumann, a brilliant Hungarian-born American, who is known for many things, including the development of game theory and his contributions to nuclear weaponry, is credited with the leading role in figuring out a way around this problem. He created the paradigm that all digital computers still follow.

Einstein's Dice and Schrödinger's Cat: How Two Great Minds Battled Quantum Randomness to Create a Unified Theory of Physics
by Paul Halpern
Published 13 Apr 2015

By touching the structure, it is in some sense taking a measurement. The house of cards topples over in one of the directions, collapsing into one of its constituent eigenstates. The process of measurement has triggered a collapse from the superposition into a single position. Hungarian mathematician John von Neumann would later show that all quantum processes obeyed one of two types of dynamics: the continuous, deterministic evolution governed by a wave equation (either the Schrödinger equation or a relativistic version such as the Dirac equation) and the discrete, probabilistic repositioning associated with wavefunction collapse.

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Causality, he argued, was a local process involving interactions between adjacent entities, spreading through space from one point to the next at the speed of light or slower. Distant things must be treated as physically distinct, not as a linked system. Otherwise a kind of “telepathy” could exist between an electron on Earth and one on, say, Mars. How could each immediately “know” what the other is doing? By then, John von Neumann had formalized the notion of wavefunction collapse, originally suggested by Heisenberg. In that formalism, a particle’s wavefunction can be expressed in terms of either position eigenstates or momentum eigenstates, but not both at once. It is something like slicing an egg. You could slice it along its length or across its width, but unless you want it to be diced instead of sliced, you’d only do one or the other.

The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity
by Amy Webb
Published 5 Mar 2019

Rather than focusing on the machine as hardware and the program as software, they imagined a new kind of symbiotic system capable of ingesting vast amounts of data, just like we humans do. Computers weren’t yet powerful enough to test this theory—but the paper did inspire others to start working toward a new kind of intelligent computer system. The link between intelligent computer systems and autonomous decision-making became clearer once John von Neumann, the Hungarian-American polymath with specializations in computer science, physics, and math, published a massive treatise of applied math. Cowritten with Princeton economist Oskar Morgenstern in 1944, the 641-page book explained, with painstaking detail, how the science of game theory revealed the foundation of all economic decisions.

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The current AI architecture has been good enough to build products with artificial narrow intelligence, like the spam filter in Gmail or Apple’s “visual voicemail” transcription service. But it must also pursue artificial general intelligence (AGI), a longer-term play that is now visible on the horizon. And that requires customized AI hardware. The reason AGI requires customized hardware has something to do with John von Neumann, the computer scientist previously mentioned who developed the theory behind the architecture of modern computers. Remember, during von Neumann’s time, computers were fed separate programs and data for processing—in his architecture, computer programs and data were both held in the memory of the machine.

Einstein's Unfinished Revolution: The Search for What Lies Beyond the Quantum
by Lee Smolin
Published 31 Mar 2019

A complete elucidation of one and the same object may require diverse points of view which defy a unique description. Indeed, strictly speaking, the conscious analysis of any concept stands in a relation of exclusion to its immediate application.4 Other quantum luminaries, such as Wolfgang Pauli, a wunderkind who published a textbook on general relativity when he was twenty-one, and John von Neumann, a Hungarian mathematician who is famous for his inventions in a broad range of fields, from the architecture of computers to the mathematics of quantum theory, taught variants of these anti-realist philosophies. Their views differed in emphasis, but anything written by them was classified as part of the “Copenhagen interpretation” of quantum mechanics.

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There were only Copenhagen textbooks. These either ignored the foundational issues with the theory or presented a confident assertion that all questions that were meaningful had already been answered by Bohr and Heisenberg. One important reason anti-realism triumphed was that the mathematician John von Neumann published a proof he claimed showed there could not be a consistent alternative to quantum mechanics. This was published a few years after the Solvay conference in a book on the mathematical structure of quantum mechanics. This claim had to be wrong, as it implied de Broglie’s pilot wave theory had to be inconsistent, which it wasn’t.

Fifty Challenging Problems in Probability With Solutions
by Frederick Mosteller
Published 15 May 1965

As is not hard to see, this can occur only if y = x, in which case he can win y with a single bold gamble with the probability w given by (2). 57 37 The problem of an exact upper bound and optimum strategies for the gambler in Red-and-Black who wants to win an amount different from x is more difficult and will not be entered into here. 38. The Thick Coin How thick should a coin be to have a ! chance of landing on edge? Solution for The Thick Coin On first hearing this question, the late great mathematician, John von Neumann, was unkind enough to solve it-including a 3-decimal answerin his head in 20 seconds in the presence of some unfortunates who had labored much longer. ~ '-J- Edge This problem has no definite answer without some simplifying conditions. The elasticity of the coin, the intensity with which it is tossed, and the properties of the surface on which it lands combine to make the reallife question an empirical one.

The Art of Computer Programming
by Donald Ervin Knuth
Published 15 Jan 2001

Fundamental Constants (octal) 727 3. Harmonic Numbers, Bernoulli Numbers, Fibonacci Numbers . . . 728 Appendix B — Index to Notations 730 Index and Glossary 735 CHAPTER THREE RANDOM NUMBERS Any one who considers arithmetical methods of producing random digits is, of course, in a state of sin. — JOHN VON NEUMANN A951) Lest men suspect your tale untrue, Keep probability in view. — JOHN GAY A727) There wanted not some beams of light to guide men in the exercise of their Stocastick faculty. — JOHN OWEN A662) 3.1. INTRODUCTION Numbers that are "chosen at random" are useful in many different kinds of applications.

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George Marsaglia helped resuscitate random tables in 1995 by preparing a demonstration disk that contained 650 random megabytes, generated by combining the output of a noise-diode circuit with deterministically scrambled rap music. (He called it "white and black noise.") The inadequacy of mechanical methods in the early days led to an interest in the production of random numbers using a computer's ordinary arithmetic operations. John von Neumann first suggested this approach in about 1946; his idea was to take the square of the previous random number and to extract the middle digits. For example, if we are generating 10-digit numbers and the previous value was 5772156649, we square it to get 33317792380594909201; the next number is therefore 7923805949.

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A new uniform deviate U is generated whenever we need it. These numbers are usually represented in a computer word with the radix point assumed at the left. 3.4.1. Numerical Distributions This section summarizes the best techniques known for producing numbers from various important distributions. Many of the methods were originally suggested by John von Neumann in the early 1950s, and they have gradually been improved upon by other people, notably George Marsaglia, J. H. Ahrens, and U. Dieter. A. Random choices from a finite set. The simplest and most common type of distribution required in practice is a random integer. An integer between 0 and 7 can be extracted from three bits of U on a binary computer; in such a case, these bits should be extracted from the most significant (left-hand) part of the computer word, since the least significant bits produced by many random number generators are not sufficiently random.

Smart Machines: IBM's Watson and the Era of Cognitive Computing (Columbia Business School Publishing)
by John E. Kelly Iii
Published 23 Sep 2013

People used them to organize data and make calculations that were helpful in everything from conducting a national population census to tracking the performance of a company’s sales force. The programmable computing era—today’s technologies—emerged in the 1940s. Programmable machines are still based on a design laid out by the Hungarian American mathematician John von Neumann. Electronic devices governed by software programs perform calculations, execute logical sequences of steps, and store information using millions of zeros and ones. Scientists built the first such computers for use in decrypting encoded messages in wartime. Successive generations of computing technology have enabled everything from space exploration to global manufacturing-supply chains to the Internet.

Fortune's Formula: The Untold Story of the Scientific Betting System That Beat the Casinos and Wall Street
by William Poundstone
Published 18 Sep 2006

Nyquist had used intelligence, and Hartley had used information. In his earliest writings, Shannon favored Nyquist’s term. The military connotation of “intelligence” was fitting for the cryptographic work. “Intelligence” also implies meaning, however, which Shannon’s theory is pointedly not about. John von Neumann of Princeton’s Institute for Advanced Study advised Shannon to use the word entropy. Entropy is a physics term loosely described as a measure of randomness, disorder, or uncertainty. The concept of entropy grew out of the study of steam engines. It was learned that it is impossible to convert all the random energy of heat into useful work.

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In the late 1950s, Shannon began an intensive study of the stock market that was motivated both by intellectual curiosity and desire for gain. He filled three library shelves with something like a hundred books on economics and investing. The titles included Adam Smith’s The Wealth of Nations, John von Neumann and Oskar Morgenstern’s Theory of Games and Economic Behavior, and Paul Samuelson’s Economics, as well as books with a more practical focus on investment. One book Shannon singled out as a favorite was Fred Schwed’s wry classic, Where Are the Customers’ Yachts? At the time he was designing the roulette computer with Thorp, Shannon kept notes in an MIT notebook.

Rise of the Robots: Technology and the Threat of a Jobless Future
by Martin Ford
Published 4 May 2015

Indeed, the arc of progress can be traced back in time at least as far as Charles Babbage’s mechanical difference engine in the early seventeenth century. The innovations that have resulted in fantastic wealth and influence in today’s information economy, while certainly significant, do not really compare in importance to the groundbreaking work done by pioneers like Alan Turing or John von Neumann. The difference is that even incremental advances are now able to leverage that extraordinary accumulated account balance. In a sense, the successful innovators of today are a bit like the Boston Marathon runner who in 1980 famously snuck into the race only half a mile from the finish line. Of course, all innovators stand on the shoulders of those who came before them.

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In the words of futurist and inventor Ray Kurzweil, it would “rupture the fabric of history” and usher in an event—or perhaps an era—that has come to be called “the Singularity.” The Singularity The first application of the term “singularity” to a future technology-driven event is usually credited to computer pioneer John von Neumann, who reportedly said sometime in the 1950s that “ever accelerating progress . . . gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue.”5 The theme was fleshed out in 1993 by San Diego State University mathematician Vernor Vinge, who wrote a paper entitled “The Coming Technological Singularity.”

Human Compatible: Artificial Intelligence and the Problem of Control
by Stuart Russell
Published 7 Oct 2019

It was all the more remarkable for the fact that, unlike monetary amounts, the utility values of various bets and prizes are not directly observable; instead, utilities are to be inferred from the preferences exhibited by an individual. It would be two centuries before the implications of the idea were fully worked out and it became broadly accepted by statisticians and economists. In the middle of the twentieth century, John von Neumann (a great mathematician after whom the standard “von Neumann architecture” for computers was named16) and Oskar Morgenstern published an axiomatic basis for utility theory.17 What this means is the following: as long as the preferences exhibited by an individual satisfy certain basic axioms that any rational agent should satisfy, then necessarily the choices made by that individual can be described as maximizing the expected value of a utility function.

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The expected monetary value of the two solutions is the same, but Sempronius clearly prefers the two-ship solution. 16. By most accounts, von Neumann did not himself invent this architecture but his name was on an early draft of an influential report describing the EDVAC stored-program computer. 17. The work of von Neumann and Morgenstern is in many ways the foundation of modern economic theory: John von Neumann and Oskar Morgenstern, Theory of Games and Economic Behavior (Princeton University Press, 1944). 18. The proposal that utility is a sum of discounted rewards was put forward as a mathematically convenient hypothesis by Paul Samuelson, “A note on measurement of utility,” Review of Economic Studies 4 (1937): 155–61.

Rage Inside the Machine: The Prejudice of Algorithms, and How to Stop the Internet Making Bigots of Us All
by Robert Elliott Smith
Published 26 Jun 2019

In Turing’s posthumously published paper, Intelligent Machinery, which included ideas about neural networks, the father of computer science speculated that some form of “genetical search” could play a role in transforming and organising the random connections of his “unorganized machines.” While Turing was working on breaking WWII codes, John Von Neumann was working on the Manhattan Project. Von Neumann was an American-Hungarian genius and polymath, and his work designing nuclear weapons was only one of the ways in which he changed the world forever. The others include devising a way to make ENIAC (arguably the world’s first real computer) programmable; making substantial contributions to quantum physics6 and equilibrium theories in economics; and, inventing game theory, an area of mathematical research which shaped cold war politics for a generation through his descriptions of a game-theoretic construct he called “mutually assured destruction.”

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Other scientific studies also show that striking a precise balance between the two may be the vital characteristic of living systems. The term edge of chaos was first used by the SFI researchers Doyne Farmer and Chris Langton, while looking at cellular automata models, simplified algorithms invented by John von Neumann to represent self-replicating biology.4 While experimenting with parameters that controlled these programs, they observed that for some parameter settings, the algorithms would settle into uninteresting equilibria, essentially static states. For others, the algorithms just generated complete randomness, never seeming to settle down into any recognizable patterns.

From eternity to here: the quest for the ultimate theory of time
by Sean M. Carroll
Published 15 Jan 2010

And that’s not the end of it; there are several other ways of thinking about entropy, and new ones are frequently being proposed in the literature. There’s nothing wrong with that; after all, Boltzmann and Gibbs were proposing definitions to supercede Clausius’s perfectly good definition of entropy, which is still used today under the rubric of “thermodynamic” entropy. After quantum mechanics came on the scene, John von Neumann proposed a formula for entropy that is specifically adapted to the quantum context. As we’ll discuss in the next chapter, Claude Shannon suggested a definition of entropy that was very similar in spirit to Gibbs’s, but in the framework of information theory rather than physics. The point is not to find the one true definition of entropy; it’s to come up with concepts that serve useful functions in the appropriate contexts.

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9 INFORMATION AND LIFE You should call it entropy, for two reasons. In the first place, your uncertainty function has been used in statistical mechanics under that name, so it already has a name. In the second place, and more important, no one knows what entropy really is, so in a debate you will always have the advantage. —John von Neumann, to Claude Shannon144 In a celebrated episode in Swann’s Way, Marcel Proust’s narrator is feeling cold and somewhat depressed. His mother offers him tea, which he reluctantly accepts. He is then pulled into an involuntary recollection of his childhood by the taste of a traditional French teatime cake, the madeleine.

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As a general rule, the more symmetries you have, the simpler things become. 110 This whole checkerboard-worlds idea sometimes goes by the name of cellular automata. A cellular automaton is just some discrete grid that follows a rule for determining the next row from the state of the previous row. They were first investigated in the 1960s, by John von Neumann, who is also the guy who figured out how entropy works in quantum mechanics. Cellular automata are fascinating for many reasons having little to do with the arrow of time; they can exhibit great complexity and can function as universal computers. See Poundstone (1984) or Shalizi (2009). Not only are we disrespecting cellular automata by pulling them out only to illustrate a few simple features of time reversal and information conservation, but we are also not speaking the usual language of cellular-automaton cognoscenti.

Information: A Very Short Introduction
by Luciano Floridi
Published 25 Feb 2010

So MTC is commonly described as a study of information at the syntactic level. And since computers are syntactical devices, this is why MTC can be applied so successfully in ICT. Entropy and randomness Information in Shannon's sense is also known as entropy. It seems we owe this confusing label to John von Neumann (1903-1957), one of the most brilliant scientists of the 20th century, who recommended it to Shannon: You should call it entropy for two reasons: first, the function is already in use in thermodynamics under the same name; second, and more importantly, most people don't know what entropy really is, and if you use the word entropy in an argument you will win every time.

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World
by Pedro Domingos
Published 21 Sep 2015

The Bible Code, a 1998 bestseller, claimed that the Bible contains predictions of future events that you can find by skipping letters at regular intervals and assembling words from the letters you land on. Unfortunately, there are so many ways to do this that you’re guaranteed to find “predictions” in any sufficiently long text. Skeptics replied by finding them in Moby Dick and Supreme Court rulings, along with mentions of Roswell and UFOs in Genesis. John von Neumann, one of the founding fathers of computer science, famously said that “with four parameters I can fit an elephant, and with five I can make him wiggle his trunk.” Today we routinely learn models with millions of parameters, enough to give each elephant in the world his own distinctive wiggle.

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His PhD advisor, Arthur Burks, nevertheless encouraged Holland’s interest in evolutionary computation and was instrumental in getting him a faculty job at Michigan and shielding him from senior colleagues who didn’t think that stuff was computer science. Burks himself was so open-minded because he had been a close collaborator of John von Neumann, who had proved the possibility of self-reproducing machines. Indeed, it had fallen to him to complete the work when von Neumann died of cancer in 1957. That von Neumann could prove that such machines are possible was quite remarkable, given the primitive state of genetics and computer science at the time.

Hacking Capitalism
by Söderberg, Johan; Söderberg, Johan;

Indeed, George Caffentzis has argued convincingly that the more technologically advanced the industrialised countries become, the worse the exploitation of labour gets in the remaining parts of the world. In an article in the Midnight Notes Collective, Caffentzis based his case on a vision of the computer scientist John von Neumann about a self-replicating, automatised factory. In this hypothetical scenario, where the production process involves no living labour whatsoever, there will be no value generated for the capitalist to exploit. The law of value postulates that only human labour can add value to a product. Human labour is unique in that it enlarges the value of a product above the sum of its own inputs.

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The expectations of early Marxists, that capitalism would spiral downwards into aggravated crises and eventually self-destruct because of falling profitability, has by now been thoroughly discredited. Nevertheless, the law of the falling rate of profit does portray a gradual movement towards a logical endpoint, suggested by Ernest Mandel—total automation, which simultaneously is inconceivable with capitalism. A state of total automation, as it was envisioned already by John von Neumann, would be reached when machinery, without any injection of living labour, spits out an infinite volume of goods at instant speed. It is hard to imagine a machine with such dimensions, less than visualising science fiction gadgets or, just slightly more down-to-earth, nanotechnologic fantasies.

The Grand Design
by Stephen Hawking and Leonard Mlodinow
Published 14 Jun 2010

In fact, all the basic functions of a modern computer, such as AND and OR gates, can also be created from gliders. In this manner, just as electrical signals are employed in a physical computer, streams of gliders can be employed to send and process information. In the Game of Life, as in our world, self-reproducing patterns are complex objects. One estimate, based on the earlier work of mathematician John von Neumann, places the minimum size of a self-replicating pattern in the Game of Life at ten trillion squares—roughly the number of molecules in a single human cell. One can define living beings as complex systems of limited size that are stable and that reproduce themselves. The objects described above satisfy the reproduction condition but are probably not stable: A small disturbance from outside would probably wreck the delicate mechanism.

Surviving AI: The Promise and Peril of Artificial Intelligence
by Calum Chace
Published 28 Jul 2015

But the first general-purpose computer to be completed was ENIAC (Electronic Numerical Integrator And Computer), built at the Moore School of Electrical Engineering in Philadelphia, and unveiled in 1946. Like so many technological advances, it was funded by the military, and one of its first assignments was a feasibility study of the hydrogen bomb. While working on ENIAC’s successor, EDVAC (Electronic Discrete Variable Automatic Computer), the brilliant mathematician and polymath John von Neumann wrote a paper describing an architecture for computers which remains the basis for today’s machines. The Dartmouth Conference The arrival of computers combined with a series of ideas about thinking by Turing and others led to “the conjecture that every . . . feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”

Literary Theory for Robots: How Computers Learned to Write
by Dennis Yi Tenen
Published 6 Feb 2024

We stand at a crossroads with Leibniz because, from here, the history of artificial reason splits into at least two diverging paths. The road more traveled leads to the development of modern calculus (literally “the small pebble” in Latin) through the work of Maria Agnesi, Augustin-­Louis Cauchy, Louis Arbogast, Bernhard Riemann, and John von Neumann. The larger, though now somewhat neglected, pebble rolled down the road of universal language, leading directly to modern conversational AIs. In 1679, Leibniz revived an earlier, monumental project, a universal encyclopedia, aimed at healing the rift between Protestant and Catholic churches.

Radical Technologies: The Design of Everyday Life
by Adam Greenfield
Published 29 May 2017

But 3D printers like the Replicator 2, as well as the computer numerically controlled (CNC) milling machines and laser cutters elsewhere in the building, might just be the most visible sign of a coming revolution: the digital fabrication of all the things we encounter in the world. We owe the conceptual genesis of the digital fabrication device to the legendary twentieth-century mathematician John von Neumann, who first broached the notion in a thought experiment he entertained as early as the mid-1940s. Von Neumann’s Theory of Self-Reproducing Automata,1 published posthumously in 1966, outlined the principles of a “universal constructor” able to pluck resources from its environment and, given enough time, arrange them into anything desired—including, crucially, exact copies of itself.

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This is, of course, an entirely reasonable expectation, not merely in the liminal space of a dive bar but anywhere in the city. Casey Newton, “Seattle dive bar becomes first to ban Google Glass,” CNET, March 8, 2013. 23.Dan Wasserman, “Google Glass Rolls Out Diane von Furstenberg frames,” Mashable, June 23, 2014. 4Digital fabrication 1.John Von Neumann, Theory of Self-Reproducing Automata, Urbana: University of Illinois Press, 1966, cba.mit.edu/events/03.11.ASE/docs/VonNeumann.pdf. 2.You may be familiar with cellular automata from John Conway’s 1970 Game of Life, certainly the best-known instance of the class. See Bitstorm.org, “John Conway’s Game of Life,” undated, bitstorm.org. 3.Adrian Bowyer, “Wealth Without Money: The Background to the Bath Replicating Rapid Prototyper Project,” February 2, 2004, reprap.org/wiki/Wealth_Without_Money; RepRap Project, “Cost Reduction,” December 30, 2014, reprap.org/wiki/Cost_Reduction.

Prime Obsession:: Bernhard Riemann and the Greatest Unsolved Problem in Mathematics
by John Derbyshire
Published 14 Apr 2003

It is not impossible for a mathematician to be worldly, and there are many 164 PRIME OBSESSION counterexamples. René Descartes was a soldier and a courtier. (He survived the first but not the second.) Karl Weierstrass spent his years at university drinking and fighting and left without a degree. John von Neumann, one of the greatest of twentieth-century mathematicians, was quite a boulevardier, fond of pretty women and fast cars. Jacques Hadamard, on the evidence, was not one of those counterexamples. Even discounting the apocrypha that develop around any great man, it seems plain that Hadamard could not knot his tie without assistance.

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Hilbert’s “metamathematics” program tried to encompass both logic and mathematics in a more waterproof symbolism. This inspired the work of Kurt Gödel and Alan Turing. Gödel proved important theorems by attaching numbers to Hilbert-type symbols; Turing coded both instructions and data as arbitrary numbers in his “Turing machine” concept. Picking up on this idea, John von Neumann developed the stored-program concept on which all modern software is based, that code and data can be represented in the same way in a computer’s memory…. EPILOGUE 138. In a letter to his brother dated June 26, 1854, he mentioned a recurrence of mein altes Übel—“my old malady”—brought on by a spell of bad weather. 139.

Debunking Economics - Revised, Expanded and Integrated Edition: The Naked Emperor Dethroned?
by Steve Keen
Published 21 Sep 2011

The concept of expected value is thus not a good arbiter for rational behavior in the way it is normally presented in Behavioral Economics and Finance experiments – why, then, is it used? If you’ve read this far into this book, you won’t be surprised to learn that it’s because economists have misread the foundation research on this topic by the mathematician John von Neumann, and his economist collaborator Oskar Morgenstern, The Theory of Games and Economic Behavior (Von Neumann and Morgenstern 1953). Misunderstanding von Neumann John von Neumann was one of the greatest intellects of all time, a child prodigy who went on to make numerous pivotal contributions to a vast range of fields in mathematics, physics, and computer science. He was a polymath at a time when it was far more difficult to make contributions across a range of fields than it had been in earlier centuries.

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However, subsequent economists have applied this theory to all behavior, including interpersonal relations. 4 Cardinal refers to the ability to attach a precise quantity, whereas ordinal refers to the ability to rank things in size order, without necessarily being able to ascribe a numeric value to each. 5 As I point out later, the mathematician John von Neumann developed a way that a cardinal measure of utility could be derived, but this was ignored by neoclassical economists (Von Neumann and Morgenstern 1953: 17–29). 6 At its base (where, using my ‘bananas and biscuits’ example, zero bananas and zero biscuits were consumed), its height was zero. Then as you walked in the bananas direction only (eating bananas but no biscuits), the mountain rose, but at an ever-diminishing rate – it was its steepest at its base, because the very first units consumed gave the greatest ‘marginal utility.’

GDP: A Brief but Affectionate History
by Diane Coyle
Published 23 Feb 2014

The electronic programmable computer was one of the basic innovations of World War II. It emerged from the wartime code-breaking work at Bletchley Park in the United Kingdom and the brilliant conceptual leaps made by Alan Turing, and, across the Atlantic during and after the war, from the work of John Von Neumann and others involved in the development of nuclear weapons. Computers began as military and academic machines, then came into use in big businesses, and in the 1980s finally became small and cheap enough to spread to all offices and gradually individual homes. Separately, the communications protocols between computers were developed in the United States in the 1970s, by DARPA (the Defense Advanced Research Projects Agency) among other groups.

What Technology Wants
by Kevin Kelly
Published 14 Jul 2010

When two world wars unleashed the full killing power of this inventiveness, it cemented the reputation of technology as a beguiling satan. As we refined this stuff through generations of technological evolution, it lost much of its hardness. We began to see through technology’s disguise as material and began to see it primarily as action. While it inhabited a body, its heart was something softer. In 1949, John von Neumann, the brainy genius behind the first useful computer, realized what computers were teaching us about technology: “Technology will in the near and in the farther future increasingly turn from problems of intensity, substance, and energy, to problems of structure, organization, information, and control.”

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Synchronicity is not just a phenomenon of the past, when communication was poor, but very much part of the present. Scientists at AT&T Bell Labs won a Nobel Prize for inventing the transistor in 1948, but two German physicists independently invented a transistor two months later at a Westinghouse laboratory in Paris. Popular accounts credit John von Neumann with the invention of a programmable binary computer during the last years of World War II, but the idea and a working punched-tape prototype were developed quite separately in Germany a few years earlier, in 1941, by Konrad Zuse. In a verifiable case of modern parallelism, Zuse’s pioneering binary computer went completely unnoticed in the United States and the UK until many decades later.

From Airline Reservations to Sonic the Hedgehog: A History of the Software Industry
by Martin Campbell-Kelly
Published 15 Jan 2003

From Airline Reservations to Sonic the Hedgehog History of Computing I. Bernard Cohen and William Aspray, editors William Aspray, John von Neumann and the Origins of Modern Computing Charles J. Bashe, Lyle R. Johnson, John H. Palmer, and Emerson W. Pugh, IBM’s Early Computers Martin Campbell-Kelly, From Airline Reservations to Sonic the Hedgehog: A History of the Software Industry Paul E. Ceruzzi, A History of Modern Computing I. Bernard Cohen, Howard Aiken: Portrait of a Computer Pioneer I. Bernard Cohen and Gregory W. Welch, editors, Makin’ Numbers: Howard Aiken and the Computer John Hendry, Innovating for Failure: Government Policy and the Early British Computer Industry Michael Lindgren, Glory and Failure: The Difference Engines of Johann Müller, Charles Babbage, and Georg and Edvard Scheutz David E.

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The Technical Computing Bureau was allocated the first production 701, which was located in IBM’s world headquarters on Madison Avenue in New York. The machine was inaugurated in April 1953 with enormous publicity to herald IBM’s arrival on the computer scene. Among the 200 guests were J. Robert Oppenheimer (the former director of the Manhattan Project), John von Neumann (one of the inventors of the stored-program computer), and William Shockley (coinventor of the transistor). The Technical Computing Bureau’s machine was in place about 6 months before the first deliveries of 701s to customers, so customers’ programmers were able to become familiar with the 701 before their machines arrived.

Misbehaving: The Making of Behavioral Economics
by Richard H. Thaler
Published 10 May 2015

This implies that if your wealth is $100,000 and I offer you a choice between an additional $1,000 for sure or a 50% chance to win $2,000, you will take the sure thing because you value the second thousand you would win less than the first thousand, so you are not willing to risk losing that first $1,000 prize in an attempt to get $2,000. The full treatment of the formal theory of how to make decisions in risky situations—called expected utility theory—was published in 1944 by the mathematician John von Neumann and the economist Oskar Morgenstern. John von Neumann, one of the greatest mathematicians of the twentieth century, was a contemporary of Albert Einstein at the Institute of Advanced Study at Princeton University, and during World War II he decided to devote himself to practical problems. The result was the 600-plus-page opus The Theory of Games and Economic Behavior, in which the development of expected utility theory was just a sideline.

The Planet Remade: How Geoengineering Could Change the World
by Oliver Morton
Published 26 Sep 2015

The idea of deliberate climate change was bound up with thinking about nuclear weaponry from the very beginning of the cold war. Control and Catastrophe In 1945 ENIAC, the first fully programmable electronic computer, was given its first problem: a simulation of the hydrogen-bomb design then being touted by Edward Teller. The task was set by John von Neumann, a mathematician at the Princeton Institute for Advanced Study who was attached to the Manhattan Project. Von Neumann was determined that the computer’s potential for solving problems should be applied to the most pressing issues of the day. And that was why, even as the million-punch-card H-bomb explosion was trundling through ENIAC’s circuits, he was already working out how the machine and its successors could be programmed to predict the weather, and produce new insights into controlling it.

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Chapter Eleven: The Ends of the World Badash (2009) provides a very full account of the origins of and arguments over nuclear winter; the key papers are Crutzen and Birks (1982) and Turco et al. (1983) – the TTAPS paper. Levenson (1990) provides excellent context. Weart (1988) and Brians (1987) are useful on the cultural ramifications of nuclear fear; Johan Rockström is quoted in Rayner and Heyward (2013). For more on Huxley’s speech, see Deese (2010). Of many sources available on John von Neumann, Dyson (2012) stands out; his supernova fears are quoted in Smith (2007) and the proceedings of the ‘Infinite Forecast’ meeting are in Pfeffer (1956). The point about the different academic milieux of weather modification and climate modelling is made in Hart and Victor (1993), and Paul Edwards’ dissection of climate-nuclear doublethink is in Edwards (2012).

People, Power, and Profits: Progressive Capitalism for an Age of Discontent
by Joseph E. Stiglitz
Published 22 Apr 2019

Robert Gordon of Northwestern University in his bestselling book The Rise and Fall of American Growth: The US Standard of Living Since the Civil War argues that the pace of innovation has actually slowed.2 Yes, we have Facebook and Google, but these innovations pale in comparison with the importance of electricity, or even indoor toilets and clean water that played such an important role in improving health and longevity. These past experiences may, however, not be a good guide for the future. More than a half century ago, John von Neumann, one of the leading mathematicians of the mid-twentieth century, suggested that there might be a point3 where it becomes less expensive to produce a machine to replace a human than to hire and train a human. These machines will, in turn, be produced by other machines that learn how to produce them.

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Some suggest that there are significant measurement errors in GDP, so that it underestimates the true rate of growth, but in my judgment, while there are significant measurement problems, they do not change the overall picture, in particular, the pace of increase in GDP today is lower than it was in earlier periods. Of course, by its very nature, we cannot be sure about the future pace of innovation. 3.Referred to as the “singularity.” See also Stanislaw Ulam, “Tribute to John von Neumann,” Bulletin of the American Mathematical Society 64, no. 3, part 2 (1958): 5. See also Anton Korinek and Joseph E. Stiglitz, “Artificial Intelligence and Its Implications for Income Distribution and Unemployment,” in Economics of Artificial Intelligence (Chicago: University of Chicago Press, forthcoming). 4.Rapid advances in artificial intelligence in the last five years has led to extensive speculation about when AI will exceed human performance in a range of jobs.

Monadic Design Patterns for the Web
by L.G. Meredith

Further, we can use our case class, Game, together with zero, to make the Conway game that represents unity, aka one. We could write this at the Scala Read-Evaluate-Print-Loop (REPL) prompt with the following: scala> val one = Game( Set( EmptyGame ), Set.empty ) one: Game = Game(Set(EmptyGame),Set()) Anyone familiar with the John von Neumann encoding of the Naturals can see where this is going. (And anyone not will get the gist with the next bit.) We’ll skip ahead soon and talk about how to model addition, but first, let’s look at Conway’s own notation. He writes games like this: G = { GL, . . . | GR, . . . } The notation is much like set (comprehension) notation except that the bar in the middle of braces denotes the separation of the left and right components.

Infinite Ascent: A Short History of Mathematics
by David Berlinski
Published 2 Jan 2005

We ourselves may allow Pedro or Fedro to suffer a cut all his own, restoring to prominence in Kurt Gödel the twenty-three-year-old director of record. The unpurged images of this spectacular argument recede; so, too, the details of Gödel’s first theorem. Directly, the second theorem appears, this one dealing directly with the issue of consistency. It is a theorem that John von Neumann noticed after Gödel had communicated his first theorem to various mathematicians; but when he wrote eagerly to Gödel to convey his discovery, he learned that Gödel had already discovered the same thing. The import of Gödel’s second theorem can be conveyed by means of only a few strokes. The first incompleteness theorem affirms that if the system of the Principia is consistent, then there exists an undecidable proposition, one that may be expressed from within the cage of its symbols.

Information Doesn't Want to Be Free: Laws for the Internet Age
by Cory Doctorow , Amanda Palmer and Neil Gaiman
Published 18 Nov 2014

They figured out how to do it with ease. 1.5 Understanding General-Purpose Computers BACK AT THE dawn of mechanical computation, computers were “special-purpose.” One computer would solve one kind of mathematical problem, and if you had a different problem, you’d build a different computer. But during World War II, thanks to the government-funded advancements made by such scientific luminaries as Alan Turing and John von Neumann, a new kind of computer came into existence: the “general-purpose” digital computer. These machines arose from a theory of general-purpose computation that showed that, with a simple set of “logic gates” and enough memory and time, you could “compute” any program that could be represented symbolically.

The Price of Tomorrow: Why Deflation Is the Key to an Abundant Future
by Jeff Booth
Published 14 Jan 2020

For a moment, though, let’s assume that we are always completely rational, making decisions that are best for ourselves, our families, our countries, and the world around us—in that order. On the face of it, it sounds simple enough—until we consider that the decisions that are best for us are oftentimes at odds with each other. Game theory applies to almost everything when competing for scarce resources. It was developed in 1928 by John von Neumann (1903–1957) and was further refined in 1944 with Oskar Morgenstern (1902–1977) and has broad implication in business, economics, biology, and war—whenever our own actions depend critically on other participants. As different “actors” or “agents” (game theory speak—in this case, you could use “individuals” or “countries”) choose different strategies to maximize their own benefit, a “game” is developed where understanding what each actor or agent will do becomes critical to who wins the game.

Physics of the Future: How Science Will Shape Human Destiny and Our Daily Lives by the Year 2100
by Michio Kaku
Published 15 Mar 2011

The word originally came from the world of relativistic physics, my personal specialty, where a singularity represents a point of infinite gravity, from which nothing can escape, such as a black hole. Because light itself cannot escape, it is a horizon beyond which we cannot see. The idea of an AI singularity was first mentioned in 1958, in a conversation between two mathematicians, Stanislaw Ulam (who made the key breakthrough in the design of the hydrogen bomb) and John von Neumann. Ulam wrote, “One conversation centered on the ever accelerating progress of technology and changes in the mode of human life, which gives the appearance of approaching some essential singularity in the history of the human race beyond which human affairs, as we know them, could not continue.”

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Matching the computing speed of the brain is just the humble beginning. Third, even if intelligent robots are possible, it is not clear if a robot can make a copy of itself that is smarter than the original. The mathematics behind self-replicating robots was first developed by the mathematician John von Neumann, who invented game theory and helped to develop the electronic computer. He pioneered the question of determining the minimum number of assumptions before a machine could create a copy of itself. However, he never addressed the question of whether a robot can make a copy of itself that is smarter than it.

The Transparent Society: Will Technology Force Us to Choose Between Privacy and Freedom?
by David Brin
Published 1 Jan 1998

In those days, long-distance call routing was a laborious task of negotiation, planned well in advance by human operators arranging connections from one zone to the next. But this drudgery might be avoided in a dispersed computer network if the messages themselves could navigate, finding their own way from node to node, carrying destination information in their lead bits like the address on the front of an envelope. Early theoretical work by Alan Turing and John Von Neumann hinted this to be possible by allowing each part of a network to guess the best way to route a message past any damaged area and eventually reach its goal. In theory, such a system might keep operating even when others lay in tatters. In retrospect, the advantages of Baranʼs insight seem obvious.

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(See the section on “public feedback regulation” in chapter 8.) Right now we should focus on the evolving way in which researchers have come to view the concept of risk and how people respond to it. Until recently, most models were based on classical decision theory, supplemented by the later game theory that John Von Neumann developed after World War II. These are essentially mathematical approaches to betting— calculating odds for success or failure when contributing factors are either well known, or partly unknown. For instance, a problem called “the prisonersʼ dilemma” explores how two parties might behave when each can make a quick, temporary score by betraying the other, or else both might prosper, moderately but indefinitely, by deciding to cooperate.

Accelerando
by Stross, Charles
Published 22 Jan 2005

"Oh, one of Johnny's toys – a micromechanical digital phonograph player," Gianni says dismissively. "He used to design Babbage engines for the Pentagon – stealth computers. (No van Eck radiation, you know.) Look." He carefully pulls a fabric-bound document out of the obsolescent data wall and shows the spine to Manfred: "On the Theory of Games, by John von Neumann. Signed first edition." Aineko meeps and dumps a slew of confusing purple finite state automata into Manfred's left eye. The hardback is dusty and dry beneath his fingertips as he remembers to turn the pages gently. "This copy belonged to the personal library of Oleg Kordiovsky. A lucky man is Oleg: He bought it in 1952, while on a visit to New York, and the MVD let him to keep it."

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Presently a braid of processes running on an abstract virtual machine asks him a question that cannot be encoded in any human grammar. Watch and wait, he replies to his passenger. They'll figure out what we are sooner or later. Part 2 Point of Inflexion Life is a process which may be abstracted from other media. – John Von Neumann Chapter 1 Halo The asteroid is running Barney: it sings of love on the high frontier, of the passion of matter for replicators, and its friendship for the needy billions of the Pacific Rim. "I love you," it croons in Amber's ears as she seeks a precise fix on it: "Let me give you a big hug … " A fraction of a light-second away, Amber locks a cluster of cursors together on the signal, trains them to track its Doppler shift, and reads off the orbital elements.

Track Changes
by Matthew G. Kirschenbaum
Published 1 May 2016

read the ad that was posted around Silicon Valley in February 1975.3 Computers themselves, of course, compute: which is to say they work by fundamentally arithmetical principles. An algorithm is a sequence of arithmetical steps. A program is a sequence of algorithms. The basic architecture of input, processing, storage, and output has been canonical in computer science since John von Neumann published his “draft” report on the EDVAC in 1945.4 All of those different abstract components, however, require a corresponding instantiation in some physical medium. Computer storage, for example, has historically taken the form of everything from disks and tape to punched cards to magnetic drums, wires, mesh, and ringlets, even cathode ray tubes (before they were used as output devices).

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The principle of storing data in the same medium and format as the programs that make use of it is a bedrock principle of computer architecture, formally instantiated in the so-called Von Neumann model that dominated computer systems design throughout the second half of the twentieth century. (As John von Neumann himself was wont to put it, it was all the same “organ.”)42 As early as 2001 the State Library of Victoria in Melbourne purchased the Macintosh laptop—reportedly missing its “o” key—that the Australian novelist Peter Carey used to write The True History of the Kelly Gang (2000); it is currently on display there under glass, alongside samples from his literary papers.43 Similar to the work at Emory, archivists there contemplate making a “clone” of the machine available so visitors can explore its electronic innards.

Robot Rules: Regulating Artificial Intelligence
by Jacob Turner
Published 29 Oct 2018

Alt and M. Ruminoff, Vol. 6 (New York: Academic Press, 1965). 106Nick Bostrom, “How Long Before Superintelligence?”, International Journal of Future Studies, 1998, vol. 2.. 107 The singularity was conceived of shortly after the advent of modern AI studies, having been introduced by John von Neumann in 1958 and then popularised by Vernor Vinge, in “The Coming Technological Singularity: How to Survive in the Post-human Era” (1993), available at: https://​edoras.​sdsu.​edu/​~vinge/​misc/​singularity.​html, accessed 22 June 2018 and subsequently by Ray Kurzweil, The Singularity Is Near: When Humans Transcend Biology (New York: Viking Press, 2005). 108In 1968, a Scottish chess champion bet AI pioneer John McCarthy £500 that a computer would not be able to beat him by 1979.

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, Scientific American, Vol. 314 (June 2016), 58–59. 140Nate Soares and Benja Fallenstein, “Aligning Superintelligence with Human Interests: A Technical Research Agenda”, in The Technological Singularity (Berlin and Heidelberg: Springer, 2017), 103–125. See also Stephen M. Omohundro, “The Basic AI Drives”, in Proceedings of the First Conference on Artificial General Intelligence, 2008. 141Ibid. 142Nick Bostrom, Superintelligence : Paths, Dangers, Strategies (Oxford: Oxford University Press, 2014), Chapter 9. 143See John von Neumann and Oskar Morgenstern, Theory of Games and Economic Behavior (Princeton, NJ: Princeton University Press, 1944). 144Nate Soares and Benja Fallenstein, “Toward Idealized Decision Theory”, Technical Report 2014–7 (Berkeley, CA: Machine Intelligence Research Institute, 2014), https://​arxiv.​org/​abs/​1507.​01986, accessed 1 June 2018. 145See, for example, Thomas Harris, The Silence of the Lambs (London: St.

Radical Uncertainty: Decision-Making for an Unknowable Future
by Mervyn King and John Kay
Published 5 Mar 2020

Supermarket shoppers filled their trolleys as if they maximised their utility. And decision-makers faced with radical uncertainty behaved as if they maximised their subjective expected utility. This extension of the axiomatic approach from the analysis of consumer choice to decision-making under uncertainty was the result of the work of several US-based scholars. John von Neumann was a polymathic genius who worked on the Manhattan Project and subsequently helped to develop the hydrogen bomb. In their classic work The Theory of Games and Economic Behavior , von Neumann and his Princeton colleague Oskar Morgenstern sought to establish that probabilistic reasoning could provide a coherent and rigorous framework for rational decision-making in a world of uncertainty.

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On what basis can we conclude that the men who attended the Paris symposium, among the cleverest people on the planet, were failing to act ‘in accordance with reason or logic’? Rational behaviour is not defined by conformity with a set of axioms set down even by such distinguished thinkers as John von Neumann and Milton Friedman. Styles of reasoning At the end of the nineteenth century, Charles Sanders Pierce, a founder of the American school of pragmatist philosophy, distinguished three broad styles of reasoning. Deductive reasoning reaches logical conclusions from stated premises. For example, ‘Evangelical Christians are Republican.

Quantum Computing for Everyone
by Chris Bernhardt
Published 19 Mar 2019

Entropy is also defined in thermodynamics. In fact, this is where Shannon got the idea. How closely are these two entropies related to one another? Can some of the theory of computation be expressed in terms of thermodynamics? In particular, can one talk about the minimum energy required performing a calculation? John von Neumann conjectured that when information was lost energy is expended—it dissipates as heat. Rolf Landauer proved the result and gave the minimum possible amount of energy to erase one bit of information. This amount of energy is called the Landauer limit. If the computation is reversible, however, no information is lost and theoretically it can be performed with no energy loss.

The Internet of Us: Knowing More and Understanding Less in the Age of Big Data
by Michael P. Lynch
Published 21 Mar 2016

Is it possible that the smartest guy in the room is the room? That is, can networks themselves know? There are a few different ways to approach this question. One way has to do with what those in the AI (artificial intelligence) biz call “the singularity”—a term usually credited to the mathematician John von Neumann. The basic idea is that at some point machines—particularly computer networks—will become intelligent enough to become self-aware, and powerful enough to take control. The possibility of the singularity raises a host of interesting philosophical questions, but I want to focus on one issue that is already with us.

Scale: The Universal Laws of Growth, Innovation, Sustainability, and the Pace of Life in Organisms, Cities, Economies, and Companies
by Geoffrey West
Published 15 May 2017

Soon it will have to take twenty-five, then twenty, then seventeen, and so on, and like Sisyphus we are destined to go on doing it, if we insist on continually growing and expanding. The resulting sequence of singularities, each of which threatens stagnation and collapse, will continue to pile up, leading to what mathematicians call an essential singularity—a sort of mother of all singularities. The great John von Neumann, mathematician, physicist, computer scientist, and polymath, a man whose ideas and accomplishments have had a huge influence on your life, made the following remarkably prescient observation more than seventy years ago: “The ever accelerating progress of technology and changes in the mode of human life . . . gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue.”7 Among von Neumann’s many accomplishments before he died at the relatively young age of fifty-three in 1957 are his seminal role in the early development of quantum mechanics, his invention of game theory, which is a major tool in economic modeling, and the conceptual design of modern computers universally referred to as the von Neumann architecture.

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Kurzweil, The Singularity Is Near: When Humans Transcend Biology (New York: Viking, 2005). 6. V. Vinge, “The Coming Technological Singularity: How to Survive in the Post-Human Era,” Whole Earth Review (1993). 7. This is quoted by the great mathematician Stanislaw Ulam in a eulogy to von Neumann following his death in 1957: “Tribute to John von Neumann,” Bulletin of the American Mathematical Society 5(3), part 2 (1958): 64. 8. C. McCarthy, The Road (New York: Alfred A. Knopf, 2006). AFTERWORD 1. Two popular nontechnical books that present a broad overview of the enormously exciting quest for the fundamental constituents of matter and a grand unified theory for understanding their interactions, including its extension to the evolution of the cosmos and the origin of space-time itself, are S.

Why Stock Markets Crash: Critical Events in Complex Financial Systems
by Didier Sornette
Published 18 Nov 2002

The most important tool in this analysis was game theory: the study of situations, like poker or chess games, in which players have to make their decisions based on guesses about what the other player is going to do next. Game theory was first adapted to economics in the 1940s by mathematician John von Neumann (the same von Neumann whose theoretical insights made the computer possible) and economist O. Morgenstern. Since then, the standard economics and social science model of a human agent is that it is like a general-purpose logic machine. All decision tasks, regardless of context, constitute optimization problems subject to external constraints whether from the physical environment or from the reaction functions of other agents.

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Gambling with the house money and trying to break even: The effects of prior outcomes on risky choice, Management Science 36, 643–660. 426. Toner, J. and Tu, Y. H. (1998). Flocks, herds, and schools: A quantitative theory of flocking, Physical Review E 58, 4828–4858. 427. Trueman, B. (1994). Analyst forecasts and herding behavior, The Review of Financial Studies 7, 97–124. 428. Ulam, S. (1959). Tribute to John von Neumann, Bulletin of the American Mathematical Society 64, 1–49. 429. U.S. Committee of the Global Atmospheric Research Program (1975). Understanding Climatic Change—A Program for Action (National Research Council, National Academy of Sciences, Washington, D.C.). 430. U.S. Postage Release No. 99-045, May 21, 1999. 431.

Red Plenty
by Francis Spufford
Published 1 Jan 2007

He could tune up the whole Soviet orchestra, if they’d let him. His left foot dripped. He really must find a way to get new shoes. Notes – I.1 The Prodigy, 1938 1 Without thinking about it, Leonid Vitalevich: Leonid Vitalevich Kantorich (1912–86), mathematician and economist, nearest Soviet equivalent to John von Neumann, later (1975) to be the only Soviet winner of the Nobel Prize for Economics (shared with Tjalling Koopmans). Calling someone by first name and patronymic expresses formal esteem, in Russian; he is mostly referred to that way here, to suggest that he is being viewed with respectful acquaintance but not intimacy.

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There are many, many editions, but see, for example, V.I.Lenin, Selected Works vol. 2 (Moscow: Progress Publishers, 1970). I.1 The Prodigy, 1938 1 Without thinking about it, Leonid Vitalevich: Leonid Vitalevich Kantorovich (1912–86), mathematician and economist, nearest Soviet equivalent to John von Neumann, later (1975) to be the only Soviet winner of the Nobel Prize for Economics (shared with Tjalling Koopmans). Calling someone by first name and patronymic expresses formal esteem, in Russian; he is mostly referred to that way here, to suggest that he is being viewed with respectful acquaintance but not intimacy.

The Moral Animal: Evolutionary Psychology and Everyday Life
by Robert Wright
Published 1 Jan 1994

THE SECRET OF LIFE On the day James Watson and Francis Crick discovered the structure of DNA, Crick, as Watson later recalled it, walked into their regular lunch place and announced that they had “found the secret of life.” With all due respect for DNA, I would like to nominate another candidate for the secret of life. Unlike Francis Crick, I can’t claim to have discovered the secret I’m touting. It was discovered—or, if you prefer, invented—about half a century ago by the founders of game theory, John von Neumann and Oskar Morgenstern. They made a basic distinction between “zero-sum” games and “non-zero-sum” games. In zero-sum games, the fortunes of the players are inversely related. In tennis, in chess, in boxing, one contestant’s gain is the other’s loss. In non-zero-sum games, one player’s gain needn’t be bad news for the other(s).

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Schelling is surely right that a zero-sum game is merely a special case of a fixed-sum game—the case in which the sum is fixed at zero. So the “fixed-sum/variable-sum” terminology is, strictly speaking, more generally applicable to life. But “zero-sum/non-zero-sum” is the terminology used by the founders of game theory, John von Neumann and Oskar Morgenstern, and it remains in common use. And its implication that there are negative-sum and positive-sum games—though slippery on close inspection—is useful for present purposes. Sun Tzu’s advice: Cotterell (1995), p. 243. †heedlessly launching war: The reasons for this are complex and variable, but one is that within a given society, per pectives differ.

Nonzero: The Logic of Human Destiny
by Robert Wright
Published 28 Dec 2010

THE SECRET OF LIFE On the day James Watson and Francis Crick discovered the structure of DNA, Crick, as Watson later recalled it, walked into their regular lunch place and announced that they had “found the secret of life.” With all due respect for DNA, I would like to nominate another candidate for the secret of life. Unlike Francis Crick, I can’t claim to have discovered the secret I’m touting. It was discovered—or, if you prefer, invented—about half a century ago by the founders of game theory, John von Neumann and Oskar Morgenstern. They made a basic distinction between “zero-sum” games and “non-zero-sum” games. In zero-sum games, the fortunes of the players are inversely related. In tennis, in chess, in boxing, one contestant’s gain is the other’s loss. In non-zero-sum games, one player’s gain needn’t be bad news for the other(s).

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Schelling is surely right that a zero-sum game is merely a special case of a fixed-sum game—the case in which the sum is fixed at zero. So the “fixed-sum/variable-sum” terminology is, strictly speaking, more generally applicable to life. But “zero-sum/non-zero-sum” is the terminology used by the founders of game theory, John von Neumann and Oskar Morgenstern, and it remains in common use. And its implication that there are negative-sum and positive-sum games—though slippery on close inspection—is useful for present purposes. Sun Tzu’s advice: Cotterell (1995), p. 243. †heedlessly launching war: The reasons for this are complex and variable, but one is that within a given society, per pectives differ.

The Singularity Is Nearer: When We Merge with AI
by Ray Kurzweil
Published 25 Jun 2024

Put the atoms down where the chemist says, and so you make the substance.”[52] Feynmann was optimistic: “The problems of chemistry and biology can be greatly helped if our ability to see what we are doing, and to do things on an atomic level, is ultimately developed—a development which I think cannot be avoided.” In order for nanotechnology to have an impact on large objects, it needs to have a self-replication system. The idea of how to create a self-replicating module was first formalized by legendary mathematician John von Neumann (1903–1957) in a series of lectures during the late 1940s and in a 1955 Scientific American article.[53] But the full range of his ideas was not collected and widely published until 1966, almost a decade after his death. Von Neumann’s approach was highly abstract and mathematical, and focused mostly on the logical underpinnings rather than the detailed physical practicalities of building self-replicating machines.

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BACK TO NOTE REFERENCE 50 For an essay closely adapted from Feynman’s December 29, 1959, lecture, see Richard Feynman, “There’s Plenty of Room at the Bottom,” Engineering and Science 23, no. 5 (February 1960): 22–26, 30–36, http://calteches.library.caltech.edu/47/2/1960Bottom.pdf. BACK TO NOTE REFERENCE 51 Feynman, “There’s Plenty of Room at the Bottom,” 22–26, 30–36. BACK TO NOTE REFERENCE 52 John von Neumann, Theory of Self-reproducing Automata (Urbana, IL: University of Illinois Press, 1966), https://archive.org/details/theoryofselfrepr00vonn_0/mode/2up; John G. Kemeny, “Man Viewed as a Machine,” Scientific American 192, no. 4 (April 1955): 58–67, in nearly complete form at https://dijkstrascry.com/sites/default/files/papers/JohnKemenyManViewedasaMachine.pdf [inactive].

Symmetry and the Monster
by Ronan, Mark
Published 14 Sep 2006

The Moonshine connections have spawned conferences where mathematicians and mathematical physicists meet to discuss these things, but let us begin with the study of symmetry itself, starting with the work of the ancient Greeks. 1 Theaetetus’s Icosahedron In mathematics you don’t understand things. You just get used to them. John von Neumann (1903–57) In 369 BCE an Athenian philosopher named Theaetetus was wounded in a battle at Corinth, and carried home. He contracted dysentery and died in Athens. None of his writings survive, but we know of his work through later commentators, and know about him personally from Plato, who records two dialogues with Theaetetus as the main character.

Hit Refresh: The Quest to Rediscover Microsoft's Soul and Imagine a Better Future for Everyone
by Satya Nadella , Greg Shaw and Jill Tracie Nichols
Published 25 Sep 2017

In other words, how can I solve a problem that has limitless possibilities in a way that is fast and good but not always optimal? Do we solve this as best we can right now, or work forever for the best solution? Theoretical computer science really grabbed me because it showed the limits to what today’s computers can do. It led me to become fascinated by mathematicians and computer scientists John Von Neumann and Alan Turing, and by quantum computing, which I will write about later as we look ahead to artificial intelligence and machine learning. And, if you think about it, this was great training for a CEO—nimbly managing within constraints. I completed my master’s in computer science at Wisconsin and even managed to work for what Microsoft would now call an independent software vendor (ISV).

The Secret War Between Downloading and Uploading: Tales of the Computer as Culture Machine
by Peter Lunenfeld
Published 31 Mar 2011

—Vannevar Bush People tend to overestimate what can be done in one year and underestimate what can be done in five to ten years. —J.C.R. Licklider 147 GENERATIONS There are many mathematicians, early computer scientists, and engineers who deserve to be considered part of the first generation of pioneering Patriarchs. They include Alan Turing, already discussed in chapter 2; mathematician and quantum theorist John von Neumann; cyberneticist Norbert Wiener; information theorist Claude Shannon; and computer architects like the German Konrad Zuse, and Americans J. Presper Eckert and John Mauchly, who developed ENIAC, the room-sized machine at the University of Pennsylvania that we recognize as the first general-purpose electronic computer.

Geek Sublime: The Beauty of Code, the Code of Beauty
by Vikram Chandra
Published 7 Nov 2013

“The telephone switchboard-like appearance of the ENIAC programming cable-and-plug panels,” Ensmenger writes, “reinforced the notion that programmers were mere machine operators, that programming was more handicraft than science, more feminine than masculine, more mechanical than intellectual.”18 The planners considered the coding process so transparently simple that they couldn’t imagine that once in the machines, their algorithms might fault and hang, might need to be stopped. One of the ENIAC programmers, Betty Holberton, had to work very hard to convince John von Neumann that programs were complex and therefore fragile: But to my astonishment, [Dr von Neumann] never mentioned a stop instruction. So I did coyly say, “Don’t we need a stop instruction in this machine?” He said, “No we don’t need a stop instruction. We have all these empty sockets here that just let it go to bed.”

You Are Not a Gadget
by Jaron Lanier
Published 12 Jan 2010

CHAPTER 2 An Apocalypse of Self-Abdication THE IDEAS THAT I hope will not be locked in rest on a philosophical foundation that I sometimes call cybernetic totalism. It applies metaphors from certain strains of computer science to people and the rest of reality. Pragmatic objections to this philosophy are presented. What Do You Do When the Techies Are Crazier Than the Luddites? The Singularity is an apocalyptic idea originally proposed by John von Neumann, one of the inventors of digital computation, and elucidated by figures such as Vernor Vinge and Ray Kurzweil. There are many versions of the fantasy of the Singularity. Here’s the one Marvin Minsky used to tell over the dinner table in the early 1980s: One day soon, maybe twenty or thirty years into the twenty-first century, computers and robots will be able to construct copies of themselves, and these copies will be a little better than the originals because of intelligent software.

Data-Ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else
by Steve Lohr
Published 10 Mar 2015

The big-data era is the next evolutionary upheaval in the landscape of computing. The things people want to do with data, like real-time analysis of data streams or continuously running machine-learning software, pose a threat to the traditional computer industry. Conventional computing—the Von Neumann architecture, named for mathematician and computer scientist John von Neumann—operates according to discrete steps of program, store, and process. Major companies and markets were built around those tiers of computing—software, disk drives, and microprocessors, respectively. Modern data computing, according to John Kelly, IBM’s senior vice president in charge of research, will “completely disrupt the industry as we know it, creating new platforms and players.”

Move Fast and Break Things: How Facebook, Google, and Amazon Cornered Culture and Undermined Democracy
by Jonathan Taplin
Published 17 Apr 2017

Ready or not, computers are coming to the people. That’s good news, maybe the best since psychedelics. It’s way off the track of the “Computers—Threat or menace?” school of liberal criticism but surprisingly in line with the romantic fantasies of the forefathers of the science such as Norbert Wiener, Warren McCulloch, J.C.R. Licklider, John von Neumann and Vannevar Bush. The trend owes its health to an odd array of influences: The youthful fervor and firm dis-Establishmentarianism of the freaks who design computer science; an astonishingly enlightened research program from the very top of the Defense Department; an unexpected market-Banking movement by the manufacturers of small calculating machines; and an irrepressible midnight phenomenon known as Spacewar.

Work in the Future The Automation Revolution-Palgrave MacMillan (2019)
by Robert Skidelsky Nan Craig
Published 15 Mar 2020

Pew Research Center. Retrieved from http://www.pewresearch.org/fact-tank/2017/10/09/most-americanswould-favor-policies-to-limit-job-and-wage-lossescaused-by-automation/ Part IV Possibilities and Limitations for AI: What Can’t Machines Do? 11 What Computers Will Never Be Able To Do Thomas Tozer In 1948, John von Neumann, a father of the computer revolution, claimed that for anything he was told a computer could not do, after this ‘thing’ had been explained to him precisely he would be able to make a machine capable of doing it. Many scientists and philosophers strongly rejected this. For, they responded, there was something unique to humans that neither von Neumann, nor anyone else, would ever be able to replicate in a computer.

Explaining Humans: What Science Can Teach Us About Life, Love and Relationships
by Camilla Pang
Published 12 Mar 2020

For that we need to delve into the science of game theory, which maps not just how different agents in a system interact, but what their motivations are, and why they make certain decisions. Game theory was pioneered by two mathematicians whose work helped lay the foundations for the modern study of artificial intelligence: John von Neumann and John Nash. Like agent-based models, it looks at how different players within a certain, rules-based system interact. But it goes further by looking at the consequences of their various choices: how will a decision by one or several players in the game affect everyone else? Game theory looks at the whole picture, assuming a player doesn’t just consider their own decisions and their consequences, but those of the other players as well – predicting both what they may know, and how they are likely to act.

The Art of Computer Programming: Fundamental Algorithms
by Donald E. Knuth
Published 1 Jan 1974

Dijkstra, CACM 18 A975), 453-457; A Discipline of Programming (Prentice-Hall, 1976).] 18 BASIC CONCEPTS 1.2.1 The concept of inductive assertions actually appeared in embryonic form in 1946, at the same time as flow charts were introduced by H. H. Goldstine and J. von Neumann. Their original flow charts included "assertion boxes" that are in close analogy with the assertions in Fig. 4. [See John von Neumann, Collected Works 5 (New York: Macmillan, 1963), 91-99. See also A. M. Turing's early comments about verification in Report of a Conference on High Speed Automatic Calculating Machines (Cambridge Univ., 1949), 67-68 and figures; reprinted with commentary by F. L. Morris and C. B. Jones in Annals of the History of Computing 6 A984), 139-143.]

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Babbage's planned machine was controlled by sequences of punched cards, as on the Jacquard loom; the Mark I was controlled by a number of paper tapes. Thus they were quite different from today's stored- program computers. Subroutine linkage appropriate to stored-program machines, with the return address supplied as a parameter, was discussed by Herman H. Goldstine and John von Neumann in their widely circulated monograph on programming, written during 1946 and 1947; see von Neumann's Collected Works 5 (New York: Macmillan, 1963), 215-235. The main routine of their programs was responsible for storing parameters into the body of the subroutine, instead of passing the necessary information in registers.

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The paper "Input- Output Buffering and FORTRAN" by David E. Ferguson, JACM 7 A960), 1-9, describes buffer circles and gives a detailed description of simple buffering with many units at once. About 1000 instructions is a reasonable upper limit for the complexity of the problems new envisioned. — HERMAN GOLDSTINE and JOHN VON NEUMANN A946) CHAPTER TWO INFORMATION STRUCTURES / think that I shall never see A poem lovely as a tree. — JOYCE KILMER A913) Yea, from the table of my memory I'll wipe away all trivial fond records. — Hamlet (Act I, Scene 5, Line 98) 2.1. INTRODUCTION Computer programs usually operate on tables of information.

Strategy: A History
by Lawrence Freedman
Published 31 Oct 2013

Nonetheless, game theory represented a way of thinking about strategic issues that was abstract and formal. Its influence on the social sciences eventually became significant. It emerged as the result of collaboration between two European émigrés working at Princeton during the war. From Hungary came John von Neumann. As a child he could astound with feats of memory and computation, and he was soon recognized as one of the mathematical geniuses of his age. He had developed the basic principle of game theory in the 1920s by contemplating poker. When Oskar Morgenstern, an economist from Vienna, got to know von Neumann at Princeton he saw the broader significance of his ideas and helped give them structure.

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In this they were influenced by Friedrich Hayek, an Austrian who had acquired British citizenship in 1938 and had been teaching at the London School of Economics until he was recruited to Chicago, though not by the economics department, in 1950. His most famous book, The Road to Serfdom, was published during the war and warned against the inclination to central planning that was gathering momentum under the combined influence of socialism and the wartime experience. Meanwhile, the Cowles Commission, influenced by John von Neumann and sponsored by RAND, was up for new methodological challenges and was more inclined to believe that robust models could support enlightened policy. Either way the assumptions and methods associated with game theory became part of a wider project to develop new forms of social science. Economics into Business The Ford Foundation was at the fore in exploring how management within big government and big business could become vital instruments of efficiency and progress.

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For a critique of the role of systems analysis, see Stephen Rosen, “Systems Analysis and the Quest for Rational Defense,” The Public Interest 76 (Summer 1984): 121–159. 17. Bernard Brodie, War and Politics (London: Cassell, 1974), 474–475. 18. Cited in William Poundstone, Prisoner’s Dilemma (New York: Doubleday, 1992), 6. 19. Oskar Morgenstern, “The Collaboration between Oskar Morgenstern and John von Neumann,” Journal of Economic Literature 14, no. 3 (September 1976): 805–816. E. Roy Weintraub, Toward a History of Game Theory (London: Duke University Press, 1992); R. Duncan Luce and Howard Raiffa, Games and Decisions; Introduction and Critical Survey (New York: John Wiley & Sons, 1957). 20. Poundstone, Prisoner’s Dilemma, 8. 21.

The Inner Lives of Markets: How People Shape Them—And They Shape Us
by Tim Sullivan
Published 6 Jun 2016

The foundation’s founding motto was “Science is Measurement.”11 The second, the RAND Corporation, first established as a joint project by the Douglas Aircraft Company and the US Department of War in 1945, used game theory to analyze the United States’s geopolitical position relative to the Soviet Union. Game theory—a mathematical approach to analyzing strategic choices—emerged from the work of Princeton mathematician John von Neumann in the 1930s, who collaborated with his economist colleague Oskar Morgenstern to write Theory of Games and Economic Behavior (published in 1944), which launched the field. Their book provided an analytical framework for figuring out, say, what Pepsi should do if Coke lowers its prices. That depends on how Pepsi’s CEO thinks Coke will respond, which in turn depends on what Coke’s CEO expects that Pepsi’s response to their price reduction will be.

Are You Smart Enough to Work at Google?: Trick Questions, Zen-Like Riddles, Insanely Difficult Puzzles, and Other Devious Interviewing Techniques You ... Know to Get a Job Anywhere in the New Economy
by William Poundstone
Published 4 Jan 2012

Another idea is to roll the die twice and total the numbers, or multiply them, or otherwise generate a large number. Then divide by 7, taking only the remainder. The remainder will be in the range of 0 to 6. We don’t need a 0, so pretend it’s a 7. That gives a “random” number in the range of 1 to 7. I put “random” in scare quotes because, as the mathematician John von Neumann wrote, “Any one who considers arithmetical methods of producing random digits is, of course, in a state of sin.” While this trick may be good enough for some purposes, the result isn’t truly random, and so this answer is not rated highly at Google or Amazon. On the web, random numbers had better be random.

Life at the Speed of Light: From the Double Helix to the Dawn of Digital Life
by J. Craig Venter
Published 16 Oct 2013

Turing also defined a universal Turing machine, which can carry out any computation for which an instruction set can be written. This is the theoretical foundation of the digital computer. Turing’s ideas were developed further in the 1940s, by the remarkable American mathematician and polymath John von Neumann, who conceived of a self-replicating machine. Just as Turing had envisaged a universal machine, so von Neumann envisaged a universal constructor. The Hungarian-born genius outlined his ideas in a lecture, “The General and Logical Theory of Automata,” at the 1948 Hixon Symposium, in Pasadena, California.

The Economic Singularity: Artificial Intelligence and the Death of Capitalism
by Calum Chace
Published 17 Jul 2016

[iii] There is no general agreement about when the information revolution started. In his 1962 book “The Production and Distribution of Knowledge in the United States”, the Austrian economist Fritz Machler suggested that with 29% of GDP accounted for by the knowledge industry, it had begun. [iv] The term was first applied to human affairs back in the 1950s by John von Neumann, a key figure in the development of the computer. The physicist and science fiction author Vernor Vinge argued in 1993 that artificial intelligence and other technologies would cause a singularity in human affairs within 30 years. This idea was picked up and popularised by the inventor and futurist Ray Kurzweil, who believes that computers will overtake humans in general intelligence in 1929, and a singularity will arrive in 2045. https://en.wikipedia.org/wiki/Technological_singularity [v] The event horizon of a black hole is the point beyond which events cannot affect an outside observer, or in other words, the point of no return.

Time Travel: A History
by James Gleick
Published 26 Sep 2016

Only at the last instant does he realize whose death he had witnessed as a child. * * * *1 A rebellious elector in Virginia refused to cast his ballot for the vote winners, Richard Nixon and Spiro Agnew, in 1972 and voted instead for John Hospers, on the Libertarian line. *2 Gödel’s proof “is more than a monument,” said John von Neumann, “it is a landmark which will remain visible far in space and time….The subject of logic has completely changed its nature and possibilities with Gödel’s achievement.” *3 Also, the Gödelian universe does not expand, whereas most cosmologists are pretty sure that ours does. *4 Gödel’s biographer Rebecca Goldstein remarked, “As a physicist and a man of common sense, Einstein would have preferred that his field equations excluded such an Alice-in-Wonderland possibility as looping time

12 Bytes: How We Got Here. Where We Might Go Next
by Jeanette Winterson
Published 15 Mar 2021

The German mathematician and philosopher Leibniz was the first champion of binary calculations, unless we go back to the Chinese and their classic book of wisdom and divination, the I Ching. Leibniz was an enthusiastic I Ching explorer. While decimal was the system initially used in computer programming and calculations, the Hungarian-American John von Neumann realised that Leibniz’s binary form was the best solution for stored-programme computing. Binary uses only two digits – zero and one. The ENIAC, launched at the University of Pennsylvania in 1946, and programmed by 6 women, used decimal. Using decimal digits, the number 128 needed 30 vacuum tubes (on/off switches that preceded transistors) to represent it.

How to Predict the Unpredictable
by William Poundstone

To add to the mystique, Shannon alone was able to beat his outguessing machine. Shannon described his device in a March 18, 1953, Bell Laboratories memorandum with the title “A Mind-Reading (?) Machine.” There he noted that the matching game had a distinguished and somewhat literary history. It was “discussed from the game theoretic angle by [John] von Neumann and [Oskar] Morgenstern and from the psychological point of view by Edgar Allan Poe in ‘The Purloined Letter.’ Oddly enough, the machine is aimed more nearly at Poe’s method than von Neumann’s.” The hero of Poe’s psychological detective tale solves crimes on the premise that people are predictable when they try not to be.

Escape From Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do About It
by Erica Thompson
Published 6 Dec 2022

More complicated models may have many thousands of parameters with much more complicated effects, but the process of fitting or calibrating the model to the observed data remains the same: change the parameters until you get a model output that is closest (in some way) to the observations. The more parameters a model has, in general, the more control we have over its behaviour and the more opportunity the model has to fit the data. Hungarian-American polymath John von Neumann is reputed to have said, ‘With four parameters I can fit an elephant. With five I can make him wiggle his trunk.’ The implication is that if we can fit anything, then the model has no explanatory power. If we can fit nothing, of course, it equally has no explanatory power. We gain confidence in a model by being able to fit the observations without going through great contortions to do so, because this shows that the variation in the observations is in some sense contained within the simple principles expressed by the model.

The Signal and the Noise: Why So Many Predictions Fail-But Some Don't
by Nate Silver
Published 31 Aug 2012

But computers are very good at computing: at repeating the same arithmetic tasks over and over again and doing so quickly and accurately. Tasks like chess that abide by relatively simple rules, but which are difficult computationally, are right in their wheelhouse. So, potentially, was the weather. The first computer weather forecast was made in 1950 by the mathematician John von Neumann, who used a machine that could make about 5,000 calculations per second.17 That was a lot faster than Richardson could manage with a pencil and paper in a French hay field. Still, the forecast wasn’t any good, failing to do any better than a more-or-less random guess. Eventually, by the mid-1960s, computers would start to demonstrate some skill at weather forecasting.

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It actually does a much worse job of explaining the real world.58 As obvious as this might seem when explained in this way, many forecasters completely ignore this problem. The wide array of statistical methods available to researchers enables them to be no less fanciful—and no more scientific—than a child finding animal patterns in clouds.* “With four parameters I can fit an elephant,” the mathematician John von Neumann once said of this problem.59 “And with five I can make him wiggle his trunk.” Overfitting represents a double whammy: it makes our model look better on paper but perform worse in the real world. Because of the latter trait, an overfit model eventually will get its comeuppance if and when it is used to make real predictions.

The Pleasure of Finding Things Out: The Best Short Works of Richard P. Feynman
by Richard P. Feynman and Jeffrey Robbins
Published 1 Jan 1999

Ultimately, for fun again and intellectual pleasure, we could imagine machines as tiny as a few microns across, with wheels and cables all interconnected by wires, silicon connections, so that the thing as a whole, a very large device, moves not like the awkward motions of our present stiff machines but in the smooth way of the neck of a swan, which after all is a lot of little machines, the cells all interconnected and all controlled in a smooth way. Why can’t we do that ourselves? ______ *John von Neumann (1903–1957), a Hungarian-American mathematician who is credited as being one of the fathers of the computer. Ed. *The jerky movements of particles caused by the constant random collisions of molecules, first noted in print in 1928 by botanist Robert Brown, and explained by Albert Einstein in a 1905 paper in Annalen der Physik.

The Supermen: The Story of Seymour Cray and the Technical Wizards Behind the Supercomputer
by Charles J. Murray
Published 18 Jan 1997

The release of energy was simply beyond the bounds of human imagination. Being anywhere near a nuclear blast was probably the closest thing on earth to hell itself. That knowledge had, in fact, been one of the driving forces behind the formation of the new lab. Legend held that on his deathbed, world-renowned mathematician John von Neumann had called for a greater push in the area of computational study of nuclear weapons. "Never let the lab be like the aircraft industry," he had said, "building, crash- ing, and then fixing." The concept of computing was not new to nuclear scientists. Those at Los Alamos National Laboratory-or more accurately, their wives-had used primitive calculating machinery to work through the mysteries of Fat Man and Little Boy.

Erwin Schrodinger and the Quantum Revolution
by John Gribbin
Published 1 Mar 2012

As the Oxford physicist David Deutsch (b. 1953) has put it, “a non-local hidden variable theory means, in ordinary language, a theory in which influences propagate across space and time without passing through the space in between: [in other words] they propagate instantaneously.”1 Apart from the momentum of the Copenhagen juggernaut, there was another reason why most physicists did not take hidden variables theory seriously in the 1950s. In 1932, John von Neumann (1903–57), a Hungarian-born mathematical genius, had published a book in which, among other things, he “proved” that hidden variables theories could not work. His contemporaries were so in awe of von Neumann’s ability that for a generation this proof was barely questioned, and it was widely cited as gospel, without being spelled out in full, in standard texts such as Max Born’s Natural Philosophy of Cause and Chance, published in 1949.

We-Think: Mass Innovation, Not Mass Production
by Charles Leadbeater
Published 9 Dec 2010

Just as the fax machine, printer and photocopier have spread the world over, so could low-cost manufacturing of machines that make reliable products, customised to local needs. One such machine could be based on Bath University’s RepRap, which looks like a large photocopier and can make three-dimensional objects from designs stored inside its computer. In the 1950s the mathematician John von Neumann imagined a universal constructor: a computer linked to a manufacturing robot that could make virtually any physical object, including replicating itself. The closest the world got to such a machine was the replicator in Star Wars, which could make any object out of thin air; the RepRap might make that a reality.

The Ages of Globalization
by Jeffrey D. Sachs
Published 2 Jun 2020

(For all his genius and his contributions, a towering figure in the entire history of mathematics, Turing was hounded by British authorities after World War II for his homosexuality, and possibly driven to suicide, as the cause of his death remains disputed.) The next step in the digital revolution came out of another remarkable mind, that of John von Neumann, who conceptualized in 1945 the basic architecture of the modern computer, with a processing unit, control unit, working memory, input and output devices, and external mass storage. Von Neumann’s computer architecture became the design of the first computers, devices using vacuum tubes to implement the computer’s logical circuitry.

The New Class Conflict
by Joel Kotkin
Published 31 Aug 2014

Clerical Dreams: A High-Tech Nirvana For at least a century or more, some scientists have dreamed of a society that was driven by the imperatives of technology, as opposed to the often messy, frequently irrational dynamics of mass democracy. This notion was noted in 1950 by the early computer designer John von Neumann, who saw that “the ever accelerating progress of technology . . . gives the appearance of approaching some essential singularity in the history of the race beyond which human affairs, as we know them, could not continue.”83 In this new formulation, technology essentially supplants divinity, community, and family as the driving force in history.

A Little History of Economics
by Niall Kishtainy
Published 15 Jan 2017

Many game theorists worked for the RAND (‘research and development’) Corporation, a military research organisation. In the film, Dr Strangelove is the American president’s director of weapons research, an eccentric genius with dark glasses and a funny accent who advises on military tactics. He’s said to have been inspired by a real genius, the Hungarian-born mathematician John von Neumann (1903–57), one of the founders of game theory who worked for RAND and became President Eisenhower’s adviser on defence strategy. Von Neumann was so clever that at the age of eight he could divide eight-digit numbers in his head. As an adult he wrote scientific papers on shockwaves, aerodynamics and the distribution of stars.

Artificial Unintelligence: How Computers Misunderstand the World
by Meredith Broussard
Published 19 Apr 2018

Together, they created HAL 9000, a computer that even today embodies all of the promise and terror of what machines might do. Most people remember HAL’s single, glowing red “eye.” That ominous eye is almost identical to an eyeball (actually, a display unit) on ENIAC, which is considered the world’s first programmable, general-purpose digital computer. John von Neumann, who came up with one of the core concepts of computer storage that led to ENIAC, was one of Minsky’s mentors. Minsky’s literary taste ran almost exclusively to science fiction. He wrote his own, and he was also friends with Isaac Asimov and other prominent science fiction writers. Sometimes in the friendships, the lines between science fiction and reality became blurred.

Wonderland: How Play Made the Modern World
by Steven Johnson
Published 15 Nov 2016

Turing’s speculations form a kind of origin point for two parallel paths that would run through the rest of the century: building intelligence into computers by teaching them to play chess, and studying humans playing chess as a way of understanding our own intelligence. Those interpretative paths would lead to some extraordinary breakthroughs: from the early work on cybernetics and game theory from people like Claude Shannon and John von Neumann, to machines like IBM’s Deep Blue that could defeat grandmasters with ease. In cognitive science, the litany of insights that derived from the study of chess could almost fill an entire textbook, insights that have helped us understand the human capacity for problem solving, pattern recognition, visual memory, and the crucial skill that scientists call, somewhat awkwardly, chunking, which involves grouping a collection of ideas or facts into a single “chunk” so that they can be processed and remembered as a unit.

The Knowledge Illusion
by Steven Sloman
Published 10 Feb 2017

Landauer was a pioneer of cognitive science, holding academic appointments at Harvard, Dartmouth, Stanford, and Princeton and also spending twenty-five years trying to apply his insights at Bell Labs. He started his career in the 1960s, a time when cognitive scientists took seriously the idea that the mind is a kind of computer. Cognitive science emerged as a field in sync with the modern computer. As great mathematical minds like John von Neumann and Alan Turing developed the foundations of computing as we know it, the question arose whether the human mind works in the same way. Computers have an operating system that is run by a central processor that reads and writes to a digital memory using a small set of rules. Early cognitive scientists ran with the idea that the mind does too.

The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity
by Byron Reese
Published 23 Apr 2018

Everything your smartphone can do can be programmed on a Turing machine, and everything IBM Watson can do can be programmed on a Turing machine. Who could have guessed that such a humble little device could do all that? Well, Turing could, of course. But no one else seems to have had that singular idea. Exit Turing. Enter John von Neumann, whom we call the father of modern computing. In 1945, he developed the von Neumann architecture for computers. While Turing machines are purely theoretical, designed to frame the question of what computers can do, the von Neumann architecture is about how to build actual computers. He suggested an internal processor and computer memory that holds both programs and data.

Broad Band: The Untold Story of the Women Who Made the Internet
by Claire L. Evans
Published 6 Mar 2018

During the war, the Computation Laboratory was isolated from the handful of other computing projects in the world, and Grace Hopper, handling the lab’s everyday computational needs, had neither the time nor the opportunity to see what the rest of the field was doing. But sometimes the field came to her. Grace had been working in the Computation Laboratory for only a few months, for instance, when the physicist John von Neumann came to visit. Von Neumann had mobility; he spent much of 1944 visiting different computing projects in the United States, looking for a machine brawny enough to crack a complex partial differential equation. The Mark I was the first large-scale computer on his tour, and for three months that summer he decamped in a conference room at Harvard, outlining his problem on a blackboard while Richard Bloch set it up on the computer.

The New Gold Rush: The Riches of Space Beckon!
by Joseph N. Pelton
Published 5 Nov 2016

Yet as just noted these service jobs are increasingly being automated or turned over to devices or robots that are artificially intelligent. Ray Kurzweil, the artificial intelligence guru that invented “Siri,” who so sweetly and competently responds to inquiries on smart phones, believes that the “singularity” is coming within the next few years. The term “singularity” was first used by John von Neumann in 1958. It was then amplified by Vernon Verre of Hungary and even more recently given a more focused meaning by Kurzweil, especially in his book The Singularity Is Near , published in 2005. Kurzweil predicted high speed processors, memory storage and artificially intelligent algorithms that would not only duplicate human reasoning, memory and processing capabilities but would be commercially available at $1000 per unit by 2029.

Concentrated Investing
by Allen C. Benello
Published 7 Dec 2016

In one of his earlier flights of fancy, Shannon had begun an intensive study of the stock market in the late 1950s.6 He wanted to know if his information theory could help him decode the market’s random walk. His research led him to fill three library shelves with books, including Adam Smith’s Wealth of Nations, John von Neumann and Oskar Morgenstern’s Theory of Games and Economic Behavior, Paul Samuelson’s Economics, and Fred Schwed’s Where Are the Customer’s Yachts? In a notebook Shannon recorded a varied list of thinkers, including French mathematician Louis Bachelier, Benjamin 74 Concentrated Investing Graham, and Benoit Mandelbrot.

The Internet Trap: How the Digital Economy Builds Monopolies and Undermines Democracy
by Matthew Hindman
Published 24 Sep 2018

In general, though, there are good reasons to prefer the power law label, even when other distributions may fit the data slightly better. Of course other related distributions often fit better: they have two or more parameters, while pure power laws have only one. Parsimony is a cardinal virtue in model building, and each additional parameter provides latitude for mischief. As John von Neumann reportedly said, “With four parameters I can fit an elephant, and with five I can make him wiggle his trunk.”1 In any case, our data show a good fit to a pure power law, the discussion in the previous paragraphs notwithstanding. A simple way to check the regularity of the traffic distribution is to estimate the exponent of the power law for each day in our data.

Climbing Mount Improbable
by Richard Dawkins and Lalla Ward
Published 1 Jan 1996

Make a new robot, then feed the same TRIP program into its on-board computer and turn it loose on the world to do the same thing.’ The hypothetical robot that we have now worked towards can be called a TRIP robot. A TRIP robot such as we are now imagining is a machine of great technical ingenuity and complexity. The principle was discussed by the celebrated Hungarian-American mathematician John von Neumann (one of two candidates for the honoured title of the father of the modern computer—the other was Alan Turing, the young British mathematician who, through his codebreaking genius, may have done more than any other individual on the Allied side to win the Second World War, but who was driven to suicide after the war by judicial persecution, including enforced hormone injections, for his homosexuality).

Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence
by Richard Yonck
Published 7 Mar 2017

The increased trend toward integration with computer technology will likely alter this to some degree. 7. The damsel in distress is also a feature of sadomasochistic fetishes, an aspect the AIs may have been manipulating in Nathan from before the beginning of this story. 8. The term “singularity” was originally used in this sense by Stanislaw Ulam in his obituary of computing giant John von Neumann. Chapter 17 1. The first two terms refer to the apocalyptic dystopia of the Terminator franchise, created by James Cameron and Gale Anne Hurd. The technological singularity is a hypothetical moment in our future when a machine intelligence rapidly self-improves, surpassing all human intelligence and severely disrupts all of society.

My Life as a Quant: Reflections on Physics and Finance
by Emanuel Derman
Published 1 Jan 2004

It looked very unprofessorally businesslike, an early precursor of soon-to-arrive European Filofaxes and, a decade later, American Palm Pilots. David clearly thought big. In those days he was planning what he called "NonVon," a parallel-processing computer comprised of many small processors and memory units. It was to be the antithesis of the standard computer with one large central processor, a design that had prevailed since John von Neumann and the ENIAC computer of the 1940s. David's confidence inspired fear and envy. John Kender complained half-jokingly to me that while he and the other assistant professors in the tenure race at Columbia were trying to get modest government grants to do their work, David was always talking about ambitious proposals on a much larger scale, with plans for NonVon eventually to require a staff of tens to hundreds.

Utopias: A Brief History From Ancient Writings to Virtual Communities
by Howard P. Segal
Published 20 May 2012

So, too, do the failures of other experts in such realms as environmental protection and nuclear power to achieve promised goals safely and efficiently.57 For that matter, predictions of an ever growing population in the developing world are now recognized as outdated, in favor of a far more complex picture.58 These dismal records have in turn led to a declining faith in forecasting as a serious intellectual and moral enterprise—just as, paradoxically, forecasting has become a highly profitable industry. A revealing footnote here is the failure of the otherwise brilliant scientists and engineers who invented computers during and after World War II to anticipate the evolution of the computers of their day. Interviews, memoirs, and other accounts from pioneers such as John Mauchly and John Von Neumann reveal no expectations of significant changes from the handful of room-sized behemoths—operated by skilled programmers and dependent on vacuum tubes that constantly needed to be replaced— that were to be used only by the largest national and international institutions to solve the most complex quantitative problems.

Powers and Prospects
by Noam Chomsky
Published 16 Sep 2015

The distinguished biologist François Jacob observes that ‘for the biologist, the living begins only with what was able to constitute a genetic program’, while ‘for the chemist, in contrast, it is somewhat arbitrary to make a demarcation where there can only be continuity’. Others might want to add crystals to the mix, or self-replicating automata of the kind pioneered by John von Neumann. There is no ‘right answer’, no reason to seek sharper boundaries to distinguish among physical, biological, chemical, and other aspects of the world. No discipline has any prior claim to particular objects in the world, whether they are complex molecules, stars, or human language. I should make it clear that these remarks are not uncontentious.

Reinventing the Bazaar: A Natural History of Markets
by John McMillan
Published 1 Jan 2002

What is sometimes called the wisdom of the market results from the dispersion of decision-making. Markets make fewer big mistakes than planners. This is not because businesspeople are necessarily smarter than bureaucrats. The folklore of the computer industry, for example, relates a host of wrong predictions from those best placed to know. In 1954, John von Neumann, the mathematical genius who helped invent the computer, said, “I think there is a world market for maybe five computers.” In 1977, Ken Olson, president of Digital Equipment Corp., said, “There is no reason anyone would want a computer in their home.” In 1981, Bill Gates, founder of Microsoft, is reported to have said, “640K ought to be enough for anybody.”

The Myth of Capitalism: Monopolies and the Death of Competition
by Jonathan Tepper
Published 20 Nov 2018

The optimal strategy is for the group to cooperate—no one talks to the blonde and they all talk to the less attractive friends. Nash's key idea was that among different players, they might all choose tacit cooperation rather than face competition. The solution to the problem of competition is called “Nash Equilibrium.” Nash didn't create game theory, but he developed it. His idea was a direct descendant of John von Neumann's Minimax theory. The idea is that players of a game won't seek to achieve the highest payout but will try to minimize their maximum loss. The easiest way to understand this is the example of a mother who allows her two children to divide a cake. The most equal division will happen if one cuts the cake and the other chooses the first piece.

Language and Mind
by Noam Chomsky
Published 1 Jan 1968

For those who sought a more mathematical formulation of the basic processes, there was the newly developed mathematical theory of communication, which, it was widely believed in the early 1950s, had provided a fundamental concept – the concept of “information” – that would unify the social and behavioral sciences and permit the development of a solid and satisfactory mathematical theory of human behavior on a probabilistic base. At about the same time, the theory of automata developed as an independent study, making use of closely related mathematical notions. And it was linked at once, and quite properly, to earlier explorations of the theory of neural nets. There were those – John von Neumann, for example – who felt that the entire development was dubious and shaky at best, and probably quite misconceived, but such qualms did not go far to dispel the feeling that mathematics, technology, and behavioristic linguistics and psychology were converging on a point of view that was very simple, very clear, and fully adequate to provide a basic understanding of what tradition had left shrouded in mystery.

Artificial Intelligence: A Guide for Thinking Humans
by Melanie Mitchell
Published 14 Oct 2019

In fact, the ideas that led to the first programmable computers came out of mathematicians’ attempts to understand human thought—particularly logic—as a mechanical process of “symbol manipulation.” Digital computers are essentially symbol manipulators, pushing around combinations of the symbols 0 and 1. To pioneers of computing like Alan Turing and John von Neumann, there were strong analogies between computers and the human brain, and it seemed obvious to them that human intelligence could be replicated in computer programs. Most people in artificial intelligence trace the field’s official founding to a small workshop in 1956 at Dartmouth College organized by a young mathematician named John McCarthy.

The Cultural Logic of Computation
by David Golumbia
Published 31 Mar 2009

As Amadae puts it, “the mathematical formalism structuring rational choice theory is impelled by the same academy-wide momentum propelling an increased emphasis on formal models as an indication of scientific standing” (158), also pointing to the influence of one of the founders of modern computing, John von Neumann, on rational choice doctrine (via his writings on game theory, von Neumann and Morgenstern 1944). Because cognition itself is formal, syntactic, and thereby instrumental, we are extending the human cognitive apparatus by building out our scientific and technological instruments; because this is the only sort of knowledge worth the name, and knowledge solves social problems, we need only build out our technology sufficiently to address any problems that emerge.

Ghost Road: Beyond the Driverless Car
by Anthony M. Townsend
Published 15 Jun 2020

Such a device would be able to continually improve its own design, kicking off a chain reaction of accelerated learning. The Singularity, as Vinge dubbed such an event, would be “an exponential runaway beyond any hope of control,” a technological revolution “comparable to the rise of human life on Earth.” This wasn’t the first airing for this radical prediction. The mathematician John von Neumann had raised the possibility in the early 1950s. But Vinge’s message was timely, concise, and eminently meme-worthy. And while his assertion sounded like the plot for a dystopian novel, many geeks and gurus welcomed the possibility of machines with superhuman intelligence. In the age of the Human Genome Project and climate modeling, the great challenges facing humanity were better seen as informational problems, they argued.

Chaos Kings: How Wall Street Traders Make Billions in the New Age of Crisis
by Scott Patterson
Published 5 Jun 2023

More often than not, rather than discussing the nature of uncertainty and Black Swans, their conversations revolved around literature, history, art, and the vast array of fascinating people Mandelbrot had encountered during his long career (Margaret Mead, Noam Chomsky, Robert Oppenheimer, Stephen Jay Gould, and John von Neumann, to name a few). Mandelbrot, who died in 2010, would have an outsize influence on The Black Swan. In fact, the book is dedicated to him. But Taleb’s new insights into fractal forces at work in market crashes didn’t help him where it really counted at the time—at Empirica. * * * Empirica was bleeding.

The Strangest Man: The Hidden Life of Paul Dirac, Mystic of the Atom
by Graham Farmelo
Published 24 Aug 2009

Wigner was fearful of the future of the country, then under Admiral Horthy’s authoritarian regime. Despite all the political upheavals, Wigner had an exceptionally fine school education in mathematics and science, even more thorough than Dirac’s. Historians still debate why Budapest in the early twentieth century produced so many intellectual innovators, including John von Neumann, whom Dirac would later rate as the world’s finest mathematician, and Wigner’s friends Leó Szilárd and Edward Teller, both to do important research into the first nuclear weapons.17 The success of this cohort of Hungarians is partly due to their education, shortly after the war, in Budapest’s excellent high schools and partly to the vibrancy and ambition of the city’s Western-focused culture.18 Wigner was one of the shyest and most uncommunicative of the quantum physicists but, compared with Dirac, he was gregariousness itself, so conversation during their evening meals together was probably strained.

…

In the summer of 1939, Wigner, Szilárd and Teller persuaded Einstein to write to President Roosevelt, drawing his attention to the possibility of nuclear weapons and the danger that the Germans might produce one first.17 After a long delay, Roosevelt invited Einstein to join a committee of government advisers but he brusquely declined and sat out the war at the Institute for Advanced Study in Princeton, where word spread that the Nazis were indeed working on a bomb. In the spring of 1940, Dirac’s friends Oswald Veblen and John von Neumann wrote to the director Frank Aydelotte, urgently seeking his assistance to fund investigations into the chain reaction. In their letter, they mentioned a recent conversation with the Dutch physical chemist Peter Debye, who had led one of Berlin’s largest research institutes until the German authorities sent him abroad in order to free his laboratories for secret war work.

The Road to Ruin: The Global Elites' Secret Plan for the Next Financial Crisis
by James Rickards
Published 15 Nov 2016

Complexity and the related field of chaos theory are two branches of the broader sciences of nonlinear mathematics and critical state systems analysis. Los Alamos has been on the cutting edge of these fields from its start. Significant breakthroughs in the 1970s were computational and built on earlier theoretical work from the 1940s and 1950s by iconic figures such as John von Neumann and Stanislaw Ulam. Theoretical constructs were harnessed to massive computing power to simulate phenomena such as hydrodynamic turbulence. Seeing a fast-flowing stream at sunset is an aesthetic experience; poets try to capture its noetic beauty. Still, an effort to write equations that precisely model the ebb and flow, twist and turn, of every molecule of H2O in the stream, not just at a point in time, but dynamically through time, presents a challenge.

Augmented: Life in the Smart Lane
by Brett King
Published 5 May 2016

It’s why, as consumers, we have come to expect major new features to be incorporated into every new iPhone.3 The graph on the following page shows what accelerated technology growth has looked like over the last 600 years. Statisticians call this sort of graph a “hockey stick curve” as it indicates evidence of an exponential growth scenario. In the 20th century, graphs like this appeared with increasing regularity, especially where technology was involved. This led to the hypothesis of what mathematician John von Neumann and futurist Ray Kurzweil dubbed the singularity (sometimes called the technological singularity)—a time when technological advancement reaches escape velocity. In theory, the singularity means that we could solve any problem mankind faces through the application of increasingly powerful computing.

What the Dormouse Said: How the Sixties Counterculture Shaped the Personal Computer Industry
by John Markoff
Published 1 Jan 2005

Composed of thirteen thousand mechanical relays, the SSEC, which could perform a lumbering twenty-five instructions per second (today an Intel Pentium microprocessor will easily surpass three billion instructions in the same second), was a computing machine that straddled the divide between calculators and modern computers. It didn’t have a memory in the modern sense, and programs were entered via punched paper tape. The skills Crane developed on the SSEC later proved useful when he was hired to work on a new computer being built by the legendary mathematician John Von Neumann at the Institute for Advanced Study in Princeton. Frustrated with the slow speed of getting data into and out of his machine, Von Neumann had persuaded IBM’s founder, Tom Watson Sr., to donate a punch-card reader to help speed up the process. Since he was one of the few people who knew how card readers worked, Crane was enlisted in the project.

This Will Make You Smarter: 150 New Scientific Concepts to Improve Your Thinking
by John Brockman
Published 14 Feb 2012

In a positive-sum game, a rational, self-interested actor may benefit the other actor with the same choice that benefits himself or herself. More colloquially, positive-sum games are called win-win situations and are captured in the cliché “Everybody wins.” This family of concepts—zero-sum, nonzero-sum, positive-sum, negative-sum, constant-sum, and variable-sum games—was introduced by John von Neumann and Oskar Morgenstern when they invented the mathematical theory of games in 1944. The Google Books Ngram tool shows that the terms saw a steady increase in popularity beginning in the 1950s, and their colloquial relative “win-win” began a similar ascent in the 1970s. Once people are thrown together in an interaction, their choices don’t determine whether they are in a zero- or nonzero-sum game; the game is a part of the world they live in.

Some Remarks
by Neal Stephenson
Published 6 Aug 2012

The present thought occurring at t1, together with the Production Rule, will determine what F will think at t2.” Combined with the monadic property of being able to perceive the states of all other monads, this comes close to being a mathematically formal definition of cellular automata, a branch of mathematics generally agreed to have been invented by Stanislaw Ulam and John von Neumann during the 1940s as an outgrowth of work at Los Alamos. The impressive capabilities of such systems have, in subsequent decades, drawn the attention of many luminaries from the worlds of mathematics and physics, some of whom have proposed that the physical universe might, in fact, consist of cellular automata carrying out a calculation—a hypothesis known as Digital Physics, or It from Bit. 4.

Trillions: How a Band of Wall Street Renegades Invented the Index Fund and Changed Finance Forever
by Robin Wigglesworth
Published 11 Oct 2021

The eclecticism of RAND’s research community is reflected in his first published works, which were a proposal for a smog tax and a review of aircraft compartment design criteria for Army deployments. The nascent field of computing also rubbed off on Sharpe. He learned to program on a hulking RAND computer designed by John von Neumann—one of the greatest American mathematicians of the twentieth century—which staff had nicknamed “Johnniac,” as well as a state-of-the-art IBM machine. This then-novel skill, honed through countless brutal nighttime keypunch sessions, would prove invaluable for the young economist. Aside from helping Sharpe counter his weakness at pure mathematics, becoming one of the first-ever economist-programmers ultimately helped him secure a doctorate.

Capital Ideas Evolving
by Peter L. Bernstein
Published 3 May 2007

Consequently, people depended on prayer and incantation, in one form or another, as the only available form of risk management. What other approach could you take when everything seemed to be God’s will or the will of the Fates? As we move toward modern times, nature has declining importance. What takes its place? I would seek the answer to that question in the words of the mathematician John von Neumann, who developed the theory of games of strategy (as opposed to games of chance) during the 1920s and 1930s. The most significant insight in game theory was to recognize that men and women are not Robinson Crusoes—each individual isolated from all other individuals. Failure to keep this distinction in mind is the primary reason the techniques and concepts of the natural sciences so often lead the social scientists astray.

Trees on Mars: Our Obsession With the Future
by Hal Niedzviecki
Published 15 Mar 2015

This was technological development of astonishing speed prompted, quite literally, by our embodied will to survive—embedded in us, as Richard Dawkins theorizes, on the molecular level. The loser is wiped out, after all; a poor result from a genetic perspective. At any rate, in the lead-up to America’s entering World War II and throughout that epic conflict, a ragtag group led by Jewish Hungarian John von Neumann worked feverishly to build the bomb and, at the same time, build one of the world’s first multiuse electronic calculating machines, the ENIAC machine housed at the Princeton University based Institute for Advanced Study. The military provided substantial funding for ENIAC, having already seen what these fellows could do when unleashed on a problem.

The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution
by Gregory Zuckerman
Published 5 Nov 2019

Most showed faith, hoping Simons could figure out a way to improve the results, but Simons himself was racked with self-doubt. The setback was “stomach-wrenching,” he told a friend. “There’s no rhyme or reason.” Simons had to find a different approach. CHAPTER FOUR Truth . . . is much too complicated to allow for anything but approximations. John von Neumann Jim Simons was miserable. He hadn’t abandoned a flourishing academic career to deal with sudden losses and grumpy investors. Simons had to find a different method to speculate on financial markets; Lenny Baum’s approach, reliant on intellect and instinct, just didn’t seem to work. It also left Simons deeply unsettled.

Dark Territory: The Secret History of Cyber War
by Fred Kaplan
Published 1 Mar 2016

In April 1967, shortly before ARPANET’s rollout, an engineer named Willis Ware wrote a paper called “Security and Privacy in Computer Systems” and delivered it at the semiannual Joint Computer Conference in New York City. Ware was a pioneer in the field of computers, dating back to the late 1940s, when there barely was such a field. At Princeton’s Institute for Advanced Studies, he’d been a protégé of John von Neumann, helping design one of the first electrical computers. For years now, he headed the computer science department at the RAND Corporation, an Air Force–funded think tank in Santa Monica, California. He well understood the point of ARPANET, lauded its goals, admired its ambition; but he was worried about some implications that its managers had overlooked.

A World Without Work: Technology, Automation, and How We Should Respond
by Daniel Susskind
Published 14 Jan 2020

Given nothing more than those, it played itself for three days to generate its own data—and it returned to thrash its older cousin, AlphaGo.31 Other systems are using similar techniques to engage in pursuits that more closely resemble the messiness of real life. Chess and go, for instance, are games of “perfect information”: both players see the entire board and all the pieces. But as the legendary mathematician John von Neumann put it, “real life is not like that. Real life consists of bluffing, of little tactics of deception, of asking yourself what is the other man going to think I mean to do.” That is why poker has fascinated researchers—and proven so hard to automate. Yet DeepStack, developed by a team in Canada and the Czech Republic in 2017, managed to defeat professional poker players in a series of more than forty-four thousand heads-up games (that is, games involving two players).

The AI Economy: Work, Wealth and Welfare in the Robot Age
by Roger Bootle
Published 4 Sep 2019

Well, it is here in this book anyway. Whether and when it will ever be there, in the outside world, and with what consequences, is what we must now consider. The first use of the term “singularity” to refer to a future technology driven event seems to have been by the legendary computer pioneer John von Neumann, in the 1950s. But it doesn’t seem to have caught on until, in 1983, the mathematician Vernor Vinge wrote about an approaching “technological singularity.”3 More recently, the “Singularity,” notably now sporting a capital “S,” has become closely associated with the name of Ray Kurzweil, who published his book The Singularity is Near: When Humans Transcend Biology in 2005.

Empire of the Sum: The Rise and Reign of the Pocket Calculator
by Keith Houston
Published 22 Aug 2023

After all, the electronic computer had shown that there was a new way to build calculating machines—one that did away with gears to be oiled and handles to be cranked, that worked near silently and with astounding speed. Back in 1949, the Mathematical Tables Project had inadvertently accelerated the transition to this brave new world. John von Neumann, a Hungarian American scientist of prodigious intellect and an alumnus of the Manhattan Project to build the first atomic bomb, had approached the MTP with a classic problem of economics: how to feed a given number of people as cheaply as possible with a fixed set of different ingredients.57 Twenty-five MTP computers toiled over the problem for twenty-one days, but von Neumann did not really care about their solution; instead, he wanted to confirm his suspicion that electronic computers were superior to the human variety.

Nuclear War: A Scenario
by Annie Jacobsen
Published 25 Mar 2024

National Archives) Thirteen-year-old Setsuko Thurlow was 1.1 miles from ground zero when this atomic weapon, code-named Little Boy, detonated over Hiroshima at an altitude of 1,900 feet—an airburst, as it’s known. This was the first nuclear weapon used in battle. Its burst height was based on a figure that had been precisely calculated by the American defense scientist John von Neumann, whose assigned task was to figure out a way to kill the most people possible on the ground below with this single atomic bomb. Exploding a nuclear bomb directly on the ground “wastes” a lot of energy, displacing massive volumes of earth, as military planners had figured out and agreed. Setsuko Thurlow was knocked unconscious by this blast.

How the Mind Works
by Steven Pinker
Published 1 Jan 1997

Strangelove, with his disconcerting tic of giving the Nazi salute, is one of cinema’s all-time eeriest characters. He was meant to symbolize a kind of intellectual who until recently was prominent in the public’s imagination: the nuclear strategist, paid to think the unthinkable. These men, who included Henry Kissinger (on whom Sellers based his portrayal), Herman Kahn, John von Neumann, and Edward Teller, were stereotyped as amoral nerds who cheerfully filled blackboards with equations about megadeaths and mutual assured destruction. Perhaps the scariest thing about them was their paradoxical conclusions—for example, that safety in the nuclear age comes from exposing one’s cities and protecting one’s missiles.

…

The thirty-six dramatic situations. Boston: The Writer, Inc. Posner, M. I. 1978. Chronometric explorations of mind. Hillsdale, N.J.: Erlbaum. Poundstone, W. 1988. Labyrinths of reason: Paradox, puzzles, and the frailty of knowledge. New York: Anchor. Poundstone, W. 1992. Prisoner’s dilemma: John von Neumann, game theory, and the puzzle of the bomb. New York: Anchor. Prasada, S., & Pinker, S. 1993. Generalizations of regular and irregular morphological patterns. Language and Cognitive Processes, 8, 1–56. Premack, D. 1976. Intelligence in ape and man. Hillsdale, N.J.: Erlbaum. Premack, D. 1990.

The Undoing Project: A Friendship That Changed Our Minds
by Michael Lewis
Published 6 Dec 2016

It effectively turned a blind eye to gambling. Odd this, as the search for a theory about how people made risky decisions had started as an attempt to make Frenchmen shrewder gamblers. Amos’s text skipped over the long, tortured history of utility theory after Bernoulli all the way to 1944. A Hungarian Jew named John von Neumann and an Austrian anti-Semite named Oskar Morgenstern, both of whom fled Europe for America, somehow came together that year to publish what might be called the rules of rationality. A rational person making a decision between risky propositions, for instance, shouldn’t violate the von Neumann and Morgenstern transitivity axiom: If he preferred A to B and B to C, then he should prefer A to C.

The Hacker and the State: Cyber Attacks and the New Normal of Geopolitics
by Ben Buchanan
Published 25 Feb 2020

David Sanger and Michael Schmidt, “More Sanctions on North Korea After Sony Case,” New York Times, January 2, 2015. 21. Symantec Security Response, “WannaCry: Ransomware Attacks Show Strong Links to Lazarus Group,” Symantec blog, May 22, 2017. 22. At some level, the idea of the worm dated back to a famous work in computer science written in 1966. John Von Neumann and Arthur W. Burks, “Theory of Self-Reproducing Automata,” IEEE Transactions on Neural Networks 5, no. 1 (1966): 3–14. 23. Details are not abundant about the initial infection vector for WannaCry. For one view, see thegrugq, “The Triple A Threat: Aggressive Autonomous Agents,” presentation deck, Comae Technologies, 2017, 22. 24.

Human Frontiers: The Future of Big Ideas in an Age of Small Thinking
by Michael Bhaskar
Published 2 Nov 2021

Thanks to the program, previously unthinkable moves are now part of the tactical lexicon. AlphaGo, like AlphaFold, jolted the game out of a local maximum. DeepMind is at the forefront of a well-publicised renaissance in AI. (AI itself is a big idea that goes back to Alan Turing and pioneers like John von Neumann and Marvin Minsky and, in the form of dreams of automata, much earlier still.) Over recent decades, computer scientists have brought together a new generation of techniques: evolutionary algorithms, reinforcement learning, deep neural networks and backpropagation, adversarial networks, logistic regression, decision trees and Bayesian networks, among others.

A People’s History of Computing in the United States
by Joy Lisi Rankin

University of Illinois Archives (2013). http://­archives​.­library​.­illinois​.­edu​/ ­blog​/ ­birth​-­of​ -­t he​-­computer​-­age. Archived at perma.cc/RJ57–9NV8. Anderson, Terry H. The Movement and the Sixties: Protest in Amer­i­ca from Greensboro to Wounded Knee. New York: Oxford University Press, 1995. Aspray, William. Computing before Computers. Ames: Iowa State University Press, 1990. —­—­—. John von Neumann and the Origins of Modern Computing. Cambridge, MA: MIT Press, 1991. Aspray, William, and Paul E. Ceruzzi, eds. The Internet and American Business. Cambridge, MA: MIT Press, 2008. Aspray, William, and Jeffrey Yost. “New Voices, New Topics.” IEEE Annals of the History of Computing 33, no. 2 (2011): 4–8. 

Visual Thinking: The Hidden Gifts of People Who Think in Pictures, Patterns, and Abstractions
by Temple Grandin, Ph.d.
Published 11 Oct 2022

It’s possible that his mathematical mind may have been stimulated by Einstein’s book on the theory of relativity, a gift from Turing’s grandfather. At King’s College in Cambridge, England, along with advanced math, Turing studied cryptology. He read several influential books, including Bertrand Russell’s Introduction to Mathematical Philosophy and John von Neumann’s text on quantum mechanics. In a course called “Foundations of Mathematics” with British mathematician and codebreaker M. H. A. Newman, Turing first encountered David Hilbert’s Entscheidungsproblem, or “decision problem”: Is it possible to use an algorithm to determine whether an inference made during an operation of formal logic is valid?

Artificial Intelligence: A Modern Approach
by Stuart Russell and Peter Norvig
Published 14 Jul 2019

He proposed instead a principle based on maximization of expected utility, and explained human investment choices by proposing that the marginal utility of an additional quantity of money diminished as one acquired more money. Léon Walras (pronounced “Valrasse”) (1834–1910) gave utility theory a more general foundation in terms of preferences between gambles on any outcomes (not just monetary outcomes). The theory was improved by Ramsey (1931) and later by John von Neumann and Oskar Morgenstern in their book The Theory of Games and Economic Behavior (1944). Economics is no longer the study of money; rather it is the study of desires and preferences. Decision theory, which combines probability theory with utility theory, provides a formal and complete framework for individual decisions (economic or otherwise) made under uncertainty—that is, in cases where probabilistic descriptions appropriately capture the decision maker’s environment.

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Like Craik (who also used control systems as psychological models), Wiener and his colleagues Arturo Rosenblueth and Julian Bigelow challenged the behaviorist orthodoxy (Rosenblueth et al., 1943). They viewed purposive behavior as arising from a regulatory mechanism trying to minimize “error”—the difference between current state and goal state. In the late 1940s, Wiener, along with Warren McCulloch, Walter Pitts, and John von Neumann, organized a series of influential conferences that explored the new mathematical and computational models of cognition. Wiener’s book Cybernetics (1948) became a bestseller and awoke the public to the possibility of artificially intelligent machines. Meanwhile, in Britain, W. Ross Ashby pioneered similar ideas (Ashby, 1940).

…

Throughout the 19th century, several leading economists created simple mathematical examples to analyze particular examples of competitive situations. The first formal results in game theory are due to Zermelo (1913) (who had, the year before, suggested a form of minimax search for games, albeit an incorrect one). Emile Borel (1921) introduced the notion of a mixed strategy. John von Neumann (1928) proved that every two-person, zero-sum game has a maximin equilibrium in mixed strategies and a well-defined value. Von Neumann’s collaboration with the economist Oskar Morgenstern led to the publication in 1944 of the Theory of Games and Economic Behavior, the defining book for game theory.

Smarter Faster Better: The Secrets of Being Productive in Life and Business
by Charles Duhigg
Published 8 Mar 2016

gambling techniques René Carmona et al., Numerical Methods in Finance: Bordeaux, June 2010, Springer Proceedings in Mathematics, vol. 12 (Berlin: Springer Berlin Heidelberg, 2012); René Carmona et al., “An Introduction to Particle Methods with Financial Application,” in Numerical Methods in Finance, 3–49; Pierre Del Moral, Mean Field Simulation for Monte Carlo Integration (Boca Raton, Fla.: CRC Press, 2013); Roger Eckhardt, “Stan Ulam, John von Neumann, and the Monte Carlo Method,” Los Alamos Science, special issue (1987): 131–37. in the shape of a hat Andrew Hargadon and Robert I. Sutton, “Technology Brokering and Innovation in a Product Development Firm,” Administrative Science Quarterly 42, no. 4 (1997): 716–49; Roger P. Brown, “Polymers in Sport and Leisure,” Rapra Review Reports 12, no. 3 (November 2, 2001); Melissa Larson, “From Bombers to Bikes,” Quality 37, no. 9 (1998): 30.

Dawn of the New Everything: Encounters With Reality and Virtual Reality
by Jaron Lanier
Published 21 Nov 2017

Hopper’s team was spectacular, even creating an optimizing compiler, way ahead of when that would become a hot topic in computer science. Text-based code demands that one particular abstraction become dominant, for it will provide the vocabulary. Therefore Hopper’s approach had the effect of making abstractions seem fundamental and unavoidable. Picture This Most of the earliest computers, such as one churning in John von Neumann’s basement lab at the Institute for Advanced Study at Princeton, included a rudimentary visual display: a light for each bit so you could watch it flipping moment to moment.3 You could watch a program running.4 That’s how I like to think about computation, as a concrete process involving materials changing states; the flipping bits.

Dreaming in Code: Two Dozen Programmers, Three Years, 4,732 Bugs, and One Quest for Transcendent Software
by Scott Rosenberg
Published 2 Jan 2006

Yet other central figures in the development of modern software share his complaint that the software profession has taken a fundamental wrong turn. As early as 1978, John Backus, the father of Fortran, was expressing parallel views. Programming, Backus argued, had grown out of the ideas of John von Neumann, the mathematician who, at the dawn of computing in the 1940s, devised the basic structure of the “stored program” of sequentially executed instructions. But those ideas had become a straitjacket. “Von Neumann languages constantly keep our noses pressed in the dirt of address computation and the separate computation of single words,” he wrote.

Stock Market Wizards: Interviews With America's Top Stock Traders
by Jack D. Schwager
Published 1 Jan 2001

A large, irregular-polygon-shaped, brushed aluminum table, which served as a desk on one end and a conference area on the other, dominated the center of the room. We sat directly across from each other at the conference end. THE Q U A N T I T A T I V E EDGE traditional von Neumann machine, named after John von Neumann, has a single central processing unit (CPU) connected to a single memory unit. Originally, the two were well matched in speed and size. Over time, however, as processors became faster and memories got larger, the connection between the two—the time it takes for the CPU to get things out of memory, perform the computations, and place the results back into memory—became more and more of a bottleneck.

When Computers Can Think: The Artificial Intelligence Singularity
by Anthony Berglas , William Black , Samantha Thalind , Max Scratchmann and Michelle Estes
Published 28 Feb 2015

Like perceptrons, it is possible to feed some of the outputs of a PLA back into some of the inputs buffered by flip-flops that store state. If that is done then they can in principle implement any general purpose computer program. Von Neumann Architecture Owned Modern computers use an architecture that was first proposed by John von Neumann in 1945, which is illustrated above. It has a memory that is organized as a series of words, each of which can contain a small number. Each word also has an address which can be used to access it. The memory provides random access, meaning that words can be efficiently accessed in a random order so there is no need to access them sequentially.

Capital Ideas: The Improbable Origins of Modern Wall Street
by Peter L. Bernstein
Published 19 Jun 2005

As Roy put it, “A man who seeks advice about his actions will not be grateful for the suggestion that he maximize his expected utility.”19 The complexity of the subject has attracted the attention of some of the best thinkers of our time, including Kenneth Arrow, a Nobel Prize-winner, and Oskar Morgenstern and John von Neumann, famous for having invented game theory. But this is not the only feature of the Markowitz paradigm with controversial implications. The calculation of the Efficient Frontier is a task that would defy the abilities and capabilities of many investors, and even the capacities of many computers. so it is fair to ask whether the relationship between risk and return is as neat as Markowitz postulates.

What to Think About Machines That Think: Today's Leading Thinkers on the Age of Machine Intelligence
by John Brockman
Published 5 Oct 2015

McKinsey predicts that these technologies will create more than $50 trillion of economic value by 2025. If this is accurate, we should expect dramatically increased investment soon. The recent successes are being driven by cheap computer power and plentiful training data. Modern AI is based on the theory of “rational agents,” arising from work on microeconomics in the 1940s by John von Neumann and others. AI systems can be thought of as trying to approximate rational behavior using limited resources. There’s an algorithm for computing the optimal action for achieving a desired outcome, but it’s computationally expensive. Experiments have found that simple learning algorithms with lots of training data often outperform complex hand-crafted models.

Samuelson Friedman: The Battle Over the Free Market
by Nicholas Wapshott
Published 2 Aug 2021

Silber, Volcker: The Triumph of Persistence (Bloomsbury Press, New York, 2012), pp. 145–46. 11.New York Times, July 29, 1979, p. F1. 12.Silber, Volcker, p. 148. 13.Personal Letters from 1979, Papers of Paul Volcker. Federal Reserve Bank of New York Archives, Box 95714. 14.Ibid. 15.Oskar Morgenstern (January 24, 1902–July 26, 1977), Princeton economist, who with mathematician John von Neumann founded the mathematical field of game theory and its application to economics. 16.Friedrich August Lutz (December 29, 1901–October 4, 1975), German-born Princeton economist who developed the expectations hypothesis. 17.Paul Volcker, “The Problems of Federal Reserve Policy since World War II,” Princeton, 1949. https://catalog.princeton.edu/catalog/dsp019019s3255. 18.Minutes of Federal Open Market Committee Meeting, August 14, 1979, p. 1. 19.Denis Winston Healey, Lord Healey (August 30, 1917–October 3, 2015), British Labour Party Secretary of State for Defence, 1964–1970; chancellor of the exchequer, 1974–1979; and deputy leader of the Labour Party, 1980–1983. 20.Healey, The Time of My Life, p. 432. 21.Ibid. 22.Presidential address to the American Economic Association and the American Finance Association, Atlantic City, N.J., September 16, 1976. https://www.newyorkfed.org/medialibrary/media/research/quarterly_review/75th/75article7.pdf. 23.Ibid. 24.Paul Volcker, “The Role of Monetary Targets in an Age of Inflation,” Journal of Monetary Economics 4, no. 2, April 1978. 25.Ibid., p. 331. 26.In particular the events in the spring and summer of 1977, when inflation leapt from 5 to 7 percent. 27.Paul Volcker and Toyoo Gyohten, Changing Fortunes: The World’s Money and the Threat to American Leadership (Times Books, New York, 1992), pp. 164–65. 28.New York Times, September 19, 1979, p. 1. 29.Volcker and Gyohten, Changing Fortunes, p. 165. 30.Paul A.

Paper: A World History
by Mark Kurlansky
Published 3 Apr 2016

Hoe invents the steam-powered rotary printing press. 1863 American papermakers start using wood pulp. 1867 English wood pulp grinder exhibited in Paris. 1867 Wood pulp for paper first ground in the United States in Stockbridge, Massachusetts. 1867 Typewriter invented. 1872 United States surpasses Britain and Germany to become the largest paper producer in the world. 1874 Yukosha Company begins making machine-made paper in Tokyo, and six other companies follow in Osaka, Kyoto, and Kobe. 1890 US census tabulated by punch-card machines. 1899 Swedish explorer Sven Hedin, while excavating the ruins of the vanished city of Lu Lan, finds paper from 252 BCE, completely upsetting the history of paper. 1931 Vannevar Bush builds an analog electromechanical computer. 1945 John von Neumann publishes a paper on using binary numbers to program electronic memory. 1947 Transistor invented at Bell Labs. 1951 Remington Rand produces forty-six UNIVAC I computers with memory storage and output on magnetic tape. 1958 Microchip invented. ACKNOWLEDGMENTS MY FIRST THANK-YOU IS TO KERMIT HUMMEL, WHO CAME TO ME out of the blue and convinced me that paper was the subject on which I ought to be writing.

The Case for Space: How the Revolution in Spaceflight Opens Up a Future of Limitless Possibility
by Robert Zubrin
Published 30 Apr 2019

This will make them expensive, as space labor will be dear and transportation from Earth will be costly. Expensive robots are acceptable for assisting in certain tasks, such as exploration, where large numbers are not required. But terraforming will need multitudes. The only solution would be robots that make themselves. Back in the 1940s, the mathematician John Von Neumann proved that self-replicating automatons are possible. That is, he proved that there is no mathematical contradiction that precludes the existence of such systems. But creating them is another issue altogether. No one today has a clue as to how to do it, but it would not be too big a leap of faith to believe that a machine could be built and programmed that, if let loose in a room filled with gears, wires, wheels, batteries, computer chips, and all its other component parts, could assemble a copy of itself.

Why Things Bite Back: Technology and the Revenge of Unintended Consequences
by Edward Tenner
Published 1 Sep 1997

In the 195os, American notables helped celebrate the twenty-fifth anniversary of Fortune magazineby painting the year 198o in tones that rivaled the most radiant forecasts of the Soviet Politburo. David Sarnoff of RCA predicted: "Small atomic generators, installed in homes and industrial plants, will provide power for years and ultimately for a lifetime without recharging." John von Neumann of the Institute for Advanced Study and the Atomic Energy Commission speculated that energy might even "be free—just like the unmetered air." Henry R. Luce himself foresaw the global stewardship of consciousness by "High Organization" as represented by the multinational American corporation, bureaucracies, and labor unions.

The Net Delusion: The Dark Side of Internet Freedom
by Evgeny Morozov
Published 16 Nov 2010

Name a problem that has to deal with information, and Google is already on top of it. Why the Ultimate Technological Fix Is Online It’s not all Google’s fault. There is something about the Internet and its do-it-yourself ethos that invites an endless production of quick fixes, bringing to mind the mathematician John von Neumann’s insightful observation that “technological possibilities are irresistible to man. If man can go to the moon, he will. If he can control the climate, he will” (even though on that last point, von Neumann may have been a bit off ). With the Internet, it seems, everything is irresistible, if only because everything is within easy grasp.

Culture and Prosperity: The Truth About Markets - Why Some Nations Are Rich but Most Remain Poor
by John Kay
Published 24 May 2004

Behavioral economics contemplates alternative assumptions about motives and the nature of economic behavior. I will introduce game theory and institutional economics in the present chapter and take up behavioral economics in the chapter that follows. Economic Theory After Arrow and Debreu eeeeeeeee&ee&oeeeeeeoeeeoeeoeeooeeeoe In 1944,John von Neumann and Oskar Morgenstern published The Theory ofGames and Economic Behavior. This approach was, after an interval, to revolutionize economic theory. The analysis of competitive markets supposes anonymous interactions among many buyers and many sellers. The fragmentation and impersonality of these markets leads to incentive compatibility-there is no need to consider the behavior and responses of other market participants.

Near and Distant Neighbors: A New History of Soviet Intelligence
by Jonathan Haslam
Published 21 Sep 2015

Finally, all Moscow’s communications with the outside world were suddenly severed. For Washington, this was Black Friday: October 29, 1948. When signals resumed on Monday, nothing could be deciphered. Computer Catch-Up At the Institute for Advanced Study the publication in 1946 by the Hungarian-born mathematician John von Neumann of a seminal article on the construction of the computer and the appearance of the first American civilian computer, ENIAC, that same year served to warn the Russians that the United States was moving into more innovative computation. This was despite the fact that it was a symbolic rather than a genuine threat, since the machine was digital with an outside program driven slowly, step by step, with only a single memory.

How I Became a Quant: Insights From 25 of Wall Street's Elite
by Richard R. Lindsey and Barry Schachter
Published 30 Jun 2007

As I said, the math core offerings were nothing new, so I looked around. Then came another fortuitous break: my discoveries of game theory and of my eventual thesis advisor, William F. Lucas. This guy is a math legend, though unlike most, as humble a man as you will ever meet. What made him a legend was an elegant counter example to John von Neumann’s and Oskar Morganstern’s conjecture that all cooperative N-person games have solutions according to their self-proclaimed definition of such. Until that time, cooperative game theory was thought uninteresting with no open issues. However, once Lucas proved the case was not closed, the whole subject blossomed with theories of solution concepts, some of which have proved extremely valuable in applications such as voting analysis and fair division.

The God Delusion
by Richard Dawkins
Published 12 Sep 2006

The ‘crime’ itself being a private act, performed by consenting adults who were doing nobody else any harm, we again have here the classic hallmark of religious absolutism. My own country has no right to be smug. Private homosexuality was a criminal offence in Britain up until – astonishingly – 1967. In 1954 the British mathematician Alan Turing, a candidate along with John von Neumann for the title of father of the computer, committed suicide after being convicted of the criminal offence of homosexual behaviour in private. Admittedly Turing was not buried alive under a wall pushed over by a tank. He was offered a choice between two years in prison (you can imagine how the other prisoners would have treated him) and a course of hormone injections which could be said to amount to chemical castration, and would have caused him to grow breasts.

Surveillance Valley: The Rise of the Military-Digital Complex
by Yasha Levine
Published 6 Feb 2018

The paper essentially described a modern multipurpose computer, complete with a display, keyboard, speech recognition software, networking capabilities, and applications that could be used in real time for a variety of tasks.27 It seems obvious to us now, but back then Lick’s ideas were visionary. His paper was widely circulated in defense circles and earned him an invitation by the Pentagon to do a series of lectures on the topic.28 “My first experience with computers had been listening to a talk by [mathematician John] von Neumann in Chicago back in nineteen forty-eight. It sounded like science fiction then: a machine that could carry out algorithms automatically,” recalled Charles Herzfeld, a physicist who would go on to serve as the director of ARPA in the mid-1960s.29 “But the next big shock was Lick: not only could we use these machines for massive calculations, but we could make them useful in our everyday lives.

The Search for Life on Mars
by Elizabeth Howell
Published 14 Apr 2020

Chapter 4: The Road to Utopia 86 visitors from Mars: So claimed one who knew von Kármán and his fellow Hungarians, the German physicist Otto Frisch. They were a “galaxy of brilliant Hungarian expatriates,” as he termed them, whose number included Edward Teller, Leo Szilard, Eugene Wigner, and John von Neumann. All made fundamental contributions to modern science, not least in the years before and during World War II. 90 Deep Space Network: Originally, one of the “southern hemisphere” dishes was located in South Africa, but growing concerns over the apartheid regime in the 1960s meant that it was abandoned in favor of the Spanish station outside of Madrid. 91 exobiologists: The term exobiology was coined by Nobel laureate Joshua Lederberg, who was involved in the Viking missions.

Open: The Story of Human Progress
by Johan Norberg
Published 14 Sep 2020

One of Haber’s prominent colleagues pleaded to Hitler to spare Haber and told him that these purges would set Germany back a hundred years in physics and chemistry. Hitler retorted: ‘If Jews are so important to physics and chemistry, then we’ll just have to work one hundred years without physics and chemistry.’45 The list of thinkers who escaped Hitler reads like a Who’s Who of the scientific world: Fritz Haber, Albert Einstein, John von Neumann, Niels Bohr, Edward Teller, Erwin Schrödinger, and many more. Most of them escaped to the US, which was safe and far away. ‘It was the most significant influx of ability of which there is any record,’ wrote the novelist and chemist C. P. Snow. ‘The refugees made [the US], in a very short time, the world’s dominant force in pure science.’46 It was an incalculable loss to Germany, not least in terms of military capability.

Spin
by Robert Charles Wilson
Published 2 Jan 2005

Given the inherent difficulty of sublight-speed travel as a way of exploring the galaxy, most technological cultures eventually settle for an expanding grid of von Neumann machines—which is what the replicators are—that costs nothing to maintain and generates a trickle of scientific information that expands exponentially over historical time." "Okay," I said, "I understand that. The Martian replicators aren't unique. They ran into what you call an ecology—" "A von Neumann ecology." (After the twentieth-century mathematician John von Neumann, who first suggested the possibility of self-reproducing machines.) "A von Neumann ecology, and they were absorbed by it. But that doesn't tell us anything about the Hypotheticals or the Spin." Jason pursed his lips impatiently. "Tyler, no. You don't understand. The Hypotheticals are the von Neumann ecology.

The Age of Spiritual Machines: When Computers Exceed Human Intelligence
by Ray Kurzweil
Published 31 Dec 1998

—IBM Chairman Thomas Watson, 1943 “Computers in the future may weigh no more than 1.5 tons.” —Popular Mechanics, 1949 “It would appear that we have reached the limits of what is possible to achieve with computer technology, although one should be careful with such statements, as they tend to sound pretty silly in five years.” —John von Neumann, 1949 “There’s no reason for individuals to have a computer in their home.” —Ken Olson, 1977 “640,000 bytes of memory ought to be enough for anybody.” —Bill Gates, 1981 “Long before the year 2000, the entire antiquated structure of college degrees, majors and credits will be a shambles.”

Secrets and Lies: Digital Security in a Networked World
by Bruce Schneier
Published 1 Jan 2000

Almost every computer security system that uses cryptography needs random numbers—for keys, unique values in protocols, and so on—and the security of those systems is often dependent on the randomness of those random numbers. If the random number generator is insecure, the entire system breaks. Depending on who you talk to, generating random numbers from a computer is either trivial or impossible. Theoretically, it’s impossible. John von Neumann, the father of computers, said: “Anyone who considers arithmetic methods of producing random digits is, of course, in a state of sin.” What he means is that it is impossible to get something truly random out of a deterministic beast like a computer. This is true, but luckily we can get by anyway.

Into the Black: The Extraordinary Untold Story of the First Flight of the Space Shuttle Columbia and the Astronauts Who Flew Her
by Rowland White and Richard Truly
Published 18 Apr 2016

As with Hans Mark, Morgenstern was resident in the United States as a consequence of Hitler’s annexation of Austria in 1938. A professor of economics at the University of Vienna, Morgenstern was visiting Princeton when the Nazis seized Vienna. He remained at the American university, where he met the Hungarian-born mathematical genius John von Neumann. A prodigy who as a child could memorize and recite the phone book, von Neumann had earned his PhD in mathematics at just twenty-two. Still in his twenties, he took up, alongside Albert Einstein, one of five professorships at Princeton’s Institute for Advanced Study. Von Neumann’s polymathic brilliance ranged from quantum mechanics to the hydrogen bomb.

Big Bang
by Simon Singh
Published 1 Jan 2004

THOMAS HENRY HUXLEY (1825-95), English biologist The sciences do not try to explain, they hardly even try to interpret, they mainly make models. By a model is meant a mathematical construct which, with the addition of certain verbal interpretations, describes observed phenomena. The justification of such a mathematical construct is solely and precisely that it is expected to work. JOHN VON NEUMANN (1903-57), Hungarian-born mathematician The science of today is the technology of tomorrow. EDWARD TELLER (1908-2003), American physicist Every great advance in science has issued from a new audacity of imagination. JOHN DEWEY (1859-1952), American philosopher Four stages of acceptance: i) this is worthless nonsense, ii) this is an interesting, but perverse, point of view, iii) this is true, but quite unimportant, iv) I always said so.

Lila: An Inquiry Into Morals
by Robert M. Pirsig
Published 1 Jan 1991

With more than ten-thousand trees that kept wanting to expand to one-hundred thousand, the PROGRAM slips were absolutely necessary to keep from getting lost. What made them so powerful was that they too were on slips, one slip for each instruction. This meant the PROGRAM slips were random access too and could be changed and resequenced as the need arose without any difficulty. He remembered reading that John Von Neumann, an inventor of the computer, had said the single thing that makes a computer so powerful is that the program is data and can be treated like any other data. That seemed a little obscure when Phædrus had read it but now it was making sense. The next slips were the GRIT slips. These were for days when he woke up in a foul mood and could find nothing but fault everywhere.

Connectography: Mapping the Future of Global Civilization
by Parag Khanna
Published 18 Apr 2016

As Guangzhou has graduated from factory town to financial center, its glittering central business district features the aerodynamic 103-story-tall IFC tower, modern art museums one would expect to find in Zurich, and an opera house designed by Zaha Hadid. Just outside the city, the Singapore-run Knowledge City and Guangzhou Science City were built to resemble a low-rise version of Silicon Valley, with leafy boulevards that feature bronze statues of Albert Einstein and the mathematician John von Neumann. Singapore has opened a branch of its elite Chinese-language Hwa Chong Institution while also partnering with the local government to develop new curricula for the South China University of Technology, which already graduates some of the country’s top entrepreneurs establishing companies in digital industries such as cloud computing and GPS navigation, materials engineering, renewable energy, biotechnology, and pharmaceuticals.

From Bacteria to Bach and Back: The Evolution of Minds
by Daniel C. Dennett
Published 7 Feb 2017

Before turning to some of the specialized ways brains are designed to extract semantic information, it is time to address the issue of how dramatically brains differ from the computers that have flooded our world. Control tasks formerly performed by human brains have recently been usurped by computers, which have taken charge of many things, from elevators to airplanes to oil refineries. Turing’s theoretical idea, made operational by John von Neumann’s implementation, the serial stored program computer, has multiplied exponentially in the last sixty years and now occupies every environment on Earth and has sent thousands, perhaps millions, of descendants into space; the most traveled brainchildren in history. The brilliant idealizations of Shannon, Turing, von Neumann, McCulloch, and Pitts have led to such an explosion in information-handling competence that today it is commonly supposed not only that brains are just organic digital computers of one sort or another but that also silicon-based computers will soon embody Artificial Intelligence that will surpass human brains in “all the achievements of creative skill” (to echo Beverley’s outraged charge that Darwin thought that “Absolute Ignorance” could do the same trick).

Model Thinker: What You Need to Know to Make Data Work for You
by Scott E. Page
Published 27 Nov 2018

The figure shows three trees: Tree 1: If (age < 30) and (internet hours per week in [15, 25]) Tree 2: If (age in [20, 45]) and (internet hours per week > 30) Tree 3: If (age > 40) and (internet hours per week < 20) Figure M3: A Forest of Decision Trees Classifying Conference Attendees The collection of trees are called a forest. Machine learning algorithms create trees randomly on a training set and then keep those that classify accurately on the testing set and on a training set. 8. Concavity and Convexity To say nonlinear science is akin to saying non-elephant zoology. —John von Neumann We now introduce nonlinear models and nonlinear functions. Nonlinear functions can curve downward or upward, they can form S-shapes, they can kink, jump, and squiggle. In time, we cover all of these possibilities. We start here with models that rely on convexity and concavity. We show how growth and positive feedbacks produce convexity and how diminishing returns and negative feedbacks produce concavity.

The Art of Computer Programming: Sorting and Searching
by Donald Ervin Knuth
Published 15 Jan 1998

If we want to speed up the insertion process we can consider inserting several elements at a time, "batching" them, and this leads naturally to the general idea of merge sorting. From a historical point of view, merge sorting was one of the very first methods proposed for computer sorting; it was suggested by John von Neumann as early as 1945 (see Section 5.5). We shall study merging in considerable detail in Section 5.4, with regard to external sorting algorithms; our main concern in the present section is the somewhat simpler question of merge sorting within a high-speed random-access memory. Table 1 shows a merge sort that "burns the candle at both ends" in a manner similar to the scanning procedure we have used in quicksort and radix exchange: We examine the input from the left and from the right, working towards the 160 SORTING 5.2.4 middle.

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The designers of EDVAC were especially interested in sorting, because it epitomized the potential nonnumerical applications of computers; they realized that a satisfactory order code should not only be capable of expressing programs for the solution of differ- difference equations, it must also have enough flexibility to handle the combinatorial "decision-making" aspects of algorithms. John von Neumann therefore prepared programs for internal merge sorting in 1945, in order to test the adequacy of some instruction codes he was proposing for the EDVAC computer. The existence of efficient special-purpose sorting machines provided a natural standard by which the merits of his proposed computer organization could be evaluated.

The Beginning of Infinity: Explanations That Transform the World
by David Deutsch
Published 30 Jun 2011

As a matter of fact, there is no such thing as mathematical ‘inspiration’ (mathematical knowledge coming from an infallible source, traditionally God): as I explained in Chapter 8, our knowledge of mathematics is not infallible. But if Representative Mills meant that mathematicians are, or somehow ought to be, society’s best judges of fairness, then he was simply mistaken.* The National Academy of Sciences panel that reported to Congress in 1948 included the mathematician and physicist John von Neumann. It decided that a rule invented by the statistician Joseph Adna Hill (which is the one in use today) is the most impartial between states. But the mathematicians Michel Balinski and Peyton Young have since concluded that it favours smaller states. This illustrates again that different criteria of ‘impartiality’ favour different apportionment rules, and which of them is the right criterion cannot be determined by mathematics.

Fantasyland: How America Went Haywire: A 500-Year History
by Kurt Andersen
Published 4 Sep 2017

In the early 1960s, a mania for a certain kind of hyperrationalist abstraction had U.S. leaders in its thrall. It came along at just the right moment, as the Cold War and then the Vietnam War reached their horrific peaks, to help give reason itself a permanent taint in the American mind. The mathematician John von Neumann, a father of both the digital and the nuclear ages, left Germany for the United States just before the Nazis took power. As a young man, he created game theory, the distillation of human decision making to its underlying, purely mathematical essentials. He helped to create the atomic bomb and to choose the Japanese cities to be incinerated, work about which he seemed blithe and unchastened.

Hands-On Machine Learning With Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
by Aurélien Géron
Published 13 Mar 2017

Since AdaGrad, RMSProp, and Adam optimization automatically reduce the learning rate during training, it is not necessary to add an extra learning schedule. For other optimization algorithms, using exponential decay or performance scheduling can considerably speed up convergence. Avoiding Overfitting Through Regularization With four parameters I can fit an elephant and with five I can make him wiggle his trunk. John von Neumann, cited by Enrico Fermi in Nature 427 Deep neural networks typically have tens of thousands of parameters, sometimes even millions. With so many parameters, the network has an incredible amount of freedom and can fit a huge variety of complex datasets. But this great flexibility also means that it is prone to overfitting the training set.

Editing Humanity: The CRISPR Revolution and the New Era of Genome Editing
by Kevin Davies
Published 5 Oct 2020

He was formerly an advisor to BGI’s controversial Cognitive Genomics project, since aborted. Now he believes he can apply AI to the prediction of complex polygenic traits including cognitive ability. Once, when asked to give his view of a superior human intelligence, Hsu offered as an example John von Neumann, the 20th-century polymath, developer of game theory, and computer science, who was capable of total recall and a photographic memory. “In my opinion,” Hsu says, “genotypes exist that correspond to phenotypes as far beyond von Neumann as he was beyond a normal human.” Hsu cofounded a PGT clinic called Genomic Prediction, located in an unremarkable office park off the New Jersey Turnpike, a short drive from Manhattan.

After Apollo?: Richard Nixon and the American Space Program
by John M. Logsdon
Published 5 Mar 2015

It had little credibility when it was submitted to the new Office of Management and Budget (OMB) in August 1970. NASA selected Mathematica, Inc. of Princeton, NJ to lead an independent study of shuttle economics. Mathematica had been founded by prestigious economist Oskar Morgenstern of the Institute for Advanced Studies; there he had worked with mathematician John von Neumann to develop game theory, an approach to analyzing situations in which actors with conflicting interests pursue independent courses of action. Morgenstern had founded Mathematica to pursue practical applications of this approach. At 190 A f t e r A p o l l o? Mathematica, a young Austrian-born economist named Klaus Heiss was put in charge of the space shuttle study.

Fantasyland
by Kurt Andersen
Published 5 Sep 2017

In the early 1960s, a mania for a certain kind of hyperrationalist abstraction had U.S. leaders in its thrall. It came along at just the right moment, as the Cold War and then the Vietnam War reached their horrific peaks, to help give reason itself a permanent taint in the American mind. The mathematician John von Neumann, a father of both the digital and the nuclear ages, left Germany for the United States just before the Nazis took power. As a young man, he created game theory, the distillation of human decision making to its underlying, purely mathematical essentials. He helped to create the atomic bomb and to choose the Japanese cities to be incinerated, work about which he seemed blithe and unchastened.

Thinking, Fast and Slow
by Daniel Kahneman
Published 24 Oct 2011

Consider this example: If you prefer an apple to a banana, then you also prefer a 10% chance to win an apple to a 10% chance to win a banana. The apple and the banana stand for any objects of choice (including gambles), and the 10% chance stands for any probability. The mathematician John von Neumann, one of the giant intellectual figures of the twentieth century, and the economist Oskar Morgenstern had derived their theory of rational choice between gambles from a few axioms. Economists adopted expected utility theory in a dual role: as a logic that prescribes how decisions should be made, and as a description of how Econs make choices.

Thank You for Being Late: An Optimist's Guide to Thriving in the Age of Accelerations
by Thomas L. Friedman
Published 22 Nov 2016

Norm Ornstein Dear Tom, It is an oddity often remarked upon that at the turn of the century, one small, obscure provincial area of Hungary, then under the benign tutelage of Emperor Franz Josef, spawned several towering figures in the fields of physics and mathematics—among them Edward Teller, George de Hevesy, Eugene Wigner, Leo Szilard and John von Neumann. This group, many of them Nobel Prize winners, all of them products of the Jewish middle class, were referred to in their diaspora as the “Men from Mars” because of their obscure provenance and their thick Finno-Ugaric accents. What explosive tinder in this remote corner of the Carpathians had nourished such a forest fire of genius?

Architects of Intelligence
by Martin Ford
Published 16 Nov 2018

These two paradigms were completely different, they aimed to try and solve different problems, and they used completely different methods and different kinds of mathematics. Back then, it wasn’t at all clear which was going to be the winning paradigm. It’s still not clear to some people today. What was interesting, was that some of the people most associated with logic actually believed in the neural net paradigm. The biggest examples are John von Neumann and Alan Turing, who both thought that big networks of simulated neurons were a good way to study intelligence and figure out how those things work. However, the dominant approach in AI was symbol processing inspired by logic. In logic, you take symbol strings and alter them to arrive at new symbol strings, and people thought that must be how reasoning works.

Write Great Code, Volume 1
by Randall Hyde
Published 6 Aug 2012

Knowing about memory performance characteristics, data locality, and cache operation can help you design software that runs as fast as possible. Writing great code requires a strong knowledge of the computer’s architecture. 6.1 The Basic System Components The basic operational design of a computer system is called its architecture. John von Neumann, a pioneer in computer design, is given credit for the principal architecture in use today. For example, the 80x86 family uses the von Neumann architecture (VNA). A typical von Neumann system has three major components: the central processing unit (CPU), memory, and input/output (I/O), as shown in Figure 6-1.

Elon Musk
by Walter Isaacson
Published 11 Sep 2023

At some point, biological brainpower would be dwarfed by digital brainpower. In addition, new AI machine-learning systems could ingest information on their own and teach themselves how to generate outputs, even upgrade their own code and capabilities. The term “singularity” was used by the mathematician John von Neumann and the sci-fi writer Vernor Vinge to describe the moment when artificial intelligence could forge ahead on its own at an uncontrollable pace and leave us mere humans behind. “That could happen sooner than we expected,” Musk said in an ominous, flat tone. For a moment I was struck by the oddness of the scene.

Liberalism at Large: The World According to the Economist
by Alex Zevin
Published 12 Nov 2019

: A Study of the Expedient Pledge on Rents Included in the Conservative Election Manifesto in October, London 1959; Macrae, Sunshades in October: An Analysis of the Main Mistakes in British Economic Policy Since the Mid Nineteen-fifties, London 1963; Macrae, Homes for the People, London 1967; Macrae, The Neurotic Trillionaire; A Survey of Mr. Nixon’s America, New York 1970; Macrae, The 2025 Report: A Concise History of the Future, 1975–2025, New York 1984; Macrae, The Hobart Century, London 1984; Macrae, John von Neumann: The Scientific Genius Who Pioneered the Modern Computer, Game Theory, Nuclear Deterrence, and Much More, Providence, RI 1999. 45.‘The Unacknowledged Giant’, 17 June 2010. 46.‘The Risen Sun’, 27 May 1967; ‘Consider Japan’, 1 September 1962. 47.‘Consider Japan’, 1 September 1962. 48.‘The Risen Sun’, 27 May 1967. 49.

Engineering Security
by Peter Gutmann

The standard economic decisionmaking model, also known as the Bayesian decision-making model, assumes that someone making a decision will carefully take all relevant information into account in order to come up with an optimal decision [5]. As one observer put it, this model “took its marching orders from standard American economics, which assumes that people always know what they want and choose the optimal course of action for getting it” [6]. This model, called Utility Theory, goes back to at least 1944 and John von Neumann’s work on game theory [7], although some trace its origins (in somewhat distant forms) as far back as the early 1700s [8]. The formalisation of the economic decision-making model, Subjective Expected Utility Theory (SEU), makes the following assumptions about the decision-making process [9][10][11][12]: 1.

…

“Herding, social influence and economic decision-making: sociopsychological and neuroscientific analyses”, Michelle Baddeley, Philosophical Transactions of the Royal Society B (Biological Sciences), Vol.365, No.1538 (27 January 2010), p.281. “Decision making in complex systems”, Baruch Fischhoff, Proceedings of the NATO Advanced Study Institute on Intelligent Decision Support on Intelligent Decision Support in Process Environments, Springer-Verlag, 1986, p.61. “Theory of Games and Economic Behaviour”, John von Neumann and Oskar Morgenstern, Princeton University Press, 1944. “Emotion and Reason: The Cognitive Neuroscience of Decision Making”, Alain Berthoz, Oxford University Press, 2006. “Models of Man : Social and Rational”, Herbert Simon, John Wiley and Sons, 1957. “Reason in Human Affairs”, Herbert Simon, Stanford University Press, 1983.

Rationality: From AI to Zombies
by Eliezer Yudkowsky
Published 11 Mar 2015

Maybe before you pull the dualist fire alarm on human brains being physically special, you should provide experimental proof that a rock can’t play the same role in dispelling the Mysterious Phenomenon as a human researcher? But that’s hindsight, and it’s easy to call the shots in hindsight. Do you really think you could’ve done better than John von Neumann, if you’d been alive at the time? The point of this kind of retrospective analysis is to ask what kind of fully general clues you could have followed, and whether there are any similar clues you’re ignoring now on current mysteries. Though it is a little embarrassing that even after the theory of amplitudes and configurations had been worked out—with the theory now giving the definite prediction that any nudged particle would do the trick—early scientists still didn’t get it.

…

That’s like, like some kind of comedy routine where the guy opens a box, and it contains a spring-loaded pie, so the guy opens another box, and it contains another spring-loaded pie, and the guy just keeps doing this without even thinking of the possibility that the next box contains a pie too. You think John von Neumann, who may have been the highest-g human in history, wouldn’t think of it?” “That’s right,” Huve says, “He wouldn’t. Ponder that.” “This is the world where my good friend Ernest formulates his Schrödinger’s Cat thought experiment, and in this world, the thought experiment goes: ‘Hey, suppose we have a radioactive particle that enters a superposition of decaying and not decaying.

Masterminds of Programming: Conversations With the Creators of Major Programming Languages
by Federico Biancuzzi and Shane Warden
Published 21 Mar 2009

He served as chair of the department from 1995 to 1997, and in the spring of 2003. Professor Aho has a B.A.Sc. in engineering physics from the University of Toronto and a Ph.D. in electrical engineering/computer science from Princeton University. Professor Aho won the Great Teacher Award for 2003 from the Society of Columbia Graduates. Professor Aho has won the IEEE John von Neumann Medal and is a Member of the U.S. National Academy of Engineering and the American Academy of Arts and Sciences. He received honorary doctorates from the Universities of Helsinki and Waterloo, and is a Fellow of the American Association for the Advancement of Science, the ACM, Bell Labs, and the IEEE.

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
by Martin Kleppmann
Published 17 Apr 2017

.: “Jupiter Rising: A Decade of Clos Topologies and Centralized Control in Google’s Datacenter Network,” at Annual Conference of the ACM Special Interest Group on Data Communication (SIGCOMM), August 2015. doi:10.1145/2785956.2787508 [10] Glenn K. Lockwood: “Hadoop’s Uncomfortable Fit in HPC,” glennklock‐ wood.blogspot.co.uk, May 16, 2014. 312 | Chapter 8: The Trouble with Distributed Systems [11] John von Neumann: “Probabilistic Logics and the Synthesis of Reliable Organ‐ isms from Unreliable Components,” in Automata Studies (AM-34), edited by Claude E. Shannon and John McCarthy, Princeton University Press, 1956. ISBN: 978-0-691-07916-5 [12] Richard W. Hamming: The Art of Doing Science and Engineering.

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
by Martin Kleppmann
Published 16 Mar 2017

.: “Jupiter Rising: A Decade of Clos Topologies and Centralized Control in Google’s Datacenter Network,” at Annual Conference of the ACM Special Interest Group on Data Communication (SIGCOMM), August 2015. doi:10.1145/2785956.2787508 [10] Glenn K. Lockwood: “Hadoop’s Uncomfortable Fit in HPC,” glennklockwood.blogspot.co.uk, May 16, 2014. [11] John von Neumann: “Probabilistic Logics and the Synthesis of Reliable Organisms from Unreliable Components,” in Automata Studies (AM-34), edited by Claude E. Shannon and John McCarthy, Princeton University Press, 1956. ISBN: 978-0-691-07916-5 [12] Richard W. Hamming: The Art of Doing Science and Engineering.

God Created the Integers: The Mathematical Breakthroughs That Changed History
by Stephen Hawking
Published 28 Mar 2007

It is hard to imagine the development of either electrodynamics or quantum theory without the methods of Jean Baptiste Joseph Fourier or the work on calculus and the theory of complex functions pioneered by Carl Friedrich Gauss and Augustin-Louis Cauchy—and it was Henri Lebesgue’s work on the theory of measure that enabled John von Neumann to formulate the rigorous understanding of quantum theory that we have today. Albert Einstein could not have completed his general theory of relativity had it not been for the geometric ideas of Bernhard Riemann. And practically all of modern science would be far less potent (if it existed at all) without the concepts of probability and statistics pioneered by Pierre Simon Laplace.

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Students jokingly called it the “Ivory Tower,” not because of the often ethereal intellectual activity it over-looked, but because it overlooked “Procter Hall,” the graduate college’s chief public room, built with a donation from William Cooper Procter, a founder of the American company Procter & Gamble, the manufacturers of Ivory Soap! Within the confines of the mathematics department, Turing could pursue whatever he pleased. There was no need to conform. He felt at home in the department. Yet, in spite of John von Neumann’s offer to stay on at the Institute for Advanced Study as a research assistant, Turing decided to return to England after receiving his Ph.D. in 1938. He realized that America, in general, and Presbyterian Princeton, in particular, would not easily tolerate a nonconformist such as himself. In the summer of 1938, he returned to a Europe on the brink of war.

The Golden Passport: Harvard Business School, the Limits of Capitalism, and the Moral Failure of the MBA Elite
by Duff McDonald
Published 24 Apr 2017

Born in Louisville, Kentucky, in 1866, Flexner had staked out permanent territory as an educational authority with the publication two decades earlier of the Flexner Report, which is credited with sparking the reform of medical education in the United States and Canada. He also cofounded (with Louis Bamberger) the Institute for Advanced Study at Princeton, with the immodest ambition of “[advancing] the frontiers of knowledge.”1 The institute, which later counted Albert Einstein and John Von Neumann among its faculty, has more than met that goal. Flexner was both a critic and an innovator—a man to be listened to, even if HBS didn’t like what he had to say. And they most certainly did not, starting with his conclusion that the rapid proliferation of professional schools within the university system—with the exception of medicine and law—was a threat to the university’s sacred purpose of the advancement of knowledge.2 And what society most certainly did not need, Flexner argued, was a graduate school of business at Harvard, which, ironically, the Rockefellers’ own GEB had helped bring into being.

On Thermonuclear War
by Herman Kahn
Published 16 Jul 2007

This is an understatement of the things that are now technologically feasible but that "cost a little too much." I have not seen any figures, but I surmise that relatively thin margins of cost prevent us from doing such extraordinary projects as melting ice caps and diverting ocean currents. The coming crisis in technology was described by the late John von Neumann in an article entitled "Can We Survive Technology?" 8 To quote von Neumann: "'The great globe itself is in a rapidly maturing crisis—a crisis attributable to the fact that the environment in which technological progress must occur has become both undersized and underorganized. . . . "In the first half of this century the accelerating Industrial Revolution encountered an absolute limitation—not on technological progress as such, but on an essential safety factor.

Behave: The Biology of Humans at Our Best and Worst
by Robert M. Sapolsky
Published 1 May 2017

In a world of noncooperators it’s disadvantageous to be the first altruist. How do systems of cooperation ever start?* Gigantic Question #1: What Strategy for Cooperating Is Optimal? While biologists were formulating these questions, other scientists were already starting to answer them. In the 1940s “game theory” was founded by the polymath John von Neumann, one of the fathers of computer science. Game theory is the study of strategic decision making. Framed slightly differently, it’s the mathematical study of when to cooperate and when to cheat. The topic was already being explored with respect to economics, diplomacy, and warfare. What was needed was for game theorists and biologists to start talking.

Concepts, Techniques, and Models of Computer Programming
by Peter Van-Roy and Seif Haridi
Published 15 Feb 2004

Talk given at the Newcastle Seminar on the Teaching of Computing Science, Newcastle, UK. [232] Matthias Zenger and Martin Odersky. Implementing extensible compilers. In 1st International Workshop on Multiparadigm Programming with Object-Oriented Languages, pages 61–80, Budapest, Hungary, June 2001. John von Neumann Institute for Computing (NIC). Workshop held as part of ECOOP 2001. Foundations of Index ! (cut) operation (in Prolog), 662, 666, 669 ! (escaped variable marker), 500, 509 !! (read-only) operation, 206, 799 " (double quote), 53, 821 $ (nesting marker), 53, 83, 355, 365 ´ (single quote), 35, 52, 821, 824 ’ (single quote) operation (in Lisp), 39 * (multiplication) operation, 54, 821 */ (comment end), 841 + (addition) operation, 54, 821 - (subtraction) operation, 54, 821 .

Applied Cryptography: Protocols, Algorithms, and Source Code in C
by Bruce Schneier
Published 10 Nov 1993

If you are depending on your random-number generator for security, weird correlations and strange results are the last things you want. The problem is that a random-number generator doesn’t produce a random sequence. It probably doesn’t produce anything that looks even remotely like a random sequence. Of course, it is impossible to produce something truly random on a computer. Donald Knuth quotes John von Neumann as saying: “Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin” [863]. Computers are deterministic beasts: Stuff goes in one end, completely predictable operations occur inside, and different stuff comes out the other end. Put the same stuff in on two separate occasions and the same stuff comes out both times.

The Codebreakers: The Comprehensive History of Secret Communication From Ancient Times to the Internet
by David Kahn
Published 1 Feb 1963

The problem is then to discover the transformation rule, or the nature of the filter, when given the statistics of the input and output. It is like finding the structure of an electrical filter by passing random noise through it and measuring the statistical distributions of the input and output voltages.” Cryptology may also be regarded as a conflict in the sense employed in The Theory of Games and Economic Behavior by John Von Neumann and Oskar Morgenstern. As Shannon, who first made the allusion, puts it: “The situation between the cipher designer and cryptanalyst can be thought of as a ‘game’ of a very simple structure; a zero-sum two-person game with complete information, and just two ‘moves.’ [A zero-sum game is one in which one contestant’s advances are made at the expense of the other.]