The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence
by Sebastian Mallaby; · 30 Mar 2026 · 607pp · 161,998 words
PhD, and Suleyman talked about what he was reading. In contrast to Hassabis, who loved books about science, Suleyman devoured volumes on politics, sociology, and complexity theory, logging every title that he went through. In December 2007, the two friends met for lunch at a steakhouse, right by the Smithfield meat market
Strategy: A History
by Lawrence Freedman · 31 Oct 2013 · 1,073pp · 314,528 words
these he drew language and insights to describe the sort of conflicts that interested him. From Norbert Wiener’s cybernetics to Murray Gell-Mann’s complexity theory emerged some core themes about the interaction of parts within systems, adaptation to changing environments, and outcomes that seemed indeterminate but were not beyond explanation
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for matters that were already well understood. Many of the emerging themes were present, for example, in Schelling’s writings. The most important contribution of complexity theory was to underline the importance of considering individual actors as part of complex systems, so that they must always be assessed in relation to their
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Brazil Set Off a Tornado in Texas?” For a history of chaos theory, see James Gleick, Chaos: Making a New Science (London: Cardinal, 1987). On complexity theory, see Murray Gell-Man, The Quark and the Jaguar: Adventures in the Simple and the Complex (London: Little, Brown & Co., 1994); Mitchell Waldrop, Complexity: The
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League, 254, 262 Communist Manifesto, The (Marx and Engels), 254, 256–257, 260–261, 270, 329 compellence theory, 163, 190–191 Competitive Advantage (Porter), 522 complexity theory, 197–198 Concept of the Corporation, The (Drucker), 493–495 Condition of the Working Class in England, The (Engels), 252 Confederación Nacional del Trabajo (CNT
In Our Own Image: Savior or Destroyer? The History and Future of Artificial Intelligence
by George Zarkadakis · 7 Mar 2016 · 405pp · 117,219 words
the nature of these interactions? And how can we test whether they exist or not? For many scientists, emergentist theories that spring from cybernetics and complexity theory do not seem falsifiable, and are therefore suspiciously non-scientific. Suspicion about theories of emergence reflects the ideological divide between traditional scientific methods of reductionism
Cognitive Gadgets: The Cultural Evolution of Thinking
by Cecilia Heyes · 15 Apr 2018
The Singularity Is Near: When Humans Transcend Biology
by Ray Kurzweil · 14 Jul 2005 · 761pp · 231,902 words
surprised by the idea that simple mechanisms can produce results more complicated than their starting conditions. We've seen this phenomenon in fractals, chaos and complexity theory, and self-organizing systems (such as neural nets and Markov models), which start with simple networks but organize themselves to produce apparently intelligent behavior. At
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can rapidly understand and apply this information by building models and working simulations. These simulations of brain regions are based on the mathematical principles of complexity theory and chaotic computing and are already providing results that closely match experiments performed on actual human and animal brains. As noted in chapter 2, the
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and those that comprise stochastic "noise" or chaos. The chaotic (random and unpredictable) aspects of neural function can be modeled using the mathematical techniques of complexity theory and chaos theory.16 ·The brain uses emergent properties. Intelligent behavior is an emergent property of the brain's chaotic and complex activity. Consider the
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only in the exact location on the table where it was asked to solve the problem. Genetic algorithms, part of the field of chaos or complexity theory, are increasingly being used to solve otherwise intractable business problems, such as optimizing complex supply chains. This approach is beginning to supplant more analytic methods
The Myth of the Rational Market: A History of Risk, Reward, and Delusion on Wall Street
by Justin Fox · 29 May 2009 · 461pp · 128,421 words
, 69–72, 133, 145, 194–95 Commodity Futures Trading Commission, 244 Common Stocks as Long-Term Investments (Smith), 22 competition, 160, 181, 353–54n. 25 complexity theory, 134, 301–2, 304 Complexity (Waldrop), 302 computers, 29, 86–87, 99–101, 204, 219, 224, 232, 234, 303–4 The Condition of the Working
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environmental risk, 185 equilibrium theory and the Arrow-Debreu framework, 77–78 and asset pricing, 87 background of, 9–12 and behavioral finance, 301 and complexity theory, 304–6 and derivatives, 237 and intrinsic values, 193 and Keynesian economics, 35 and mathematics, 49–50 and Pareto’s Law, 349–50n. 2 and
Masterminds of Programming: Conversations With the Creators of Major Programming Languages
by Federico Biancuzzi and Shane Warden · 21 Mar 2009 · 496pp · 174,084 words
the laws of physics and engineering principles of a particular discipline. In computing that would mean computer science principles such as algorithms, data structures, and complexity theory, as well as the principles of software engineering. In any field, it is important to develop a feel for how things are done. If software
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of emergent systems. That’s what science is all about and language is all about. That’s one of the concepts from the concept of complexity theory. You get the sense of emergence, and there’s no one fundamental layer. Certainly people have understood that in computing for a long time. You
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have gotten so much faster, that doesn’t matter. They’re still obsessed with what I’ll call the micro-optimizations. They don’t know complexity theory, they don’t understand the “order-of” stuff, but they’re worried about little, itty-bitty speed improvements. I wrote a subroutine package and then
The Blockchain Alternative: Rethinking Macroeconomic Policy and Economic Theory
by Kariappa Bheemaiah · 26 Feb 2017 · 492pp · 118,882 words
an economy are reflective of the study of complex systems, an academic discipline that observes the deep laws of complexity and emergence in any system. Complexity theory was born in the 1970’s (Wilson, 1998) and was originally inspired by 19th century physics, specifically the fields of classical mechanics, statistical non-equilibrium
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physics and thermodynamics (Helbing and Kirman, 2014). The main tenets of complexity theory borrow their conceptions from chaos theory, self-criticality and adaptive landscapes, to bring into focus the way complex systems grow, persist and collapse. The first
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scholars of complexity theory began their formulations at the Santa Fe institute, and based their study of complex systems on abstract non-linear transformative computer simulations. They attempted to
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. It was, and will be, one of the most important scientific discoveries of the century. 11Some of the early trailblazers who combined the study of complexity theory with economics include, Kenneth Arrow (economist), Philip Anderson (physicist), Larry Summers (economist), John Holland (physicist), Tom Sargent (economist), Stuart Kauffman (physicist), David Pines (physicist), José
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-1e7744c66818 Smaghi, L. B. (2010). The paradigm shift after the financial crisis. Nomura Seminar. Kyoto: European Central Bank. Stefano Battiston, J. D. (2016, February 19). Complexity theory and financial regulation - Economic policy needs interdisciplinary network analysis and behavioral modeling. Science , pp. 818-819. Sweney, M. (2008, November 28 ). FT rolls out “St
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). Whom or What Does the Representative Individual Represent? Journal of Economic Perspectives - Volume 6, Number 2, 117-136. Kirman, D. H. (2014). Rethinking Economics Using Complexity Theory . Iowa State University. Kirman, M. G. (2013). Reconstructing economics: Agent based models and complexity . Baltzer Science Publishers, DOI : 10.7564/12-COEC2 . Lawrence J. Christiano
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Learned and to What End? LeBaron, B. (2002). Building the Santa Fe Artificial Stock Market . Brandeis University. Levy, D. L. (2000). Applications and Limitations of Complexity Theory in Organization Theory and Strategy. In G. J. Jack Rabin, Handbook of Strategic Management (pp. 67-87). Routledge. Levy, M. (2012). Agent Based Computational Economics
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. In R. A. Meyers, Computational Complexity: Theory, Techniques, and Applications (pp. 18-39). Springer. Manson, S. M. (2001). Simplifying complexity: a review of complexity theory. Geoforum, Volume 32, Issue 3, 405-414. Morçöl, G. (2008). A Complexity Theory for Policy Analysis. In K. A. Richardson and Linda F. Dennard (Editors
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: 10.2478/revecp-2014-0008 , pp. 145-164,. Stefania Bandini, S. M. (2012). Chapter 7 : Agent Based Modeling and Simulation. In R. A. Meyers, Computational Complexity: Theory, Techniques, and Applications (pp. 105 - 121). Springer. Stuart Russell, P. N. (2009). Artificial Intelligence: A Modern Approach . Pearson. Syll, L. P. (2016, April 7). Deductivism
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) Dodd-Frank Act Dynamic Stochastic General Equilibrium (DSGE) model E EBM SeeEquation based modelling (EBM) Economic entropy vs. economic equilibrium assemblages and adaptations complexity economics complexity theory DSGE based models EMH human uncertainty principle’ LHC machine-like system operating neuroscience findings reflexivity RET risk assessment scientific method technology and economy Economic flexibility
The Road to Ruin: The Global Elites' Secret Plan for the Next Financial Crisis
by James Rickards · 15 Nov 2016 · 354pp · 105,322 words
forecasting extreme events was based on analytic methods similar to ones used in this book. He did not use the same names we use today; complexity theory and behavioral economics were still far in the future when he was engaged with markets. Still, his methods are visible from his writings. A vivid
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-old theorem, scorned for centuries, triumphantly reemerged to solve otherwise unsolvable problems. The three most important new tools in the finance toolkit are behavioral psychology, complexity theory, and causal inference. These tools can be used separately to solve a particular problem or combined to build more robust models. All three tools seem
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ignore second-order costs (the cat). Investors can use this book to see the saber-toothed cats. The second new tool in the toolkit is complexity theory. The crucial question in economics today is whether capital markets are complex systems. If the answer is yes, then every equilibrium model used in financial
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when you enter that data into a deficient model, you get deficient output. Investors who use complexity theory can leave mainstream analysis behind and get better forecasting results. The third tool in addition to behavioral psychology and complexity theory is Bayesian statistics, a branch of etiology also referred to as causal inference. Both terms
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lately merged into the neoliberal consensus, a nightmarish surf and turf presenting the worst of both. In this book, I write as a theorist using complexity theory, Bayesian statistics, and behavioral psychology to study economics. That approach is unique and not yet a “school” of economic thought. This book also uses one
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. Over a bottle of white Burgundy, we conversed about old times, mutual friends, and the crowd at the lecture. I had addressed the audience on complexity theory and hard data that showed the financial system moving toward collapse. My friend on the right didn’t need any lectures on systemic risk; she
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conversation with Keynes recounted by Felix Somary in The Raven of Zurich (1986) No key unlocks the mysteries of capital markets with more ease than complexity theory. That theory formally dates from the 1960s, but observation of complex dynamics is as old as humanity. An ancient astronomer seeing a supernova in the
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Street. Few have walked that path, as evidenced by the continued dominance of obsolete equilibrium models in central bank policymaking and private risk management. Modern complexity theory began in 1960 with the work of Edward Lorenz, an MIT mathematician and meteorologist. Lorenz was modeling atmospheric flows and discovered that minute changes in
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critical state models is vast. Climate, biology, solar flares, forest fires, traffic jams, and other natural and man-made behaviors can all be described using complexity theory. Lorenz’s observation that long-run forecasting in nonlinear systems is impossible given minute differences in initial conditions did not mean that no valuable information
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is derived from the models. Applied complexity theory is interdisciplinary. Complex systems all have behaviors in common, yet have dynamics unique to each domain. A team out to crack the code in applied
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complexity theory would include physicists, mathematicians, computer modelers, and subject matter experts from the field being addressed. Biologists, climatologists, hydrologists, psychologists, and other domain experts work together
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I sat there watching physicists demonstrate Bayesian technique for nuclear weapons testing, my mind turned to applications in capital markets. In fact, there are many. Complexity theory is a branch of physics. Bayes’ theorem is applied mathematics. Complexity and Bayes fit together hand in glove for solving capital markets problems. Capital markets
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Bayesian technique is not a science in itself, it’s an applied mathematical tool with robust predictive properties. The prime science of capital markets is complexity theory. Capital markets are complex systems, yet complexity is little understood and even less used in financial economics. From the 1998 global liquidity crisis, to the
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2000 tech bubble collapse, to the 2008 panic, policymakers have led the world into one crash after another. Their failure to use complexity theory explains why. The case for complexity theory is straightforward. It’s not difficult to grasp. Investors must grasp it now if they wish to preserve wealth. The next panic
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is a combination because the natural complexity of uranium atoms is engineered by scientists to the supercritical state that releases a bomb’s destructive power. Complexity theory begins with two tools. The first is the agent. An agent is simply an actor in a system. An agent can be a human in
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a in real markets is approximately 0.7. This empirical result falls between 0.5 and 1.0, somewhere between random and ordered, exactly what complexity theory predicts. This is strong evidence that capital markets are complex systems. Not only do capital markets fit a descriptive definition of complex systems based on
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. And this is far from being a minor flaw since it is precisely these moments when your money is most at risk. A grasp of complexity theory is a powerful tool for assessing risk in capital markets. We see how diverse groups of agents can self-organize into a crowd and anticrowd
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Moment” given by sociologists, economists, and media. Those concepts, colorful as they may be, are not science. The dynamics of ruin are best understood using complexity theory, a hard science that offers tools to see collapse coming in advance. The term “complexity” is often used loosely as synonymous with complication or connectedness
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’t just happen. Crises emerge because regulators don’t comprehend the statistical properties of the systems they regulate. LTCM was a textbook case in ignoring complexity theory. For example, traders at LTCM frequently constructed two-sided strategies using real government notes and synthetic notes in swap form. If the spread between the
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were at the golf course or racetrack thinking risk was under control, LTCM was a ticking time bomb. In August 1998, the bomb exploded. Aftermath Complexity theory is not understood by regulators today, so perhaps LTCM partners can be forgiven for not understanding complexity in 1998. Yet once that collapse occurred, it
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. Still, after Glass-Steagall there was no general banking crisis of the kind seen from 1929 to 1933. Glass-Steagall worked for exactly the reason complexity theory suggests. By breaking the banking system into two parts, Glass-Steagall made each part stronger by shrinking systemic scale, diminishing dense connections, and truncating channels
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model because of this strong feedback effect. Neil F. Johnson, Ph.D., professor of physics, University of Miami A New Crisis From the perspective of complexity theory, the 2008 collapse was easily foreseen. A dynamically identical collapse happened in 1998. The scope of the 2008 panic was greater than in the 1998
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and applied at the CIA were the same ones I was using to untangle LTCM’s collapse. By 2005, I had worked out complexity theory dynamics in finance. The main complexity theory tenets were expounded by physicists years before, and applied in various scientific fields including seismology, meteorology, and biology. Still, physicists were making
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late summer of 2008. On August 16, just one month before the Lehman blow-up, I provided the McCain campaign with this written advice using complexity theory–based models. The advice was sent as an email with the subject line “Storm Warning.” The text reads: Here’s a quick take on the
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, and the McCain campaign in 2008, about the coming collapse failed. Bush and McCain were not alone in their inability to comprehend insights offered by complexity theory. Policymakers from Paulson to Bernanke, and CEOs from Merrill Lynch’s John Thain to Lehman’s Dick Fuld, were as dazed. On September 29, 2008
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Washington Post published my op-ed “A Mountain, Overlooked: How Risk Models Failed Wall St. and Washington.” This was my first public effort to use complexity theory to explain the ongoing financial collapse. In the op-ed I wrote: Since the 1990s, risk management on Wall Street has been dominated by a
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. Capital markets are an example of such complex dynamic systems. The Washington Post takes an extremely rigorous approach to guest op-eds. My contribution on complexity theory coming at the height of the crisis was published only after a series of conference calls with Vincent Reinhart, a former monetary economist for the
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the Treasury in Washington. The briefing took place on Friday, April 12. The Treasury official who organized the briefing was interested in learning more about complexity theory and its use in identifying systemic risk in capital markets. I was encouraged by the invitation. Perhaps glasnost had arrived in Washington a quarter century
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profits. Later in the presentation, I asked the senior OFR official what models they used to assess systemic risk. I knew they were not using complexity theory, and still used value at risk. I wanted to know whether refinements or advances to VaR had been adopted. The official said, “Well, we’re
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the point that economists, especially those at the Fed, simply don’t see bubbles. There are models that do a good job identifying bubbles using complexity theory, causal inference, and behavioral economics, although the exact timing of collapse remains difficult to predict due to the minuteness of catalysts, and the stochastics of
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his “escape from the Fed.” Unfortunately there is no escape for the global economy. The Power of Gold Simply seeing market collapse, even through a complexity theory lens, is unsatisfying to investors who don’t care why things end, but want to know when. Greed plays a part. Investors may concur that
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network density was resistant to political and economic contagion. Since 1500, increasing scale and density in Europe have led to exponentially larger breakdowns, exactly what complexity theory predicts. The Concert of Europe collapsed in stages about sixty years after Vienna. German and Italian unification, completed in 1871, signaled a sharp increase in
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of equilibrium models, monetarism, Keynesianism, floating exchange rates, free trade, globalization, and fiat money. Meanwhile, the real world is best understood through the lens of complexity theory, conditional probability, behavioral psychology, currency wars, neomercantilism, and gold. Cognitive dissonance between the elite worldview and real-world economics is taking its toll on elite
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years before complexity was discovered as a branch of physics. Yet his view of historical processes fits well with the extended time frames for which complexity theory holds the most explanatory power. Complexity provides models for comprehension of the slow, steady buildup of dense networks that suddenly, catastrophically collapse. Seismic faults, forests
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destroys everything. Complexity theorists know that apparent stability is a mask for rising tension. The rise and fall of civilizations is the grandest example of complexity theory applied to human affairs. Schumpeter’s consideration of capitalism’s rise and fall, while not specific to one civilization, is the kind of study to
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which complexity theory lends valuable tools. Schumpeter eschewed Keynesian models because of the artifice in holding most variables constant while monotonically isolating one as the “cause” of the
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surely look benignly on such efforts as a reasonable simulacrum of his deep historical processes. Society stands on Schumpeter’s shoulders with new tools of complexity theory to look over a ridgeline at the rise of socialism and fascism, one and the same. The New Praetorians In ancient Rome, the Praetorian Guard
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cockcrow, or in the morning. Mark 13:22–35 There is a high likelihood of … another global crisis. “The Geneva Report 16” (2014) Countdown Clock Complexity theory says we will not know the time of the next financial collapse in advance. This conclusion is not a case of throwing up one’s
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ten-year Treasury note intraday yield ranges since 1998 is not a normal distribution as VaR advocates expect, but a perfect power curve—exactly what complexity theory predicts.) The rarity of the intraday 37-basis-point flash crash in yields is troubling enough. More troubling is the observation of a 16-basis
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liquidity crises sketched above are not the only crisis catalysts. Natural disasters, cyberwarfare, and nuclear arms in the Middle East are all on the table. Complexity theory teaches that what counts is not the proximate cause of a collapse, but the density, interactions, and systemic scale that make collapse inevitable. The greatest
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be governments and banks. The losers would be investors, except those elites who were in on the plan or could catch glimpses and prepare accordingly. Complexity theory makes a mockery of plans. The most likely path is the one no one sees. A systemic crisis could erupt at any time. Monetary elites
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of collapse, I concluded my case for America in decline. I trusted the audience to realize this has all happened before, and would happen again. Complexity theory is a guide to the future, yet there is no greater guide than the past. About fifty yards off Turkey’s southern coast, near a
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social systems, including crowd and anticrowd behavior, is based on similar examples and related experiments presented in Neil Johnson, Simply Complexity: A Clear Guide to Complexity Theory (London: Oneworld, 2012), 72–85. Research conducted by physicists Neil Johnson, Pak Ming Hui, and Paul Jefferies: Pak Ming Hui, Paul Jefferies, and Neil F
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of America’s Decline: Politics, Economics, and a Half Century of False Prophecies. New York: Liveright, 2015. Johnson, Neil. Simply Complexity: A Clear Guide to Complexity Theory. London: Oneworld, 2012. Kahneman, Daniel. Thinking, Fast and Slow. New York: Farrar, Straus and Giroux, 2011. Keynes, John Maynard. The General Theory of Employment, Interest
Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the Agi Workshop 2006
by Ben Goertzel and Pei Wang · 1 Jan 2007 · 303pp · 67,891 words
us with some puzzles. A first important one is: what about this particular code causes it to understand? A second important one is: given that complexity theory has indicated that many computations are inherently time consuming, how does the mind work so amazingly fast? Computational learning theory has explained generalization as arising
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learning so innately programmed appears quite automatic, reliably resulting in creatures with similar abilities provided that they are allowed interaction with the world during development. Complexity theory suggests that learning is a hard problem, requiring vast computation to extract structure. Yet we learn so fast that we do not have time to
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to extract specific kinds of meaning. The bulk of the requisite computation, and thus the guts of the process from the point of view of complexity theory, went into the evolution of the genome. Empirical evidence shows that creatures are in fact programmed with specific inductive biases. If a rat is shocked
Artificial Intelligence: A Modern Approach
by Stuart Russell and Peter Norvig · 14 Jul 2019 · 2,466pp · 668,761 words
Silence on the Wire: A Field Guide to Passive Reconnaissance and Indirect Attacks
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Connectography: Mapping the Future of Global Civilization
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Algorithms to Live By: The Computer Science of Human Decisions
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We-Think: Mass Innovation, Not Mass Production
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Public Places, Urban Spaces: The Dimensions of Urban Design
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Unweaving the Rainbow
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Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist
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The Rise of the Network Society
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Complexity: A Guided Tour
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Science Fictions: How Fraud, Bias, Negligence, and Hype Undermine the Search for Truth
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Horizons: The Global Origins of Modern Science
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Why Machines Learn: The Elegant Math Behind Modern AI
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The Fabric of Reality
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Where Good Ideas Come from: The Natural History of Innovation
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Applied Cryptography: Protocols, Algorithms, and Source Code in C
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The Human Cosmos: A Secret History of the Stars
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Life Is Simple: How Occam's Razor Set Science Free and Shapes the Universe
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Uncharted: How to Map the Future
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The Classical School
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Nomad Citizenship: Free-Market Communism and the Slow-Motion General Strike
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The Butterfly Defect: How Globalization Creates Systemic Risks, and What to Do About It
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Advances in Financial Machine Learning
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The Art of Statistics: Learning From Data
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Grand Transitions: How the Modern World Was Made
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The Friendly Orange Glow: The Untold Story of the PLATO System and the Dawn of Cyberculture
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Data and the City
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Diverse Bodies, Diverse Practices: Toward an Inclusive Somatics
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Nuts and Bolts: Seven Small Inventions That Changed the World (In a Big Way)
by Roma Agrawal · 2 Mar 2023 · 290pp · 80,461 words
Rethinking Capitalism: Economics and Policy for Sustainable and Inclusive Growth
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What to Think About Machines That Think: Today's Leading Thinkers on the Age of Machine Intelligence
by John Brockman · 5 Oct 2015 · 481pp · 125,946 words
How the Mind Works
by Steven Pinker · 1 Jan 1997 · 913pp · 265,787 words
Life on the Edge: The Coming of Age of Quantum Biology
by Johnjoe McFadden and Jim Al-Khalili · 14 Oct 2014 · 476pp · 120,892 words
Quantum Computing for Everyone
by Chris Bernhardt · 19 Mar 2019 · 211pp · 57,618 words
Cogs and Monsters: What Economics Is, and What It Should Be
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The Art of Statistics: How to Learn From Data
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Growth: From Microorganisms to Megacities
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The Art of Computer Programming
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When More Is Not Better: Overcoming America's Obsession With Economic Efficiency
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From Counterculture to Cyberculture: Stewart Brand, the Whole Earth Network, and the Rise of Digital Utopianism
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Why People Believe Weird Things: Pseudoscience, Superstition, and Other Confusions of Our Time
by Michael Shermer · 1 Jan 1997 · 404pp · 134,430 words
Everything Is Obvious: *Once You Know the Answer
by Duncan J. Watts · 28 Mar 2011 · 327pp · 103,336 words
The Age of Spiritual Machines: When Computers Exceed Human Intelligence
by Ray Kurzweil · 31 Dec 1998 · 696pp · 143,736 words
The Ancestor's Tale: A Pilgrimage to the Dawn of Evolution
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Debunking Economics - Revised, Expanded and Integrated Edition: The Naked Emperor Dethroned?
by Steve Keen · 21 Sep 2011 · 823pp · 220,581 words
Darwin's Dangerous Idea: Evolution and the Meanings of Life
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The Barbell Prescription: Strength Training for Life After 40
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The Swerve: How the Renaissance Began
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Scale: The Universal Laws of Growth, Innovation, Sustainability, and the Pace of Life in Organisms, Cities, Economies, and Companies
by Geoffrey West · 15 May 2017 · 578pp · 168,350 words
The Patterning Instinct: A Cultural History of Humanity's Search for Meaning
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Human Compatible: Artificial Intelligence and the Problem of Control
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Making Sense of Chaos: A Better Economics for a Better World
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The Demon in the Machine: How Hidden Webs of Information Are Finally Solving the Mystery of Life
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Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets
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More Than You Know: Finding Financial Wisdom in Unconventional Places (Updated and Expanded)
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The Idea Factory: Bell Labs and the Great Age of American Innovation
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A Devil's Chaplain: Selected Writings
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Currency Wars: The Making of the Next Gobal Crisis
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The Art of Computer Programming: Sorting and Searching
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Thinking, Fast and Slow
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The Golden Ticket: P, NP, and the Search for the Impossible
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Average Is Over: Powering America Beyond the Age of the Great Stagnation
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The Irrational Economist: Making Decisions in a Dangerous World
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The Death of Money: The Coming Collapse of the International Monetary System
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How to Read a Paper: The Basics of Evidence-Based Medicine
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The Organized Mind: Thinking Straight in the Age of Information Overload
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The Signal and the Noise: Why So Many Predictions Fail-But Some Don't
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The Future of the Brain: Essays by the World's Leading Neuroscientists
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City: Urbanism and Its End
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On Nature and Language
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The Most Human Human: What Talking With Computers Teaches Us About What It Means to Be Alive
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Emergence
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The Denial of Death
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Physics in Mind: A Quantum View of the Brain
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Culture and Prosperity: The Truth About Markets - Why Some Nations Are Rich but Most Remain Poor
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