How to Create a Mind: The Secret of Human Thought Revealed
by Ray Kurzweil · 13 Nov 2012 · 372pp · 101,174 words
quiz show, and Ken Jennings, who had previously held the Jeopardy! championship for the record time of seventy-five days. By way of context, I had predicted in my first book, The
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and, 253, 255 I, Robot (film), 210 Jacquard loom, 189, 190 James, William, 75–76, 98–99 Jeffers, Susan, 104 Jennings, Ken, 157–58, 165 Jeopardy! (TV show), 6–7, 108, 157–58, 160, 165, 166, 167, 168, 169, 172, 178, 232–33, 270 Joyce, James, 55 Kasparov, Garry, 39, 166 K Computer
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, 115 Science, 82–83 “scientist’s pessimism,” 272–73 Searle, John, 170, 201, 205, 206, 222 “Chinese room” thought experiment of, 170, 274–75 Seinfeld (TV show), 75 selective serotonin reuptake inhibitors, 106 self-organizing systems, 144, 147, 149, 150, 154–55, 162, 168, 171–72, 175, 197, 270 sensorimotor area, 77
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, 99 sensory-touch pathway, 58, 60, 94–98, 95, 97–100, 97, 99 serotonin, 105, 106, 107, 118 Seung, Sebastian, 10 Sex and the City (TV show), 117 sexual reproduction, 118 simulated, 148 Shakespeare, William, 39, 114–15, 209 Shannon, Claude, 183–84, 190 Shashua, Amnon, 159 Shaw, J. C., 181 Short
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spinal cord, 36, 99 spindle neurons, 109–11 split-brain patients, 70, 226–27 Stanford Encyclopedia of Philosophy, The, 232 Star Trek: The Next Generation (TV show), 210 Star Wars films, 210 stochastic variation, 9 supercomputer power, growth in, 258, 301n–3n survival: as evolutionary goal, 79, 104, 242 as individual goal
The Glass Cage: Automation and Us
by Nicholas Carr · 28 Sep 2014 · 308pp · 84,713 words
seems clear that computers are a long way from bumping up against those limits. When, in early 2011, the IBM supercomputer Watson took the crown as the reigning champion of Jeopardy!, thrashing two of the quiz show’s top players, we got a preview of where computers’ analytical talents are heading. Watson’s ability to
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, 214 iPads, 136, 153, 203 iPhones, 13, 136 Ironstone Group, 116 “Is Drawing Dead?” (symposium), 144 Jacquard loom, 36 Jainism, 185 Jefferson, Thomas, 160, 222 Jeopardy! (quiz show), 118–19, 121 Jobless Future, The (Aronowitz and DiFazio), 27–28 jobs, 14–17, 27–33, 85, 193 automation’s altering of, 67, 112–20
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Shushwap tribe, 228–29, 232 Silicon Valley, 7, 33, 133, 194, 226, 227 Simons, Daniel, 201 simplicity, 180, 181 Singhal, Amit, 78–79 60 Minutes (TV show), 29 Sketchpad, 138 SketchUp, 146 Skidelsky, Robert, 31–32 Skiles, Jeffrey, 154 skill fade, 58 skills, 80–85, 161, 216–17, 218, 219 degradation of
Machine, Platform, Crowd: Harnessing Our Digital Future
by Andrew McAfee and Erik Brynjolfsson · 26 Jun 2017 · 472pp · 117,093 words
2010, Google unexpectedly announced that a fleet of completely autonomous cars had been driving on US roads without mishap. In 2011, IBM’s Watson supercomputer beat two human champions at the TV quiz show Jeopardy! By the third quarter of 2012, there were more than a billion users of smartphones, devices that combined the communication
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work of coming up with new television commercials, but also with the task of figuring out exactly when and where to show them: identifying which TV shows, geographic markets, and times were the best match for the advertisers’ goals and budget. Data and technology have long been used for this work—the
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responded well to very different types of ads, so they needed to differentiate the groups when buying time on TV shows. By 2012, some ratings companies had gone far beyond capturing demographic data on TV shows and were instead able to specify which individuals were watching them.‡‡ This was exactly the second type of
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have a pretty good idea who your target people are, but you’ve never been able to know with the same precision and confidence what TV shows they’re watching. Well, now you can.” For advertisers, placing TV commercials is an important decision that has been made with some data, but also
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provide valuable signals about the level of interest and enthusiasm for some types of offerings, particularly those likely to appeal to a niche audience. The TV show Veronica Mars, for example, which was about a teenage detective played by Kristen Bell, had a devoted but relatively small following when it aired between
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ask and answer questions. These cover every conceivable topic, from makeup to car repair to analyzing what happened on the last episode of a hit TV show. As fans of innovation, we’re particularly excited about the “maker movement,” a broad term for the tinkerers, do-it-yourselfers, spare-time fabricators, engineers
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one of the most maligned groups within the standard arrangement. Their portrayals in popular culture, from the movie Office Space to the British and American TV shows The Office, are almost always negative. They are seen as bumblers who have no value while sapping employees’ enthusiasm, wasting their time, and thwarting their
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the Apple Store,” TaskRabbit (blog), September 17, 2012, https://blog.taskrabbit.com/2012/09/17/were-first-in-line-at-the-apple-store. 261 The TV show Veronica Mars: IMDb, s. v. “Veronica Mars: TV Series (2004–2007),” accessed February 8, 2017, http://www.imdb.com/title/tt0412253. 262 To find out
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Isaacson, Walter, 152, 165 iteration, 173, 323; See also experimentation iTunes, 217–18 iTunes Store, 145, 165 Jackson, Michael, 131 Java, 204n Jelinek, Frederick, 84 Jeopardy! (TV show), 17 Jeppesen, Lars Bo, 259 Jobs, Steve curation of iPhone platform, 165 Dropbox acquisition offer, 162 and iPhone apps, 151–53, 157, 163 joint-stock
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, Uber prohibition in, 202 venture capital, DAO vs., 302 verifiability, 248 verifiable/reversible contributions, 242–43 Verizon, 96, 232n Veronica Mars (movie), 262 Veronica Mars (TV show), 261–62 Viant, 171 video games, AI research and, 75 videos, crowd-generated, 231–32 Viper, 163 virtualization, 89–93; See also robotics vision, Cambrian
From Bacteria to Bach and Back: The Evolution of Minds
by Daniel C. Dennett · 7 Feb 2017 · 573pp · 157,767 words
, 188, 273n, 274–75, 279, 303, 353 Jackson, John Hughlings, 345 Jacob, François, 161 Java applets, 188, 301–2, 304 Jennings, Ken, 389, 395, 398 Jeopardy (TV show), 389–90, 395, 398 Jesus units, 113 Johansson, Scarlett, 399n Johst, Hans, 25n joint attention, 286 Jonze, Spike, 399n just-so stories, 121, 248 K
Warnings
by Richard A. Clarke · 10 Apr 2017 · 428pp · 121,717 words
’s warning and prediction, 64–74 Israel, Arab-Israeli War, 35–36 Israeli Defense Force, 35 Jaffe, Robert, 101, 108 Japanese Nuclear Safety Commission, 92 Jeopardy! (TV show), 202 Jōgan earthquake of 869, 77–81, 91, 97–98 Joy, Bill, 355 JPMorgan, 159 Jupiter, 306–7 Kan, Naoto, 84, 88, 92 Karachi, Pakistan
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Global Institute, 212 Mackowiak, Joseph, 122, 133–42 background of, 134 UBB ventilation system, 133–37 Macmillan, Harold, 10–11 McNeill, William, 217 Madame Secretary (TV show), 298 Madarame, Haruki, 92 Madeira School, 153 Madoff, Andrew, 107, 112, 113–14 Madoff, Bernard “Bernie,” 6, 100–120, 178 Madoff, Mark, 107, 112, 113
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noise, separating, 356–58 Silver, Nate, 13, 15 Silver mining, 128–29 Simon, Herbert, 180–81, 322 Simons, Daniel, 175 Singularity, the, 209 60 Minutes (TV show), 119, 162, 244 Skepticism, 151–53, 168, 185, 240, 248–49 Skynet, 205 Smith & Wesson, 99, 109 Snowden, Edward, 211 Solid rocket boosters, and Challenger
Overcomplicated: Technology at the Limits of Comprehension
by Samuel Arbesman · 18 Jul 2016 · 222pp · 53,317 words
of, 101–2 interoperability, 47–48 optimal vs. maximum, 62–63, 64–65 interpreters, of complex systems, 166–67, 229 Ionia, 138–39 iPad, 162 Jeopardy! (TV show), 142, 169 Jobs, Steve, 161 Jones, Benjamin, 90 July 8, 2015, system crashes on, 1, 4 Kant Generator, 74 Kasparov, Garry, 84 Katsuyama, Brad, 189
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–67 software bugs in, 97 Scientific Reports, 4 Scientific Revolution, optimistic view of human comprehension in, 152–53 security, software bugs and, 97–98 Seinfeld (TV show), 130 sentences: garden path, 74–75 parsing of, 73–74 sewage systems, complexity of, 101 Shakespeare, William, 55 Shatner, William, 160 Shepard, Alan, 200 sickle
Final Jeopardy: Man vs. Machine and the Quest to Know Everything
by Stephen Baker · 17 Feb 2011 · 238pp · 77,730 words
like Ken Jennings seemed to be the model of human intelligence. They aced exams. They had dozens of facts at their fingertips. In one quiz show that predated Jeopardy, College Bowl, teams of the brainiest students would battle one another for the honor of their universities. Later in life, people turned to them
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to look smart, and they want people at home to feel smart, too. That’s critical to Jeopardy’s popularity. “You can’t forget that it’s a TV show,” said Roger Craig, a six-time Jeopardy champion. “They’re writing for the person in the living room.” And that viewer, like Ken Jennings
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This is where Harry Friedman worked. Friedman, then in his late fifties, was the executive producer of both Wheel of Fortune and Jeopardy, the top- and second-ranked game shows in America. Wheel, as it was known, relied on the chance of a spinning wheel and required only the most rudimentary knowledge of
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for the Lincoln Star. After graduating, in 1971, he traveled to Hollywood. He eventually landed a part-time job at Hollywood Squares, a popular daytime game show, where he wrote for $5 a joke. Friedman climbed the ladder at Hollywood Squares, eventually producing the show. He also wrote stand-up acts for
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shift to electronic letters. The game speeded up. Ratings improved. Two years later, he was offered the top job at Jeopardy. The game, which today radiates such wholesomeness, emerged from the quiz show scandals of the 1950s. “That’s where we came from. That’s our history,” Friedman said. Back then, millions
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hearings, a condemnation by President Eisenhower—“a terrible thing to do to the American people”—and stricter regulations covering the industry. For a few years, quiz shows all but disappeared. In 1963, Merv Griffin, the talk show host and entrepreneur, was wondering how to resurrect the format. According to a corporate history
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seemed like forever. But this, it turned out, wasn’t a bad thing at all. Ratings soared. Jeopardy had hatched its first celebrity. His name was Ken Jennings. Nothing about the man suggested quiz show dominance. Unlike basketball, where a phenom like LeBron James emerged in high school, amid monster dunks, as the
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been another prominent female track star named Jones, Jennings, like thousands of others, would have been a one-time loser on America’s most popular quiz show. But the judges knew no other stars named Jones and approved his vague answer. “We’ll accept that,” Trebek said. Ken Jennings won the
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Jennings extended his streak to thirty-eight games, ratings jumped 50 percent from those of the previous year, reaching a daily audience of fifteen million. Jeopardy rose to be the second-ranked TV show of the month, trailing only the CBS prime-time crime series CSI. In an added dividend for Friedman
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that the answers were on call in his head somewhere led him to a remarkable 92 percent precision rate, according to statistics compiled by the quiz show’s fans. This topped the average champion by 10 percent. As IBM’s scientists contemplated building a machine that could compete with the likes of
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software developers, librarians—all with one section of their mind specially adapted—or possibly overfitted—to a TV quiz show. David Ferrucci spent his days swimming in statistics. They defined every aspect of the Jeopardy project. Blue J’s analysis of data was statistical. Its confidence algorithms and learning programs were fed entirely
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imagination as a company that took risks and was engaged in changing the world with bleeding-edge technology. The Jeopardy challenge, with this talking IBM machine on national television matching wits with game-show luminaries, was the branding opportunity of the decade. The name had to be good. Was THINQ the right
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names stressed intelligence. Qwiz, for example, blended “Q,” for question, with “wiz” to suggest that the technology had revolutionized search. The pronunciation—quiz—fit the game show theme. Another choice, nSight, referred to “n,” representing infinite possibilities. And EureQA blended “eureka” with the Q-A for question-answering. Another candidate “Mined,” pointed
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any other contestant, would be limited to the face behind the podium—or whatever fit there. Jeopardy held the power and exercised it. If IBM’s computer was to benefit from an appearance on Jeopardy, the quiz show would lay down the rules. Now that Watson was reduced from a possible Jumbotron to a
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Culver City empty-handed, with no promises of extra airtime or other promotional concessions. Not everything hinged on the final game. IBM hoped that Watson would enjoy a career long after the Jeopardy showdown. They had plans for it to tour extensively, perhaps at company events or schools. This mobile Watson might be
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Crain, from Illinois. It would amount to an entire research project—which would likely be useless to IBM outside the narrow confines of a specific game show. Ferrucci wouldn’t even consider it. Loughran thought Ferrucci and Friedman could iron out many of these points with a one-on-one conversation. “
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sitting in his office on the Sony lot in Culver City. The walls were plastered with photographs and awards from his forty-year career in game shows, his seven Emmys, and his Cable and Broadcasting Hall of Fame plaque. It had been a tense day. That morning he had had another
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the other—or at least be perceived as doing so. Ferrucci was always careful to ascribe this possibility to unconscious bias. But for Jeopardy, a franchise born from the quiz show scandals of the 1950s, the hint of such bias—conscious or not—was poisonous. And even if Ferrucci kept this concern to
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for the upcoming season, with taping starting in July. A few days before taping, an official from Sullivan Compliance Company, an outside firm that monitors game shows, would select thirty of those games. He would not see the clues or categories and would pick two of the games only by numbers given
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three seconds. All of that engineering, and those thousands of processors were harnessed, just to be able to beat humans to a buzzer in a quiz show. Yet as Watson casts about for work, speed will be a crucial factor. Often it takes a company a day or two to make sense
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warned all along. Still, despite Watson’s virtuosity with the buzzer and its remarkable performance on Jeopardy clues, the machine’s education is far from complete. As this question-answering technology expands from its quiz show roots into the rest of our lives, engineers at IBM and elsewhere must sharpen its understanding of
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things. ‘What’s the name of the bassist in that band again?’ Or ‘What’s the movie where . . . ?’ Or ‘Who’s that guy on the TV show . . . he’s got the mustache?’ You always know who the guy to ask is, right?” I knew how he felt. And it hit me harder
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something else.” Notes [>] It was a September morning: Like Yahoo! and a handful of other businesses, the official name of the quiz show in this story ends in an exclamation point: Jeopardy! Initially, I tried using that spelling, but I thought it made reading harder. People see a word like this! and they
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, W. W. Norton & Co., 1997 Rasskin-Gutman, Diego, Chess Metaphors: Artificial Intelligence and the Human Mind, MIT Press, 2009 Richmond, Ray, This Is Jeopardy!: Celebrating America’s Favorite Quiz Show, Barnes & Noble Books, 2004 Storrs Hall, J., Beyond AI: Creating the Conscience of the Machine, Prometheus Books, 2007 Wright, Alex, Glut: Mastering Information
Thinking Machines: The Inside Story of Artificial Intelligence and Our Race to Build the Future
by Luke Dormehl · 10 Aug 2016 · 252pp · 74,167 words
, of course, to the existence of the fully fledged smart home. The House of the Future When I was growing up, it seemed that every TV show sooner or later featured an episode based around the idea of the ‘house of the future’. One of my favourite such episodes came from the
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shows, but it was enough to remind him of home. Two of Jennings’ favourite shows were the original Star Trek and the American general-knowledge game show Jeopardy! Aside from TV, Jennings gravitated towards computers. He was part of the first generation of children to have personal computers in the home. He still
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Jeopardy! champion with $37,201. The following episode he won again. And again. And again. As the weeks passed, the game show seemed to get easier for him. The margin between himself as the winner and the other
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get stronger, not more fatigued, the more rounds that went by. The public took notice, too. Ratings for Jeopardy! jumped 50 per cent compared to the previous year. In July 2004, the game show was America’s second most popular TV programme – losing out only to CBS’s crime investigation drama CSI. And
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never happened in my lifetime that Americans cared so much about who was on a quiz show.’ Jennings’ streak eventually came to an end following a record seventy-four consecutive shows. He was sad to lose, but Jeopardy! had done him wonders. He was smart, he was in-demand, and – thanks to his
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billions of unique words. For a computer, it means that it isn’t enough to simply build the quiz show version of Google. A regular search engine can answer around 30 per cent of Jeopardy! questions by looking for statistically likely answers based on keywords, but struggles when it comes to the remaining
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$1 million prize money. Although the human players put up a good showing, there was no doubt who was the game show’s new king. Jennings, in particular, was shocked. ‘It really stung to lose that badly,’ he admits. At the end of the game, the dejected
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lot of the jobs we currently do – but humans are far from irrelevant. After all, several years after Ken Jennings was roundly beaten by IBM’s Watson AI, we’re not yet letting our dinners grow cold to go and watch two AIs battle it out in trivia shows on TV. Despite
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Watson isn’t the end goal for IBM’s Artificial Intelligence. Like getting one of the world’s most powerful AIs to compete on a game show, at its root, Chef Watson is a metaphor – a proof of concept to show off the way Watson can use its enormous database of natural
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we blog on WordPress or LiveJournal, post a new status update on Twitter or Facebook, comment on the news using Contextly, choose a movie or TV show to watch on Netflix, send IMs, or simply make searches with Google, our digital identity is updated and curated. The result is an increasingly accurate
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intelligence. AI is now capable of beating humans at a wide range of specific domains, whether this be playing chess or answering questions on the TV show Jeopardy! As discussed in chapter five, this range of capabilities is expanding all the time, and may well rise to cover around half of all current
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AI has made. Some of these are showy illustrations, whether that be AI defeating world champions at chess or beating human brain-boxes at the quiz show Jeopardy! However, AI is also playing a key role in discovering new types of medicine, making information accessible and useful to people around the world, allowing
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, 83, 249, 254 invention 174, 178, 179, 182–5, 187–9 Jawbone 78–9, 92–3, 254 Jennings, Ken 133–6, 138–9, 162, 189 Jeopardy! (TV show) 135–9, 162, 189–90, 225, 254 Jobs, Steve 6–7, 32, 35, 108, 113, 181, 193, 231 Jochem, Todd 55–6 judges 153–4
Tomorrowland: Our Journey From Science Fiction to Science Fact
by Steven Kotler · 11 May 2015 · 294pp · 80,084 words
, 168 Janiger, Oscar, 169 Japan asteroid mining missions by, 146–47 nuclear power in, 117, 122–23 Jekot, Walter, 195, 196, 197 Jennings, Ken, 223 Jeopardy (TV show), 223 Jet Propulsion Laboratory, 149 John of God, 159 Johns Hopkins University, 162 Johnson, Brittany, 254 Johnson, Diane, 254 Johnson, Ronald, 254 Johnson Space Center
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, 3 Shell, 148 Siegel, Ronald, 167 Simak, Paul, 145 Single Mothers by Choice, 253 The Singularity Is Near (Kurzweil), 28 The Six Million Dollar Man (TV show), 11 ski-BASE, 130 Skycar, 100 skydiving, 35–36, 125–30 Skygrabber, 235 slavery, financial profitability of, 51–53 smallpox, 236–37 Small Scale Nuclear
Falter: Has the Human Game Begun to Play Itself Out?
by Bill McKibben · 15 Apr 2019
Iowa IQ scores Iran Iraq Ireland irrigation Italy IVF treatment Jackson, Jesse Jacobs, Jane Jacobson, Mark Jaeger, John Jakarta Japan Java Sea jellyfish Jenner, Kylie Jeopardy! (TV show) Jetnil-Kijiner, Kathy Jobs, Steve John Birch Society Johnson, Lyndon B. Journal of Mathematical Biology Journal of Physical Therapy Science Joy, Bill Joyce, James Kac
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, Nicole Shanghai sharks Shell Oil Shetland Islands Short, Marc Siberia Siberian Traps Sierra Nevada Silent Spring (Carson) Silicon Valley Silver, Lee Sinovation Ventures 60 Minutes (TV show) slowdown smallpox smartphones Smart Replay program Smith, Adam Snowden, Edward socialism social isolation social media social safety net Social Security Solar City solar power lobbying
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Union. See also Russia soy space colonization SpaceX Spanish flu SpinVox Spotify Stalin, Joseph Standing Rock protests Staples, Sam Starr, Ken Startling Stories Star Trek (TV show) state governments Steffen, Alex stem cells Sternberg, Sam St. Louis World’s Fair Stock, Gregory Stonehenge stress hormones submarine landslides suicide SunEdison Sun Microsystems Sweden
Upstream: The Quest to Solve Problems Before They Happen
by Dan Heath · 3 Mar 2020
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Homo Deus: A Brief History of Tomorrow
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Human Compatible: Artificial Intelligence and the Problem of Control
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Hit Refresh: The Quest to Rediscover Microsoft's Soul and Imagine a Better Future for Everyone
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The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do
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Data-Ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else
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What to Think About Machines That Think: Today's Leading Thinkers on the Age of Machine Intelligence
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A World Without Work: Technology, Automation, and How We Should Respond
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Fully Automated Luxury Communism
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