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The Economic Singularity: Artificial intelligence and the death of capitalism by Calum Chace

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[clxxix] We drive rather than use public transport because there is no appropriate public transport available, or sometimes because we prefer travelling in our own space. Self-driving cars could give us the best of both worlds, allowing us to read, sleep, watch video or chat as we travel. Finally, self-driving cars will enable us to use our environments more sensibly, especially our cities. Most cars spend 95% of their time parked.[clxxx] This is a waste of an expensive asset, and a waste of the land they occupy while sitting idle. We will consider later how far self-driving cars could alleviate this problem. To autonomy and beyond Self-driving cars, like our artificially intelligent digital assistants, are still waiting to receive their generic name. “Self-driving cars” is the name we are stuck with for the time being, but it is all clunk and no click. At the end of the 19th century it was becoming obvious that horseless carriages were here to stay, and needed a shorter name.

[clxxxi] Perhaps we will contract the phrase “autonomous vehicle”, and call them “autos”. Some people are going to hate self-driving cars, whatever they are called: petrol-heads like Jeremy Clarkson are unlikely to be enthusiastic about the objects of their devotion being replaced by machines with all the romance of a horizontal elevator. Some people are already describing a person who has been relegated from driver to chaperone as a “meat puppet”.[clxxxii] The US Department of Transport draws a distinction between (partly) autonomous cars and (fully) self-driving cars.[clxxxiii] The former still have steering wheels, and require a human driver to take over when they encounter a tricky situation. Self-driving cars, by contrast, are fully independent, and the steering wheel has been removed to save space. Autonomous cars will probably be merely a staging post en route to the completely self-driving variety.

They struggle with heavy rain or snow, they can get confused by potholes or debris obstructing the road, and they cannot always discern between a pedestrian and a policeman indicating for the vehicle to stop. A self-driving car which travelled 3,400 miles from San Francisco to New York in March 2015 did 99% of the driving itself, but that means it had to hand over to human occupants for 1% of the journey.[clxxxv] With many technology projects, resolving the last few issues is more difficult than the bulk of the project: edge cases are the acid test. Nevertheless, those edge cases are being tackled, and will be resolved. It is well-known that Google's self-driving cars have travelled well over a million miles in California without causing a significant accident, but what is less well-known is that the cars also drive millions of miles every day in simulators. Chris Urmson, head of the Google project, expects self-driving cars to be in general use by 2020.[clxxxvi] Sceptics point out that Google's self-driving cars depend on detailed maps.


pages: 215 words: 56,215

The Second Intelligent Species: How Humans Will Become as Irrelevant as Cockroaches by Marshall Brain

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Amazon Web Services, clean water, cloud computing, computer vision, en.wikipedia.org, full employment, income inequality, job automation, knowledge worker, mutually assured destruction, Occupy movement, Search for Extraterrestrial Intelligence, self-driving car, Stephen Hawking, working poor

The system also includes front- and rear-facing radar with a longer range, as well as an optical camera that is helpful in determining, among other things, whether a traffic light is red or green. In addition, self-driving cars drive on roads that have been pre-scanned. How can a self-driving car tell where the lanes are if it is night time, raining and the lane marking lines have faded? The car knows where the lanes are because of this pre-scanning. By putting all of this together, a self-driving car can do a great job on public roads. In fact, self-driving cars are far better at driving than human beings are. Self-driving cars never get distracted, never blink, never doze off, never talk on cell phones, never get drunk, etc. In addition, the self-driving car has a 360-degree view and multiple sensors that humans will never have. The only problem at this moment (2015) is that all of the equipment for a self-driving car is fairly expensive. As in, more expensive than the car itself.

Chapter 5 - How Computer Vision Systems will Destroy Jobs If you look back at the description of self-driving cars in the previous chapter, notice that computer vision does not really play a role. Current self-driving cars do not have two eyes on the roof or the hood looking out at the road and deciding what to do based on visual input. Self-driving cars do have an optical camera, but it plays a small role. For example, it helps the car decide if a traffic light at an intersection is red or green. This might seem odd to many people. When humans drive a car, visual input through our eyes is essential. Why don't self-driving cars do it the same way? Why doesn't a self-driving car use optical cameras and binocular vision in the same way that human beings use their eyes to sense the world? Instead of cameras, a self-driving car uses different sensors to detect the world around it.

Imagine a search engine like Google, but you can ask it any question you like in English and get a great answer immediately. There once was a day when the idea of a self-driving car operating on city streets seemed way off in the future. How could a computer possibly handle the vagaries of pedestrians, wildlife, drunk drivers, weather, etc.? Then one day in 2012 Google announced that it had a self-driving system that had logged 100,000 accident-free miles on normal roads in normal traffic. No longer were self-driving cars the stuff of SciFi novels – they were here driving around amongst us. One day, as far as the general public knew, there were zero real self-driving cars on normal roadways. The next day we discovered that Google had made self-driving cars a fait accompli. The exact same thing will happen with machine consciousness. One day it will seem impossible that a computer could think and talk and act just like a normal human being.


pages: 484 words: 104,873

Rise of the Robots: Technology and the Threat of a Jobless Future by Martin Ford

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See ibid. for Chris Urmson’s comments. 11. “The Self-Driving Car Logs More Miles on New Wheels” (Google corporate blog), August 7, 2012, http://googleblog.blogspot.co.uk/2012/08/the-self-driving-car-logs-more-miles-on.html. 12. As quoted in Heather Kelly, “Driverless Car Tech Gets Serious at CES,” CNN, January 9, 2014, http://www.cnn.com/2014/01/09/tech/innovation/self-driving-cars-ces/. 13. For US accident statistics, see http://www.census.gov/compendia/statab/2012/tables/12s1103.pdf; for global accident statistics, see http://www.who.int/gho/road_safety/mortality/en/. 14. Information on collision avoidance systems can be found at http://www.iihs.org/iihs/topics/t/crash-avoidance-technologies/qanda. 15. As quoted in Burkhard Bilger, “Auto Correct: Has the Self-Driving Car at Last Arrived?,” New Yorker, November 25, 2013, http://www.newyorker.com/reporting/2013/11/25/131125fa_fact_bilger?

Perhaps the most important thing to understand about a future in which your car is fully autonomous is that it probably won’t be your car. Most people who have given serious thought to the optimal role of self-driving cars seem to agree that, at least in densely populated areas, they are likely to be a shared resource. This has been Google’s intent from the start. As Google co-founder Sergey Brin explained to the New Yorker’s Burkhard Bilger, “[L]ook outside, and walk through parking lots and past multilane roads: the transportation infrastructure dominates. It’s a huge tax on the land.”15 Google hopes to smash the prevailing owner-operator model for the automobile. In the future, you’ll simply reach for your smart phone or other connected device and call for a self-driving car whenever you need it. Rather than spending 90 percent or more of their time parked, cars will see much higher utilization rates.

To avoid a feeling of being closed in, virtual windows could be mounted on the dividing walls; high resolution screens would display images captured by cameras mounted on the exterior of the car. By the time self-driving cars are in routine operation, the hardware to accomplish all this will be remarkably inexpensive. The vehicle would stop, a green light would flash on one of the doors, and you would get in and ride to your destination just as if you were traveling alone. You’d be sharing the vehicle, but riding in your own virtual commuter pod. Other vehicles might be designed to carry groups (or more sociable solo travelers), or perhaps the barriers could slide away upon mutual consent.* Then, again, the commuter pod might not need to be “virtual.” In May 2014, Google announced that the next phase of its research into self-driving cars would focus on the development of two-passenger electric vehicles with a top speed of 25 miles per hour and specifically geared toward urban environments.


pages: 265 words: 74,807

Our Robots, Ourselves: Robotics and the Myths of Autonomy by David A. Mindell

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Air France Flight 447, autonomous vehicles, Captain Sullenberger Hudson, Chris Urmson, en.wikipedia.org, Erik Brynjolfsson, fudge factor, index card, Mars Rover, ride hailing / ride sharing, Ronald Reagan, self-driving car, Silicon Valley, telepresence, telerobotics, trade route

“We want to make cars that are better than drivers”: Burkhard Bilger, “Auto Correct,” The New Yorker, November 25, 2013, http://www.newyorker.com/reporting/2013/11/25/131125fa_fact_bilger?currentPage=all. “without traffic accidents or congestion”: Sebastian Thrun, “Self-Driving Cars Can Save Lives, and Parking Spaces,” New York Times, December 5, 2011, http://www.nytimes.com/2011/12/06/science/sebastian-thrun-self-driving-cars-can-save-lives-and-parking-spaces.html. Sebastian Thrun, “What We’re Driving At,” Google official blog, http://googleblog.blogspot.com/2010/10/what-were-driving-at.html, accessed July 10, 2014. John Markoff, “A Trip in a Self-Driving Car Now Seems Routine,” Bits Blog, http://bits.blogs.nytimes.com/2014/05/13/a-trip-in-a-self-driving-car-now-seems-routine, accessed July 10, 2014. John Markoff, “Google Cars Drive Themselves, in Traffic,” New York Times, October 9, 2010, http://www.nytimes.com/2010/10/10/science/10google.html.

John Markoff, “Police, Pedestrians and the Social Ballet of Merging: The Real Challenges for Self-Driving Cars,” Bits Blog, http://bits.blogs.nytimes.com/2014/05/29/police-bicyclists-and-pedestrians-the-real-challenges-for-self-driving-cars/, accessed July 10, 2014. We know that driverless cars will be susceptible: John Markoff, “Collision in the Making Between Self-Driving Cars and How the World Works,” New York Times, January 23, 2012, http://www.nytimes.com/2012/01/24/technology/googles-autonomous-vehicles-draw-skepticism-at-legal-symposium.html. Will Knight, “Proceed with Caution toward the Self-Driving Car,” MIT Technology Review, April 16, 2013, http://www.technologyreview.com/review/513531/proceed-with-caution-toward-the-self-driving-car/. M. L. Cummings and Jason Ryan, “Shared Authority Concerns in Automated Driving Applications,” Journal of Ergonomics, S3:001. doi:10.4172/2165-7556.S3-001 how will they rush into the loop quickly enough: Bianca Bosker, “No One Understands the Scariest, Most Dangerous Part of a Self-Driving Car: Us,” Huffington Post, September 16, 2013, accessed July 10, 2014.

., “These Are the Secrets Google Wanted to Keep about Its Self-Driving Cars,” Quartz, http://qz.com/252817/these-are-the-secrets-google-wanted-to-keep-about-its-self-driving-cars/, accessed November 18, 2014. Mark Harris, “How Much Training Do You Need to Be a Robocar Test Driver? It Depends On Whom You Work For,” IEEE Spectrum Cars That Think, February 24, 2015, http://spectrum.ieee.org/cars-that-think/transportation/human-factors/how-much-training-do-you-need-to-be-a-robocar-test-driver-it-depends-on-whom-you-work-for. He put a video camera on the dashboard of his car: John Leonard, “Conversations on Autonomy,” presentation, MIT, March 13, 2014. John Markoff, “Police, Pedestrians and the Social Ballet of Merging: The Real Challenges for Self-Driving Cars,” Bits Blog, http://bits.blogs.nytimes.com/2014/05/29/police-bicyclists-and-pedestrians-the-real-challenges-for-self-driving-cars/, accessed July 10, 2014.


pages: 144 words: 43,356

Surviving AI: The Promise and Peril of Artificial Intelligence by Calum Chace

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Some 30 US cities will be experimenting with self-driving cars by the end of 2016, for instance. (22) There are 3.5 million truck drivers in the US alone, (23) 650,000 bus drivers (24) and 230,000 taxi drivers. (25) There are numerous hurdles to be overcome before all these jobs become vulnerable. At the time of writing, Google’s self-driving cars have travelled a million miles without causing an accident. As we saw in chapter 1 they are not perfect, but none of the challenges facing them look insurmountable: Google was recently awarded a patent for a system which can tell whether a cyclist is signalling a turn. Politicians worldwide have understood that they need to agree and implement policies and procedures to cope with the arrival of this technology. (The impetus to introduce self-driving cars is enormous. Around 1.2m lives are lost on the world’s roads each year and most of these deaths are due to driver error.

Around 1.2m lives are lost on the world’s roads each year and most of these deaths are due to driver error. Self-driving cars don’t get tired, distracted or drunk. Accidents are also a major cause of traffic congestion, so average journey times would be significantly reduced if most cars were self-driving. Car-sharing is expected to become more common, and parking should become much easier. There are always unforeseen consequences, of course. In 2014, Los Angeles generated $160m from parking violations, much of which could have to come from somewhere else in future.) The second wave of automation forecast by the Oxford Martin School report will affect jobs in the heartland of the middle and upper-middle class: professional occupations like medicine and the law, managerial jobs, and even in the arts.

from intelligent algorithms which match adverts with readers and viewers, and it is busily looking for more and more new ways to exploit its world-leading expertise in AI in as many industries as it can manage. The huge collection of servers which comprise the distributed computing platform for the AI which drives the company’s numerous services is often called the Google Brain. Sometimes Google enters a new industry using home-grown talent, as with its famous self-driving cars, and with Calico, which is looking to apply Big Data to healthcare. Other times it acquires companies with the expertise not already found inside Google, or “acqui-hires” their key talent. Its rate of acquisition reached one company a week in 2010, and by the end of 2014 it had acquired 170 of them. Significant industries where Google has engaged by acquiring include smartphones (Android, Motorola), voice over IP telephony (GrandCentral, Phonetic Arts), intelligent house management (Nest Labs, Dropcam and Revolv), robotics (eight robot manufacturers acquired in 2013 alone), publishing (reCAPTCHA and eBook Technologies), banking (TxVia), music (Songza), and drones (Titan Aerospace).


pages: 308 words: 84,713

The Glass Cage: Automation and Us by Nicholas Carr

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Chapter Seven: AUTOMATION FOR THE PEOPLE 1.Kevin Kelly, “Better than Human: Why Robots Will—and Must—Take Our Jobs,” Wired, January 2013. 2.Jay Yarow, “Human Driver Crashes Google’s Self Driving Car,” Business Insider, August 5, 2011, businessinsider.com/googles-self-driving-cars-get-in-their-first-accident-2011-8. 3.Andy Kessler, “Professors Are About to Get an Online Education,” Wall Street Journal, June 3, 2013. 4.Vinod Khosla, “Do We Need Doctors or Algorithms?,” TechCrunch, January 10, 2012, techcrunch.com/2012/01/10/doctors-or-algorithms. 5.Gerald Traufetter, “The Computer vs. the Captain: Will Increasing Automation Make Jets Less Safe?,” Spiegel Online, July 31, 2009, spiegel.de/international/world/the-computer-vs-the-captain-will-increasing-automation-make-jets-less-safe-a-639298.html. 6.See Adam Fisher, “Inside Google’s Quest to Popularize Self-Driving Cars,” Popular Science, October 2013. 7.Tosha B. Weeterneck et al., “Factors Contributing to an Increase in Duplicate Medication Order Errors after CPOE Implementation,” Journal of the American Medical Informatics Association 18 (2011): 774–782. 8.Sergey V.

NOTES Introduction: ALERT FOR OPERATORS 1.Federal Aviation Administration, SAFO 13002, January 4, 2013, faa.gov/other_visit/aviation_industry/airline_operators/airline_safety/safo/all_safos/media/2013/SAFO13002.pdf. Chapter One: PASSENGERS 1.Sebastian Thrun, “What We’re Driving At,” Google Official Blog, October 9, 2010, googleblog.blogspot.com/2010/10/what-were-driving-at.html. See also Tom Vanderbilt, “Let the Robot Drive: The Autonomous Car of the Future Is Here,” Wired, February 2012. 2.Daniel DeBolt, “Google’s Self-Driving Car in Five-Car Crash,” Mountain View Voice, August 8, 2011. 3.Richard Waters and Henry Foy, “Tesla Moves Ahead of Google in Race to Build Self-Driving Cars,” Financial Times, September 17, 2013, ft.com/intl/cms/s/0/70d26288-1faf-11e3-8861-00144feab7de.html. 4.Frank Levy and Richard J. Murnane, The New Division of Labor: How Computers Are Creating the Next Job Market (Princeton: Princeton University Press, 2004), 20. 5.Tom A. Schweizer et al., “Brain Activity during Driving with Distraction: An Immersive fMRI Study,” Frontiers in Human Neuroscience, February 28, 2013, frontiersin.org/Human_Neuroscience/10.3389/fnhum.2013.00053/full. 6.N.

Outfitted with laser range-finders, radar and sonar transmitters, motion detectors, video cameras, and GPS receivers, the car can sense its surroundings in minute detail. It can see where it’s going. And by processing all the streams of incoming information instantaneously—in “real time”—its onboard computers are able to work the accelerator, the steering wheel, and the brakes with the speed and sensitivity required to drive on actual roads and respond fluidly to the unexpected events that drivers always encounter. Google’s fleet of self-driving cars has now racked up close to a million miles, and the vehicles have caused just one serious accident. That was a five-car pileup near the company’s Silicon Valley headquarters in 2011, and it doesn’t really count. It happened, as Google was quick to announce, “while a person was manually driving the car.”2 Autonomous automobiles have a ways to go before they start chauffeuring us to work or ferrying our kids to soccer games.

Big Data at Work: Dispelling the Myths, Uncovering the Opportunities by Thomas H. Davenport

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Automated Insights, autonomous vehicles, bioinformatics, business intelligence, business process, call centre, chief data officer, cloud computing, data acquisition, Edward Snowden, Erik Brynjolfsson, intermodal, Internet of things, Jeff Bezos, knowledge worker, Mark Zuckerberg, move fast and break things, Narrative Science, natural language processing, Netflix Prize, New Journalism, recommendation engine, RFID, self-driving car, sentiment analysis, Silicon Valley, smart grid, smart meter, social graph, sorting algorithm, statistical model, Tesla Model S, text mining

The scenarios will require a number of smart people to work for several years to make them possible. In some cases, they will require regulatory change (e.g., to allow self-driving cars on the nation’s highways). It’s not entirely clear what the viable business model for each of these innovations is—how companies can make money with them. It’s also not clear that customers will want these innovations—­particularly Chapter_02.indd 41 03/12/13 11:42 AM 42  big data @ work those like the pet store video cameras that pose a risk to human and pet ­privacy. However, it seems likely that some organizations will pull them off, and that they will make those organizations very successful. Just as Google, for example, decided to make the self-driving car a reality, there are other organizations that will succeed with integrating it into a comprehensive travel management capability.

Without any actions on Lynda’s part, she receives a proposed itinerary with the f­ ollowing components: • A flight on her preferred airline, with a frequent flyer upgrade already arranged and her preferred aisle seat reserved • A hotel reservation for all the nights of the conference • A self-driving rental car reservation at the airport (because the conference hotel is forty miles away, and the travel management application has compared the cost at prevailing rates of taxi, limo, and rental car for that distance) • A reservation at the best Italian restaurant in the conference city—Lynda’s favorite dining option—for the “on your own” night of the conference, with three suggestions (and three alternate suggestions) for dining companions who are valued members of her social network and who will also be attending the conference; Lynda needs only to touch her tablet screen once to invite them Chapter_02.indd 33 03/12/13 11:42 AM 34  big data @ work Lynda’s self-driving car delivers her to the conference hotel with no problems; the travel management system had downloaded her destination address, preferred air-conditioning temperature, and favorite satellite music station to the car. Lynda’s only complaint about selfdriving rental cars is that antiquated regulations force her to sit in the driver’s seat, which limits her tablet access. She also resents the laws that prevent her from watching movies and TV while the car drives her; soon, she expects, these would be relaxed.

Her notes suggest that insurance will never be the same after massive data, and neither will the experience of traveling to learn about the coming changes. (Note to skeptics: Although many of these automated travel features are not yet available, travel management experts I interviewed suggested that they would be plausible in the fairly near future. And we Chapter_02.indd 34 03/12/13 11:42 AM How Big Data Will Change Your Job, Company, and Industry   35 know that the self-driving car already exists—described by Google as a big data project—and will probably be incorporated into the transportation system in some fashion.) A Big Data Scenario for Energy Management David Byron is a corporate facilities and energy manager for ­Bathworks, a large US plumbing fixtures manufacturing company. He is in charge of facilities and energy management for Bathworks’s twenty office campuses and facilities around the country.


pages: 23 words: 5,264

Designing Great Data Products by Jeremy Howard, Mike Loukides, Margit Zwemer

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AltaVista, Filter Bubble, PageRank, pattern recognition, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, text mining

We need to define the models we will need, such as physics models to predict the effects of steering, braking and acceleration, and pattern recognition algorithms to interpret data from the road signs. As one engineer on the Google self-driving car project put it in a recent Wired article, “We’re analyzing and predicting the world 20 times a second.” What gets lost in the quote is what happens as a result of that prediction. The vehicle needs to use a simulator to examine the results of the possible actions it could take. If it turns left now, will it hit that pedestrian? If it makes a right turn at 55 mph in these weather conditions, will it skid off the road? Merely predicting what will happen isn’t good enough. The self-driving car needs to take the next step: after simulating all the possibilities, it must optimize the results of the simulation to pick the best combination of acceleration and braking, steering and signaling, to get us safely to Santa Clara.

We call it the Drivetrain Approach, inspired by the emerging field of self-driving vehicles. Engineers start by defining a clear objective: They want a car to drive safely from point A to point B without human intervention. Great predictive modeling is an important part of the solution, but it no longer stands on its own; as products become more sophisticated, it disappears into the plumbing. Someone using Google’s self-driving car is completely unaware of the hundreds (if not thousands) of models and the petabytes of data that make it work. But as data scientists build increasingly sophisticated products, they need a systematic design approach. We don’t claim that the Drivetrain Approach is the best or only method; our goal is to start a dialog within the data science and business communities to advance our collective vision.


pages: 268 words: 75,850

The Formula: How Algorithms Solve All Our Problems-And Create More by Luke Dormehl

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3D printing, algorithmic trading, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, big data - Walmart - Pop Tarts, call centre, Cass Sunstein, Clayton Christensen, computer age, death of newspapers, deferred acceptance, Edward Lorenz: Chaos theory, Erik Brynjolfsson, Filter Bubble, Flash crash, Florence Nightingale: pie chart, Frank Levy and Richard Murnane: The New Division of Labor, Google Earth, Google Glasses, High speed trading, Internet Archive, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, Kevin Kelly, Kodak vs Instagram, Marshall McLuhan, means of production, Nate Silver, natural language processing, Netflix Prize, pattern recognition, price discrimination, recommendation engine, Richard Thaler, Rosa Parks, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, Slavoj Žižek, social graph, speech recognition, Steve Jobs, Steven Levy, Steven Pinker, Stewart Brand, the scientific method, The Signal and the Noise by Nate Silver, upwardly mobile, Wall-E, Watson beat the top human players on Jeopardy!, Y Combinator

As a straightforward example, what would happen if the passenger in the car needed to reach a hospital as a matter of urgency—and that this meant breaking the speed limit on a largely empty stretch of road? It is one thing if the driver/passenger was ticketed at a later date thanks to the car’s built-in speed tracker. But what if the self-driving car, bound by fixed Ambient Laws, refused to break the regulated speed limit under any conditions? You might not even have to wait for the arrival of self-driving cars for such a scenario to become reality. In 2013, British newspapers reported on road-safety measures being drawn up by EC officials in Brussels that would see all new cars fitted with “Intelligent Speed Adaptation” measures similar to those already installed in many heavy-goods vehicles and buses. Using satellite feeds, or cameras designed to automatically detect and read road signs, vehicles could be forced to conform to speed limits.

“Do Robots Dream of Electric Laws: An Experiment in the Law as Algorithm,” March 29, 2013. rumint.org/gregconti/publications/201303_AlgoLaw.pdf. 43 Reiser, Stanley. Medicine and the Reign of Technology (Cambridge, UK; New York: Cambridge University Press, 1978). 44 Gusfield, Joseph. The Culture of Public Problems: Drinking-Driving and the Symbolic Order (Chicago: University of Chicago Press, 1981). 45 “Google’s Self-Driving Cars Are Safer Than Human Drivers.” Macworld, August 8, 2012. macworld.com.au/news/googles-self-driving-cars-are-safer-than-human-drivers-67261/#.Uh2-DLyE5eo. 46 Owen, Glen. “Britain Fights EU’s ‘Big Brother’ Bid to Fit Every Car with Speed Limiter.” Daily Mail, August 31, 2013. dailymail.co.uk/news/article-2408012/Britain-fights-EUs-Big-Brother-bid-fit-car-speed-limiter.html. 47 Moskvitch, Katia, and Richard Fisher. “Penal Code.” New Scientist, September 7, 2013. 48 Hook, P.

Both at law and in the research “laboratory,” the technology of the blood level sample and the Breathalyzer meant a definitive and easily validated measure of the amount of alcohol in the blood and, consequently, an accentuated law enforcement and a higher expectancy of convictions.44 In other words, the arrival of the Breathalyzer turned a person’s ability to drive after several drinks from abstract “standard” into concrete “rule” in the eyes of the law. This issue will become even more pressing as the rise of Ambient Law continues—with technologies not only having the power to regulate behavior but to dictate it as well, sometimes by barring particular courses of action from being taken. Several years ago, Google announced that it was working on a fleet of self-driving cars, in which algorithms would be used for everything from planning the most efficient journey routes, to changing lanes on the motorway by determining the smoothest path combining trajectory, speed and safe distance from nearby obstacles. At the time of writing, these cars have completed upward of 300,000 miles of test drives in a wide range of conditions, without any reported accidents—leading to the suggestion that a person is safer in a car driven by an algorithm than they are in one driven by a human.45 Since cars driven by an algorithm already conform to a series of preprogrammed rules, it is understandable why specific laws would become just more to add to the collection.


pages: 413 words: 119,587

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

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A Declaration of the Independence of Cyberspace, AI winter, airport security, Apple II, artificial general intelligence, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, Bill Duvall, bioinformatics, Brewster Kahle, Burning Man, call centre, cellular automata, Chris Urmson, Claude Shannon: information theory, Clayton Christensen, clean water, cloud computing, collective bargaining, computer age, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data acquisition, Dean Kamen, deskilling, don't be evil, Douglas Engelbart, Douglas Hofstadter, Dynabook, Edward Snowden, Elon Musk, Erik Brynjolfsson, factory automation, From Mathematics to the Technologies of Life and Death, future of work, Galaxy Zoo, Google Glasses, Google X / Alphabet X, Grace Hopper, Gödel, Escher, Bach, Hacker Ethic, haute couture, hive mind, hypertext link, indoor plumbing, industrial robot, information retrieval, Internet Archive, Internet of things, invention of the wheel, Jacques de Vaucanson, Jaron Lanier, Jeff Bezos, job automation, John Conway, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, knowledge worker, Kodak vs Instagram, labor-force participation, loose coupling, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, Mother of all demos, natural language processing, new economy, Norbert Wiener, PageRank, pattern recognition, pre–internet, RAND corporation, Ray Kurzweil, Richard Stallman, Robert Gordon, Rodney Brooks, Sand Hill Road, Second Machine Age, self-driving car, semantic web, shareholder value, side project, Silicon Valley, Silicon Valley startup, Singularitarianism, skunkworks, Skype, social software, speech recognition, stealth mode startup, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, strong AI, superintelligent machines, technological singularity, Ted Nelson, telemarketer, telepresence, telepresence robot, Tenerife airport disaster, The Coming Technological Singularity, the medium is the message, Thorstein Veblen, Turing test, Vannevar Bush, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, William Shockley: the traitorous eight

Hagerty, “A Roboticist’s Trip from Mines to the Moon,” Wall Street Journal, July 2, 2011, http://www.wsj.com/articles/SB10001424052702304569504576405671616928518. 4.John Markoff, “The Creature That Lives in Pittsburgh,” New York Times, April 21, 1991, http://www.nytimes.com/1991/04/21/business/the-creature-that-lives-in-pittsburgh.html. 5.John Markoff, “Google Cars Drive Themselves, in Traffic,” New York Times, October 9, 2010, http://www.nytimes.com/2010/10/10/science/10google.html?pagewanted=all. 6.“Electronic Stability Control Systems for Heavy Vehicles,” National Highway Traffic Safety Administration, 2012, http://www.nhtsa.gov/Laws+&+Regulations/Electronic+Stability+Control+(ESC). 7.John Markoff, “Police, Pedestrians and the Social Ballet of Merging: The Real Challenges for Self-Driving Cars,” New York Times, May 29, 2014, http://bits.blogs.nytimes.com/2014/05/29/police-bicyclists-and-pedestrians-the-real-challenges-for-self-driving-cars/?_php=true&_type=blogs&_r=0. 8.Lawrence D. Burns, William C. Jordan, and Bonnie A. Scarborough, “Transforming Personal Mobility,” The Earth Institute, Columbia University, January 27, 2013, http://sustainablemo bility.ei.columbia.edu/files/2012/12/Transforming-Personal-Mobility-Jan-27-20132.pdf. 9.William Grimes, “Philippa Foot, Renowned Philosopher, Dies at 90,” New York Times, October 9, 2010, http://www.nytimes.com/2010/10/10/us/10foot.html. 10.

The philosophers convinced him that there were real limits to the capabilities of intelligent machines. Winograd’s conversion coincided with the collapse of a nascent artificial intelligence industry known as the “AI Winter.” Several decades later, Winograd, who was faculty advisor for Google cofounder Larry Page at Stanford, famously counseled the young graduate student to focus on the problem of Web search rather than self-driving cars. In the intervening decades Winograd had become acutely aware of the importance of the designer’s point of view. The separation of the fields of AI and human-computer interaction, or HCI, is partly a question of approach, but it’s also an ethical stance about designing humans either into or out of the systems we create. More recently at Stanford Winograd helped create an academic program focusing on “Liberation Technologies,” which studies the construction of computerized systems based on human-centered values.

Vehicles tipped over, drove in circles, and ignominiously knocked down fences. Even the most successful entrant had gotten stuck in the dust just seven miles from the starting line in a 120-mile race, with one wheel spinning helplessly as it teetered off the edge of the road. When the dust settled, a reporter flying overhead in a light plane saw brightly colored vehicles scattered motionless over the desert floor. At the time it seemed obvious that self-driving cars were still years away, and Tether was criticized for organizing a publicity stunt. Now, just a little more than a year later, Thrun was behind the wheel in a second-generation robot contestant. It felt like the future had arrived sooner than expected. It took only a dozen miles, however, to realize that techno-enthusiasm is frequently premature. Stanley crested a rise in the desert and plunged smartly into a swale.


pages: 237 words: 64,411

Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence by Jerry Kaplan

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Affordable Care Act / Obamacare, Amazon Web Services, asset allocation, autonomous vehicles, bank run, bitcoin, Brian Krebs, buy low sell high, Capital in the Twenty-First Century by Thomas Piketty, combinatorial explosion, computer vision, corporate governance, crowdsourcing, en.wikipedia.org, Erik Brynjolfsson, estate planning, Flash crash, Gini coefficient, Goldman Sachs: Vampire Squid, haute couture, hiring and firing, income inequality, index card, industrial robot, invention of agriculture, Jaron Lanier, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, Loebner Prize, Mark Zuckerberg, mortgage debt, natural language processing, Own Your Own Home, pattern recognition, Satoshi Nakamoto, school choice, Schrödinger's Cat, Second Machine Age, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, Skype, software as a service, The Chicago School, Turing test, Watson beat the top human players on Jeopardy!, winner-take-all economy, women in the workforce, working poor, Works Progress Administration

The split-second decisions these contraptions will have to make pose ethical questions that have bedeviled deep thinkers for millennia. Imagine that my car is crossing a narrow bridge and a school bus full of children suddenly enters from the other side. The bridge can’t accommodate both vehicles, so to avoid destroying both it’s clear that one of them will have to go over the edge. Would I buy a car that is willing to sacrifice my life to save the children? Will the aggressiveness of a self-driving car become a selling point like gas mileage? Moral quandaries like this, no longer confined to the musings of philosophers, will urgently arrive on our courthouse steps. The emergence of synthetic intellects and forged laborers that act as our individual agents will raise a raft of practical conundrums. What should “one per customer” mean when a robot is the customer, and I own a whole fleet of them?

Looking further to the future while staying rooted in today’s technologies, imagine the fire extinguishers, shrunk to the size of insects, digging themselves into miniature foxholes awaiting a command to spring into action. When summoned, they might self-assemble to form a protective dome or blanket around homes, infrastructure, even individual people. Research on concepts like this is active enough to have earned the name “swarm robotics.” Even self-driving cars aren’t going to be nearly as self-contained or autonomous as they appear. Standards for vehicles and roadside sensors to share information wirelessly, essentially becoming one interconnected system of eyes and ears, are close to completion. The U.S. Department of Transportation, among other institutions, is developing so-called V2V (vehicle to vehicle) communications protocols by piggybacking on the Federal Communications Commission’s allocation of radio spectrum for dedicated short-range communications (DSRC) specifically intended for automotive applications.

This same principle, appropriately generalized, can apply to just about any circumstance where electronic agents compete with humans—not just to lines. Do the participants differ in their ability, or the cost they pay, to access the resource? This question needs to be answered on a case-by-case basis, but the concept is clear. For instance, suppose I send my robot to move my car every two hours to avoid a parking ticket, or instruct my self-driving car to repark itself. Will we judge that cost sufficiently equivalent to doing it myself to consider it fair to those without a robotic driver or car to spare? What if it costs me as much to send the robot as it would for you to send your human administrative assistant? I contend that the brawl for the right to display an ad to you seems a lot fairer than having HFT programs participate in the securities markets.


pages: 339 words: 88,732

The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson, Andrew McAfee

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2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, access to a mobile phone, additive manufacturing, Airbnb, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, American Society of Civil Engineers: Report Card, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, barriers to entry, Baxter: Rethink Robotics, British Empire, business intelligence, business process, call centre, clean water, combinatorial explosion, computer age, computer vision, congestion charging, corporate governance, crowdsourcing, David Ricardo: comparative advantage, employer provided health coverage, en.wikipedia.org, Erik Brynjolfsson, factory automation, falling living standards, Filter Bubble, first square of the chessboard / second half of the chessboard, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, full employment, game design, global village, happiness index / gross national happiness, illegal immigration, immigration reform, income inequality, income per capita, indoor plumbing, industrial robot, informal economy, inventory management, James Watt: steam engine, Jeff Bezos, jimmy wales, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Khan Academy, knowledge worker, Kodak vs Instagram, law of one price, low skilled workers, Lyft, Mahatma Gandhi, manufacturing employment, Mark Zuckerberg, Mars Rover, means of production, Narrative Science, Nate Silver, natural language processing, Network effects, new economy, New Urbanism, Nicholas Carr, Occupy movement, oil shale / tar sands, oil shock, pattern recognition, payday loans, price stability, Productivity paradox, profit maximization, Ralph Nader, Ray Kurzweil, recommendation engine, Report Card for America’s Infrastructure, Robert Gordon, Rodney Brooks, Ronald Reagan, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Simon Kuznets, six sigma, Skype, software patent, sovereign wealth fund, speech recognition, statistical model, Steve Jobs, Steven Pinker, Stuxnet, supply-chain management, TaskRabbit, technological singularity, telepresence, The Bell Curve by Richard Herrnstein and Charles Murray, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Tyler Cowen: Great Stagnation, Vernor Vinge, Watson beat the top human players on Jeopardy!, winner-take-all economy, Y2K

But our experience on the highway convinced us that it’s a viable approach for the large and growing set of everyday driving situations. Self-driving cars went from being the stuff of science fiction to on-the-road reality in a few short years. Cutting-edge research explaining why they were not coming anytime soon was outpaced by cutting-edge science and engineering that brought them into existence, again in the space of a few short years. This science and engineering accelerated rapidly, going from a debacle to a triumph in a little more than half a decade. Improvement in autonomous vehicles reminds us of Hemingway’s quote about how a man goes broke: “Gradually and then suddenly.”5 And self-driving cars are not an anomaly; they’re part of a broad, fascinating pattern. Progress on some of the oldest and toughest challenges associated with computers, robots, and other digital gear was gradual for a long time.

Murnane, The New Division of Labor: How Computers Are Creating the Next Job Market (Princeton, NJ: Princeton University Press, 2004). 2. Michael Polanyi, The Tacit Dimension (Chicago, IL: University of Chicago Press, 2009), p. 4. 3. Joseph Hooper, “DARPA’s Debacle in the Desert,” Popular Science, June 4, 2004, http://www.popsci.com/scitech/article/2004-06/darpa-grand-challenge-2004darpas-debacle-desert. 4. Mary Beth Griggs, “4 Questions About Google’s Self-Driving Car Crash,” Popular Mechanics, August 11, 2011, http://www.popularmechanics.com/cars/news/indus try/4-questions-about-googles-self-driving-car-crash; John Markoff, “Google Cars Drive Themselves, in Traffic,” New York Times, October 9, 2010, http://www.nytimes.com/2010/10/10/science/10google.html. 5. Ernest Hemingway, The Sun Also Rises (New York: HarperCollins, 2012), p. 72. 6. Levy and Murnane, The New Division of Labor, p. 29. 7. “Siri Is Actually Incredibly Useful Now,” Gizmodo, accessed August 4, 2013, http://gizmodo.com/5917461/siri-is-better-now. 8.

According to an initial specification supplied by the agency, they will have to be able to drive a utility vehicle, remove debris blocking an entryway, climb a ladder, close a valve, and replace a pump.34 These seem like impossible requirements, but we’ve been assured by highly knowledgeable colleagues—ones competing in the DRC, in fact—that they’ll be met. Many saw the 2004 Grand Challenge as instrumental in accelerating progress with autonomous vehicles. There’s an excellent chance that the DRC will be similarly important at getting us past Moravec’s paradox. More Evidence That We’re at an Inflection Point Self-driving cars, Jeopardy! champion supercomputers, and a variety of useful robots have all appeared just in the past few years. And these innovations are not just lab demos; they’re showing off their skills and abilities in the messy real world. They contribute to the impression that we’re at an inflection point—a bend in the curve where many technologies that used to be found only in science fiction are becoming everyday reality.


pages: 349 words: 95,972

Messy: The Power of Disorder to Transform Our Lives by Tim Harford

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affirmative action, Air France Flight 447, Airbnb, airport security, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, Atul Gawande, autonomous vehicles, banking crisis, Barry Marshall: ulcers, Basel III, Berlin Wall, British Empire, Broken windows theory, call centre, Cass Sunstein, Chris Urmson, cloud computing, collateralized debt obligation, crowdsourcing, deindustrialization, Donald Trump, Erdős number, experimental subject, Ferguson, Missouri, Filter Bubble, Frank Gehry, game design, global supply chain, Googley, Guggenheim Bilbao, high net worth, Inbox Zero, income inequality, Internet of things, Jane Jacobs, Jeff Bezos, Loebner Prize, Louis Pasteur, Mark Zuckerberg, Menlo Park, Merlin Mann, microbiome, out of africa, Paul Erdős, Richard Thaler, Rosa Parks, self-driving car, side project, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, Steven Levy, Stewart Brand, telemarketer, the built environment, The Death and Life of Great American Cities, Turing test, urban decay

Sarah O’Connor, “Leave the Robotic Jobs to Robots and Improve Humans’ Lives,” Financial Times, January 5, 2016, https://next.ft.com/content/da557b66-b09c-11e5-993b-c425a3d2b65a. 18. Klein, Streetlights and Shadows, pp. 123–124. 19. “Will Self-Driving Cars Spell the End of the American Road Trip?” 99% Invisible (podcast), available on The Eye: Slate’s Design Blog, July 3, 2015, http://www.slate.com/blogs/the_eye/2015/07/03/self_driving_cars_and_the_paradox_of_automation_from_99_invisible.html. Raj Rajkumar’s comments below are from the same podcast. 20. Jack Stewart, “What May Be Self-Driving Cars’ Biggest Problem,” BBC Future, August 25, 2015, http://www.bbc.com/future/story/20150824-what-may-be-self-driving-cars-biggest-problem. 21. Cited in Langewiesche, “The Human Factor.” 22. M. L. Cummings, C. Mastracchio, K. M. Thornburg, and A. Mkrtchyan, “Boredom and Distraction in Multiple Unmanned Vehicle Supervisory Control,” Interacting with Computers 25, no. 1 (2013), pp. 34–47, http://hdl.handle.net/1721.1/86942. 23.

Once the veterans retire, the human expertise to intuit when the computer has screwed up will be lost forever.18 • • • We’ve seen the problems with GPS systems and with autopilot. Put the two ideas together, and you get the self-driving car. Chris Urmson, who runs Google’s self-driving car program, hopes that the cars will soon be so widely available that his sons will never need to have a driving license. (His oldest son will be sixteen in 2020—Urmson is in a hurry.) There’s a revealing implication in that target: that unlike a plane’s autopilot, a self-driving car will never need to cede control to a human being. True to form, Google’s autonomous vehicles have no steering wheel, though one hopes there will be some way to jump out if they start heading for the ocean.19 Not everyone thinks it is plausible for cars to be completely autonomous—or, at least, not soon enough for Urmson junior.


pages: 364 words: 99,897

The Industries of the Future by Alec Ross

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23andMe, 3D printing, Airbnb, algorithmic trading, AltaVista, Anne Wojcicki, autonomous vehicles, banking crisis, barriers to entry, Bernie Madoff, bioinformatics, bitcoin, blockchain, Brian Krebs, British Empire, business intelligence, call centre, carbon footprint, cloud computing, collaborative consumption, connected car, corporate governance, Credit Default Swap, cryptocurrency, David Brooks, disintermediation, Dissolution of the Soviet Union, distributed ledger, Edward Glaeser, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, fiat currency, future of work, global supply chain, Google X / Alphabet X, industrial robot, Internet of things, invention of the printing press, Jaron Lanier, Jeff Bezos, job automation, knowledge economy, knowledge worker, litecoin, M-Pesa, Mark Zuckerberg, Mikhail Gorbachev, mobile money, money: store of value / unit of account / medium of exchange, new economy, offshore financial centre, open economy, peer-to-peer lending, personalized medicine, Peter Thiel, precision agriculture, pre–internet, RAND corporation, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Satoshi Nakamoto, self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, social graph, software as a service, special economic zone, supply-chain management, supply-chain management software, technoutopianism, underbanked, Vernor Vinge, Watson beat the top human players on Jeopardy!, women in the workforce, Y Combinator, young professional

Worldwide, those statistics are enormous: “Annual Global Road Crash Statistics,” Association for Safe International Road Travel, http://asirt.org/Initiatives/Informing-Road-Users/Road-Safety-Facts/Road-Crash-Statistics. Accidents are caused by the four Ds: Bilger, “Auto-Correct.” There remain many gaps: Lee Gomes, “Hidden Obstacles for Google’s Self-Driving Cars,” MIT Technology Review, August 28, 2014, http://www.technologyreview.com/news/530276/hidden-obstacles-for-googles-self-driving-cars/. Uber has already built: John Biggs, “Uber Opening Robotics Research Facility in Pittsburgh to Build Self-Driving Cars,” TechCrunch, February 2, 2015, http://techcrunch.com/2015/02/02/uber-opening-robotics-research-facility-in-pittsburgh-to-build-self-driving-cars/. At last count there were 162,037 active drivers: Emily Badger, “Now We Know How Many Drivers Uber Has—and Have a Better Idea of What They’re Making,” Washington Post, January 22, 2015, http://www.washingtonpost.com/blogs/wonkblog/wp/2015/01/22/now-we-know-many-drivers-uber-has-and-how-much-money-theyre-making%E2%80%8B/.

And it turns out that the development of a driverless car is deeply personal. As Sebastian Thrun explained in a TED talk, his best friend was killed in a car accident, spurring his personal crusade to innovate the car accident out of existence: “I decided I’d dedicate my life to saving 1 million people every year.” Google has hired the former deputy director of the National Highway Traffic Safety Administration, Ron Medford, to be its director of safety for self-driving cars. Medford explained that Americans collectively drive approximately 3 trillion miles per year, and more than 30,000 people die in the process. Worldwide, those statistics are enormous; approximately 1.3 million people die every year in car crashes. Google, of course, also has an interest in allowing consumers to have more time on their hands—quite literally, to have their hands free. The average American spends 18.5 hours a week driving, and Europeans spend about half that.

More to the point, even if passengers end up preferring robot drivers to humans, what happens to the human taxi driver who loses his job because service industry jobs are at risk in the next wave of innovation as never before? This isn’t just about taxi drivers; the delivery driver may be replaced by Amazon’s airborne delivery drones or automated delivery trucks. UPS and Google are also testing their own versions of the delivery drone. Two and a half million people in the United States make their living from driving trucks, taxis, or buses, and all of them are vulnerable to displacement by self-driving cars. It’s hard to wrap your head around all the changes this might mean. I met the CEO of a company that develops high-tech access control systems (like the new parking garage system at the airport that tells you how many open spaces are available on each floor) and asked him what worries him about the future. He cited a disruption that I’d never considered before: what driverless cars might mean for parking garages.


pages: 247 words: 81,135

The Great Fragmentation: And Why the Future of All Business Is Small by Steve Sammartino

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3D printing, additive manufacturing, Airbnb, augmented reality, barriers to entry, Bill Gates: Altair 8800, bitcoin, BRICs, Buckminster Fuller, citizen journalism, collaborative consumption, cryptocurrency, Elon Musk, fiat currency, Frederick Winslow Taylor, game design, Google X / Alphabet X, haute couture, helicopter parent, illegal immigration, index fund, Jeff Bezos, jimmy wales, Kickstarter, knowledge economy, Law of Accelerating Returns, market design, Metcalfe's law, Minecraft, minimum viable product, Network effects, new economy, post scarcity, prediction markets, pre–internet, profit motive, race to the bottom, random walk, Ray Kurzweil, recommendation engine, remote working, RFID, self-driving car, sharing economy, side project, Silicon Valley, Silicon Valley startup, skunkworks, Skype, social graph, social web, software is eating the world, Steve Jobs, too big to fail, web application

Apps could enable the driver to change dashboard visuals, designs and colours, as well as controlling music, tracking devices, voice-activated enhancements, and general driving efficiency and enjoyment hacks. Who knows, the crowd may come up with significant safety enhancements that use sensors to wake sleepy drivers. glass cockpit: an aircraft cockpit that features electronic (digital) instrument displays, typically large LCD screens (Wikipedia) Moreover, cars are very soon going to evolve into lounge rooms once self-driving cars become the norm. The technology for safe self-driving cars already exists. Millions of kilometres have been driven without incident. The cost of the technology that makes it possible is in rapid freefall. It’s hard to predict when autonomous driving cars will be available to the public, and estimates range from a few years to up to 20 years.1 Google, a leading developer of the technology, claims its technology will be ready to commercialise with major auto manufacturers by the year 2018.

When this happens, the possibilities of dashboard technology will no longer be restricted by safe driving practices. Given we’re talking about years, rather than decades, car companies should probably prepare for the inevitable now. A world of entirely new revenue streams awaits the auto industry if they follow the playbook already evidenced in both media evolution and personal computing technology. All they need is to have the courage to let other people get involved. The technology for safe self-driving cars already exists. From products to platforms Being able to thrive going forward is about removing the finality that comes with the launch mentality: not assuming that a product is finished when we deliver it to the market. Brands that survive the current reconfiguration of economics will understand that a product or service is a continuum of development, a continuum that people take from the company and invent the next stages of.

When the car arrives, we create road rules, speed limits and signs to avoid crashing into each other. As car ownership widens and we start to seal the roads, we make highways, roundabouts and traffic lights. We invent maps for directions as the number and complexity of roads increases. We use GPS devices and live traffic reports (via the web) for more efficient movement on the roads. And our next stack will be the self-driving car, which will do it all for us. Each layer is needed before the subsequent layer can make any sense or be needed. While the industrial revolution created a machine-based layer of technology in business and lifestyle, we’re now entering a stage where a digital layer is being added. Cheap, disposable technology will give us a new layer that augments both how we live and how we do business.


pages: 240 words: 65,363

Think Like a Freak by Steven D. Levitt, Stephen J. Dubner

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Albert Einstein, Anton Chekhov, autonomous vehicles, Barry Marshall: ulcers, call centre, Cass Sunstein, colonial rule, Edward Glaeser, food miles, Gary Taubes, income inequality, Internet Archive, Isaac Newton, medical residency, microbiome, prediction markets, randomized controlled trial, Richard Thaler, Scramble for Africa, self-driving car, Silicon Valley, Tony Hsieh, transatlantic slave trade, éminence grise

: See Robert Hornik, Lela Jacobsohn, Robert Orwin, Andrea Piesse, Graham Kalton, “Effects of the National Youth Anti-Drug Media Campaign on Youths,” American Journal of Public Health 98, no. 12 (December 2008). 174 SELF-DRIVING CARS: Among the many people who informed our thinking on the driverless-car future, we are especially indebted to Raj Rajkumar and his colleagues at Carnegie Mellon, who let us ride in their driverless vehicle and answered every question. / 175 Google has already driven its fleet of autonomous cars: See Angela Greiling Keane, “Google’s Self-Driving Cars Get Boost from U.S. Agency,” Bloomberg.com, May 30, 2013; “The Self-Driving Car Logs More Miles on New Wheels,” Google official blog, August 7, 2012. (Our text contains updated mile figures from a Google spokesperson as of October 2013.) / 174 Ninety percent of traffic deaths due to driver error: Per Bob Joop Goos, chairman of the International Organization for Road Accident Prevention; also per National Highway Traffic Safety Administration (NHTSA) statistics. / 174 Worldwide traffic deaths: Most of the statistics in this section are drawn from World Health Organization and NHTSA reports. / 175 In many U.S. cities, 30 to 40 percent of the downtown surface area is devoted to parking: See Stephen J.

If you make an argument that promises all benefits and no costs, your opponent will never buy it—nor should he. Panaceas are almost nonexistent. If you paper over the shortcomings of your plan, that only gives your opponent reason to doubt the rest of it. Let’s say you’ve become a head-over-heels advocate for a new technology you think will change the world. Your argument goes like this: The era of the self-driving car—a.k.a. the driverless car, or autonomous vehicle—is just around the corner, and we should embrace it as vigorously as possible. It will save millions of lives and improve just about every facet of our society and economy. You could go on and on. You could talk about how the toughest challenge—the technology itself—has largely been conquered. Nearly every major automaker in the world, as well as Google, has successfully tested cars that use an onboard computer, GPS, cameras, radar, laser scanners, and actuators to do everything a human driver can do—but better.

Wade, 93 Rolling Stone, 140 Romania, witches in, 30–31 Roth, David Lee, 152, 154 and game theory, 142–43 and King Solomon, 137–38, 142–43 and M&M clause, 141–42 and Van Halen, 137, 138, 140–42 running with the herd, 10, 112–15, 172 salt sensitivity, 76–77 Sargent, Thomas, 26–27 “Save to Win,” 99 savings: prize-linked (PLS) account, 98–99 rate of, 97–99 scams, 154–61 schoolteachers, early retirement of, 180–81 “Scramble for Africa,” 74 Seeger, Pete, 138 self-assessment, 27 self-driving car, 174–77 self-interest, 7 self-sterilizing surface, invention of, 194–95 Sen, Amartya, 66 separating equilibrium, 143, 154 September 11 attacks, 22, 161–62 seriousness, 96 shame, fear of, 6 Shaw, George Bernard, 10–11 shoes, selling, 128–30 Silva, Rohan, 12 simplicity, 94 Singer, Isaac Bashevis, “Why I Write for Children,” 104 slavery: and Caribbean blacks, 77 and salt sensitivity, 76–77 in South America, 74–77 Smile Pinki, 120 Smile Train, 119–24, 130 Smith, Adam, 58 Smith, Billie June, 99 soccer, penalty kick in, 3–7, 29 Soccer Boy, 119 social-gaming site, 100 social issues: and corruption, 66–67 experiments in, 39–40 incentives in, 112, 113 problem solving, 66–67 Society of Fellows, Harvard, 42 Solomon, King, 152, 165 and David Lee Roth, 137–38, 142–43 First Temple built by, 137 and game theory, 142–43 maternity dispute settled by, 58, 139–40, 154, 187 Solomon method, 58, 140n solution, “perfect,” 173–74 sophistication, 88n South America: colonialism in, 74 slavery in, 74–77 Spanish Prisoner, 156 speculation, 90 Spenkuch, Jörg, 71–72 SpinForGood.com, 100 sports: brain as critical organ in, 63 competitive eating, 62–64 expectations in, 64 training for, 62 tricking athletes into improvement, 63 Springsteen, Bruce, 208 Standards of Conduct Office, 184 starvation, causes of, 66–67 status quo, 10 status-quo bias, 206 stock markets, predictions of, 24–25, 29–30 stomach acid, 78, 79–80, 95 Stone, Alex, 101–3 storytelling, 181–88 anecdotes vs., 181–82 in the Bible, 185–88 data in, 182 and narcissism, 183 teaching via, 183 time frame in, 182 truth vs. falsity of, 182–83 suicide, 32–34 getting help, 34 impulse toward, 34 “no one left to blame” theory of, 33–34 sunk-cost fallacy, 191, 192, 199 Sunstein, Cass, 172 SuperFreakonomics, 11–12, 161, 164 swimming accidents, 91 table manners, Japanese, 57 talent: as overrated, 96 self-assessment of, 27 teacher quality, 50 Teach Your Garden to Weed Itself, 143, 145, 149, 154 Ten Commandments, 185–86 terrorists: and banks, 161–65 and education, 171 and life insurance, 163–65 Tetlock, Philip, 23–25, 171 Thaler, Richard, 172 thinking: big, 89 with different muscles, 8 like a child, 87, 92, 95, 100 like a Freak, 8, 10–11, 87 small, 88–92 time spent in, 10–11 Thomas, Sonya, 61 time frame, 182 total internal reflection, 195 tradition, 39, 78, 82 traffic accidents, 178–79 “transpoosion,” 87 trial by ordeal, 144–49, 154 tricks: fun in, 152 improving athletes via, 63 “Turn!


pages: 481 words: 125,946

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

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3D printing, agricultural Revolution, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, artificial general intelligence, augmented reality, autonomous vehicles, bitcoin, blockchain, clean water, cognitive dissonance, Colonization of Mars, complexity theory, computer age, computer vision, constrained optimization, corporate personhood, cosmological principle, cryptocurrency, cuban missile crisis, Danny Hillis, dark matter, discrete time, Elon Musk, Emanuel Derman, endowment effect, epigenetics, Ernest Rutherford, experimental economics, Flash crash, friendly AI, Google Glasses, hive mind, income inequality, information trail, Internet of things, invention of writing, iterative process, Jaron Lanier, job automation, John von Neumann, Kevin Kelly, knowledge worker, loose coupling, microbiome, Moneyball by Michael Lewis explains big data, natural language processing, Network effects, Norbert Wiener, pattern recognition, Peter Singer: altruism, phenotype, planetary scale, Ray Kurzweil, recommendation engine, Republic of Letters, RFID, Richard Thaler, Rory Sutherland, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, superintelligent machines, supervolcano, the scientific method, The Wisdom of Crowds, theory of mind, Thorstein Veblen, too big to fail, Turing machine, Turing test, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Y2K

And the operation would have to survive the hazards of detection, betrayal, stings, blunders, and bad luck. In theory it could happen, but we have more pressing things to worry about. Once we put aside the sci-fi disaster plots, the possibility of advanced artificial intelligence is exhilarating—not just for the practical benefits, like the fantastic gains in safety, leisure, and environment-friendliness of self-driving cars but also for the philosophical possibilities. The computational Theory of Mind has never explained the existence of consciousness in the sense of first-person subjectivity (though it’s perfectly capable of explaining the existence of consciousness in the sense of accessible and reportable information). One suggestion is that subjectivity is inherent to any sufficiently complicated cybernetic system.

Should we worry that we’re building systems whose increasingly accurate decisions are based on incomprehensible foundations? First, and most simply, it matters because we regularly find ourselves in everyday situations where we need to know why. Why was I denied a loan? Why was my account blocked? Why did my condition suddenly get classified as “severe”? And sometimes we need to know why in cases where the machine made a mistake. Why did the self-driving car abruptly go off the road? It’s hard to troubleshoot problems when you don’t understand why they’re happening. There are deeper troubles, too; to talk about them, we need to understand more about how these algorithms work. They’re trained on massive quantities of data and they’re remarkably good at picking up on the subtle patterns these data contain. We know, for example, how to build systems that can look at millions of identically structured loan applications from the past, all encoded the same way, and start to identify the recurring patterns in the loans that—in retrospect—were the right ones to grant.

Conceptually, autonomous or artificial intelligence systems can develop in two ways: either as an extension of human thinking or as radically new thinking. Call the first “Humanoid Thinking,” or Humanoid AI, and the second “Alien Thinking,” or Alien AI. Almost all AI today is Humanoid Thinking. We use AI to solve problems too difficult, time-consuming, or boring for our limited brains to process: electrical-grid balancing, recommendation engines, self-driving cars, face recognition, trading algorithms, and the like. These artificial agents work in narrow domains with clear goals their human creators specify. Such AI aims to accomplish human objectives—often better, with fewer cognitive errors, distractions, outbursts of bad temper, or processing limitations. In a couple of decades, AI agents might serve as virtual insurance sellers, doctors, psychotherapists, and maybe even virtual spouses and children.


pages: 396 words: 117,149

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

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3D printing, Albert Einstein, Amazon Mechanical Turk, Arthur Eddington, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, crowdsourcing, Danny Hillis, data is the new oil, double helix, Douglas Hofstadter, Erik Brynjolfsson, experimental subject, Filter Bubble, future of work, global village, Google Glasses, Gödel, Escher, Bach, information retrieval, job automation, John Snow's cholera map, John von Neumann, Joseph Schumpeter, Kevin Kelly, lone genius, mandelbrot fractal, Mark Zuckerberg, Moneyball by Michael Lewis explains big data, Narrative Science, Nate Silver, natural language processing, Netflix Prize, Network effects, NP-complete, P = NP, PageRank, pattern recognition, phenotype, planetary scale, pre–internet, random walk, Ray Kurzweil, recommendation engine, Richard Feynman, Richard Feynman, Second Machine Age, self-driving car, Silicon Valley, speech recognition, statistical model, Stephen Hawking, Steven Levy, Steven Pinker, superintelligent machines, the scientific method, The Signal and the Noise by Nate Silver, theory of mind, transaction costs, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, white flight

Satellites, DNA sequencers, and particle accelerators probe nature in ever-finer detail, and learning algorithms turn the torrents of data into new scientific knowledge. Companies know their customers like never before. The candidate with the best voter models wins, like Obama against Romney. Unmanned vehicles pilot themselves across land, sea, and air. No one programmed your tastes into the Amazon recommendation system; a learning algorithm figured them out on its own, by generalizing from your past purchases. Google’s self-driving car taught itself how to stay on the road; no engineer wrote an algorithm instructing it, step-by-step, how to get from A to B. No one knows how to program a car to drive, and no one needs to, because a car equipped with a learning algorithm picks it up by observing what the driver does. Machine learning is something new under the sun: a technology that builds itself. Ever since our remote ancestors started sharpening stones into tools, humans have been designing artifacts, whether they’re hand built or mass produced.

But, more surprisingly, computers can learn programs that people can’t write. We know how to drive cars and decipher handwriting, but these skills are subconscious; we’re not able to explain to a computer how to do these things. If we give a learner a sufficient number of examples of each, however, it will happily figure out how to do them on its own, at which point we can turn it loose. That’s how the post office reads zip codes, and that’s why self-driving cars are on the way. The power of machine learning is perhaps best explained by a low-tech analogy: farming. In an industrial society, goods are made in factories, which means that engineers have to figure out exactly how to assemble them from their parts, how to make those parts, and so on—all the way to raw materials. It’s a lot of work. Computers are the most complex goods ever invented, and designing them, the factories that make them, and the programs that run on them is a ton of work.

“All humans are mortal” is a piece of knowledge. Riding a bicycle is a skill. In machine learning, knowledge is often in the form of statistical models, because most knowledge is statistical: all humans are mortal, but only 4 percent are Americans. Skills are often in the form of procedures: if the road curves left, turn the wheel left; if a deer jumps in front of you, slam on the brakes. (Unfortunately, as of this writing Google’s self-driving cars still confuse windblown plastic bags with deer.) Often, the procedures are quite simple, and it’s the knowledge at their core that’s complex. If you can tell which e-mails are spam, you know which ones to delete. If you can tell how good a board position in chess is, you know which move to make (the one that leads to the best position). Machine learning takes many different forms and goes by many different names: pattern recognition, statistical modeling, data mining, knowledge discovery, predictive analytics, data science, adaptive systems, self-organizing systems, and more.


pages: 372 words: 101,174

How to Create a Mind: The Secret of Human Thought Revealed by Ray Kurzweil

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Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Albert Michelson, anesthesia awareness, anthropic principle, brain emulation, cellular automata, Claude Shannon: information theory, cloud computing, computer age, Dean Kamen, discovery of DNA, double helix, en.wikipedia.org, epigenetics, George Gilder, Google Earth, Isaac Newton, iterative process, Jacquard loom, Jacquard loom, John von Neumann, Law of Accelerating Returns, linear programming, Loebner Prize, mandelbrot fractal, Norbert Wiener, optical character recognition, pattern recognition, Peter Thiel, Ralph Waldo Emerson, random walk, Ray Kurzweil, reversible computing, self-driving car, speech recognition, Steven Pinker, strong AI, the scientific method, theory of mind, Turing complete, Turing machine, Turing test, Wall-E, Watson beat the top human players on Jeopardy!, X Prize

Calculations per second per (constant) thousand dollars of different computing devices.10 Floating-point operations per second of different supercomputers.11 Transistors per chip for different Intel processors.12 Bits per dollar for dynamic random access memory chips.13 Bits per dollar for random access memory chips.14 The average price per transistor in dollars.15 The total number of bits of random access memory shipped each year.16 Bits per dollar (in constant 2000 dollars) for magnetic data storage.17 Even the predictions that were “wrong” were not all wrong. For example, I judged my prediction that we would have self-driving cars to be wrong, even though Google has demonstrated self-driving cars, and even though in October 2010 four driverless electric vans successfully concluded a 13,000-kilometer test drive from Italy to China.18 Experts in the field currently predict that these technologies will be routinely available to consumers by the end of this decade. Exponentially expanding computational and communication technologies all contribute to the project to understand and re-create the methods of the human brain.

Watson’s ability to intelligently master the knowledge in natural-language documents is coming to a search engine near you, and soon. People are already talking to their phones in natural language (via Siri, for example, which was also contributed to by Nuance). These natural-language assistants will rapidly become more intelligent as they utilize more of the Watson-like methods and as Watson itself continues to improve. The Google self-driving cars have logged 200,000 miles in the busy cities and towns of California (a figure that will undoubtedly be much higher by the time this book hits the real and virtual shelves). There are many other examples of artificial intelligence in today’s world, and a great deal more on the horizon. As further examples of the LOAR, the spatial resolution of brain scanning and the amount of data we are gathering on the brain are doubling every year.

If all the AI systems decided to go on strike tomorrow, our civilization would be crippled: We couldn’t get money from our bank, and indeed, our money would disappear; communication, transportation, and manufacturing would all grind to a halt. Fortunately, our intelligent machines are not yet intelligent enough to organize such a conspiracy. What is new in AI today is the viscerally impressive nature of publicly available examples. For example, consider Google’s self-driving cars (which as of this writing have gone over 200,000 miles in cities and towns), a technology that will lead to significantly fewer crashes, increased capacity of roads, alleviating the requirement of humans to perform the chore of driving, and many other benefits. Driverless cars are actually already legal to operate on public roads in Nevada with some restrictions, although widespread usage by the public throughout the world is not expected until late in this decade.


pages: 361 words: 81,068

The Internet Is Not the Answer by Andrew Keen

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3D printing, A Declaration of the Independence of Cyberspace, Airbnb, AltaVista, Andrew Keen, augmented reality, Bay Area Rapid Transit, Berlin Wall, bitcoin, Black Swan, Burning Man, Cass Sunstein, citizen journalism, Clayton Christensen, clean water, cloud computing, collective bargaining, Colonization of Mars, computer age, connected car, cuban missile crisis, David Brooks, disintermediation, Downton Abbey, Edward Snowden, Elon Musk, Erik Brynjolfsson, Fall of the Berlin Wall, Filter Bubble, Francis Fukuyama: the end of history, Frank Gehry, Frederick Winslow Taylor, frictionless, full employment, future of work, gig economy, global village, Google bus, Google Glasses, Hacker Ethic, happiness index / gross national happiness, income inequality, index card, informal economy, information trail, Innovator's Dilemma, Internet of things, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, Joseph Schumpeter, Julian Assange, Kevin Kelly, Kickstarter, Kodak vs Instagram, Lean Startup, libertarian paternalism, Lyft, Mark Zuckerberg, Marshall McLuhan, Martin Wolf, move fast and break things, Nate Silver, Network effects, new economy, Nicholas Carr, nonsequential writing, Norbert Wiener, Occupy movement, packet switching, PageRank, Paul Graham, Peter Thiel, Plutocrats, plutocrats, Potemkin village, precariat, pre–internet, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Skype, smart cities, Snapchat, social web, South of Market, San Francisco, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, TaskRabbit, Ted Nelson, telemarketer, the medium is the message, Thomas L Friedman, Tyler Cowen: Great Stagnation, Uber for X, urban planning, Vannevar Bush, Whole Earth Catalog, WikiLeaks, winner-take-all economy, working poor, Y Combinator

“The prevailing methods of computerized communication pretty much ensure that the role of people will go on shrinking,” Carr writes in The Glass Cage. “Society is reshaping itself to fit the contours of the new computing infrastructure. The infrastructure orchestrates the instantaneous data exchanges that make fleets of self-driving cars and armies of killer robots possible. It provides the raw materials for the predictive algorithms that inform the decisions of individuals and groups. It underpins the automation of classrooms, libraries, hospitals, shops, churches, and homes.”24 With its massive investment in the development of intelligent labor-saving technologies like self-driving cars and killer robots, Google—which has imported Ray Kurzweil, the controversial evangelist of “singularity,” to direct its artificial intelligence engineering strategy25—is already invested in the building and management of the glass cage.

Thanks to cloud computing, robotics, Facebook, Google, LinkedIn, Twitter, the iPad, and cheap Internet-enabled smartphones, Friedman says, “the world has gone from connected to hyper-connected.”13 Runciman, Lanchester, and Friedman are all describing the same great economic, cultural, and, above all, intellectual transformation. “The Internet,” Joi Ito, the director of the MIT Media Lab, notes, “is not a technology; it’s a belief system.”14 Everything and everyone are being connected in a network revolution that is radically disrupting every aspect of today’s world. Education, transportation, health care, finance, retail, and manufacturing are now being reinvented by Internet-based products such as self-driving cars, wearable computing devices, 3-D printers, personal health monitors, massive open online courses (MOOCs), peer-to-peer services like Airbnb and Uber, and currencies like Bitcoin. Revolutionary entrepreneurs like Sean Parker and Kevin Systrom are building this networked society on our behalf. They haven’t asked our permission, of course. But then the idea of consent is foreign, even immoral, to many of these architects of what the Columbia University historian Mark Lilla calls our “libertarian age.”

Google, for example, still prides itself as being an “uncompany,” a corporation without the traditional structures of power—even though the $400 billion leviathan is, as of June 2014, the world’s second most valuable corporation. It’s active and in some cases brutally powerful in industries as varied as online search, advertising, publishing, artificial intelligence, news, mobile operating systems, wearable computing, Internet browsers, video, and even—with its fledgling self-driving cars—the automobile industry. In the digital world, everyone wants to be an unbusiness. Amazon, the largest online store in the world and a notorious bully of small publishing companies, still thinks of itself as the scrappy “unstore.” Internet companies like the Amazon-owned shoe store Zappos, and Medium, an online magazine founded by billionaire Twitter founder Ev Williams, are run on so-called holacratic principles—a Silicon Valley version of communism where there are no hierarchies, except, of course, when it comes to wages and stock ownership.


pages: 340 words: 92,904

Street Smart: The Rise of Cities and the Fall of Cars by Samuel I. Schwartz

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2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, active transport: walking or cycling, Affordable Care Act / Obamacare, American Society of Civil Engineers: Report Card, autonomous vehicles, car-free, City Beautiful movement, collaborative consumption, congestion charging, crowdsourcing, desegregation, Enrique Peñalosa, Ford paid five dollars a day, Frederick Winslow Taylor, if you build it, they will come, intermodal, invention of the wheel, lake wobegon effect, Loma Prieta earthquake, Lyft, Masdar, megacity, meta analysis, meta-analysis, moral hazard, Nate Silver, oil shock, Productivity paradox, Ralph Nader, rent control, ride hailing / ride sharing, Rosa Parks, self-driving car, skinny streets, smart cities, smart grid, smart transportation, the built environment, the map is not the territory, transportation-network company, Uber and Lyft, Uber for X, Unsafe at Any Speed, urban decay, urban planning, urban renewal, walkable city, Wall-E, white flight, Works Progress Administration, Yogi Berra, Zipcar

In the view of former PRT advocate Alain Kornhauser, who is now convinced of the practicality of street-useful driverless cars, the beauty of these technological improvements is that, because they increase driving safety, they even have the potential to be self-financing: so long as collision avoidance and other autonomous driving modules cost less than the potential liability from future accidents, it will be in the interest of automobile insurance companies to pay for them. Even better: so long as more autonomy equals more safety, there is no point where the cost of the technology exceeds its added value. The most prominent player in the world of autonomous driving, though, isn’t Allstate or Geico. It isn’t Mercedes-Benz or Ford, or even Tesla. It’s Google. The Google Self-Driving Car is a project that the Internet giant saw as a natural outgrowth of its existing mapping software, particularly the technology from Google Street View, which stitches together panoramic photos of more than five million miles of roads in more than forty countries. Google’s versions of the driverless car—refitted Toyotas, Audis, and Lexuses—combine real-time access to all that data with a laser rangefinder that creates and refreshes three-dimensional maps of the area immediately around the car.

Transit riders use more than 20 percent more calories than drivers on a per-trip basis, which gives buses, subways, and streetcars a giant health advantage over cars. In fact, after five years of taking transit, the obese percentage of a given population—those with a Body Mass Index greater than 30—drops by more than half. And, as long as cities create plazas and piazzas where cars are banned but not people, self-driving cars offer no advantage, even without recognizing the mathematical impossibility of moving thousands of people through a city center in single-occupant vehicles. This doesn’t mean there isn’t a place for cars, with or without laser-rangefinders and GPS mapping. In less dense parts of cities, suburbs, and rural areas, all the safety aspects developed by automated cars make sense. In some ways, the driverless car is a natural next step following all the technological and demographic changes that contributed to the original Millennial-led driving revolution that is the subject of this book, especially the information oversupply that made smartphones into a tool for transportation planning.

A group of simulated driverless cars negotiating a typical urban intersection at the same (slow) acceleration of a commuter train increases the time needed to cross the intersection by anywhere from 36 percent to more than 2,000 percent. If you want to browse the Internet while commuting, and still want to get to work on time, trains look like a much better option. There are other reasons to be suspicious of the brave new world represented by Google’s self-driving cars and others of similar ambition. On a purely personal level, I’m a little taken aback by the promise that autonomous vehicles will be able to collect you at your front door and deposit you at the front door of a supermarket or shopping mall—or even at your desk or workstation—without your feet ever touching the ground. In the Disney movie Wall-E, spaceship-bound refugees from an Earth destroyed by environmental catastrophe are so well cared for by their robot transportation devices that hardly anyone even stands up anymore, with the result that the universe’s entire remaining population of Homo sapiens is morbidly obese.


pages: 588 words: 131,025

The Patient Will See You Now: The Future of Medicine Is in Your Hands by Eric Topol

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23andMe, 3D printing, Affordable Care Act / Obamacare, Anne Wojcicki, Atul Gawande, augmented reality, bioinformatics, call centre, Clayton Christensen, clean water, cloud computing, computer vision, conceptual framework, connected car, correlation does not imply causation, crowdsourcing, dark matter, data acquisition, disintermediation, don't be evil, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Firefox, global village, Google Glasses, Google X / Alphabet X, Ignaz Semmelweis: hand washing, interchangeable parts, Internet of things, Isaac Newton, job automation, Joseph Schumpeter, Julian Assange, Kevin Kelly, license plate recognition, Lyft, Mark Zuckerberg, Marshall McLuhan, meta analysis, meta-analysis, microbiome, Nate Silver, natural language processing, Network effects, Nicholas Carr, obamacare, pattern recognition, personalized medicine, phenotype, placebo effect, RAND corporation, randomized controlled trial, Second Machine Age, self-driving car, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, Snapchat, social graph, speech recognition, stealth mode startup, Steve Jobs, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, Turing test, Uber for X, Watson beat the top human players on Jeopardy!, X Prize

Sullivan, “Google’s New Driverless Car Has No Brakes or Steering Wheel,” Washington Post, May 28, 2014, http://www.washingtonpost.com/news/morning-mix/wp/2014/05/28/googles-new-driverless-car-has-no-brakes-or-steering-wheel//?print=1. 29. R. Lawler, “Google X Built a Fully Self-Driving Car from Scratch, Sans Steering Wheel and Pedals,” TechCrunch, May 27, 2014, http://techcrunch.com/2014/05/27/google-x-introduces-a-fully-self-driving-car-sans-steering-wheel-and-pedals/. 30. L. Gannes, “Google’s New Self-Driving Car Ditches the Steering Wheel,” Recode, May 27, 2014, http://recode.net/2014/05/27/googles-new-self-driving-car-ditches-the-steering-wheel/. 31. R. W. Lucky, “The Drive for Driverless Cars,” IEEE Spectrum, June 26, 2014, http://spectrum.ieee.org/computing/embedded-systems/the-drive-for-driverless-cars. 32. C. Smith, “‘I No Longer Have to Go to See the Doctor’: How the Patient Portal is Changing Medical Practice,” Journal of Participatory Medicine 6 (2014): e6. 33.

Sarasohn-Kahn, “Why Having Access to Your Health Information Matters,” Healthcare DIY, March 1, 2014, http://healthcarediy.com/technology/your-medical-records-are-your-medical-records/. 24. B. Dolan and A. Pai, “In-Depth: Providers’ Inevitable Acceptance of Patient Generated Health Data,” MobiHealthNews, March 21, 2014, http://mobihealthnews.com/31268/in-depth-providers-inevitable-acceptance-of-patient-generated-health-data/. 25. J. Markoff, “A Trip in a Self-Driving Car Now Seems Routine,” New York Times, May 13, 2014, http://bits.blogs.nytimes.com/2014/05/13/a-trip-in-a-self-driving-car-now-seems-routine/?smid=tw-nytimesbits. 26. A. Salkever, “What Google’s Driverless Car Future Might Really Look Like,” Read Write, May 28, 2014, http://readwrite.com/2014/05/28/googles-driverless-car-future?awesm=readwr.it_p20r-awesm=~oFWmYrlzbbCpi0. 27. C. C. Miller, “When Driverless Cars Break the Law,” New York Times, May 14, 2014, http://www.nytimes.com/2014/05/14/upshot/when-driverless-cars-break-the-law.html. 28.

The Google driverless car is now electric without brakes, an accelerator, or a steering wheel.25–31 It has a 360-degree field of view—eliminating any blind spots—with hundreds of laser and radar sensors. It can now recognize pedestrians and bicyclists, along with their hand gestures, better than human beings can, and has a sterling safety record that surpasses driving by humans. And it can be summoned by a smartphone. If we can build self-driving cars with this sensor and computing technology, are we ready to develop doctorless patients? I think the answer is much more autonomous patients, yes, without question, but truly doctorless, no. Much of the practice of medicine will reboot and bypass the current deeply engrained, sacrosanct doctor-dependent operations.32–34 Just as you can do your electrocardiogram by your smartphone today and get an immediate computer algorithm interpretation, so it will be the case for many diagnostics in the future, such as whether you have sleep apnea or hypertension—anything with simple quantitative data to record, process, and quickly return to you.


pages: 357 words: 95,986

Inventing the Future: Postcapitalism and a World Without Work by Nick Srnicek, Alex Williams

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3D printing, additive manufacturing, air freight, algorithmic trading, anti-work, back-to-the-land, banking crisis, battle of ideas, blockchain, Bretton Woods, call centre, capital controls, carbon footprint, Cass Sunstein, centre right, collective bargaining, crowdsourcing, cryptocurrency, David Graeber, decarbonisation, deindustrialization, deskilling, Doha Development Round, Elon Musk, Erik Brynjolfsson, Ferguson, Missouri, financial independence, food miles, Francis Fukuyama: the end of history, full employment, future of work, gender pay gap, housing crisis, income inequality, industrial robot, informal economy, intermodal, Internet Archive, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, late capitalism, low skilled workers, manufacturing employment, market design, Martin Wolf, means of production, minimum wage unemployment, Mont Pelerin Society, neoliberal agenda, New Urbanism, Occupy movement, oil shale / tar sands, oil shock, patent troll, pattern recognition, post scarcity, postnationalism / post nation state, precariat, price stability, profit motive, quantitative easing, reshoring, Richard Florida, rising living standards, road to serfdom, Robert Gordon, Ronald Reagan, Second Machine Age, secular stagnation, self-driving car, Slavoj Žižek, social web, stakhanovite, Steve Jobs, surplus humans, the built environment, The Chicago School, Tyler Cowen: Great Stagnation, universal basic income, wages for housework, We are the 99%, women in the workforce, working poor, working-age population

These are tasks that computers are perfectly suited to accomplish once a programmer has created the appropriate software, leading to a drastic reduction in the numbers of routine manual and cognitive jobs over the past four decades.22 The result has been a polarisation of the labour market, since many middle-wage, mid-skilled jobs are routine, and therefore subject to automation.23 Across both North America and Western Europe, the labour market is now characterised by a predominance of workers in low-skilled, low-wage manual and service jobs (for example, fast-food, retail, transport, hospitality and warehouse workers), along with a smaller number of workers in high-skilled, high-wage, non-routine cognitive jobs.24 The most recent wave of automation is poised to change this distribution of the labour market drastically, as it comes to encompass every aspect of the economy: data collection (radio-frequency identification, big data); new kinds of production (the flexible production of robots,25 additive manufacturing,26 automated fast food); services (AI customer assistance, care for the elderly); decision-making (computational models, software agents); financial allocation (algorithmic trading); and especially distribution (the logistics revolution, self-driving cars,27 drone container ships and automated warehouses).28 In every single function of the economy – from production to distribution to management to retail – we see large-scale tendencies towards automation.29 This latest wave of automation is predicated upon algorithmic enhancements (particularly in machine learning and deep learning), rapid developments in robotics and exponential growth in computing power (the source of big data) that are coalescing into a ‘second machine age’ that is transforming the range of tasks that machines can fulfil.30 It is creating an era that is historically unique in a number of ways.

These are tasks that computers are perfectly suited to accomplish once a programmer has created the appropriate software, leading to a drastic reduction in the numbers of routine manual and cognitive jobs over the past four decades.22 The result has been a polarisation of the labour market, since many middle-wage, mid-skilled jobs are routine, and therefore subject to automation.23 Across both North America and Western Europe, the labour market is now characterised by a predominance of workers in low-skilled, low-wage manual and service jobs (for example, fast-food, retail, transport, hospitality and warehouse workers), along with a smaller number of workers in high-skilled, high-wage, non-routine cognitive jobs.24 The most recent wave of automation is poised to change this distribution of the labour market drastically, as it comes to encompass every aspect of the economy: data collection (radio-frequency identification, big data); new kinds of production (the flexible production of robots,25 additive manufacturing,26 automated fast food); services (AI customer assistance, care for the elderly); decision-making (computational models, software agents); financial allocation (algorithmic trading); and especially distribution (the logistics revolution, self-driving cars,27 drone container ships and automated warehouses).28 In every single function of the economy – from production to distribution to management to retail – we see large-scale tendencies towards automation.29 This latest wave of automation is predicated upon algorithmic enhancements (particularly in machine learning and deep learning), rapid developments in robotics and exponential growth in computing power (the source of big data) that are coalescing into a ‘second machine age’ that is transforming the range of tasks that machines can fulfil.30 It is creating an era that is historically unique in a number of ways. New pattern-recognition technologies are rendering both routine and non-routine tasks subject to automation: complex communication technologies are making computers better than humans at certain skilled-knowledge tasks, and advances in robotics are rapidly making technology better at a wide variety of manual-labour tasks.31 For instance, self-driving cars involve the automation of non-routine manual tasks, and non-routine cognitive tasks such as writing news stories or researching legal precedents are now being accomplished by robots.32 The scope of these developments means that everyone from stock analysts to construction workers to chefs to journalists is vulnerable to being replaced by machines.33 Workers who move symbols on a screen are as at risk as those moving goods around a warehouse.

While full automation of the economy is presented here as an ideal and a demand, in practice it is unlikely to be fully achieved.45 In certain spheres, human labour is likely to continue for technical, economic and ethical reasons. On a technical level, machines today remain worse than humans at jobs involving creative work, highly flexible work, affective work and most tasks relying on tacit rather than explicit knowledge.46 The engineering problems involved in automating these tasks appear insurmountable for the next two decades (though similar claims were made about self-driving cars ten years ago), and a programme of full automation would aim to invest research money into overcoming these limits. A second barrier to full automation occurs for economic reasons: certain tasks can already be completed by machines, but the cost of the machines exceeds the cost of the equivalent labour.47 Despite the efficiency, accuracy and productivity of machine labour, capitalism prefers to make profits, and therefore uses human labour whenever it is cheaper than capital investment.


pages: 304 words: 82,395

Big Data: A Revolution That Will Transform How We Live, Work, and Think by Viktor Mayer-Schonberger, Kenneth Cukier

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23andMe, Affordable Care Act / Obamacare, airport security, AltaVista, barriers to entry, Berlin Wall, big data - Walmart - Pop Tarts, Black Swan, book scanning, business intelligence, business process, call centre, cloud computing, computer age, correlation does not imply causation, dark matter, double entry bookkeeping, Eratosthenes, Erik Brynjolfsson, game design, IBM and the Holocaust, index card, informal economy, Internet of things, invention of the printing press, Jeff Bezos, Louis Pasteur, Mark Zuckerberg, Menlo Park, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, obamacare, optical character recognition, PageRank, performance metric, Peter Thiel, Post-materialism, post-materialism, random walk, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, smart grid, smart meter, social graph, speech recognition, Steve Jobs, Steven Levy, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, Turing test, Watson beat the top human players on Jeopardy!

Though it is described as part of the branch of computer science called artificial intelligence, and more specifically, an area called machine learning, this characterization is misleading. Big data is not about trying to “teach” a computer to “think” like humans. Instead, it’s about applying math to huge quantities of data in order to infer probabilities: the likelihood that an email message is spam; that the typed letters “teh” are supposed to be “the”; that the trajectory and velocity of a person jaywalking mean he’ll make it across the street in time—the self-driving car need only slow slightly. The key is that these systems perform well because they are fed with lots of data on which to base their predictions. Moreover, the systems are built to improve themselves over time, by keeping a tab on what are the best signals and patterns to look for as more data is fed in. In the future—and sooner than we may think—many aspects of our world will be augmented or replaced by computer systems that today are the sole purview of human judgment.

Its controversial Street View cars cruised around snapping pictures of houses and roads, but also gobbling up GPS data, checking mapping information, and even sucking in wifi network names (and, perhaps illegally, the content that flowed over open wireless networks). A single Google Street View drive amassed a myriad of discrete data streams at every moment. The extensibility comes in because Google applied the data not just for a primary use but for lots of secondary uses. For example, the GPS data it garnered improved the company’s mapping service and was indispensable for the functioning of its self-driving car. The extra cost of collecting multiple streams or many more data points in each stream is often low. So it makes sense to gather as much data as possible, as well as to make it extensible by considering potential secondary uses at the outset. That increases the data’s option value. The point is to look for “twofers”—where a single dataset can be used in multiple instances if it can be collected in a certain way.

Its stock market prospectus in 1997 described “collaborative filtering” before Amazon knew how it would work in practice or had enough data to make it useful. Both Google and Amazon span the categories, but their strategies differ. When Google first sets out to collect any sort of data, it has secondary uses in mind. Its Street View cars, as we have seen, collected GPS information not just for its map service but also to train self-driving cars. By contrast, Amazon is more focused on the primary use of data and only taps the secondary uses as a marginal bonus. Its recommendation system, for example, relies on clickstream data as a signal, but the company hasn’t used the information to do extraordinary things like predict the state of the economy or flu outbreaks. Despite Amazon’s Kindle e-book readers’ being capable of showing whether a certain page has been heavily annotated and underlined by users, the firm does not sell that information to authors and publishers.


pages: 239 words: 70,206

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

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23andMe, Affordable Care Act / Obamacare, Albert Einstein, big data - Walmart - Pop Tarts, bioinformatics, business intelligence, call centre, cloud computing, computer age, conceptual framework, Credit Default Swap, crowdsourcing, Daniel Kahneman / Amos Tversky, Danny Hillis, data is the new oil, David Brooks, East Village, Edward Snowden, Emanuel Derman, Erik Brynjolfsson, everywhere but in the productivity statistics, Frederick Winslow Taylor, Google Glasses, impulse control, income inequality, indoor plumbing, industrial robot, informal economy, Internet of things, invention of writing, John von Neumann, Mark Zuckerberg, market bubble, meta analysis, meta-analysis, natural language processing, obamacare, pattern recognition, payday loans, personalized medicine, precision agriculture, pre–internet, Productivity paradox, RAND corporation, rising living standards, Robert Gordon, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, six sigma, skunkworks, speech recognition, statistical model, Steve Jobs, Steven Levy, The Design of Experiments, the scientific method, Thomas Kuhn: the structure of scientific revolutions, unbanked and underbanked, underbanked, Von Neumann architecture, Watson beat the top human players on Jeopardy!

In an immense economy, like that of the United States, with its gross domestic product of $17 trillion, an amalgam of factors affects performance, including business cycles, financial crises, and demographic trends, not just technology. But Brynjolfsson sees a pattern playing out with big data that is comparable to past technologies. Innovations that have been percolating for years in research labs are making their way into products. An industry or two leads the way, like online advertising, and showcase projects point toward the future, like IBM’s Watson or Google’s self-driving cars (robotic incarnations of big data). Enthusiasm fans investment by companies and start-ups. But a broad-based payoff has not yet emerged. Debate rages between the techno-optimists and the pessimists. In his office, I ask Brynjolfsson to describe the steps that led him to become a big-data believer. He starts by observing that the data groundwork has been laid in the steady digitization of business in recent years.

By December of 2013, however, Krugman had become more impressed by advances in computing and he wrote an article, published on the Times’s Web site, explaining why he thinks Gordon is “probably wrong.” A decade ago, Krugman writes, “the field of artificial intelligence had marched from failure to failure. But something has happened—things that were widely regarded as jokes not long ago, like speech recognition, machine translation, self-driving cars, and so on, have suddenly become more or less working reality.” Data and software, Krugman observes, have forged the path to working artificial intelligence. “They’re using big data and correlations and so on,” he writes, “to implement algorithms—mindless algorithms, you might say. But if they can take people’s place, does it matter?” Krugman’s tentative conversion is noteworthy because it comes from someone of his stature who has a deep understanding of the economy.

People check in on temperatures and energy savings nearly twice a day on average, from smartphones and Web apps. The human users are interested partners and can override the machine, but most of the time they let the Nest algorithms take over. The issue of when to trust the machine—a mechanical one or a virtual one, a software algorithm—is going to play out repeatedly in the future. Appeals to efficiency alone will not carry the day. Advocates for self-driving cars marshal safety statistics and logical-sounding arguments to push their case—about accident rates and the human foibles of drowsiness, distractedness, and drunkenness. Those arguments help, but they do not speak to the issues of trust and comfort with the machines. People are not aggregates; we all experience the world as individuals. So declaring that something will be good for the population, on average, isn’t entirely persuasive.


pages: 202 words: 59,883

Age of Context: Mobile, Sensors, Data and the Future of Privacy by Robert Scoble, Shel Israel

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Albert Einstein, Apple II, augmented reality, call centre, Chelsea Manning, cloud computing, connected car, Edward Snowden, Elon Musk, factory automation, Filter Bubble, Google Earth, Google Glasses, Internet of things, job automation, Kickstarter, Mars Rover, Menlo Park, New Urbanism, PageRank, pattern recognition, RFID, ride hailing / ride sharing, Saturday Night Live, self-driving car, sensor fusion, Silicon Valley, Skype, smart grid, social graph, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Tesla Model S, Tim Cook: Apple, urban planning, Zipcar

When we talk about “the system knowing about you,” that knowledge depends on machine learning and database computation breakthroughs that couldn’t be imagined when Microsoft researcher Jim Gray turned on Microsoft’s first terabyte database back in December 1997. Similarly, significant innovations and accuracy improvements in voice recognition make systems like Apple’s Siri, Google Now and Google Voice Search possible. The foundation for the Age of Context—all of these technologies working together—is the cloud computing infrastructure, which continues to grow exponentially in capability and capacity. And it had better keep growing: A self-driving car, which we describe in Chapter 5, generates about 700 megabytes of data per second. We talked with GM, Ford, Toyota—and Google—about what would happen if every car had that technology. Well, for one thing, today’s cloud computing technology would melt down. Rackspace, a cloud hosting provider and Scoble’s employer, was the first and largest sponsor of this book. Since 2009, it has funded Scoble to travel the world interviewing hundreds of entrepreneurs and innovators.

We learned it’s more complicated than that. Prior to CES, thousands of Northern California drivers had already been startled, while driving along public roads, to pass vehicles with odd spinning devices mounted on their roofs. These cars usually moved at precisely the speed limit and contained passengers. Normal enough, except that no one was behind the wheel. These were part of Google’s growing fleet of experimental self-driving cars. They employ short-range radar, laser beams and motion and 3D sensors. The technology allows the cars to discern what’s around them in all directions and decide what, if anything, to do about it. The rooftop spinners contain a new technology, called “lidar” (Laser Imaging Detection and Ranging). It’s a technical cousin to radar that’s offered by at least two companies, Aerometric and Velodyne.

Lien walked us through a multitude of issues that dampen our hopes that the self-driving objects we see in the future are closer than they appear. To get from a consumer exhibition to general use on public roads will require “a great many incremental steps” in technology refinement, user acceptance and cost, as well as institutional adjustments such as legislation, liability and mixed-use roadways. Lien predicts that self-driving cars will first be available “in urban scenarios, because the technology can understand terrain and traffic patterns more easily and cars move at lower speeds.” Industrywide, some cars have already instituted features that are helpful in urban settings: Automatic parking features use sensors to back into tight spots without curb scrape or bumper tapping, and traffic-jam assistance recognizes patterns and adjusts lanes or routes for the driver, thus burning less fuel.


pages: 219 words: 63,495

50 Future Ideas You Really Need to Know by Richard Watson

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23andMe, 3D printing, access to a mobile phone, Albert Einstein, artificial general intelligence, augmented reality, autonomous vehicles, BRICs, Buckminster Fuller, call centre, clean water, cloud computing, collaborative consumption, computer age, computer vision, crowdsourcing, dark matter, dematerialisation, digital Maoism, Elon Musk, energy security, failed state, future of work, Geoffrey West, Santa Fe Institute, germ theory of disease, happiness index / gross national happiness, hive mind, hydrogen economy, Internet of things, Jaron Lanier, life extension, Marshall McLuhan, megacity, natural language processing, Network effects, new economy, oil shale / tar sands, pattern recognition, peak oil, personalized medicine, phenotype, precision agriculture, profit maximization, RAND corporation, Ray Kurzweil, RFID, Richard Florida, Search for Extraterrestrial Intelligence, self-driving car, semantic web, Skype, smart cities, smart meter, smart transportation, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, supervolcano, telepresence, The Wisdom of Crowds, Thomas Malthus, Turing test, urban decay, Vernor Vinge, Watson beat the top human players on Jeopardy!, web application, women in the workforce, working-age population, young professional

So what’s the future of technology-aided farming? In the UK, 13 percent of farmers are now using autosteering on farm machinery and we will soon see the development of automated, driverless vehicles that work, alone, out in the fields. From there it’s not much of a leap to semi-intelligent harvester robots or robots that can go off by themselves to look for weeds or sick animals. The idea of self-driving cars has been a staple of science fiction for many decades, but in reality we are likely to see self-driving farm machinery and military vehicles far sooner than we will see serious numbers of automated vehicles on public roads, largely because many of the safety and legal concerns that apply to road use do not apply quite so much on private farmland or battlefields. “In the long term, economic sustainability depends on ecological sustainability.”

Eventually, all buses and trains will become driverless and so too, one day, will all planes. the condensed idea Reinventing our wheels timeline 1769 First self-propelled mechanical vehicle 1885 Karl Benz invents the modern motorcar 1960s Personal jetpack technology becomes a reality 2004 China unveils a high-speed magnetic levitation train 2016 35 percent of cars now hybrids 2022 Self-driving cars start to appear in China and India 2039 High-speed rail networks link Europe with North Africa 2036 Solar-powered planes widely used in Africa and Australia 15 Extra-legal & feral slums According to a UN estimate, 1 in 7 people worldwide now live in slums and in many cases these slums, which are not regulated or sanctioned by law, are set to become major cities in the near future.

But in the future, machines with strong AI will be able to learn as they go and respond to unexpected events. The implications? Think of automated disease diagnosis and surgery, military planning and battle command, customer-service avatars, artificial creativity and autonomous robots that predict then respond to crime (a “Department of Future Crime”—see also Chapter 32 and Biocriminology). Self-driving cars Gone are the days when Google was just a search engine and cars needed a driver. Google’s autonomous car project, started by Sebastian Thrun of Stanford Artificial Intelligence Laboratory, uses a Toyota Prius equipped with sensors to follow a GPS route all by itself. A robotics scientist sits in the car, but doesn’t actually drive it. Already, seven cars have traveled 1,600km (1,000 miles) with no driver and 225,000km (140,000 miles) with occasional human intervention.


pages: 138 words: 40,787

The Silent Intelligence: The Internet of Things by Daniel Kellmereit, Daniel Obodovski

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3D printing, Airbnb, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, barriers to entry, business intelligence, call centre, Clayton Christensen, cloud computing, connected car, crowdsourcing, data acquisition, en.wikipedia.org, Erik Brynjolfsson, first square of the chessboard, first square of the chessboard / second half of the chessboard, Freestyle chess, Google X / Alphabet X, Internet of things, Network effects, Paul Graham, Ray Kurzweil, RFID, self-driving car, Silicon Valley, smart cities, smart grid, software as a service, Steve Jobs, web application, Y Combinator, yield management

Manufacturing is one example of an area where things started moving much faster than human speed long ago. Machines are often not only faster than humans, but also more accurate, and they dramatically minimize the chance of human error caused by fatigue. As things around us become smarter due to remotely controlled sensors, machines will take over more and more tasks. One of the amazing things coming down the pipeline is the self-driving car. Says Astro Teller: Self-driving cars in the not-too-distant future are just going to be meaningfully better than people. It will become irresponsible and antiquated for people to drive cars. That is absolutely going to happen in the next decade. I believe that very strongly. Whether Google does it or not, reasonable people could disagree, but whether that generally is going to happen, that I feel very strongly about.

Many cars have drive by wire implemented, whereby the steering wheel is not mechanically connected to the wheels anymore, but controls a motor, which controls the wheels. In addition, modern cars are filled with sensors: detecting light for the mirrors and headlights and rain for the windshield wipers, tire pressure monitors, accelerometers, gyroscopes, and compasses. Going further down this path, the digitalization of mechanics in cars allows for the arrival of driverless cars or self-driving cars, which Google has successfully tested over the past few years. But connectivity offers even more: communication between cars to optimize traffic flow and make better decisions on behalf of the driver. Volvo, for example, has successfully demonstrated road trains as part of the EU’s SARTRE (Safe Road Trains for the Environment) Project, which has several cars following one another in a platoon formation; the lead car has a professional driver taking responsibility for the platoon, while following vehicles operate in a semi-autonomous mode, reducing the distance between the vehicles, and reducing drag and fuel consumption, while getting to their destination faster.23 You may be familiar with the crowd-sourced navigation application Waze, which is one of the most accurate personal navigation applications today because it uses real-time traffic and construction information provided by users.


pages: 222 words: 53,317

Overcomplicated: Technology at the Limits of Comprehension by Samuel Arbesman

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3D printing, algorithmic trading, Anton Chekhov, Apple II, Benoit Mandelbrot, citation needed, combinatorial explosion, Danny Hillis, David Brooks, discovery of the americas, en.wikipedia.org, Erik Brynjolfsson, Flash crash, friendly AI, game design, Google X / Alphabet X, Googley, HyperCard, Inbox Zero, Isaac Newton, iterative process, Kevin Kelly, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, mandelbrot fractal, Minecraft, Netflix Prize, Nicholas Carr, Parkinson's law, Ray Kurzweil, recommendation engine, Richard Feynman, Richard Feynman, Richard Feynman: Challenger O-ring, Second Machine Age, self-driving car, software studies, statistical model, Steve Jobs, Steve Wozniak, Steven Pinker, Stewart Brand, superintelligent machines, Therac-25, Tyler Cowen: Great Stagnation, urban planning, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, Y2K

I’ve noticed that when faced with such massive complexity, we tend to respond at one of two extremes: either with fear in the face of the unknown, or with a reverential and unquestioning approach to technology. Fear is a natural response, given how often we are confronted with articles on such topics as the threat of killer machines, the dawn of superintelligent computers with powers far beyond our ken, or the question of whether we can program self-driving cars to avoid hitting jaywalkers. These are technologies so complex that even the experts don’t completely understand them, and they also happen to be quite formidable. This combination often leads us to approach them with alarm and worry. Even if we aren’t afraid of our technological systems, many of us still maintain an attitude of wariness and distaste toward the algorithms and technologies that surround us, particularly when we are confronted with their phenomenal power.

Systems we build to reflect the world: That the complexity of the world is reflected in the complexity of our systems is also discussed in Vikram Chandra, Geek Sublime: The Beauty of Code, the Code of Beauty (Minneapolis: Graywolf Press, 2014). One need not always end up with messy code because the world is messy, but it does often happen. Fortunately, there are ways to mitigate it. See Steve McConnell, Code Complete: A Practical Handbook of Software Construction, 2nd ed. (Redmond, WA: Microsoft Press, 2004), 583. building a self-driving vehicle: The complexity of building self-driving cars was discussed by Google[x]’s “Captain of Moonshots” in his closing keynote address at South by Southwest Interactive (SXSW) 2015: Astro Teller, “How to Make Moonshots,” Backchannel, March 17, 2015, https://medium.com/backchannel/how-to-make-moonshots-65845011a277. the exceptions that nonetheless have to be dealt with: One solution is to use humans to manually troubleshoot, or at least hard-code, the exceptions.

shifting the car into neutral: “Customer FAQs Regarding the Sticking Accelerator Pedal and Floor Mat Pedal Entrapment Recalls,” Toyota Pressroom, accessed April 27, 2015, http://pressroom.toyota.com/article_print.cfm?article_id=1861. isn’t the worst thing to tell someone: I am thankful for this insight, as well the insights related to limitative theorems, from discussion with folks from the Department of Philosophy at the University of Kansas. incomprehensible systems are the new reality: For example, just because we might not fully grasp all the details of a self-driving car, that doesn’t mean that it can’t be much safer than one driven by a person. And by the way, we already don’t really understand the car driven by a person, let alone the driver himself! the “unthinkable present”: Quoted in Carlin Romano, America the Philosophical (New York: Alfred A. Knopf, 2012), 501. Index The page numbers in this index refer to the printed version of this book.


pages: 179 words: 43,441

The Fourth Industrial Revolution by Klaus Schwab

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3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, barriers to entry, Baxter: Rethink Robotics, bitcoin, blockchain, Buckminster Fuller, call centre, clean water, collaborative consumption, conceptual framework, continuous integration, crowdsourcing, disintermediation, distributed ledger, Edward Snowden, Elon Musk, epigenetics, Erik Brynjolfsson, future of work, global value chain, Google Glasses, income inequality, Internet Archive, Internet of things, invention of the steam engine, job automation, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, life extension, Lyft, megacity, meta analysis, meta-analysis, more computing power than Apollo, mutually assured destruction, Narrative Science, Network effects, Nicholas Carr, personalized medicine, precariat, precision agriculture, Productivity paradox, race to the bottom, randomized controlled trial, reshoring, RFID, rising living standards, Second Machine Age, secular stagnation, self-driving car, sharing economy, Silicon Valley, smart cities, smart contracts, software as a service, Stephen Hawking, Steve Jobs, Steven Levy, Stuxnet, The Spirit Level, total factor productivity, transaction costs, Uber and Lyft, Watson beat the top human players on Jeopardy!, WikiLeaks, winner-take-all economy, women in the workforce, working-age population, Y Combinator, Zipcar

Positive impacts – Improved safety – More time for focusing on work and/or consuming media content – Effect on the environment – Less stress and road rage – Improved mobility for those older and disabled, among others – Adoption of electric vehicles Negative impacts – Job losses (taxi and truck drivers, car industry) – Upending of insurance and roadside assistance (“pay more to drive yourself”) – Decreased revenue from traffic infringements – Less car ownership – Legal structures for driving – Lobbying against automation (people not allowed to drive on freeways) – Hacking/cyber attacks The shift in action In October 2015, Tesla made its cars that were sold over the last year in the US semi-autonomous via a software update. Source: http://www.wired.com/2015/10/tesla-self-driving-over-air-update-live Google plans to make autonomous cars available to the public in 2020. Source: Thomas Halleck, 14 January 2015, “Google Inc. Says Self-Driving Car Will Be Ready By 2020”, International Business Times: http://www.ibtimes.com/google-inc-says-self-driving-car-will-be-ready-2020-1784150 In the summer of 2015, two hackers demonstrated their ability to hack into a moving car, controlling its dashboard functions, steering, brakes etc., all through the vehicle’s entertainment system. Source: http://www.wired.com/2015/07/hackers-remotely-kill-jeep-highway/ The first state in the United States (Nevada) to pass a law allowing driverless (autonomous) cares did so in 2012.

In doing so, they are making (and even “growing”) objects that are continuously mutable and adaptable (hallmarks of the plant and animal kingdoms).4 In The Second Machine Age, Brynjolfsson and McAfee argue that computers are so dexterous that it is virtually impossible to predict what applications they may be used for in just a few years. Artificial intelligence (AI) is all around us, from self-driving cars and drones to virtual assistants and translation software. This is transforming our lives. AI has made impressive progress, driven by exponential increases in computing power and by the availability of vast amounts of data, from software used to discover new drugs to algorithms that predict our cultural interests. Many of these algorithms learn from the “bread crumb” trails of data that we leave in the digital world.


pages: 602 words: 177,874

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

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3D printing, additive manufacturing, affirmative action, Airbnb, AltaVista, Amazon Web Services, autonomous vehicles, Ayatollah Khomeini, barriers to entry, Berlin Wall, Bernie Sanders, bitcoin, blockchain, business process, call centre, centre right, Clayton Christensen, clean water, cloud computing, corporate social responsibility, crowdsourcing, David Brooks, demand response, demographic dividend, demographic transition, Deng Xiaoping, Donald Trump, Erik Brynjolfsson, failed state, Fall of the Berlin Wall, Ferguson, Missouri, first square of the chessboard / second half of the chessboard, Flash crash, game design, gig economy, global supply chain, illegal immigration, immigration reform, income inequality, indoor plumbing, Internet of things, invention of the steam engine, inventory management, Jeff Bezos, job automation, John von Neumann, Khan Academy, Kickstarter, knowledge economy, knowledge worker, land tenure, linear programming, low skilled workers, Lyft, Mark Zuckerberg, Maui Hawaii, Menlo Park, Mikhail Gorbachev, mutually assured destruction, pattern recognition, planetary scale, pull request, Ralph Waldo Emerson, ransomware, Ray Kurzweil, Richard Florida, ride hailing / ride sharing, Robert Gordon, Ronald Reagan, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Skype, smart cities, South China Sea, Steve Jobs, TaskRabbit, Thomas L Friedman, transaction costs, Transnistria, urban decay, urban planning, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y2K, Yogi Berra

When you keep doubling something for fifty years you start to get to some very big numbers, and eventually you start to see some very funky things that you have never seen before. The authors argued that Moore’s law just entered the “second half of the chessboard,” where the doubling has gotten so big and fast we’re starting to see stuff that is fundamentally different in power and capability from anything we have seen before—self-driving cars, computers that can think on their own and beat any human in chess or Jeopardy! or even Go, a 2,500-year-old board game considered vastly more complicated than chess. That is what happens “when the rate of change and the acceleration of the rate of change both increase at the same time,” said McAfee, and “we haven’t seen anything yet!” So, at one level, my view of the Machine today is built on the shoulders of Brynjolfsson and McAfee’s fundamental insight into how the steady acceleration in Moore’s law has affected technology—but I think the Machine today is even more complicated.

This mismatch, as we will see, is at the center of much of the turmoil roiling politics and society in both developed and developing countries today. It now constitutes probably the most important governance challenge across the globe. Astro Teller’s Graph The most illuminating illustration of this phenomenon was sketched out for me by Eric “Astro” Teller, the CEO of Google’s X research and development lab, which produced Google’s self-driving car, among other innovations. Appropriately enough, Teller’s formal title at X is “Captain of Moonshots.” Imagine someone whose whole mandate is to come to the office every day and, with his colleagues, produce moonshots—turning what others would consider science fiction into products and services that could transform how we live and work. His paternal grandfather was the physicist Edward Teller, designer of the hydrogen bomb, and his maternal grandfather was Gérard Debreu, a Nobel Prize–winning economist.

At first it moves up very gradually, then it starts to slope higher as innovations build on innovations that have come before, and then it starts to soar straight to the sky. What would be on that line? Think of the introduction of the printing press, the telegraph, the manual typewriter, the Telex, the mainframe computer, the first word processors, the PC, the Internet, the laptop, the mobile phone, search, mobile apps, big data, virtual reality, human-genome sequencing, artificial intelligence, and the self-driving car. A thousand years ago, Teller explained, that curve representing scientific and technological progress rose so gradually that it could take one hundred years for the world to look and feel dramatically different. For instance, it took centuries for the longbow to go from development into military use in Europe in the late thirteenth century. If you lived in the twelfth century, your basic life was not all that different than if you lived in the eleventh century.


pages: 352 words: 104,411

Rush Hour by Iain Gately

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Albert Einstein, autonomous vehicles, Beeching cuts, blue-collar work, British Empire, business intelligence, business process, business process outsourcing, call centre, car-free, Clapham omnibus, cognitive dissonance, congestion charging, connected car, DARPA: Urban Challenge, Dean Kamen, decarbonisation, Deng Xiaoping, Detroit bankruptcy, don't be evil, Elon Musk, extreme commuting, Google bus, Henri Poincaré, Hyperloop, Jeff Bezos, low skilled workers, postnationalism / post nation state, Ralph Waldo Emerson, remote working, self-driving car, Silicon Valley, stakhanovite, Steve Jobs, telepresence, Tesla Model S, urban planning, éminence grise

qt=qt0375405. 320 For Chunka Mui and driverless cars, see: http://www.forbes.com/sites/chunkamui/. 321 ‘typical commuter arteries’, Chris Knapman, ‘Large-scale trial of driverless cars to begin on public roads’, Daily Telegraph, 2 December 2013: http://www.telegraph.co.uk/motoring/news/10484839/Large-scale-trial-of-driverless-cars-to-begin-on-public- roads.html. 321 For KPMG and driverless cars, see ‘Self-driving cars: the next revolution’: https://www.kpmg.com/US/en/IssuesAndInsights/ArticlesPublications/Documents/self-driving-cars-next-revolution.pdf. 326 ‘Something suddenly falls off a truck ahead of the car. Can the system react faster’, Dan Neil, ‘Driverless Cars’. 328 A ‘bold commitment to modernisation’, Jim Pickard, ‘Mandelson fears HS2 will prove an “expensive mistake”’, Financial Times, 2 July 2013. 328 ‘live in a rail bubble’, Matt Ridley, ‘Hadrian’s wall was a marvellous mistake; so is HS2’, 26 July 2013: http://www.rationaloptimist.com/blog/hadrian%27s-wall-was-a-marvellous-mistake-so- is-hs2.aspx. 329 For ‘reached the end of its life’, see: http://www.businessweek.com/articles/2013-07-12/french-wreck-reveals-hidden-danger-in-its-vaunted-train-system. 331 For ‘barf ride’, see: http://pedestrianobservations.wordpress.com/2013/08/13/loopy-ideas-are-fine-if- youre-an-entrepreneur/. 333 For population estimates and the world’s carrying capacity, see World Population Monitoring 2001, United Nations, Department of Economic and Social Affairs, Population Division, New York, 2001, pp. 31ff. 333 ‘occasional vertical and random horizontal low speed vehicular or moving-belt travel’, John Heaver Fremlin, ‘How Many People Can the World Support?’

Every car behind the leader would benefit from ‘drafting’ (slipstreaming) and cut its fuel use by a quarter. Traffic density could double or treble in rush hour, without creating the need to build any new roads. The vehicles could be fun to ride in, too. Instead of a row or two of forward-facing seats, there’d be space for beds, chandeliers, mini-bars, workstations, or a croupier and a roulette wheel. Commuting would become an adventure, or a form of luxury travel once again. Self-driving cars will also be wonderfully easy to use – the ultimate taxi service. You’ll be able to text for one, anywhere, anytime. Those with handicaps, alcoholics, and other classes of people currently disbarred from driving would also be empowered by the hands-free revolution. Elderly people who, whether through sight loss or growing immobility, are unable to drive themselves, will not have to lose the freedom to ride in a car that they have enjoyed all their lives.

Even auto-makers have spotted the growing indifference towards wheels. In the opinion of Catherine Lovazzano, senior manager for consumer trends at Chrysler, the motorcar is no longer ‘the iconic freedom machine that it might have been for a baby boomer’. Generation Now, together with the Digital Natives in the age cohort beneath them, make up nearly half the population of America, and if the Natives are as apathetic as the Nows, then self-driving cars will have a giant market. A similar lack of interest in driving among the young is apparent throughout the West. In a 2012 feature, ‘Seeing the back of the car’, the UK news magazine The Economist noted that ‘All over the rich world, young people are getting their licences later than they used to… in Britain, Canada, France, Norway, South Korea and Sweden’ as well as in America. ‘Even in Germany, car-culture-vulture of Europe, the share of young households without cars increased from 20% to 28% between 1998 and 2008.’


pages: 319 words: 90,965

The End of College: Creating the Future of Learning and the University of Everywhere by Kevin Carey

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Albert Einstein, barriers to entry, Berlin Wall, business intelligence, carbon-based life, Claude Shannon: information theory, complexity theory, declining real wages, deliberate practice, discrete time, double helix, Douglas Engelbart, Downton Abbey, Drosophila, Firefox, Frank Gehry, Google X / Alphabet X, informal economy, invention of the printing press, inventory management, Khan Academy, Kickstarter, low skilled workers, Lyft, Mark Zuckerberg, meta analysis, meta-analysis, natural language processing, Network effects, open borders, pattern recognition, Peter Thiel, pez dispenser, ride hailing / ride sharing, Ronald Reagan, Sand Hill Road, self-driving car, Silicon Valley, Silicon Valley startup, social web, South of Market, San Francisco, speech recognition, Steve Jobs, technoutopianism, transcontinental railway, Vannevar Bush

He dressed in jeans and stylish T-shirts and married a beautiful professor of comparative literature who liked to tease him about his techno-utopian ways. In March of that year, Thrun was invited to TED (Technology, Education, Design), the annual festival of technologist self-congratulation, where he stood before a rapt audience and described how he and his colleagues at Google had built a self-driving car. Afterward, Thrun hung around the conference to watch the other presenters, including an energetic former hedge fund analyst named Salman Khan. Khan had computer science degrees from MIT and an MBA from Harvard, and had become recently famous for creating a series of instructional videos for elementary, middle, and high school children that had attracted millions of views on YouTube. The videos became the basis for Khan’s hugely popular education web site, Khan Academy.

While they were shuttling through a series of meetings with university lawyers and bureaucrats, CS221 online enrollment continued to grow. Then a New York Times reporter wrote a story about the course, and suddenly word shot around the world. Enrollment reached six figures and continued to climb. Sebastian Thrun had done nothing particularly interesting from an educational or technological perspective. He did not invent the college equivalent of a self-driving car. There were already thousands of lecture videos on YouTube and iTunes by 2011 and millions of students enrolled in online courses offered by accredited colleges and universities. Because it was huge and free, CS221 was soon described as a “massive open online course,” or MOOC. But Thrun hadn’t invented MOOCs, either; the term had first been used three years earlier to describe a course on the nature of learning taught at the University of Manitoba by a pair of Canadian professors named George Siemens and Stephen Downes.

In describing how the brain reacts to surprise, Lue said that “everything is a function of risk and opportunity.” To survive and prosper in the world with limited cognitive capacity, humans filter waves of constant sensory information through neural patterns—heuristics and mental shortcuts that our minds use to weigh the odds that what we are sensing is familiar and categorizable based on our past experience. Sebastian Thrun’s self-driving car does this with Bayesian statistics built into silicon and code, while the human mind uses electrochemical processes that we still don’t fully understand. But the underlying principle is the same: Based on the pattern of lines and shapes and edges, that is probably a boulder and I should drive around it. That is probably a group of three young women eating lunch at a table near the sushi bar and I should pay them no mind.


pages: 696 words: 143,736

The Age of Spiritual Machines: When Computers Exceed Human Intelligence by Ray Kurzweil

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Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Any sufficiently advanced technology is indistinguishable from magic, Buckminster Fuller, call centre, cellular automata, combinatorial explosion, complexity theory, computer age, computer vision, cosmological constant, cosmological principle, Danny Hillis, double helix, Douglas Hofstadter, first square of the chessboard / second half of the chessboard, fudge factor, George Gilder, Gödel, Escher, Bach, I think there is a world market for maybe five computers, information retrieval, invention of movable type, Isaac Newton, iterative process, Jacquard loom, Jacquard loom, John von Neumann, Lao Tzu, Law of Accelerating Returns, mandelbrot fractal, Marshall McLuhan, Menlo Park, natural language processing, Norbert Wiener, optical character recognition, pattern recognition, phenotype, Ralph Waldo Emerson, Ray Kurzweil, Richard Feynman, Richard Feynman, Schrödinger's Cat, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, speech recognition, Steven Pinker, Stewart Brand, stochastic process, technological singularity, Ted Kaczynski, telepresence, the medium is the message, traveling salesman, Turing machine, Turing test, Whole Earth Review, Y2K

• Prediction: The technology for the “cybernetic chauffeur” (self-driving cars using special sensors in the roads) will become available by the end of the 1990s with implementation on major highways feasible during the first decade of the twenty-first century.What Happened: Self-driving cars are being tested in Los Angeles, London, Tokyo, and other cities. There were extensive successful tests on Interstate 15 in southern California during 1997. City planners now realize that automated driving technologies will greatly expand the capacity of existing roads. Installing the requisite sensors on a highway costs only about $10,000 per mile, compared to $1 to $10 million per mile for building new highways. Automated highways and self-driving cars will also eliminate most accidents on these roads. The U.S.

This three-dimensional storage method requires a million atoms for each bit and could achieve a trillion bits of storage for each cubic centimeter. Crystalline computing also refers to the possibility of growing computers as crystals. CSR See Continuous speech recognition. Cybernetic artist A computer program that is able to create original artwork in poetry, visual art, or music. Cybernetic artists will become increasingly commonplace starting in 2009. Cybernetic chauffeur Self-driving cars that use special sensors in the roads. Self driving cars are being experimented with in the late 1990s, with implementation on major highways feasible during the first decade of the twenty-first century. Cybernetic poet A computer program that is able to create original poetry. Cybernetics A term coined by Norbert Wiener to describe the “science of control and communication in animals and machines.” Cybernetics is based on the theory that intelligent living beings adapt to their environments and accomplish objectives primarily by reacting to feedback from their surroundings.


pages: 525 words: 116,295

The New Digital Age: Transforming Nations, Businesses, and Our Lives by Eric Schmidt, Jared Cohen

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3D printing, access to a mobile phone, additive manufacturing, airport security, Amazon Mechanical Turk, Amazon Web Services, anti-communist, augmented reality, Ayatollah Khomeini, barriers to entry, bitcoin, borderless world, call centre, Chelsea Manning, citizen journalism, clean water, cloud computing, crowdsourcing, data acquisition, Dean Kamen, Elon Musk, failed state, fear of failure, Filter Bubble, Google Earth, Google Glasses, hive mind, income inequality, information trail, invention of the printing press, job automation, Julian Assange, Khan Academy, Kickstarter, knowledge economy, Law of Accelerating Returns, market fundamentalism, means of production, mobile money, mutually assured destruction, Naomi Klein, offshore financial centre, peer-to-peer lending, personalized medicine, Peter Singer: altruism, Ray Kurzweil, RFID, self-driving car, sentiment analysis, Silicon Valley, Skype, Snapchat, social graph, speech recognition, Steve Jobs, Steven Pinker, Stewart Brand, Stuxnet, The Wisdom of Crowds, upwardly mobile, Whole Earth Catalog, WikiLeaks, young professional, zero day

While some of the very exciting new possibilities in transportation, like supersonic tube commutes and suborbital space travel, are still far in the distance, ubiquitous self-driving cars are imminent. Google’s fleet of driverless cars, built by a team of Google and Stanford University engineers, has logged hundreds of thousands of miles without incident, and other models will soon join it on the road. Rather than replacing drivers altogether, the liminal step will be a “driver-assist” approach, where the self-driving option can be turned on, just as an airline captain turns on the autopilot. Government authorities are already well versed on self-driving cars and their potential—in 2012, Nevada became the first state to issue licenses to driverless cars, and later that same year California also affirmed their legality.

distributing preloaded tablets to primary-age kids: David Talbot, “Given Tablets but No Teachers, Ethiopian Children Teach Themselves,” Technology Review, October 29, 2012, http://www.technologyreview.com/news/506466/given-tablets-but-no-teachers-ethiopian-children-teach-themselves/. one of the lowest rates of literacy in the world: “Field Listing: Literacy,” CIA, World Fact Book, accessed October 11, 2012, https://www.cia.gov/library/publications/the-world-factbook/fields/2103.html#af. in 2012, Nevada became the first state to issue licenses to driverless cars: Chris Gaylord, “Ready for a Self-Driving Car? Check Your Driveway,” Christian Science Monitor, June 25, 2012, http://www.csmonitor.com/Innovation/Tech/2012/0625/Ready-for-a-self-driving-car-Check-your-driveway. California also affirmed their legality: James Temple, “California Affirms Legality of Driverless Cars,” The Tech Chronicles (blog), San Francisco Chronicle, September 25, 2012, http://blog.sfgate.com/techchron/2012/09/25/california-legalizes-driverless-cars/; Florida has passed a similar law. See Joann Muller, “With Driverless Cars, Once Again It Is California Leading the Way,” Forbes, September 26, 2012, http://www.forbes.com/sites/joannmuller/2012/09/26/with-driverless-cars-once-again-it-is-california-leading-the-way/.

Sarkozy, Nicolas satellite positioning Saud, Alwaleed bin Talal al- Saudi Arabia, 2.1, 2.2, 3.1, 4.1, 6.1 “Saudi People Demand Hamza Kashgari’s Execution, The” (Facebook group) Save the Children scale effects Schengen Agreement Scott-Railton, John search-engine optimization (SEO), n secession movements secure sockets layer (SSL) security, 2.1, 2.2, 2.3, 2.4 in autocracies censorship and company policy on, 2.1, 2.2 privacy vs., itr.1, 5.1, 5.2 in schools selective memory self-control self-driving cars, itr.1, 1.1, 1.2 September 11, 2001, terrorist attacks of, 3.1, 5.1 Serbia, 4.1, 6.1 servers Shafik, Ahmed shanzhai network, 1.1 sharia Shia Islam Shia uprising Shiites Shock Doctrine, The (Klein), 7.1n short-message-service (SMS) platform, 4.1, 7.1 Shukla, Prakash Sichuan Hongda SIM cards, 5.1, 5.2, 5.3, 6.1, 6.2, nts.1 Singapore, 2.1, 4.1 Singer, Peter, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8 singularity SkyGrabber Skype, 2.1, 2.2, 2.3, 3.1, 5.1 sleeping rhythms Slim Helú, Carlos smart phones, itr.1, 1.1, 1.2, 5.1, 5.2, 7.1 in failed states peer-to-peer capability on Snapchat Snoad, Nigel social networking, 2.1, 4.1, 5.1 social-networking profiles social prosthetics social robots “socioeconomically at risk” people Solidarity Somalia, 2.1, 5.1, 5.2, 5.3, 6.1n, 210, 7.1, 7.2, 7.3 Sony South Africa, 4.1, 7.1 South Central Los Angeles Southern African Development Community (SADC) South Korea, 3.1, 3.2 South Sudan Soviet Union, 4.1, 6.1 Spain Speak2Tweet Special Weapons Observation Reconnaissance Detection System (SWORDS), 6.1, 6.2 speech-recognition technology spoofing Spotify Sputnik spyware, 3.1, 6.1 Stanford University statecraft State Department, U.S., 5.1, 7.1 states: ambition of future of Storyful, n Strategic Arms Limitation Talks (SALT) Stuxnet worm, 3.1, 3.2 suborbital space travel Sudan suggestion engines Summit Against Violent Extremism Sunni Web supersonic tube commutes supplements supply chains Supreme Council of the Armed Forces (SCAF) surveillance cameras Sweden switches Switzerland synthetic skin grafts Syria, 2.1, 3.1, 4.1, 4.2 uprising in Syrian Telecommunications Establishment tablets, 1.1, 1.2, 7.1 holographic Tacocopter Tahrir Square, 4.1, 4.2, 4.3 Taiwan Taliban, 2.1, 5.1, 7.1 TALON Tanzania technology companies, 2.1, 3.1 Tehran Telecom Egypt telecommunications, reconstruction of telecommunications companies Télécoms Sans Frontières television terrorism, terrorists, 4.1, 5.1, con.1 chat rooms of connectivity and cyber, 3.1n, 153–5, 5.1 hacking by Thailand Thomson Reuters Foundation thought-controlled robotic motion 3-D printing, 1.1, 2.1, 2.2, 5.1 thumbprints Tiananmen Square protest, 3.1, 4.1 Tibet time zones tissue engineers to-do lists Tor service, 2.1, 2.2, 2.3, 3.1, 5.1n Total Information Awareness (TIA) trade transmission towers transparency, 2.1, 4.1 “trespass to chattels” tort, n Trojan horse viruses, 2.1, 3.1 tsunami Tuareg fighters Tumblr Tunisia, 4.1, 4.2, 4.3, 4.4, 4.5 Turkey, 3.1, 3.2, 4.1, 5.1, 6.1 Tutsis Twa Twitter, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 3.1, 3.2, 4.1, 4.2, 5.1, 5.2, 6.1, 7.1, 7.2, nts.1 Uganda Uighurs, 3.1, 6.1 Ukraine unemployment UNESCO World Heritage Centre unique identification (UID) program United Arab Emirates, 2.1, 2.2, 2.3 United Kingdom, 2.1, 2.2, 2.3, 3.1 United Nations, 4.1, 5.1, 6.1, 7.1 United Nations Security Council, 3.1n, 214, 7.1 United Russia party United States, 3.1, 3.2, 3.3, 4.1, 5.1, 7.1 engineering sector in United States Agency for International Development (USAID) United States Cyber Command (USCYBERCOM) unmanned aerial vehicles (UAVs), 6.1, 6.2, 6.3, 6.4, 6.5 Ürümqi riots user-generated content Ushahidi vacuuming, 1.1, 1.2 Valspar Corporation Venezuela, 2.1, 2.2, 6.1 verification video cameras video chats video games videos Vietcong Vietnam vigilantism violence virtual espionage virtual governance virtual identities, itr.1, 2.1, 2.2 virtual juvenile records virtual kidnapping virtual private networks (VPNs), 2.1, 3.1 virtual reality virtual statehood viruses vitamins Vodafone, 4.1, 7.1 Vodafone/Raya voice-over-Internet-protocol (VoIP) calls, 2.1, 5.1 voice-recognition software, 1.1, 2.1, 5.1 Voilà VPAA statute, n Walesa, Lech walled garden Wall Street Journal, 97 war, itr.1, itr.2, 6.1 decline in Wardak, Abdul Rahim warfare: automated remote warlords, 2.1, 2.2 Watergate Watergate break-in Waters, Carol weapons of mass destruction wearable technology weibos, 62 Wen Jiabao Wenzhou, China West Africa whistle-blowers whistle-blowing websites Who Controls the Internet?


pages: 606 words: 157,120

To Save Everything, Click Here: The Folly of Technological Solutionism by Evgeny Morozov

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3D printing, algorithmic trading, Amazon Mechanical Turk, Andrew Keen, augmented reality, Automated Insights, Berlin Wall, big data - Walmart - Pop Tarts, Buckminster Fuller, call centre, carbon footprint, Cass Sunstein, choice architecture, citizen journalism, cloud computing, cognitive bias, crowdsourcing, data acquisition, Dava Sobel, disintermediation, East Village, en.wikipedia.org, Fall of the Berlin Wall, Filter Bubble, Firefox, Francis Fukuyama: the end of history, frictionless, future of journalism, game design, Gary Taubes, Google Glasses, illegal immigration, income inequality, invention of the printing press, Jane Jacobs, Jean Tirole, Jeff Bezos, jimmy wales, Julian Assange, Kevin Kelly, Kickstarter, license plate recognition, lone genius, Louis Pasteur, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, Narrative Science, Nicholas Carr, packet switching, PageRank, Paul Graham, Peter Singer: altruism, Peter Thiel, pets.com, placebo effect, pre–internet, Ray Kurzweil, recommendation engine, Richard Thaler, Ronald Coase, Rosa Parks, self-driving car, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, Slavoj Žižek, smart meter, social graph, social web, stakhanovite, Steve Jobs, Steven Levy, Stuxnet, technoutopianism, the built environment, The Chicago School, The Death and Life of Great American Cities, the medium is the message, The Nature of the Firm, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, transaction costs, urban decay, urban planning, urban sprawl, Vannevar Bush, WikiLeaks

It’s easy to mistake technostructuralists for pessimists, but this is not who they are. Their point is not that such liberation through technology is illusory or inconsequential but, rather, that such liberation never happens in a vacuum and may, all things considered, actually enslave. Yes, Google’s self-driving cars would make driving easier and perhaps even cut the number of deaths on the road, but a reasonable transportation system ought to pursue many other objectives. Would self-driving cars result in inferior public transportation as more people took up driving? Would it lead to even greater suburban sprawl as, now that they no longer had to drive, people could do e-mail during their commute and thus would tolerate spending more time in the car? To technostructuralists, Amazon’s foray into publishing cannot just be a tale of individual empowerment through new, better tools for reading and publishing.

As digital media make participation easier, more and more citizens ditch bowling alone—only to take up blogging together. Even those who’ve never bothered to vote in the past are finally provided with the right incentives—naturally, as a part of an online game where they collect points for saving humanity—and so they rush to use their smartphones to “check in” at the voting booth. Thankfully, getting there is no longer a chore; self-driving cars have been invented for the purpose of getting people from place to place. Streets are clean and shiny; keeping them that way is also part of an elaborate online game. Appeals to civic duty and responsibility to fellow citizens have all but disappeared—and why wouldn’t they, when getting people to do things by leveraging their eagerness to earn points, badges, and virtual currencies is so much more effective?

See Perversity-futility-jeopardy triad Galileo Galison, Peter Galton, Francis Gambling addiction Game mechanics Games and gamification and humanitarianism and smartphones vs. reality Gamification and adversarial design and degrading environment, enjoyment in and efficiency vs. inefficiency and games and games vs. reality literature and motivation and rewards vs. citizenship Gardner, James Garland, David Gasto Público Bahiense (website) Gatekeepers Gates, Bill Gates, Kelly Gawker Gender discrimination Generativity theory Genetic engineering Gertner, Joe Ghonim, Wael Gillespie, Tarleton Global Integrity Godin, Benoit Google AdSense and advertising and algorithms and algorithms, and democracy and algorithms, neutrality and objectivity of and badges and citizenship and content business and ethics GPS-enabled Android phones and Huffington Post and information organization and legal challenges and mirror imagery and openness PageRank Places and predictive policing and privacy Project Glass goggles and scientific credentials and self-driving cars values and WiFi networks and Zagat Google+ Google Autocomplete Google Buzz Google News and badges Google Scholar Government and networks role of Government, US, and WikiLeaks GPS driving data GPS-enabled Android phones (Google) Grafton, Anthony Graham, Paul Grant, Ruth Green, Donald Green, Shane Greenwald, Glenn Guernica Gutenberg, Johannes Gutshot-detection systems Hanrahan, Nancy Harvey, David Hayek, Friedrich Heald, David Health and gamification monitoring device Heller, Nathaniel Hibbing, John Hierarchies, and networks Hieronymi, Pamela Highlighting and shading Hildebrandt, Mireille Hill, Kashmir Hirschman, Albert Historians, and Internet debate History as irrelevant of technology Hoffman, Reid Holiday, Ryan Holocaust Horkheimer, Max Howard Dean for Iowa Game Huffington Post, The Humanitarianism, and games Hunch.com Hypocrisy Illich, Ivan Image-recognition software Imperfection Impermium Incentives Information cascades theory Information consumption self-tracking of Information emperors Information industries and government history of Information organization Information-processing imperative Information reductionism Information technology InfoWorld (website) Innovation and justice and technology unintended consequences of Innovation talk Institutions, and networks Intel Intermediaries.


pages: 479 words: 144,453

Homo Deus: A Brief History of Tomorrow by Yuval Noah Harari

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23andMe, agricultural Revolution, algorithmic trading, Anne Wojcicki, anti-communist, Anton Chekhov, autonomous vehicles, Berlin Wall, call centre, Chris Urmson, cognitive dissonance, Columbian Exchange, computer age, Deng Xiaoping, don't be evil, European colonialism, experimental subject, falling living standards, Flash crash, Frank Levy and Richard Murnane: The New Division of Labor, glass ceiling, global village, invention of writing, invisible hand, Isaac Newton, job automation, Kevin Kelly, means of production, Mikhail Gorbachev, Minecraft, Moneyball by Michael Lewis explains big data, mutually assured destruction, new economy, pattern recognition, Peter Thiel, placebo effect, Ray Kurzweil, self-driving car, Silicon Valley, Silicon Valley ideology, stem cell, Steven Pinker, telemarketer, too big to fail, trade route, Turing machine, Turing test, ultimatum game, Watson beat the top human players on Jeopardy!

In fact, the vast majority of our conscious decisions do not involve such issues at all. 6. Christopher Steiner, Automate This: How Algorithms Came to Rule Our World (New York: Penguin, 2012), 215; Tom Vanderbilt, ‘Let the Robot Drive: The Autonomous Car of the Future is Here’, Wired, 20 January 2012, accessed 21 December 2014, http://www.wired.com/2012/01/ff_autonomouscars/all/; Chris Urmson, ‘The Self-Driving Car Logs More Miles on New Wheels’, Google Official Blog, 7 August 2012, accessed 23 December 2014, http://googleblog.blogspot.hu/2012/08/the-self-driving-car-logs-more-miles-on.html; Matt Richtel and Conor Dougherty, ‘Google’s Driverless Cars Run into Problem: Cars with Drivers’, New York Times, 1 September 2015, accessed 2 September 2015, http://www.nytimes.com/2015/09/02/technology/personaltech/google-says-its-not-the-driverless-cars-fault-its-other-drivers.html?_r=1. 7.

But in the twenty-first century, this is becoming an urgent political and economic issue. And it is sobering to realise that, at least for armies and corporations, the answer is straightforward: intelligence is mandatory but consciousness is optional. Armies and corporations cannot function without intelligent agents, but they don’t need consciousness and subjective experiences. The conscious experiences of a flesh-and-blood taxi driver are infinitely richer than those of a self-driving car, which feels absolutely nothing. The taxi driver can enjoy music while navigating the busy streets of Seoul. His mind may expand in awe as he looks up at the stars and contemplates the mysteries of the universe. His eyes may fill with tears of joy when he sees his baby girl taking her very first step. But the system doesn’t need all that from a taxi driver. All it really wants is to bring passengers from point A to point B as quickly, safely and cheaply as possible.

And the autonomous car will soon be able to do that far better than a human driver, even though it cannot enjoy music or be awestruck by the magic of existence. Indeed, if we forbid humans to drive taxis and cars altogether, and give computer algorithms monopoly over traffic, we can then connect all vehicles to a single network, and thereby make car accidents virtually impossible. In August 2015, one of Google’s experimental self-driving cars had an accident. As it approached a crossing and detected pedestrians wishing to cross, it applied its brakes. A moment later it was hit from behind by a sedan whose careless human driver was perhaps contemplating the mysteries of the universe instead of watching the road. This could not have happened if both vehicles were steered by interlinked computers. The controlling algorithm would have known the position and intentions of every vehicle on the road, and would not have allowed two of its marionettes to collide.


pages: 252 words: 70,424

The Self-Made Billionaire Effect: How Extreme Producers Create Massive Value by John Sviokla, Mitch Cohen

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Cass Sunstein, Colonization of Mars, Daniel Kahneman / Amos Tversky, Elon Musk, Frederick Winslow Taylor, game design, global supply chain, James Dyson, Jeff Bezos, John Harrison: Longitude, Jony Ive, loss aversion, Mark Zuckerberg, market design, paper trading, RAND corporation, randomized controlled trial, Richard Thaler, risk tolerance, self-driving car, Silicon Valley, smart meter, Steve Ballmer, Steve Jobs, Steve Wozniak, Tony Hsieh, Toyota Production System, young professional

The results are marvelous, but there is no mistake that it moves—and should move—against the institutional grain in a way that will make Performers uncomfortable. One example of this discomfort and its negative effects comes from an effort that has been getting a great deal of press in recent months: the self-driving car. Right now Google owns this space. Google is the brand that has been working on the technology, and there are Google leaders out there making it real in the marketplace and evangelizing its positive applications. Reportedly, the team that went to Sergey Brin and Larry Page for permission to work on the self-driving car was pushed to be more ambitious by the founding Producers—Page and Brin gave the development team the green light to make the car only if it would be able to travel a thousand miles in both highway and city contexts with limited GPS access, a challenge the car achieved in fifteen months.6 But Google’s prominence in this market raises a thorny question: Why aren’t leading car manufacturers pursuing that charge?

They have difficulty with the idea that their innovations—historically built for proprietary use—might reside in a competitor’s product; they can’t or won’t construct a narrative of change that shows employees, customers, and stakeholders the ways in which this new disruptive model is simply a natural evolution of what they have always done. Compare the way the automotive industry has recused itself from Inventive Execution on the self-driving car with the way the Producer Bill Gates inserted Microsoft into the Internet space. In 1995, Gates knew Microsoft was late to the Internet party, and that Netscape, Sun Microsystems, and other competitors had already taken over and defined the browser and net server markets which served as the gateways to defining how users store, find, organize, and use information. In a letter he wrote to all Microsoft employees, Gates articulated his vision for why telecommunications mattered, why the Internet was at the center at that time of the new era of networked communications, and why the browser and other Web-enabling technologies were not a top priority but rather the top priority for Microsoft.7 Gates sold his organization on the Web.


pages: 260 words: 76,223

Ctrl Alt Delete: Reboot Your Business. Reboot Your Life. Your Future Depends on It. by Mitch Joel

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3D printing, Amazon Web Services, augmented reality, call centre, clockwatching, cloud computing, Firefox, future of work, ghettoisation, Google Chrome, Google Glasses, Google Hangouts, Khan Academy, Kickstarter, Kodak vs Instagram, Lean Startup, Mark Zuckerberg, Network effects, new economy, Occupy movement, place-making, prediction markets, pre–internet, recommendation engine, Richard Florida, risk tolerance, self-driving car, Silicon Valley, Silicon Valley startup, Skype, social graph, social web, Steve Jobs, Steve Wozniak, Thomas L Friedman, Tim Cook: Apple, Tony Hsieh, WikiLeaks

The answer is, of course, yes. Instagram started out as a very derivative copy of foursquare before switching its focus to mobile photos with a social edge. Google continues to fascinate as the search engine expands into areas like online video (YouTube), mobile (Android and the Nexus line of devices), email services (Gmail), Web browsers (Google Chrome), online social networking (Google+), and beyond (self-driving cars and Google Glasses). Amazon continues to squiggle by pushing beyond selling books online into e-readers (Kindle), selling shoes (Zappos), offering cloud computing technology (Amazon Web Services), and beyond. When you actually start digging down deep into how these companies have evolved and stayed relevant, you won’t see business models that look like anything from the playbooks of Kodak or RIM.

This means that we can’t be afraid to have a more squiggly career path, and we also have to be more open to doing the big, big stuff (Steve Jobs would often talk about making a “dent in the universe”). You will hear Mark Zuckerberg talk about Facebook as the place to connect the world. Sergey Brin and Larry Page of Google often talk about Google’s mission to organize the world’s information and knowledge (and, with that, they squiggle to create self-driving cars!). It’s one thing to dream big. It’s another thing to think and do big. In this new world, the squiggle is about not being afraid of the big stuff within whatever industry you serve. If you’re not thinking about the bigger problems that face your industry, someone else is. Squiggle… uncover and go after the big ideas. Lesson #3—Get squiggly. It’s not about doing what you love or being passionate about the work that you do, but about recognizing that you may be stuck or afraid to change simply because of the decisions that you made and followed back in high school and university (or because your predecessor did things a certain way).

From smartphones and Kindles to iPads and laptops. I can’t help but wonder and think about a time in the not-too-distant future when we’re no longer carrying these devices around but they are actually in us… a part of us (subdermal implants or brain-activity-activated… who knows?). As I was walking through the airport, I noticed that the current cover story for Wired magazine is all about self-driving cars. Such instances of science fiction catching up to reality get me excited. They get me thinking more about how much I love business, and it makes me hopeful that I’ll be privileged enough to be alive long enough to see how we innovate from this very powerful moment in time that we currently find ourselves in. When I think about my career (or when I see other people thinking about their careers), it strikes me that even with goal setting and planning, it’s usually a very shortsighted vision.


pages: 677 words: 206,548

Future Crimes: Everything Is Connected, Everyone Is Vulnerable and What We Can Do About It by Marc Goodman

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23andMe, 3D printing, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, airport security, Albert Einstein, algorithmic trading, artificial general intelligence, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, Bill Joy: nanobots, bitcoin, Black Swan, blockchain, borderless world, Brian Krebs, business process, butterfly effect, call centre, Chelsea Manning, cloud computing, cognitive dissonance, computer vision, connected car, corporate governance, crowdsourcing, cryptocurrency, data acquisition, data is the new oil, Dean Kamen, disintermediation, don't be evil, double helix, Downton Abbey, Edward Snowden, Elon Musk, Erik Brynjolfsson, Filter Bubble, Firefox, Flash crash, future of work, game design, Google Chrome, Google Earth, Google Glasses, Gordon Gekko, high net worth, High speed trading, hive mind, Howard Rheingold, hypertext link, illegal immigration, impulse control, industrial robot, Internet of things, Jaron Lanier, Jeff Bezos, job automation, John Harrison: Longitude, Jony Ive, Julian Assange, Kevin Kelly, Khan Academy, Kickstarter, knowledge worker, Kuwabatake Sanjuro: assassination market, Law of Accelerating Returns, Lean Startup, license plate recognition, litecoin, M-Pesa, Mark Zuckerberg, Marshall McLuhan, Menlo Park, mobile money, more computing power than Apollo, move fast and break things, Nate Silver, national security letter, natural language processing, obamacare, Occupy movement, Oculus Rift, offshore financial centre, optical character recognition, pattern recognition, personalized medicine, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, RAND corporation, ransomware, Ray Kurzweil, refrigerator car, RFID, ride hailing / ride sharing, Rodney Brooks, Satoshi Nakamoto, Second Machine Age, security theater, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart grid, smart meter, Snapchat, social graph, software as a service, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, supply-chain management, technological singularity, telepresence, telepresence robot, Tesla Model S, The Wisdom of Crowds, Tim Cook: Apple, trade route, uranium enrichment, Wall-E, Watson beat the top human players on Jeopardy!, Wave and Pay, We are Anonymous. We are Legion, web application, WikiLeaks, Y Combinator, zero day

Though the evidence and my gut tell me there are significant bumps in the road ahead—ones that government and industry are not dedicating sufficient resources to address or combat—I want to believe in the techno-utopia promised to us by Silicon Valley. This book is the story of the society we are building with our technological tools and how these very same implements can be used against us. The more we plug our devices and our lives into the global information grid—whether via mobile phones, social networks, elevators, or self-driving cars—the more vulnerable we become to those who know how the underlying technologies work and how to exploit them to their advantage and to the detriment of the common man. Simply stated, when everything is connected, everyone is vulnerable. The technology we routinely accept into our lives with little or no self-reflection or examination may very well come back to bite us. By shedding light on the very latest in criminal and terrorist tradecraft, I hope to kick off a vibrant and long-overdue discussion among my friends and colleagues in policing and national security.

The vast levels of cyber crime we currently face make it abundantly clear we cannot even adequately protect the standard desktops and laptops we presently have online, let alone the hundreds of millions of mobile phones and tablets we are adding annually. In what vision of the future, then, is it conceivable that we will have any clue how to protect the next fifty billion things to go online? Given our inability to secure today’s global information matrix, how might we ever protect a world in which every physical object, from pets to pacemakers to self-driving cars, is connected to the Net and hackable from anywhere on the planet? The obvious reality is that we cannot. The Internet of Things will become nothing more than the Internet of Things to be hacked, a cornucopia of malicious opportunity for those with the means and motivation to exploit our common technological insecurity. The IoT and its underlying insecure protocols will open a Pandora’s box of security vulnerabilities on an unprecedented scale, potentially creating systemic malfunctions whose reach will be simultaneously unpredictable, extraordinary, and terrifying.

In the same way both Crime, Inc. and crazed exes are targeting computers and mobile phones, it’s only logical that they will go after cars in the future too, bringing scenes like those in Stephen King’s 1983 horror thriller about a possessed car named Christine many steps closer to reality. Law enforcement officials clearly see the threat, and in July 2014 the FBI warned in an internal report that driverless cars could be used as “lethal weapons, with terrorists potentially packing explosives into a self-driving car aimed at a specific destination.” Autonomous vehicles could also potentially be turned off en masse, bringing traffic to a complete standstill in a city or country. To be certain, some of these vehicular attacks require a high degree of computer savvy to pull off, but as we have seen with other exploits, soon there will be point-and-click crimeware options for car hacking as well. Automakers are starting to take notice, particularly as “most hackable car” lists come out.


pages: 598 words: 134,339

Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World by Bruce Schneier

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23andMe, Airbnb, airport security, AltaVista, Anne Wojcicki, augmented reality, Benjamin Mako Hill, Black Swan, Brewster Kahle, Brian Krebs, call centre, Cass Sunstein, Chelsea Manning, citizen journalism, cloud computing, congestion charging, disintermediation, Edward Snowden, experimental subject, failed state, fault tolerance, Ferguson, Missouri, Filter Bubble, Firefox, friendly fire, Google Chrome, Google Glasses, hindsight bias, informal economy, Internet Archive, Internet of things, Jacob Appelbaum, Jaron Lanier, Julian Assange, Kevin Kelly, license plate recognition, linked data, Lyft, Mark Zuckerberg, Nash equilibrium, Nate Silver, national security letter, Network effects, Occupy movement, payday loans, pre–internet, price discrimination, profit motive, race to the bottom, RAND corporation, recommendation engine, RFID, self-driving car, Silicon Valley, Skype, smart cities, smart grid, Snapchat, social graph, software as a service, South China Sea, stealth mode startup, Steven Levy, Stuxnet, TaskRabbit, telemarketer, Tim Cook: Apple, transaction costs, Uber and Lyft, urban planning, WikiLeaks, zero day

Modern cars are loaded with computers: Ben Wojdyla (21 Feb 2012), “How it works: The computer inside your car,” Popular Mechanics, http://www.popularmechanics.com/cars/how-to/repair/how-it-works-the-computer-inside-your-car. Much of that is automatically recorded: Nate Cardozo (11 Feb 2013), “Mandatory black boxes in cars raise privacy questions,” Electronic Frontier Foundation, https://www.eff.org/press/releases/mandatory-black-boxes-cars-raise-privacy-questions. A self-driving car: Lucas Mearian (23 Jul 2013), “Self-driving cars could create 1GB of data a second,” Computer World, http://www.computerworld.com/s/article/9240992/Self_driving_cars_could_create_1GB_of_data_a_second. Embedded in digital photos: Benjamin Henne, Maximilian Koch, and Matthew Smith (3–7 Mar 2014), “On the awareness, control and privacy of shared photo metadata,” Distributed Computing & Security Group, Leibniz University, presented at the Eighteenth International Conference for Financial Cryptography and Data Security, Barbados, http://ifca.ai/fc14/papers/fc14_submission_117.pdf. 15 If you upload the photo: This is a particularly creepy story about camera metadata.

Modern cars are loaded with computers, producing data on your speed, how hard you’re pressing on the pedals, what position the steering wheel is in, and more. Much of that is automatically recorded in a black box recorder, useful for figuring out what happened in an accident. There’s even a computer in each tire, gathering pressure data. Take your car into the shop, and the first thing the mechanic will do is access all that data to diagnose any problems. A self-driving car could produce a gigabyte of data per second. Snap a photo, and you’re at it again. Embedded in digital photos is information such as the date, time, and location—yes, many cameras have GPS—of the photo’s capture; generic information about the camera, lens, and settings; and an ID number of the camera itself. If you upload the photo to the web, that information often remains attached to the file.


pages: 527 words: 147,690

Terms of Service: Social Media and the Price of Constant Connection by Jacob Silverman

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23andMe, 4chan, A Declaration of the Independence of Cyberspace, Airbnb, airport security, Amazon Mechanical Turk, augmented reality, Brian Krebs, California gold rush, call centre, cloud computing, cognitive dissonance, correlation does not imply causation, Credit Default Swap, crowdsourcing, don't be evil, Edward Snowden, feminist movement, Filter Bubble, Firefox, Flash crash, game design, global village, Google Chrome, Google Glasses, hive mind, income inequality, informal economy, information retrieval, Internet of things, Jaron Lanier, jimmy wales, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, late capitalism, license plate recognition, life extension, Lyft, Mark Zuckerberg, Mars Rover, Marshall McLuhan, meta analysis, meta-analysis, Minecraft, move fast and break things, national security letter, Network effects, new economy, Nicholas Carr, Occupy movement, optical character recognition, payday loans, Peter Thiel, postindustrial economy, prediction markets, pre–internet, price discrimination, price stability, profit motive, quantitative hedge fund, race to the bottom, Ray Kurzweil, recommendation engine, rent control, RFID, ride hailing / ride sharing, self-driving car, sentiment analysis, shareholder value, sharing economy, Silicon Valley, Silicon Valley ideology, Snapchat, social graph, social web, sorting algorithm, Steve Ballmer, Steve Jobs, Steven Levy, TaskRabbit, technoutopianism, telemarketer, transportation-network company, Turing test, Uber and Lyft, Uber for X, universal basic income, unpaid internship, women in the workforce, Y Combinator, Zipcar

Google then had a challenge: how to get the hundreds of millions of people using its other products, like Search, Gmail, Docs, and YouTube, to embrace Google+. And it was a problem they desperately needed to solve, since Facebook’s huge, active membership and its use of Like buttons and other tracking tools meant that it had a glut of information that advertisers wanted. No matter its various ambitious projects (self-driving cars, mapping the world) or its widely used e-mail service, Google is, at heart, an advertising company—about 91 percent of its 2013 revenue came from its advertising arm, down from 95 percent the previous year. So how could Google both rope more users into Google+ and maintain its informational supremacy in the advertising arms race? The answer, it turned out, was to remove the element of choice.

In the meantime, those whose sweat this industry still relies upon find themselves submitting to exploitative conditions, whether as a Foxconn worker in Shenzhen or a Postmates courier in San Francisco. As one Uber driver complained to a reporter: “We have a real person performing a function, not a Google automatic car. We have become the functional end of the app.” It might not be long before he is traded in for a self-driving car. They don’t need breaks, they don’t worry about safety conditions or unions, they don’t complain about wages. Compared to a human being, automatic cars are perfectly efficient. And who will employ him then? Who will be interested in someone who’s spent a few years bouncing between gray-market transportation facilitation services, distributed labor markets, and other hazy digital makework?

F.A.T., or Free Art and Technology, may be the leaders in this genre. They are culture jammers par excellence. A collection of about twenty artists and pranksters, F.A.T. distributes all of their material free of copyright. Often, they produce 3-D models, instruction kits, masks, and other materials that they encourage people to disseminate and to make at home. F.A.T. has produced a fake Google self-driving car that they drove around New York City. They built a fake Google Street View car and took it around Berlin, where concern about Google’s privacy and surveillance practices runs high. (The car also appeared in New York.) They created BRICKiPhone, a functional case for the iPhone (“the most ubiquitous device of the yuppie class”), which turns the phone into a blocky, gray, late-eighties-style cell phone, complete with protruding black antenna.


pages: 323 words: 90,868

The Wealth of Humans: Work, Power, and Status in the Twenty-First Century by Ryan Avent

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3D printing, Airbnb, American energy revolution, autonomous vehicles, Bakken shale, barriers to entry, Bernie Sanders, BRICs, call centre, Capital in the Twenty-First Century by Thomas Piketty, Clayton Christensen, cloud computing, collective bargaining, computer age, dark matter, David Ricardo: comparative advantage, deindustrialization, dematerialisation, Deng Xiaoping, deskilling, Dissolution of the Soviet Union, Donald Trump, Downton Abbey, Edward Glaeser, Erik Brynjolfsson, eurozone crisis, everywhere but in the productivity statistics, falling living standards, first square of the chessboard, first square of the chessboard / second half of the chessboard, Ford paid five dollars a day, Francis Fukuyama: the end of history, future of work, gig economy, global supply chain, global value chain, hydraulic fracturing, income inequality, indoor plumbing, industrial robot, interchangeable parts, Internet of things, inventory management, invisible hand, Jacquard loom, James Watt: steam engine, Jeff Bezos, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph-Marie Jacquard, knowledge economy, low skilled workers, lump of labour, Lyft, manufacturing employment, means of production, new economy, performance metric, pets.com, price mechanism, quantitative easing, Ray Kurzweil, rent-seeking, reshoring, rising living standards, Robert Gordon, Ronald Coase, savings glut, Second Machine Age, secular stagnation, self-driving car, sharing economy, Silicon Valley, single-payer health, software is eating the world, supply-chain management, supply-chain management software, TaskRabbit, The Nature of the Firm, The Spirit Level, The Wealth of Nations by Adam Smith, Thomas Malthus, trade liberalization, transaction costs, Tyler Cowen: Great Stagnation, Uber and Lyft, Uber for X, very high income, working-age population

Acemoglu, Daron ageing populations agency, concept of Airbnb Amazon American Medical Association (AMA) anarchism Andreessen, Marc Anglo-Saxon economies Apple the iPhone the iPod artisanal goods and services Atkinson, Anthony Atlanta, Georgia austerity policies automation in car plants fully autonomous trucks of ‘green jobs’ during industrial revolution installation work as resistant to low-pay as check on of menial/routine work self-driving cars and technological deskilling automobiles assembly-line techniques automated car plants and dematerialization early days of car industry fully autonomous trucks self-driving cars baseball Baumol, William Belgium Bernanke, Ben Bezos, Jeff black plague (late Middle Ages) Boston, Massachusetts Brazil BRIC era Bridgewater Associates Britain deindustrialization education in extensions of franchise in financial crisis (2008) Great Exhibition (London 1851) housing wealth in and industrial revolution Labour Party in liberalization in political fractionalization in real wages in social capital in surpassed by US as leading nation wage subsidies in Brontë, Charlotte Brynjolfsson, Erik bubbles, asset-price Buffalo Bill (William Cody) BuzzFeed Cairncross, Frances, The Death of Distance (1997) capital ‘deepening’ infrastructure investment investment in developing world career, concept of cars see automobiles Catalan nationalism Central African Republic central banks Chait, Jonathan Charlotte chemistry, industrial Chicago meat packers in nineteenth-century expansion of World’s Columbia Exposition (1893) China Deng Xiaoping’s reforms economic slow-down in era of rapid growth foreign-exchange reserves ‘green jobs’ in illiberal institutions in inequality in iPod assembly in technological transformation in wage levels in Chorus (content-management system) Christensen, Clayton Cisco cities artisanal goods and services building-supply restrictions growth of and housing costs and industrial revolution and information membership battles in rich/skilled and social capital clerical work climate change Clinton, Hillary Coase, Ronald Columbia University, School of Mines communications technology communism communities of affinity computing app-based companies capability thresholds cloud services cycles of experimentation desktop market disk-drive industry ‘enterprise software’ products exponential progress narrative as general purpose technology hardware and software infrastructure history of ‘Moore’s Law’ and productivity switches transistors vacuum tubes see also digital revolution; software construction industry regulations on Corbyn, Jeremy Corliss steam engine corporate power Cowen, Tyler craft producers Craigslist creative destruction the Crystal Palace, London Dalio, Ray Dallas, Texas debt deindustrialization demand, chronically weak dematerialization Detroit developing economies and capital investment and digital revolution era of rapid growth and industrialization pockets of wealth in and ‘reshoring’ phenomenon and sharp slowdown and social capital see also emerging economies digital revolution and agency and company cultures and developing economies and distance distribution of benefits of dotcom tech boom emergence of and global imbalances and highly skilled few and industrial institutions and information flows investment in social capital niche markets pace of change and paradox of potential productivity and output and secular stagnation start-ups and technological deskilling techno-optimism techno-pessimism as tectonic economic transformation and trading patterns web journalism see also automation; computing; globalization discrimination and exclusion ‘disruption’, phenomenon of distribution of wealth see inequality; redistribution; wealth and income distribution dotcom boom eBay economics, classical The Economist education in emerging economies during industrial revolution racial segregation in USA and scarcity see also university education electricity Ellison, Glenn Ellison, Sara Fisher emerging economies deindustrialization economic growth in education in foreign-exchange reserves growth in global supply chains highly skilled workers in see also developing economies employment and basic income policy cheap labour as boost to and dot.com boom in Europe and financial crisis (2008) ‘green jobs’ low-pay sector minimum wage impact niche markets in public sector ‘reshoring’ phenomenon as rising globally and social contexts and social membership as source of personal identity and structural change trilemma in USA see also labour; wages Engels, Friedrich environmental issues Etsy euro- zone Europe extreme populist politics liberalized economies political fractionalization in European Union Facebook face-recognition technology factors of production land see also capital; labour ‘Factory Asia’ factory work assembly-line techniques during industrial revolution family fascism Federal Reserve financial crisis (2008) financial markets cross-border capital flows in developing economies Finland firms and companies Coase’s work on core competencies culture of dark matter (intangible capital) and dematerialization and ‘disruption’ ‘firm-specific’ knowledge and information flows internal incentive structures pay of top executives shifting boundaries of social capital of and social wealth start-ups Ford, Martin, Rise of the Robots (2015) Ford Motor Company fracking France franchise, electoral Friedman, Milton Fukuyama, Francis Gates, Bill gender discrimination general purpose technologies enormous benefits from exponential progress and skilled labour supporting infrastructure and time lags see also digital revolution Germany ‘gig economy’ Glaeser, Ed global economy growth in supply chains imbalances lack of international cooperation savings glut and social consensus globalization hyperglobalization and secular stagnation and separatist movements Goldman Sachs Google Gordon, Robert Gothenburg, Sweden Great Depression Great Depression (1930s) Great Exhibition, London (1851) Great Recession Great Stagnation Greece ‘green jobs’ growth, economic battle over spoils of boom (1994-2005) and classical economists as consistent in rich countries decline of ‘labour share’ dotcom boom emerging economies gains not flowing to workers and industrial revolution Kaldor’s ‘stylized facts of’ and Keynes during liberal era pie metaphor in post-war period and quality of institutions and rich/elite cities rich-poor nation gap and skilled labour guilds Hansen, Alvin Hayes, Chris, The Twilight of the Elites healthcare and medicine hedge funds and private equity firms Holmes, Oliver Wendell Hong Kong housing in Bay-Area NIMBY campaigns against soaring prices pre-2008 crisis zoning and regulations Houston, Texas Huffington Post human capital Hungary IBM identity, personal immigration and ethno-nationalist separatism and labour markets in Nordic countries and social capital income distribution see inequality; redistribution; wealth and income distribution India Indonesia industrial revolution automation during and economic growth and growth of cities need for better-educated workers and productivity ‘second revolution’ and social change and wages and World’s Fairs inequality and education levels between firms and housing wealth during industrial revolution during liberal era between nations pay of top executives rise of in emerging economies and secular stagnation in Sweden wild contingency of wealth see also rich people; wealth and income distribution inflation in 1970s hyperinflation information technology see computing Intel interest rates International Space Station (ISS) iRobot ISIS Italy Jacksonville, Florida Jacquard, Joseph Marie Japan journalism Kaldor, Nicholas Keynes, John Maynard Kurzweil.

Ray labour abundance as good problem bargaining power cognitive but repetitive collective bargaining and demographic issues discrimination and exclusion global growth of workforce and immigration liberalization in 1970s/80s ‘lump of labour’ fallacy occupational licences organized and proximity reallocation to growing industries retraining and skill acquisition and scarcity and social value work as a positive good see also employment Labour Party, British land scarcity Latvia Le Pen, Jean-Marie Le Pen, Marine legal profession Lehman Brothers collapse (2008) Lepore, Jill liberalization, economic (from 1970s) Linkner, Josh, The Road to Reinvention London Lucas, Robert Lyft maker-taker distinction Malthus, Reverend Thomas Manchester Mandel, Michael Mankiw, Gregory marketing and public relations Marshall, Alfred Marx, Karl Mason, Paul, Postcapitalism (2015) McAfee, Andrew medicine and healthcare ‘mercantilist’ world Mercedes Benz Mexico Microsoft mineral industries minimum wage Mokyr, Joel Monroe, President James MOOCs (‘massive open online courses’) Moore, Gordon mortality rates Mosaic (web browser) music, digital nation states big communities of affinity inequality between as loci of redistribution and social capital nationalist and separatist movements Netherlands Netscape New York City Newsweek NIMBYism Nordic and Scandinavian economies North Carolina North Dakota Obama, Barack oil markets O’Neill, Jim Oracle Orbán, Viktor outsourcing Peretti, Jonah Peterson Institute for International Economics pets.com Philadelphia Centennial Fair (1876) Philippines Phoenix, Arizona Piketty, Thomas, Capital in the Twenty-First Century (2013) Poland political institutions politics fractionalization in Europe future/emerging narratives geopolitical forces human wealth narrative left-wing looming upheaval/conflict Marxism nationalist and separatist movements past unrest and conflict polarization in USA radicalism and extremism realignment revolutionary right-wing rise of populist outsiders and scarcity social membership battles Poor Laws, British print media advertising revenue productivity agricultural artisanal goods and services Baumol’s Cost Disease and cities and dematerialization and digital revolution and employment trilemma and financial crisis (2008) and Henry Ford growth data in higher education of highly skilled few and industrial revolution minimum wage impact paradox of in service sector and specialization and wage rates see also factors of production professional, technical or managerial work and education levels and emerging economies the highly skilled few and industrial revolution and ‘offshoring’ professional associations skilled cities professional associations profits Progressive Policy Institute property values proximity public spending Putnam, Robert Quakebot quantitative easing Race Against the Machine, Brynjolfsson and McAfee (2011) railways Raleigh, North Carolina Reagan, Ronald redistribution and geopolitical forces during liberal era methods of nation state as locus of as a necessity as politically hard and societal openness wealth as human rent, economic Republican Party, US ‘reshoring’ phenomenon Resseger, Matthew retail sector retirement age Ricardo, David rich people and maker-taker distinction wild contingency of wealth Robinson, James robots Rodrik, Dani Romney, Mitt rule of law Russia San Francisco San Jose Sanders, Bernie sanitation SAP Saudi Arabia savings glut, global ‘Say’s Law’ Scalia, Antonin Scandinavian and Nordic economies scarcity and labour political effects of Schleicher, David Schwartz, Anna scientists Scotland Sears Second World War secular stagnation global spread of possible solutions shale deposits sharing economies Silicon Valley Singapore skilled workers and education levels and falling wages the highly skilled few and industrial revolution ‘knowledge-intensive’ goods and services reshoring phenomenon technological deskilling see also professional, technical or managerial work Slack (chat service) Slate (web publication) smartphone culture Smith, Adam social capital and American Constitution baseball metaphor and cities ‘deepening’ definition/nature of and dematerialization and developing economies and erosion of institutions of firms and companies and good government and housing wealth and immigration and income distribution during industrial revolution and liberalization and nation-states productive application of and rich-poor nation gap and Adam Smith and start-ups social class conflict middle classes and NIMBYism social conditioning of labour force working classes social democratic model social reform social wealth and social membership software ‘enterprise software’ products supply-chain management Solow, Robert Somalia South Korea Soviet Union, dissolution of (1991) specialization Star Trek state, role of steam power Subramanian, Arvind suburbanization Sweden Syriza party Taiwan TaskRabbit taxation telegraphy Tesla, Nikola Thatcher, Margaret ‘tiger’ economies of South-East Asia Time Warner Toyota trade China as ‘mega-trader’ ‘comparative advantage’ theory and dematerialization global supply chains liberalization shaping of by digital revolution Adam Smith on trade unions transhumanism transport technology self-driving cars Trump, Donald Twitter Uber UK Independence Party United States of America (USA) 2016 Presidential election campaign average income Bureau of Labour Statistics (BLS) Constitution deindustrialization education in employment in ethno-nationalist diversity of financial crisis (2008) housing costs in housing wealth in individualism in industrialization in inequality in Jim Crow segregation labour scarcity in Young America liberalization in minimum wage in political polarization in post-crisis profit rates productivity boom of 1990s real wage data rising debt levels secular stagnation in shale revolution in social capital in and social wealth surpasses Britain as leading nation wage subsidies in university education advanced degrees downward mobility of graduates MOOCs (‘massive open online courses’) and productivity see also education urbanization utopias, post-work Victoria, Queen video-gamers Virginia, US state Volvo Vox wages basic income policy Baumol’s Cost Disease cheap labour and employment growth and dot.com boom and financial crisis (2008) and flexibility and Henry Ford government subsidies and housing costs and immigration and industrial revolution low-pay as check on automation minimum wage and productivity the ‘reservation wage’ as rising in China rising in emerging economies and scarcity in service sector and skill-upgrading approach stagnation of and supply of graduates Wandsworth Washington D.C.


pages: 502 words: 107,657

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel

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Albert Einstein, algorithmic trading, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, backtesting, Black Swan, book scanning, bounce rate, business intelligence, business process, call centre, computer age, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data is the new oil, en.wikipedia.org, Erik Brynjolfsson, experimental subject, Google Glasses, happiness index / gross national happiness, job satisfaction, Johann Wolfgang von Goethe, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, Norbert Wiener, personalized medicine, placebo effect, prediction markets, Ray Kurzweil, recommendation engine, risk-adjusted returns, Ronald Coase, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, software as a service, speech recognition, statistical model, Steven Levy, text mining, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Turing test, Watson beat the top human players on Jeopardy!, X Prize, Yogi Berra

Cruise’s bravado does not go unchecked. Colin Farrell’s Department of Justice agent confronts Cruise, and the two brutes stand off, mano a mano. “You ever get any false positives?” accuses Farrell. A false positive, aka false alarm, is when a model incorrectly predicts yes, when the correct answer is no. It says you’re guilty, convicting you of a crime you didn’t (or in this case, won’t) commit. As self-driving cars emerge from Google and BMW and begin to hit the streets, a new cultural acceptance of machine risk will emerge as well. The world will see automobile collision casualty rates decrease overall, and eventually, among waves of ire and protest, learn to accept that on some occasions the computer is to blame for an accidental death. But when a criminal who would not reoffend is kept in prison because of a false prediction, we will never have the luxury of knowing.

See crime fighting and fraud detection frequency Freud, Sigmund Friedman, Jerome friendships, predicting Fukuman, Audrey Fulcher, Christopher fund-raising, predicting in Furnas, Alexander future, views on human nature and knowing about predictions for 2020 uncertainty of G Galileo generalization paradox Ghani, Rayid Gilbert, Eric Gimpert, Ben Gladwell, Malcolm GlaxoSmithKline (UK) Gmail Goethe, Johann Wolfgang von Goldbloom, Anthony Gondek, David Google ineffective ads, predicting mouse clicks, measuring for predictions privacy policies Schmidt, Eric searches for playing Jeopardy! self-driving cars spam filtering Google Adwords Google Flu Trends Google Glass Google Page Rank government data storage by fraud detection for invoices PA for public access to data GPS data grades, predicting Granger, Clive grant awards, predicting Greenspan, Alan Grockit Groundhog Day (film) Grundhoefer, Michael H hackers, predicting Halder, Gitali HAL (intelligent computer) Hansell, Saul happiness, social effect and Harbor Sweets Harcourt, Bernard Harrah’s Las Vegas Harris, Jeanne Harvard Medical School Harvard University Hastings, Reed healthcare death predictions in health risks, predicting hospital admissions, predicting influenza, predicting medical research, predicting in medical treatments, risks for wrong predictions in medical treatments, testing persuasion in PA for personalized medicine, uplift modeling applications for health insurance companies, PA for Hebrew University Heisenberg, Werner Karl Helle, Eva Helsinki Brain Research Centre Hennessey, Kathleen Heraclitus Heritage Health Prize Heritage Provider Network Hewlett Foundation Hewlett-Packard (HP) employee data used by financial savings and benefits of PA Global Business Services (GBS) quitting and Flight Risks, predicting sales leads, predicting turnover rates at warranty claims and fraud detection High Anxiety (film) HIV progression, predicting HIV treatments, uplift modeling for Hollifield, Stephen Holmes, Sherlock hormone replacement, coronary disease and hospital admissions, predicting Hotmail.com House (TV show) “How Companies Learn Your Secrets” (Duhigg) Howe, Jeff HP.

See PA (predictive analytics) Predictive Analytics World (PAW) conferences predictive models defined marketing models overlearning and assuming response modeling response uplift modeling univariate vs. multivariate See also ensemble models predictive models, launching about action and decision making causality and deployment phase Elder’s success in going live machine learning and building observation and personalization and risks in uplift modeling predictive technology See also machine learning predictor variables pregnancy and birth, predicting customer pregnancy and buying behavior premature births prejudice, risk of PREMIER Bankcard privacy Google policies on insight vs. intrusion regarding predicted consumer data and profiling customers Progressive Insurance psychology emotions, cause and effect of Freud on emotions predictive analysis in schizophrenia, predicting psychopathy, predicting Psych (TV show) purchases, predicting Q Quadstone R Radcliffe, Nicholas Radica Games Ralph’s random forests Rebellion Research recency recidivism prediction for law enforcement recommendation systems Reed Elsevier reliability modeling REO Speedwagon (band) response modeling drawbacks of examples of targeted marketing with response rates response uplift modeling retail websites, behavior on retirement, health and Richmond (VA) Police Department Rio Salado Community College Riskprediction.org.uk risk management risk scores Risky Business (film) Robin, Leo Romney, Mitt Royal Astronomy Society R software Russell, Bertrand Rutter, Brad S Saaf, Randy safety and efficiency, PA for Safeway sales leads, predicting Salford Systems Salsburg, David Santa Cruz (CA) Police Department sarcasm, in reviews Sartre, Jean-Paul SAS satellites, predicting fault in satisficing Schamberg, Lisa schizophrenia, predicting Schlitz, Don Schmidt, Eric Schwartz, Ari Science magazine security levels, predicting self-driving cars Selfridge, Oliver Semisonic (band) sepsis, predicting Sessions, Roger Shakespeare, William Shaw, George Bernard Shearer, Colin shopping habits, predicting sickness, predicting Siegel, Eric silence, concept of Silver, Nate Simpsons, The (TV show) Siri Sisters of Mercy Health Systems small business credit risks Smarr, Larry smoking and smokers health problems and causation for motion disorders and social effect and quitting SNTMNT Sobel, David social computing social effect social media networks data glut on happiness as contagious on healthcare LinkedIn PA for spam filtering on Twitter viral tweets and posts on YouTube See also Facebook sociology, uplift modeling applications for SpaceShipOne spam filtering Spider-Man (film) sporting events, crime rates and sports cars Sprint SPSS staff behavior.


pages: 326 words: 103,170

The Seventh Sense: Power, Fortune, and Survival in the Age of Networks by Joshua Cooper Ramo

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Airbnb, Albert Einstein, algorithmic trading, barriers to entry, Berlin Wall, bitcoin, British Empire, cloud computing, crowdsourcing, Danny Hillis, defense in depth, Deng Xiaoping, Edward Snowden, Fall of the Berlin Wall, Firefox, Google Chrome, income inequality, Isaac Newton, Jeff Bezos, job automation, market bubble, Menlo Park, natural language processing, Network effects, Norbert Wiener, Oculus Rift, packet switching, Paul Graham, price stability, quantitative easing, RAND corporation, recommendation engine, Republic of Letters, Richard Feynman, Richard Feynman, road to serfdom, Sand Hill Road, secular stagnation, self-driving car, Silicon Valley, Skype, Snapchat, social web, sovereign wealth fund, Steve Jobs, Steve Wozniak, Stewart Brand, Stuxnet, superintelligent machines, technological singularity, The Coming Technological Singularity, The Wealth of Nations by Adam Smith, too big to fail, Vernor Vinge, zero day

As Baran’s fishnet grows, it locks everything it touches into a new structure. The connected devices themselves are constantly improving too. Back in Baran’s day, dozens of scientists counted themselves lucky to share a single computer. A few decades later, the PC revolution gave everyone a machine. And now, of course, we each have many computers in our lives: phones, wired TVs, soon smart self-driving cars. Because of connection, we have access to thousands of such devices in data centers, a fusion of software and hardware and connection that we are starting to lean on as “everyware.” This now-commonplace magic was formalized in 1965 by Gordon Moore, one of the founding engineers at Intel, who noticed that since the introduction of integrated chips, in 1959, the number of transistors on each tiny chip had been doubling every two years.

If they can’t innovate fast enough to develop tools to manage massive data flows or are unable to absorb the best new technology, they will be the new divergence club. Fast networks will elude them. Self-defense will be impossible; their time will be as vulnerable to manipulation by enemies as the resources of Africa and Latin America were to colonialist plunder several hundred years ago. In the next decade, everything from self-driving cars to war-fighting robots will begin to become commonplace in the most advanced nations. Think of the efficiencies these will bring: cheaper logistics and transport in a world of self-handled and self-unloaded trucks. A country such as the United States, which might be a leader in adopting such systems, would outperform even further a nation such as Chile or Nigeria, which may wait years before automated logistics can be implemented.

“Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an ‘intelligence explosion,’ and the intelligence of man would be left far behind.… Thus the first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control.” Vinge labeled this instant “the singularity”: “It is a point,” he wrote, “where our models must be discarded.” The trivial version of this would be an age of autonomous armed drones, self-driving cars, and electrical grids that flipped nuclear plants on or off according to a logic only they understood. The more profound version, however, would be the arrival of AI that really did think and create and intuit tremors too subtle for the human mind to sense. Like so much of our connected age, such machines would arrive, Vinge felt, because we wanted and even needed them to achieve our dreams.


pages: 200 words: 47,378

The Internet of Money by Andreas M. Antonopoulos

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AltaVista, altcoin, bitcoin, blockchain, clean water, cognitive dissonance, cryptocurrency, ethereum blockchain, global reserve currency, litecoin, London Interbank Offered Rate, Oculus Rift, packet switching, peer-to-peer lending, Ponzi scheme, ransomware, reserve currency, Satoshi Nakamoto, self-driving car, Skype, smart contracts, the medium is the message, trade route, underbanked, WikiLeaks

Here’s a little thought experiment. Let’s take three radically disruptive technologies and mash them together. Bitcoin. Uber. Self-driving cars. What happens when you mash the three together? The self-owning car. A car that pays for its Toyota lease, its insurance, and its gas, by giving people rides. A car that is not owned by a corporation. A car that is a corporation. A car that is a shareholder and owner of its own corporation. A car that exists as an autonomous financial entity with no human ownership. This has never happened before, and that’s just the beginning. Audience member gasps: "Oh shit!" "Let’s take three radically disruptive technologies and mash them together. Bitcoin. Uber. Self-driving cars. What happens when you mash the three together? The self-owning car." I can guarantee you that one of the first distributed autonomous corporations is going to be a fully autonomous, artificial-intelligence-based ransomware virus that will go out and rob people online of their bitcoin, and use that money to evolve itself to pay for better programming, to buy hosting, and to spread.


pages: 233 words: 58,561

Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days by Jake Knapp, John Zeratsky, Braden Kowitz

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23andMe, 3D printing, Airbnb, Anne Wojcicki, Google Earth, Google Hangouts, Google X / Alphabet X, self-driving car, side project, Silicon Valley, Wall-E

At GV, we’ve run sprints with companies like Foundation Medicine (makers of advanced cancer diagnostics), Nest (makers of smart home appliances), and Blue Bottle Coffee (makers of, well, coffee). We’ve used sprints to assess the viability of new businesses, to make the first version of new mobile apps, to improve products with millions of users, to define marketing strategies, and to design reports for medical tests. Sprints have been run by investment bankers looking for their next strategy, by the team at Google building the self-driving car, and by high school students working on a big math assignment. This book is a DIY guide for running your own sprint to answer your pressing business questions. On Monday, you’ll map out the problem and pick an important place to focus. On Tuesday, you’ll sketch competing solutions on paper. On Wednesday, you’ll make difficult decisions and turn your ideas into a testable hypothesis. On Thursday, you’ll hammer out a realistic prototype.

., 169–70 finance experts, 34 Fitbit, 171 fitness training, automated, 171–74 FitStar sprint, 171–74, 189, 206 Flatiron Health sprint, 60–64, 76, 85, 88, 100–101, 153, 176, 224 Flickr, 143 focus, sprint process emphasis on, 32 Foundation Medicine sprint, 16, 176–77, 185 FoundationOne, 176 Freeman, James, 21–25, 30, 103 Gebbia, Joe, 210–11 genetic analysis, in cancer treatments, 176 George Mason University, 38 Getting Things Done (Allen), 108–9 Giarusso, Serah, 24, 103 Glitch (video game), 128–29, 143 Gmail, 2, 4 goals, ambitious, 229 goals, long-term, 55–57, 61, 67, 110, 138, 141, 147 dangerous assumptions and, 56–57 in Flatiron Health sprint, 62–63 Goldilocks quality, 170, 207 Gonzalez, Tony, 171–72 Google, 60 experimentation culture of, 1 self-driving car of, 16 Google Earth, 83 Google Forms, 121 Google Hangouts, 3 Google Search, 4 Google Ventures (GV), 4–6, 7, 12, 15, 16, 60, 85, 113, 130, 171, 176, 201, 231 Google X, 4 Grace, Merci, 130, 131, 143–44, 152, 156, 175, 216–17, 221, 222 Graco sprint, 27–28 Green, Bobby, 76, 85, 86 Grijalva, Dave, 171–74 Harry Potter and the Philosopher’s Stone (Rowling), 196, 196n heat map, in deciding process, 131, 132–35 high stakes, as challenge, 26 honesty, in deciding process, 139–40 hotels, guest satisfaction and, 10, 56 Howard, Ron, 53 How Might We notes, 68, 73–82, 110 in Blue Bottle sprint, 73–74 challenges and, 77–78 in Flatiron Health sprint, 76–78 maps and, 81–82 organizing, 79–80 prioritizing, 80–81 target and, 87 HTML, 184 Hurley, Chad, 6 IdeaPaint, 44 IDEO, 73 illusion, 165–66 see also façades Incredibles, The (film), 149 Indian Ocean, 84 industrial companies, sprints and, 27–28 Ingram, Alex, 62, 76 interruptions, productivity and, 38–39 Interviewer, 188, 190, 204–5, 217, 225 tips for, 212–15 interviews, 196–200, 201–15 being a good host in, 212 broken questions in, 214–15 context questions in, 202, 205–6 curiosity mindset in, 215 debriefing in, 202, 209–10 detailed tasks in, 202, 208–9 as emotional roller coaster for sprint team, 197 feedback in, 207 in FitStar sprint, 197, 206 in FitStar test, 208 five-act structure of, 202 ideal number of customers for, 197–99 introducing prototypes in, 202, 206–7 in One Medical sprint, 199–200 open-ended vs. leading questions in, 212–13 power of, 210–11 schedule of, 199 in Slack sprint, 217 team observation of, see interviews, learning from thinking aloud in, 207–8 welcome in, 202, 204–5 “why” questions in, 199–200 interviews, learning from: in Blue Bottle sprint, 223–24 in Flatiron Health sprint, 224 group note-taking in, 219–21 importance of real-time team observation in, 202–4, 218–19 looking for patterns in, 222 in Savioke sprint, 223 in Slack sprint, 220–21, 223 sprint questions and, 222–23 Invite Media, 60 iPads, 171–73, 178, 189 as banned from sprint room, 41 JavaScript, 184 Keynote, 171, 173, 175, 176, 177, 178, 184–85, 186 Knapp, Jake, 24, 27–28, 30, 47, 48, 60, 62, 76, 77, 85, 107n, 109 Kowitz, Braden, 5, 22, 23–24, 30, 43, 60, 76, 156, 216 Kranz, Gene, 53, 55, 85 Lachapelle, Serge, 3 Lancelotta, Mary Pat, 176 Landauer, Thomas K., 198n laptops, as banned from sprint room, 41 Lau, Tessa, 11, 12, 178 lean development, 17 learning, see interviews, learning from Lightning Demos, 96–101, 110 Lord of the Rings, The (Tolkien), 59, 60 Lowe, David, 27 McKinsey & Company, 230 Makers, 187, 188 mapping the problem, 16, 59–67, 110, 230 in Blue Bottle sprint, 23–24, 65, 66 division of labor and, 101–2 experts and, 69–70, 76, 77 in Flatiron Health sprint, 62–63 How Might We notes and, 81–82, 85 in Savioke sprint, 10, 64–65, 66 steps in, 66 as story, 65–66 target and, 84, 85–86 Margolis, Michael, 5, 12, 60, 62, 201–2, 203, 204, 206, 208, 209, 212, 214, 216, 217 Maris, Bill, 4–5 markers, dry-erase, 75 marketing experts, 34 Maser, Mike, 171–73 “Mathematical Model of the Finding of Usability Problems, A” (Nielsen and Landauer), 198n mechanics, of product or service, 70–71 Medium, 6 Medium sprint, 224 Meehan, Bryan, 22 meetings, frustrations of, 127–28, 230 Microsoft Word, 186 Mid-Ocean Ridge, 83–84, 87 “Mind Reader, The” (Blue Bottle solution sketch), 104–6, 115 Mission Control, 53–54, 225 momentum, regaining, 26 Move Loot sprint, 113 movies, façades in, 165–66, 173 My Neighbor Totoro (film), 98 NASA, 54 Nest, 16 Newton, Alice, 195–96 Newton, Nigel, 195–96 New York Times, 15, 130, 152, 153, 188 Nielsen, Jakob, 197–98, 198n no-devices rule, 41, 110 Note-and-Vote, 146–47 note-taking: on interviews, 219–21 sketching and, 109, 110 see also How Might We notes Ocean’s Eleven (film), 29–30, 36, 37, 225 office supplies, for sprint rooms, 45 One Medical Group sprint, 180–82, 185–86, 199 opening scene, 188 OstrichCo, 139–40 paper, for sprint rooms, 44 paper coffee filters, 95–96 patterns, in customer reactions to prototypes, 222 permission, Facilitators and, 89 personal trainers, 171 phones, as banned from sprint room, 41 Photoshop, 184 Pitt, Brad, 29, 36 Pixar, 149 plate tectonics, 84 PlayStation, 178 Porter, Josh, 89 Post-It notes, see sticky notes PowerPoint, 184, 186 previous efforts, see existing solutions priorities, setting, 54–55 “Priority Inbox” project, 2–3 Procter & Gamble, 73 productivity, interruptions and, 38–39 progress, rapid, from sprint process, 31 prototype mindset, 168–69, 230 prototypes, prototyping, 16, 60, 183–90 actors and scripts in, 186 appearance of reality in, 169–70 Asset Collector in, 188 in Blue Bottle sprint, 25, 28, 104–6 Brochure Façades in, 185 Deciders and, 31, 32 deciding on, see deciding as disposable, 169 division of labor in, 183, 187 façades and, see façades Facilitator and, 187 in FitStar sprint, 189 focus on learning from, 169 in Foundation Medicine sprint, 185 Goldilocks quality in, 170 in Graco sprint, 27–28 Interviewer in, 188, 190 Makers in, 187 mindset and, 168–69 in One Medical sprint, 199 picking right tools for, 183–86 in Priority Inbox sprint, 3 Rumbles and, 143–47 in Savioke sprint, 9, 10, 11–12, 185 sketching and, 104–6 in SquidCo sprint, 30–31 Stitcher in, 183, 187, 189 storyboard scenes and, 188, 189–90 trial run in, 183, 189–90 universal application of, 169 using existing objects or spaces in, 186 Writer in, 187–88 questions: in interviews, 212–14 obvious, Facilitators and, 90 questions, finding answers to, 138, 141, 147 in Blue Bottle sprint, 23 in FitStar sprint, 171 in Flatiron Health sprint, 62–63, 88 in Foundation Medicine sprint, 176–77 in Graco sprint, 27–28 and learning from interviews, 222–23 in One Medical sprint, 180 role of sprints in, 15, 16–17, 67 in Savioke sprint, 9, 10, 178 in Slack sprint, 175, 216–17, 222–23 Starting at the End and, 55–58 surface and, 28 see also How Might We notes reaction, feedback vs., 169–70 Relay robot, 7, 14, 56 eyes of, 97–98 guest satisfaction and, 10 guests’ responses to, 13 “personality” of, 11, 13, 71, 178, 179 risk-taking, 156, 166 robot helpers, human interaction with, 8–9, 10 Rogers, Jan, 46–47 Rogers, Loran, 46, 48 rooms, for sprints, 41–45 Rumbles, 143–47, 223 in Blue Bottle sprint, 146 Deciders in, 145, 146 fake brands in, 145–46 Note-and-Vote in, 146–47 single-prototype vs., 145, 147 in Slack sprint, 144, 145 Savioke Labs sprint, 7–15, 26, 33, 64, 66, 71, 119, 145, 153, 157, 178–79, 185, 223 better guest experience as goal of, 56, 84 schedule, clearing space for sprints in, 10, 39, 40–41 screener surveys, in recruiting test customers, 119–21 Scribe, in speed critique, 135–36 Seattle, Wash., 229 Sharpies, 75n simplicity, in maps, 66 sketching, 16, 60, 102, 103–18 abstract ideas and, 106–7 in Blue Bottle sprint, 24, 103–4, 108, 113 Crazy 8s exercise in, 109, 111–13 in Move Loot sprint, 113 prototypes and, 104–6 of rough ideas, 109, 111 solution sketches in, see solution sketches taking notes in, 109, 110 as working alone together, 107–9 Slack sprint, 129–31, 143–44, 149–58, 175, 216, 217, 220–21, 222, 223 expansion into new markets as challenge for, 129–30 Smithsonian Institute, 228 snacks, for sprints, 45 solution sketches, 109, 114–18 anonymity of, 114–15 in Blue Bottle sprint, 116–17 deciding on, see deciding as explanatory, 114 importance of words in, 115 maybe-laters in, 142, 155 single-scene, 114, 117 in Slack sprint, 130 sticky notes and, 114 storyboard format in, 114, 116 titles for, 115 winners in, 141–42 speed critique: in deciding process, 131, 135–37 Scribe in, 135–36 sprints: checklists for, 232–49 clearing calendars for, 10, 39, 40–41 concept of, 3 daily schedule in, 39, 40–41, 90–91 deciding process in, see deciding façades in, see façades as five-day process, 5–6, 9, 16, 40–41 frequently asked questions about, 251–57 learning from, see interviews, learning from no-devices rule in, 41, 110 origin of, 2–5 prototypes in, see prototypes, prototyping questions to be answered in, see questions, finding answers to; tests, real-world risk-taking in, 166 Rumbles in, 143–47 setting priorities in, 54–55 storyboards in, see storyboarding time allocation in, 38–41 timers for, 46–48 uncovering dangerous assumptions through, 56–57 universal application of, 229–30 versatility of, 5–6, 229–30 wide application of, 5–6 working alone together in, 107–9 work rooms for, 41–45 Squarespace, 186 SquidCo sprint, 30–31, 32, 139 Starting at the End, 5, 53–58 in Apollo 13 rescue, 53–54 in Blue Bottle sprint, 55–56, 57 in Flatiron Health sprint, 62–63 long-term goals and, 55–57, 61, 62–63, 67 questions to be answered in, 55–58, 62–63, 67 in Savioke sprint, 56 setting priorities in, 54–55 startups, 231 sprints and, 4–5, 15–16, 27 Starwood, 9 sticky notes: poster-size, 43, 44 solution sketches and, 114 see also How Might We notes Stitcher, 187, 189 storyboarding, 125, 148–58 “artist” for, 151, 154–55, 156 assigning prototyping tasks from, 188, 189–90 in Blue Bottle sprint, 153, 157, 188 competitors’ products in, 154 copywriting in, 155–56 Decider in, 156 detail in, 156 in Flatiron Health sprint, 153 maybe-laters in, 155 opening scene in, 152–53 resisting new ideas in, 155 risk-taking in, 156 in Savioke sprint, 153, 157 in Slack sprint, 149–53, 156 solution sketches as, 114, 116 test-time limits and, 157 story-centered design, 5 strategy, 70 straw polls, 87–88 in deciding process, 131, 138–40 successes, flawed, 223–24 supervotes, 143, 144 in deciding process, 131, 140–42, 143 surface, as contact point between product and customer, 28 target, 82, 83–88 in Blue Bottle sprint, 84–85, 101 Decider and, 31, 32, 85–88 in Flatiron Health sprint, 85–87, 88 How Might We notes and, 87 key customers in, 85–86 key event in, 85–86 maps and, 84, 85–86 in Savioke sprint, 84 straw polls and, 87–88 Tcho, 97 team processes, 1 teams, 29–37, 218 in Blue Bottle sprint, 22–24, 33 challenges and, 68 choosing members of, 33, 34–36 Deciders in, see Deciders division of labor in, 101–2 experts and, see Ask the Experts Facilitators in, see Facilitators ideal size of, 33 interviews observed by, see interviews, learning from in Ocean’s Eleven, 29–30 in Savioke sprint, 9–11, 33 in SquidCo sprint, 30–31 troublemakers in, 35 tech/logistic experts, 34 “Tenacious Tour, The” (Slack solution sketch), 144, 175, 217, 220–21, 222 tests, real-world, 5, 16, 231 in Blue Bottle sprint, 25 competitors’ products in, 154 Deciders and, 31, 32 in FitStar sprint, 173–74 in Graco sprint, 27–28 interview in, see interviews recruiting customers for, 119–23, 197 in Savioke sprint, 10, 11–13, 15 time units in, 157 Tharp, Marie, 83–84 3D printing, 27, 185, 186 tight deadlines, 109 time, allocation of, for sprints, 38–41 timers, in deciding process, 136, 138 Time Timers, 46–48 Tolkien, J.


pages: 257 words: 80,100

Time Travel: A History by James Gleick

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Ada Lovelace, Albert Einstein, Albert Michelson, Arthur Eddington, augmented reality, butterfly effect, crowdsourcing, Doomsday Book, index card, Isaac Newton, John von Neumann, luminiferous ether, Marshall McLuhan, Norbert Wiener, pattern recognition, Richard Feynman, Richard Feynman, Schrödinger's Cat, self-driving car, Stephen Hawking, telepresence, wikimedia commons

But not much space travel. Warp drive and wormholes notwithstanding, we seem to have given up on populating the galaxy. Nanorobots. Remote-control warfare. The internet in your contact lens or brain implant. Self-driving cars, a comedown, somehow, from i futuristi and their fearsome roaring racing machines. The aesthetic of futurism changed, too, without anyone issuing a manifesto—from big and bold, primary colors and metallic shine to grim, dank rot and ruins. Genetic engineering and/or species extinctions. Is that all the future we have to look forward to? Nanobots and self-driving cars? Credit 14.1 Card produced c. 1900 by Hildebrands chocolate company If we lack space travel, we do have telepresence. “Present” in this context pertains to space, not to time. Telepresence was born in the 1980s, when remotely controlled cameras and microphones came into their own.


pages: 72 words: 21,361

Race Against the Machine: How the Digital Revolution Is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy by Erik Brynjolfsson

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Amazon Mechanical Turk, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, business process, call centre, combinatorial explosion, corporate governance, crowdsourcing, David Ricardo: comparative advantage, easy for humans, difficult for computers, Erik Brynjolfsson, factory automation, first square of the chessboard, first square of the chessboard / second half of the chessboard, Frank Levy and Richard Murnane: The New Division of Labor, hiring and firing, income inequality, job automation, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Khan Academy, Kickstarter, knowledge worker, labour mobility, Loebner Prize, low skilled workers, minimum wage unemployment, patent troll, pattern recognition, Ray Kurzweil, rising living standards, Robert Gordon, self-driving car, shareholder value, Skype, too big to fail, Turing test, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, winner-take-all economy

Although multiplying five-digit numbers is an unnatural and difficult skill for the human mind to master, the visual cortex routinely does far more complex mathematics each time it detects an edge or uses parallax to locate an object in space. Machine computation has surpassed humans in the first task but not yet in the second one. As digital technologies continue to improve, we are skeptical that even these skills will remain bastions of human exceptionalism in the coming decades. The examples in Chapter 2 of Google’s self-driving car and IBM’s Watson point to a different path going forward. The technology is rapidly emerging to automate truck driving in the coming decade, just as scheduling truck routes was increasingly automated in the last decade. Likewise, the high end of the skill spectrum is also vulnerable, as we see in the case of e-discovery displacing lawyers and, perhaps, in a Watson-like technology, displacing human medical diagnosticians.


pages: 468 words: 124,573

How to Build a Billion Dollar App: Discover the Secrets of the Most Successful Entrepreneurs of Our Time by George Berkowski

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Airbnb, Amazon Web Services, barriers to entry, Black Swan, business intelligence, call centre, crowdsourcing, en.wikipedia.org, game design, Google Glasses, Google Hangouts, Google X / Alphabet X, iterative process, Jeff Bezos, Jony Ive, Kickstarter, knowledge worker, Lean Startup, loose coupling, Mark Zuckerberg, minimum viable product, move fast and break things, Network effects, Oculus Rift, Paul Graham, self-driving car, Silicon Valley, Silicon Valley startup, Skype, Snapchat, social graph, software as a service, software is eating the world, Steve Jobs, Steven Levy, Y Combinator

.’, article and video interview on FirstRound.com, firstround.com/article/how-dave-goldberg-of-surveymonkey-built-a-billion-dollar-business-and-still-gets-home-by-5-30. 4 Ibid. 5 Mike Rose, ‘Supercell’s Secret Sauce’, article on Gamasutra.com, 7 December 2012, www.gamasutra.com/view/feature/183064/supercells_secret_sauce.php. 6 Ibid. 7 Alyson Shontell and Andrea Huspeni, ‘15 Incredible Employee Perks That Will Make You Wish You Worked at a Startup’, article on BusinessInsider.com, 31 May 2012, www.BusinessInsider.com/killer-startup-perks-2012-5. 8 Heather Leonard, ‘Facebook Generates Over $1 Million in Revenue Per Employee’, article on BusinessInsider.com, 19 March 2013, www.BusinessInsider.com/facebook-has-high-revenue-per-employee-2013-3. 9 Megan Rose Dickey, ‘“Clash of Clans” Maker Had a Monster Year in 2013: Revenue Increased Nearly Ninefold’, article on BusinessInsider.com, 12 February 2014, www.BusinessInsider.com/gaming-startup-supercell-2013-revenue-2014-2. 10 Steven Levy, ‘Google’s Larry Page on Why Moon Shots Matter’, article on Wired.com, 17 January 2013, www.wired.com/business/2013/01/ff-qa-larry-page/all/. 11 Peter Murray, ‘Google’s Self-Driving Car Passes 300,000 Miles’, article on Forbes.com, 15 August 2012, www.forbes.com/sites/singularity/2012/08/15/googles-self-driving-car-passes-300000-miles/. 12 For more information about Project Loon, visit www.google.com/loon/. 13 ‘Google X’, entry on Wikipedia, en.wikipedia.org/wiki/Google_X. Chapter 38: Advice from Billion-Dollar CEOs 1 Will Oremus, ‘Google’s Big Break’, article on Slate.com, 13 October 2013, www.slate.com/articles/business/when_big_businesses_were_small/2013/10/google_s_big_break_how_bill_gross_goto_com_inspired_the_adwords_business.html. 2 Ibid. 3 ‘Drew Houston’s Morph from Hacker to Hyper-Growth CEO’, article on FirstRound.com, www.firstround.com/article/Drew-Houstons-morph-from-hacker-to-hyper-growth-CEO. 4 Peter Kafka, ‘Larry Page on Speed: “There are no companies that have good slow decisions”’, article on AllThingsD.com, 27 September 2011, allthingsd.com/20110927/larry-page-on-speed-there-are-no-companies-that-have-good-slow-decisions/. 5 Glen Cathey, ‘LinkedIn Traffic Statistics and User Demographics 2013’, article on BooleanBlackBelt.com, 24 July 2013, booleanblackbelt.com/2013/07/linkedin-traffic-statistics-and-user-demographics-2013/. 6 Juhana Hietala, ‘Rovio Mobile Company Presentation – Dynamic World of Mobile Game Business’, 1 April 2005, www.soberit.hut.fi/T-76.640/Slides/T-76.640_Rovio2005_04_01HUT.pdf. 7 ‘The 30 Best Pieces of Advice for Entrepreneurs’, article on FirstRound.com, firstround.com/article/30-Best-Pieces#ixzz2pRF5EZ8a. 8 Ibid. 9 ‘Drew Houston’s Morph from Hacker to Hyper-Growth CEO’, op. cit. 10 Ibid. 11 ‘The 30 Best Pieces of Advice for Entrepreneurs’, op. cit. 12 Ibid. 13 Eric Savitz, ‘Jack Dorsey: Leadership Secrets of Twitter and Square’, article for Forbes, 5 November 2012 issue, www.forbes.com/sites/ericsavitz/2012/10/17/jack-dorsey-the-leadership-secrets-of-twitter-and-square/. 14 ‘The 30 Best Pieces of Advice for Entrepreneurs’, op. cit.

Companies that don’t have robust business models will not be able to invest in these kinds of activities, which will make it increasingly harder for them to retain the best people, who in turn, once salary is taken care of, will be looking for a job with meaning. And that comes from a company that has a culture of pure innovation and solving meaningful problems. Google X is the division of Google that is home to the company’s moonshots. Since 2010 it has delivered a variety of seemingly impossible fantasies, such as the self-driving car (which has travelled over 500,000 km without a single accident11), Google Glass (a wearable computer with an optical head-mounted display), Project Loon (which provides rural Internet connectivity via high-altitude autonomous balloons12) and more than 100 other projects.13 So, when you think about the future of your app, it’s important to think about how big your ambition and vision are – and how you are going to take people on that journey.


pages: 510 words: 120,048

Who Owns the Future? by Jaron Lanier

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3D printing, 4chan, Affordable Care Act / Obamacare, Airbnb, augmented reality, automated trading system, barriers to entry, bitcoin, book scanning, Burning Man, call centre, carbon footprint, cloud computing, computer age, crowdsourcing, David Brooks, David Graeber, delayed gratification, digital Maoism, en.wikipedia.org, facts on the ground, Filter Bubble, financial deregulation, Fractional reserve banking, Francis Fukuyama: the end of history, George Akerlof, global supply chain, global village, Haight Ashbury, hive mind, if you build it, they will come, income inequality, informal economy, invisible hand, Jacquard loom, Jaron Lanier, Jeff Bezos, job automation, Kevin Kelly, Khan Academy, Kickstarter, Kodak vs Instagram, life extension, Long Term Capital Management, Mark Zuckerberg, meta analysis, meta-analysis, moral hazard, mutually assured destruction, Network effects, new economy, Norbert Wiener, obamacare, packet switching, Peter Thiel, place-making, Plutocrats, plutocrats, Ponzi scheme, post-oil, pre–internet, race to the bottom, Ray Kurzweil, rent-seeking, reversible computing, Richard Feynman, Richard Feynman, Ronald Reagan, self-driving car, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, smart meter, stem cell, Steve Jobs, Steve Wozniak, Stewart Brand, Ted Nelson, The Market for Lemons, Thomas Malthus, too big to fail, trickle-down economics, Turing test, Vannevar Bush, WikiLeaks

We kill each other in car accidents so frequently that the toll has become a more deadly problem than wars or terrorism. It’s one of our biggest sources of death and pain. A team of Google and Stanford researchers has famously demonstrated cars that are quite effective at driving themselves. (They are not alone; similar developments are occurring around the world.) The motivations for developing self-driving cars are so extraordinarily powerful that it’s hard to imagine stronger ones. Results from experiments thus far indicate that it is unlikely robots will ever drive as badly as people. My mother died in a car accident. What could be more compelling? But there’s more. Stoplights would generally go away. Cars would simply know when there’s no other car coming, and no pedestrian, so they could just proceed through without stopping when there is no need.

If it’s to only benefit a Siren Server, you can imagine that in, say, ten years, when you want to get to the airport, a robot taxi shows up. However, the chosen route might be peculiar. Maybe the taxi lingers in front of billboards along the way, or forces you to a particular convenience store if you need to pick up something, or whatever scam would come about in a Siren Server–driven car. But one thing we can guess even at this early date is that self-driving cars will depend on cloud data about streets, pedestrians, and everything else that can affect a trip. That information will be renewed constantly, with every single ride. Will the rider be compensated beyond a free ride for helping to generate this information? To do otherwise would be considered accounting fraud in a humanistic information economy. Flattening the City on a Hill The middle classes that have already lost their levees and economic dignity to Siren Servers are sometimes called the “creative classes.”

Genomics is amazing, but the benefits to medicine don’t burst forth like a lightning bolt. Instead they grow like a slow crop. The age of silver bullets seems to have retired around the time networking got good and data became big. And yet, the future hasn’t vanished completely. My daughter, who turned six as I finished this book, asks me: “Will I learn to drive, or will cars drive themselves?” In ten years, I imagine, self-driving cars will be familiar, but probably not yet ubiquitous. But it’s at least possible that learning to drive will start to feel anachronistic to my daughter and her friends, instead of a beckoning rite of passage. Driving for her might be like writing in longhand. Will she ever wear the same dress twice as an adult? Will she recycle clothes into new objects, or wash them, as we do today? At some point in her life, I suspect laundry will become obsolete.


pages: 464 words: 127,283

Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia by Anthony M. Townsend

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1960s counterculture, 4chan, A Pattern Language, Airbnb, Amazon Web Services, anti-communist, Apple II, Bay Area Rapid Transit, Burning Man, business process, call centre, carbon footprint, charter city, chief data officer, clean water, cleantech, cloud computing, computer age, congestion charging, connected car, crack epidemic, crowdsourcing, DARPA: Urban Challenge, data acquisition, Deng Xiaoping, East Village, Edward Glaeser, game design, garden city movement, Geoffrey West, Santa Fe Institute, George Gilder, ghettoisation, global supply chain, Grace Hopper, Haight Ashbury, Hedy Lamarr / George Antheil, hive mind, Howard Rheingold, interchangeable parts, Internet Archive, Internet of things, Jacquard loom, Jacquard loom, Jane Jacobs, jitney, John Snow's cholera map, Khan Academy, Kibera, knowledge worker, load shedding, M-Pesa, Mark Zuckerberg, megacity, mobile money, mutually assured destruction, new economy, New Urbanism, Norbert Wiener, Occupy movement, openstreetmap, packet switching, patent troll, place-making, planetary scale, popular electronics, RFC: Request For Comment, RFID, ride hailing / ride sharing, Robert Gordon, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart grid, smart meter, social graph, social software, social web, special economic zone, Steve Jobs, Steve Wozniak, Stuxnet, supply-chain management, technoutopianism, Ted Kaczynski, telepresence, The Death and Life of Great American Cities, too big to fail, trade route, Tyler Cowen: Great Stagnation, Upton Sinclair, uranium enrichment, urban decay, urban planning, urban renewal, Vannevar Bush, working poor, working-age population, X Prize, Y2K, zero day, Zipcar

When congestion is reduced due to the new capacity, the opportunity cost of driving falls, spurring drivers who would never have ventured onto the previously clogged road to sally forth. Over the coming decades, we’ll witness just such a process play out as automated vehicles take to the road. So far, the excitement over innovations like Google’s self-driving car has been about safety and convenience. You’ll be able to surf the net during your commute. You’ll never have to worry about your drunken teenager wrapping the family sedan around a telephone pole. But the even greater economic potential of self-driving cars is that they could potentially double road capacity by reducing spacing between cars and jams caused by a whole host of idiosyncratic human behaviors. If that spurs people who would have stayed home to take new trips, we’ll have to double fuel economy just to hold even.

., 61–62 Flood, Joe, 212 FootPath, 271–72 “forever day” vulnerabilities, 268 Forio, 83 Forrester, 2 Forrester, Jay, 76–78, 81–82, 84–85 Foundation (Asimov), 74–75, 88 Foursquare, 144–52, 319 Dodgeball compared with, 148–49 Franken, Al, 14 Frankfurt, 38 Frazer, John and Julia, 21–22 Freedom of Information Act, 296 Frenchman, Dennis, 219 Friendster, 122–23 From Warfare to Welfare: Defense Intellectuals and Urban Problems in Cold War America (Light), 78–79 G8 group of industrialized nations, 279 Gale International, 27–28, 49 Garaufis, Nicholas, 275 Garden Cities of To-Morrow (Howard), 94–95 Garden City Movement, 94–98, 107 g-cloud, 170, 289 Geddes, Patrick, 96–98, 105–6, 113–14, 235–36, 282–83, 302–3 Gelernter, David, 69–73, 89–91, 283, 298 General Electric, 8, 34, 38, 77 General Motors, 7, 18, 47, 95, 101 General Transit Feed Specification, 204 “Generator,” 21–22 as celebration of “messy human scale,” 28 Geraci, John, 154–59, 165, 202 Germany, 244 Gerstner, Louis, Jr., 62 Gettys, Jim, 266 Gibson, William, 119 GigaOM, 38 Gig.U, 289 Gilbertson, Nate, 159 Gilder, George, 6 Gilliam, Terry, 258 Gilman, Howard, 20–21 Giuliani, Rudolph “Rudy,” 205 Glaeser, Ed, 159–60, 278 GLONASS, 265 Goldman, Greg, 194–98 Goldsmith, Stephen, 205–7 Goldstein, Brett, 211 GOODBUILDINGS, 229 Google, 125, 134, 146, 157–58, 242, 272 Flu Trends of, 157 self-driving car of, 317–18 Google Maps, 200 Gordon, Robert, 280 Gottmann, Jean, 160–61 “Gov 2.0,” 237–38, 241 Gowalla, 146–47 GPS, 68, 163, 186–87, 207, 265, 272, 306 headset for blind using, 244 Gray Area Foundation for the Arts, 226–28, 230 Greenfield, Adam, 113, 303 Greenhill, Andrew, 237–38 Green Metropolis (Owen), 278 Greenwich Village, 102–4 Grossman, Nick, 158–59 Haamer, Veljo, 133–34 hackers, 119–26, 145, 153, 158, 227–30, 292, 301, 320 for open-source and free-wireless, 223 of the smart city, 227 Hagen, Erica, 186, 188 Hahn, Jury, 301–2 Hamas, 233, 273 Harrelson, Chris, 204 Harris, Josh, 121 Harrison, Colin, 64–65, 68, 69, 72, 84–85, 88, 269, 299 Harvey, Adam, 14 Haselmayer, Sascha, 243–48 HBO, 132 Hebbert, Frank, 307 Heeks, Richard, 175–76, 180, 188, 192 Heiferman, Scott, 159 Heisenberg, Werner, 88 Herron, Ron, 20 Hickenlooper, John, 206 Hills Are Evil!


pages: 309 words: 114,984

The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age by Robert Wachter

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Affordable Care Act / Obamacare, AI winter, Airbnb, Atul Gawande, Captain Sullenberger Hudson, Checklist Manifesto, Clayton Christensen, collapse of Lehman Brothers, computer age, crowdsourcing, deskilling, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, Firefox, Frank Levy and Richard Murnane: The New Division of Labor, Google Glasses, Ignaz Semmelweis: hand washing, Internet of things, job satisfaction, Joseph Schumpeter, knowledge worker, medical malpractice, medical residency, Menlo Park, minimum viable product, natural language processing, Network effects, Nicholas Carr, obamacare, pattern recognition, personalized medicine, pets.com, Productivity paradox, Ralph Nader, RAND corporation, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, six sigma, Skype, Snapchat, software as a service, Steve Jobs, Steven Levy, the payments system, The Wisdom of Crowds, Toyota Production System, Uber for X, Watson beat the top human players on Jeopardy!, Yogi Berra

Algorithm Vo-7 and Beyond,” September 22, 2014, available at http://techcrunch.com/2014/09/22/the-reinvention-of-medicine-dralgorithm-version-0-7-and-beyond/. 94 believed that another seemingly intractable problem The story of the Google car is well told in E. Brynjolfsson and A. McAfee, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (New York: W. W. Norton, 2014). While the car is an astounding achievement, there still are a few things it cannot do, described in R. Sorokanich, “Six Simple Things Google’s Self-Driving Car Still Can’t Handle,” August 30, 2014, available at http://gizmodo.com/6-simplethings-googles-self-driving-car-still-cant-han-1628040470. 94 “Executing a left turn across oncoming traffic” F. Levy and R. J. Murnane, The New Division of Labor: How Computers Are Creating the Next Job Market (Princeton, NJ: Princeton University Press, 2004). 94 “Just as factory jobs were eliminated” The story of Watson’s defeat of the Jeopardy champions is described in S.

Many offices have a dog or two happily wandering around. The juice bar is a busy congregational space, as are the small glasswalled rectangular conference rooms, in which one twentysomething, seated on a couch, is listening to another map out a company’s can’t-miss strategy on a dry-erase board. If one half expects to see a horse-drawn carriage from the window of David Blumenthal’s Fifth Avenue office in Manhattan, one half expects to see a self-driving car outside the offices of these Silicon Valley start-ups. The sense of limitless possibilities is palpable when you enter this world, but the hype can border on the farcical. In the “Health 2.0” office near San Francisco’s CalTrain station, a London-born healthcare impresario named Matthew Holt and his staff spend their days analyzing healthcare IT start-ups for a series of publications and conferences that they produce.


pages: 515 words: 126,820

Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World by Don Tapscott, Alex Tapscott

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Airbnb, altcoin, asset-backed security, autonomous vehicles, barriers to entry, bitcoin, blockchain, Bretton Woods, business process, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, clean water, cloud computing, cognitive dissonance, corporate governance, corporate social responsibility, Credit Default Swap, crowdsourcing, cryptocurrency, disintermediation, distributed ledger, Donald Trump, double entry bookkeeping, Edward Snowden, Elon Musk, Erik Brynjolfsson, ethereum blockchain, failed state, fiat currency, financial innovation, Firefox, first square of the chessboard, first square of the chessboard / second half of the chessboard, future of work, Galaxy Zoo, George Gilder, glass ceiling, Google bus, Hernando de Soto, income inequality, informal economy, interest rate swap, Internet of things, Jeff Bezos, jimmy wales, Kickstarter, knowledge worker, Kodak vs Instagram, Lean Startup, litecoin, Lyft, M-Pesa, Mark Zuckerberg, Marshall McLuhan, means of production, microcredit, mobile money, Network effects, new economy, Oculus Rift, pattern recognition, peer-to-peer lending, performance metric, Peter Thiel, planetary scale, Ponzi scheme, prediction markets, price mechanism, Productivity paradox, quantitative easing, ransomware, Ray Kurzweil, renewable energy credits, rent-seeking, ride hailing / ride sharing, Ronald Coase, Ronald Reagan, Satoshi Nakamoto, Second Machine Age, seigniorage, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, smart grid, social graph, social software, Stephen Hawking, Steve Jobs, Steve Wozniak, Stewart Brand, supply-chain management, TaskRabbit, The Fortune at the Bottom of the Pyramid, The Nature of the Firm, The Wisdom of Crowds, transaction costs, Turing complete, Turing test, Uber and Lyft, unbanked and underbanked, underbanked, unorthodox policies, X Prize, Y2K, Zipcar

“We’re still at that 1994 point in terms of applications and protocols that really take advantage of the network and show the world, ‘Here’s what you can do that is totally groundbreaking.’”19 Hill expects to see different financial instruments, from proof-of-asset authenticity to proof-of-property ownership. He also expects to see bitcoin applications in the Metaverse (a virtual world) where you can convert bitcoin into Kongbucks and hire Hiro Protagonist to hack you some data.20 Or jack yourself into the OASIS (a world of multiple virtual utopias) where you actually do discover the Easter egg, win Halliday’s estate, license OASIS’s virtual positioning rights to Google, and buy a self-driving car to navigate Toronto.21 And, of course, there’s the Internet of Things, where we register our devices, assign them an identity (Intel is already doing this), and coordinate payment among them using bitcoin rather than multiple fiat currencies. “You can define all these new business cases that you want to do, and have it interoperate within the network, and use the network infrastructure without having to bootstrap a new blockchain, just for yourself,” said Hill. 22 Unlike fiat currency, each bitcoin is divisible to eight decimal places.

In Chicago, Melissa requests a car through SUber (think blockchain Super Uber). All the available vehicles start automatically posting offers, which Melissa’s node ranks and presents to her based on her selection criteria. Melissa factors in how much she’s willing to pay for faster routes (e.g., higher-priced toll lanes). Meanwhile John, unlike most users, is a SUber vehicle owner and as his self-driving car is taking him to work, it identifies all the parking options, both public and privately owned, selects a space, and reserves and pays for it through an autonomous parking marketplace. Because John’s predetermined parameters always include seeking the cheapest available spot within a ten-minute walk of his destination, he almost always goes with his car’s first choice. The underlying parking database that supports the parking also contains information on parking rules for specific streets on different days and at different times of day, whether or not the parking space is covered or in the open, or whether the owner of the space has established a minimum price.

The underlying parking database that supports the parking also contains information on parking rules for specific streets on different days and at different times of day, whether or not the parking space is covered or in the open, or whether the owner of the space has established a minimum price. All this runs on a distributed peer-to-peer platform—connecting multiple apps—so no centralized company is mediating the orders or taking part of the fee. There is no surge pricing and no unexpected fees. What is striking about this proposed model is not the driverless vehicles, because self-driving cars will be commonplace—probably sooner rather than later. Rather, the cars could be fully autonomous agents that earn their own fares, pay for their own fuel and repair, get their own auto insurance, negotiate liability in collisions, and operate (“drive”) without outside human control, except when they need to take some entity—maybe a human being—to court. As a condition of operating, SUber administrators could program the vehicles’ protocols into the blockchain to obey all traffic rules, take the most direct, fastest, or least expensive route, and honor their bids.


pages: 351 words: 100,791

The World Beyond Your Head: On Becoming an Individual in an Age of Distraction by Matthew B. Crawford

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airport security, Cass Sunstein, choice architecture, collateralized debt obligation, David Brooks, delayed gratification, dematerialisation, deskilling, digital Maoism, Google Glasses, hive mind, index card, informal economy, Jaron Lanier, large denomination, new economy, new new economy, online collectivism, Plutocrats, plutocrats, Richard Thaler, Rodney Brooks, self-driving car, Silicon Valley, Silicon Valley ideology, the built environment, the scientific method, The Wisdom of Crowds, theory of mind, Walter Mischel, winner-take-all economy

If you have ever listened to the NPR show Car Talk and heard people mimicking the sounds their cars make when they are misbehaving in some way, then you have some idea of the role played by sound in our ongoing monitoring of our cars, which we become aware of only when there is a new sound, indicating a problem. 11. Then again, it is said that we live at the end of history, so maybe we needn’t fret about any of this. “In the future” (as Conan O’Brien used to say), we will be ferried around by Google’s self-driving cars, wearing Google Glass goggles and who knows what all. The goggles will give us something exciting to watch, like Grand Theft Auto, and we will be given a steering wheel that shakes realistically as we execute brilliant evasive maneuvers. We will make vroom vroom sounds with our mouths to preserve that “sense of involvement,” and arrive at our destination in a mood of triumph. We should have noted earlier that the passive kitten on the carousel has an enviable inner life. 12.

Sara (chimpanzee) Sartre, Jean-Paul SATs savings rate Schüll, Natasha Dow Schutz, Alfred Schweitzer, Albert science under communism scientific method scientific revolution screw transform seat belts secondary qualities Seeman, Axel self brain-centered perspective on cognitive extension and coherent in conflict with world consciousness and contingencies and in dismantling inherited cultural jigs environment and fantasy of unencumbered fragility of freedom of heteronomy vs. and inheritance of past maintaining of as result of historical polemics right vs. left politics and scientific method and situated statistical in West self-absorption self-awareness self-control self-criticism self-discipline self-doubt self-driving cars self-knowledge self-motion self-protection “Self-Reliance” (Emerson) self-responsibility self-sufficiency self-understanding Sennett, Richard sensorimotor contingency sensorimotor experience Seoul sequences Sesame Street sex, Kinsey Reports on sex addicts Sexual Behavior studies (Kinsey) Shapiro, Lawrence Sharon (gambler) Sheba (chimpanzee) Shop Class as Soulcraft (Crawford) short-order cooks background jig of jigging of environment by kitchen-self relationship of as “machine” silence, importance of Simmel, Georg situatedness situated self affordances and situation defining features of pragmatic criteria of propositional knowledge and role of risk in comprehending role of skill in comprehending 60 Minutes skateboarders, world-historical significance of skepticism skill acquisition of and perception of affordances skilled action, contingent facts in skilled practices see also specific skilled practices Sleeper (film) slot machines autoplay see also machine gambling smart technology Smith, Christian “smooth coping” social capital social engineering gambling addiction as nudging and social media social mobility social nature Socrates South Korea sovereign, relationship of citizens to sovereignty, absolute Soviet Union five-year plan in spatial categories speed, judgment and Spiegel, Bernt spontaneous encounters Springsteen, Bruce stackable self Stahl, Lesley state of nature steering head bearings stereotyping stimulation Stoicism stories Stowe, Doug Stradivari, Antonio subjectivism Kant and submission suicide Sunstein, Cass supermarket Supersizing the Mind (Clark) suppression of environment suspension Sweden symbolic representation tacit knowledge tactical flight suit Tagliapietra, Lino Tannenberg, David tannic acid tapered roll bearings taxes, taxation filing of gambling as Taylor, Charles Taylor, George reverse engineering by Taylor and Boody Tea Party technology automaticity and television children’s and orienting response in public spaces Terror (French Revolution) texting Thaler, Richard Thatcher, Margaret “thing in itself” things attention structured by encountering of thought Descartes’s belief in certainty of explicit thrift tipping Tocqueville, Alexis de toddlers mastery of body gained by will of “To Each His Own” (Fleming) tools totalitarianism Toyota recall (2008) toys agency and traction traders tradition as authority vs. self-responsibility traffic lights transvestites Trilling, Lionel truth as representation standard for TRW Turkle, Sherry Two Treatises of Government (Locke) United Kingdom United States apprenticeships criticized in belief in meritocracy of massification in science in social mobility in Upper Half of the Motorcycle, The (Spiegel) value ventilation systems Verizon Vico, Giambattista video gambling violins virtual reality virtual reel mapping, in slot machines virtual stops, in slot machines visual demand visual perception von Hebenstreit, Benedikt walking Wallace, David Foster walls, colored Wampole, Christy “war of all against all” (Hobbes) water wealth Weariness of the Self, The (Ehrenberg) Weber, Max weight lifting Weil, Simone welfare reform Whitman, Walt Who Owns the Future?


pages: 347 words: 97,721

Only Humans Need Apply: Winners and Losers in the Age of Smart Machines by Thomas H. Davenport, Julia Kirby

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AI winter, Andy Kessler, artificial general intelligence, asset allocation, Automated Insights, autonomous vehicles, Baxter: Rethink Robotics, business intelligence, business process, call centre, carbon-based life, Clayton Christensen, clockwork universe, conceptual framework, dark matter, David Brooks, deliberate practice, deskilling, Edward Lloyd's coffeehouse, Elon Musk, Erik Brynjolfsson, estate planning, follow your passion, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, game design, general-purpose programming language, Google Glasses, Hans Lippershey, haute cuisine, income inequality, index fund, industrial robot, information retrieval, intermodal, Internet of things, inventory management, Isaac Newton, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Khan Academy, knowledge worker, labor-force participation, loss aversion, Mark Zuckerberg, Narrative Science, natural language processing, Norbert Wiener, nuclear winter, pattern recognition, performance metric, Peter Thiel, precariat, quantitative trading / quantitative finance, Ray Kurzweil, Richard Feynman, Richard Feynman, risk tolerance, Robert Shiller, Robert Shiller, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, six sigma, Skype, speech recognition, spinning jenny, statistical model, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, superintelligent machines, supply-chain management, transaction costs, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, Works Progress Administration, Zipcar

Law, regulations, and union contracts can strictly limit a firm’s use of promising technologies—and even the sense that such rules might be imposed can put a damper on development. It’s important to note, however, that the same rules that constrain automation in your industry can end up serving you well, if your own strategy aims for augmentation. For example, developers of automated transportation solutions—think self-driving cars—face something between a thicket and a morass of regulations. Although it’s now pretty clear that the technical capabilities for driverless cars, trucks, and golf carts are already mastered or masterable, it’s not at all obvious when the regulatory structure will allow them on highways and fairways. Companies like Google and Tesla, as well as automotive industry mainstays Ford, General Motors, and Mercedes, could find that they have put a lot of energy into developing vehicles that drive themselves but are stuck with regulations that require an alert driver with hands on the steering wheel and feet on the pedals.

And the third: “A robot must protect its own existence, as long as such protection does not conflict with the first or second law.” Plenty of people have pointed out that the laws are problematic, because social situations are complex. Legendary investor Warren Buffett, for example, raised a common question about autonomous vehicles during a forum hosted by the National Automobile Dealers Association. What if, he asked, a toddler runs into the street in front of a self-driving car, and the robot’s only option not to hit that child is to swerve into the path of an oncoming vehicle with four people in it? After that split-second decision is made and fatal accident results, said Buffett, “I am not sure who gets sued.” More deeply, “[I]t will be interesting to know who programs that computer and what their thoughts are about the values of human lives and things.” Or think of end-of-life decisions that are not so hasty; as we increasingly depend on AI to specify, monitor, and to even administer individualized health care, will we trust a machine to say when a patient has suffered long enough and it’s time to cease heroic measures, move her to a hospice setting, and let her die in peace?


pages: 295 words: 89,430

Small Data: The Tiny Clues That Uncover Huge Trends by Martin Lindstrom

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autonomous vehicles, Berlin Wall, big-box store, correlation does not imply causation, Edward Snowden, Fall of the Berlin Wall, land reform, Mikhail Gorbachev, Murano, Venice glass, Richard Florida, rolodex, self-driving car, Skype, Snapchat, Steve Jobs, Steven Pinker, too big to fail, urban sprawl

Before sending them out, though, the bank executive discovered something surprising. Yes, indeed, “big data” had seen evidence of churning. Thing is, it wasn’t because customers were dissatisfied with the bank or its customer service. No: most were getting a divorce, which explained why they were shifting around their assets.14 A parallel small data study could have figured this out in a day or less. Then there are the issues facing Google’s new self-driving cars, most of which it seems can be credited to the mismatch between technology and humanity. According to the New York Times, last year as one of Google’s new cars approached a crosswalk, it did as it was supposed to and came to a complete stop. The pedestrian in front crossed the street safely, at which point the Google car was rammed from behind by a second non-Google automobile. Later, another self-driving Google car found that it wasn’t able to advance through a four-way stop, as its sensors were calibrated to wait for other drivers to make a complete stop, as opposed to inching continuously forward, which most did.

See also ethnography; Subtext Research Apple, 49, 59, 96, 112, 138, 172–4, 178, 180, 203 Arons, Marc de Swaan, 13 aspiration, 4, 29, 34, 60, 81, 131–7, 180, 196 and Brazil, 122, 126–35, 140–1, 144–5 and fashion, 128–9, 150–2, 155–6, 158 Australia, 15, 75, 104–5, 107–8, 115, 131, 142, 176, 180–1 Austria, 142, 151, 154, 156, 158, 168 automobiles BMW Mini Cooper, ix, 199 Chevrolet, 138 and China, 172, 179–80, 199–205 and Germany, 173, 200–202 and identity, 180 and Saudi Arabia, 35 self-driving cars, 216 and the United States, 61, 63, 200–202 beauty, 29, 77, 80, 100, 145, 177. See also cosmetics; fashion bicycling, 105, 171–2, 214 big data, vii-viii, 2–3, 12–14, 73, 160, 212–16 Boas, Franz, 11 brands and building, 1–2, 4, 14, 67, 112, 173 and aspiration, 131–7 brand ambassadors, 108, 115, 145, 191 brand loyalty, 107, 113, 163, 213 country branding, 57, 60, 175–7 definition of brand, 211 and desire, 9–11 destinations, 173–7 fear of losing branded identities, 210–11 globalization of, 147–8 and Kulturbrille (culture glasses), 11 and religion, 138–9 and Somatic Marker Hypothesis, 65–6 See also specific brands; Subtext Research Brasil Kirin, 121–3, 138.

Future Files: A Brief History of the Next 50 Years by Richard Watson

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Albert Einstein, bank run, banking crisis, battle of ideas, Black Swan, call centre, carbon footprint, cashless society, citizen journalism, computer age, computer vision, congestion charging, corporate governance, corporate social responsibility, deglobalization, digital Maoism, disintermediation, epigenetics, failed state, financial innovation, Firefox, food miles, future of work, global supply chain, global village, hive mind, industrial robot, invention of the telegraph, Jaron Lanier, Jeff Bezos, knowledge economy, linked data, low skilled workers, M-Pesa, Northern Rock, peak oil, pensions crisis, precision agriculture, prediction markets, Ralph Nader, Ray Kurzweil, rent control, RFID, Richard Florida, self-driving car, speech recognition, telepresence, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Turing test, Victor Gruen, white flight, women in the workforce, Zipcar

Hence better vehicular access (doors) and better forward, backward and side vision will become increasingly important engineering elements and testing of older drivers will eventually become commonplace globally. Automotive and Transport 165 Ultimately, though, the solution to both older and younger driver safety will be to take the necessity of driving away altogether. Along with flying cars, self-driving cars have been a feature of the sci-fi future for decades. They first appeared in the 1950s, although the idea never really progressed beyond the concept stage for a number of legal, social and technical reasons. Nevertheless, General Motors claims that it is building such a car and that it could be introduced as early as 2008. Fat chance — although if what GM is really talking about is adaptive cruise control, it’s a possibility.

A 311 Index ‘O’ Garage 170 3D printers 56 accelerated education 57 accidents 159, 161–6, 173, 246 ACNielsen 126 adaptive cruise control 165 Adeg Aktiv 50+ 208 advertising 115–16, 117, 119 Africa 70, 89, 129, 174, 221, 245, 270, 275, 290, 301 ageing 1, 10, 54, 69, 93, 139, 147–8, 164, 188, 202, 208, 221, 228–9, 237, 239, 251, 261, 292, 295, 297–8 airborne networks 56 airlines 272 allergies 196–7, 234, 236 Alliance Against Urban 4x4s 171 alternative energy 173 alternative futures viii alternative medicine 244–5 alternative technology 151 amateur production 111–12 Amazon 32, 113–14, 121 American Apparel 207 American Express 127–8 androids 55 Angola 77 anti-ageing drugs 231, 237 anti-ageing foods 188 anti-ageing surgery 2, 237 antibiotics 251 anxiety 10, 16, 30, 32, 36, 37, 128, 149, 179, 184, 197, 199, 225, 228, 243, 251, 252, 256, 263, 283–4, 295–6, 300, 301, 305 Apple 61, 115, 121, 130, 137–8, 157 Appleyard, Bryan 79 Argentina 210 Armamark Corporation 193 artificial intelliegence 22, 40, 44, 82 131, 275, 285–6, 297, 300 Asda 136, 137 Asia 11, 70, 78, 89, 129, 150, 174, 221, 280, 290, 292 Asimov, Isaac 44 Asos.com 216 asthma 235 auditory display software 29 Australia 20–21, 72–3, 76, 92, 121, 145, 196, 242, 246, 250, 270, 282 Austria 208 authenticity 32, 37, 179, 194, 203–11 authoritarianism 94 automated publishing machine (APM) 114 automation 292 automotive industry 154–77 B&Q 279 baby boomers 41, 208 bacterial factories 56 Bahney, Anna 145 Bahrain 2 baking 27, 179, 195, 199 Bangladesh 2 bank accounts, body double 132 banknotes 29, 128 banks 22, 123, 135–8, 150, 151 virtual 134 Barnes and Noble 114 bartering 151 BBC 25, 119 Become 207 Belgium 238 313 314 benriya 28 Berlusconi, Silvio 92 Best Buy 223 biofuel 64 biomechatronics 56 biometric identification 28, 35, 52, 68, 88, 132 bionic body parts 55 Biosphere Expeditions 259 biotechnology 40, 300 blended families 20 blogs 103, 107, 109, 120 Blurb 113 BMW 289 board games 225 body double bank accounts 132 body parts bionic 55 replacement 2, 188, 228 Bolivia 73 Bollywood 111 books 29, 105, 111–25 boomerang kids 145 brain transplants 231 brain-enhancing foods 188 Brazil 2, 84, 89, 173, 247, 254, 270, 290 Burger King 184 business 13, 275–92 Bust-Up 189 busyness 27, 195, 277 Calvin, Bill 45 Canada 63, 78, 240 cancer 251 car sharing 160, 169, 176 carbon credits 173 carbon footprints 255 carbon taxes 76, 172 cars classic 168–9 driverless 154–5 flying 156, 165 hydrogen-powered 12, 31, 157, 173 pay-as-you-go 167–8 self-driving 165 cascading failure 28 cash 126–7, 205 cellphone payments 129, 213 cellphones 3, 25, 35, 51, 53, 120, 121, FUTURE FILES 129, 156, 161, 251 chicken, Christian 192 childcare robots 57 childhood 27, 33–4, 82–3 children’s database 86 CHIME nations (China, India, Middle East) 2, 10, 81 China 2, 10, 11, 69–72, 75–81, 88, 92–3, 125, 137, 139–40, 142, 151, 163, 174–5, 176, 200, 222, 228, 247, 260, 270–71, 275, 279, 295, 302 choice 186–7 Christian chicken 192 Christianity, muscular 16, 73 Chrysler 176 cinema 110–11, 120 Citibank 29, 128 citizen journalism 103–4, 108 City Car Club 168 Clarke, Arthur C. 58–9 Clarke’s 187 classic cars 168–9 climate change 4, 11, 37, 43, 59, 64, 68, 74, 77–9, 93, 150, 155, 254, 257, 264, 298–9 climate-controlled buildings 254, 264 cloning 38 human 23, 249 CNN 119 coal 176 Coca-Cola 78, 222–3 co-creation 111–12, 119 coins 29, 128, 129 collective intelligence 45–6 Collins, Jim 288 comfort eating 200 Comme des Garçons 216 community 36 compassion 120 competition in financial services 124–5 low-cost 292 computers disposable 56 intelligent 23, 43 organic 56 wearable 56, 302 computing 3, 33, 43, 48, 82 connectivity 3, 10, 11, 15, 91, 120, Index 233, 261, 275–6, 281, 292, 297, 299 conscientious objection taxation 86 contactless payments 123, 150 continuous partial attention 53 control 36, 151, 225 convenience 123, 178–9, 184, 189, 212, 223, 224 Coren, Stanley 246 corporate social responsibility 276, 282, 298 cosmetic neurology 250 Costa Rica 247 Craig’s List 102 creativity 11, 286; see also innovation credit cards 141–3, 150 crime 86–9 forecasting 86–7 gene 57, 86 Croatia 200 Crowdstorm 207 Cuba 75 cultural holidays 259, 273 culture 11, 17–37 currency, global 127, 151 customization 56, 169, 221–2, 260 cyberterrorism 65, 88–9 Cyc 45 cynicism 37 DayJet 262 death 237–9 debt 123–4, 140–44, 150 defense 63, 86 deflation 139 democracy 94 democratization of media 104, 108, 113 demographics 1, 10, 21, 69, 82, 93, 202, 276, 279–81, 292, 297–8 Denmark 245 department stores 214 deregulation 11, 3 Destiny Health 149 detox 200 Detroit Project 171 diagnosis 232 remote 228 digital downloads 121 evaporation 25 315 immortality 24–5 instant gratification syndrome 202 Maoism 47 money 12, 29, 123, 126–7, 129, 132, 138, 150, 191 nomads 20, 283 plasters 241 privacy 25, 97, 108 readers 121 digitalization 37, 292 Dinner by Design 185 dirt holidays 236 discount retailers 224 Discovery Health 149 diseases 2, 228 disintegrators 57 Disney 118–19 disposable computers 56 divorce 33, 85 DNA 56–7, 182 database 86 testing, compulsory 86 do-it-yourself dinner shops 185–6 dolls 24 doorbells 32 downshifters 20 Dream Dinners 185 dream fulfillment 148 dressmaking 225 drink 178–200 driverless cars 154–5 drugs anti-ageing 231, 237 performance-improving 284–5 Dubai 264, 267, 273 dynamic pricing 260 E Ink 115 e-action 65 Earthwatch 259 Eastern Europe 290 eBay 207 e-books 29, 37, 60, 114, 115, 302 eco-luxe resorts 272 economic collapse 2, 4, 36, 72, 221, 295 economic protectionism 10, 15, 72, 298 economy travel 272 316 Ecuador 73 education 15, 18, 82–5, 297 accelerated 57 lifelong learning 290 Egypt 2 electricity shortages 301 electronic camouflage 56 electronic surveillance 35 Elephant 244 email 18–19, 25, 53–4, 108 embedded intelligence 53, 154 EMF radiation 251 emotional capacity of robots 40, 60 enclosed resorts 273 energy 72, 75, 93 alternative 173 nuclear 74 solar 74 wind 74 enhancement surgery 249 entertainment 34, 121 environment 4, 10, 11, 14, 64, 75–6, 83, 93, 155, 171, 173, 183, 199, 219–20, 252, 256–7, 271, 292, 301 epigenetics 57 escapism 16, 32–3, 121 Estonia 85, 89 e-tagging 129–30 e-therapy 242 ethical bankruptcy 35 ethical investing 281 ethical tourism 259 ethics 22, 24, 41, 53, 78, 86, 132, 152, 194, 203, 213, 232, 238, 249–50, 258, 276, 281–2, 298–9 eugenics 252 Europe 11, 70, 72, 81, 91, 141, 150, 174–5, 182, 190, 192, 209 European Union 15, 139 euthanasia 238, 251 Everquest 33 e-voting 65 experience 224 extended financial families 144 extinction timeline 9 Facebook 37, 97, 107 face-recognition doors 57 fakes 32 family 36, 37 FUTURE FILES family loans 145 fantasy-related industries 32 farmaceuticals 179, 182 fast food 178, 183–4 fat taxes 190 fear 10, 34, 36, 38, 68, 150, 151, 305 female-only spaces 210–11, 257 feminization 84 financial crisis 38, 150–51, 223, 226, 301 financial services 123–53, 252 trends 123–5 fish farming 181 fixed-price eating 200 flashpacking 273 flat-tax system 85–6 Florida, Richard 36, 286, 292 flying cars 165 food 69–70, 72, 78–9, 162, 178–201 food anti-ageing 188 brain-enhancing 188 fast 178, 183–4 functional 179 growing your own 179, 192, 195 history 190–92 passports 200 slow 178, 193 tourism 273 trends 178–80 FoodExpert ID 182 food-miles 178, 193, 220 Ford 169, 176, 213, 279–80 forecasting 49 crime 86–7 war 49 Forrester Research 132 fractional ownership 168, 175, 176, 225 France 103, 147, 170, 189, 198, 267 Friedman, Thomas 278–9, 292 FriendFinder 32 Friends Reunited 22 frugality 224 functional food 179 Furedi, Frank 68 gaming 32–3, 70, 97, 111–12, 117, 130, 166, 262 Gap 217 Index gardening 27, 148 gas 176 GE Money 138, 145 gendered medicine 244–5 gene silencing 231 gene, crime 86 General Motors 157, 165 Generation X 41, 281 Generation Y 37, 41, 97, 106, 138, 141–2, 144, 202, 208, 276, 281, 292 generational power shifts 292 Genes Reunited 35 genetic enhancement 40, 48 history 35 modification 31, 182 testing 221 genetics 3, 10, 45, 251–2 genomic medicine 231 Germany 73, 147, 160, 170, 204–5, 216–17, 261, 267, 279, 291 Gimzewski, James 232 glamping 273 global currency 127 global warming 4, 47, 77, 93, 193, 234 globalization 3, 10, 15–16, 36–7, 63–7, 72–3, 75, 81–2, 88, 100, 125, 139, 143, 146, 170, 183, 189, 193–5, 221, 224, 226, 233–4, 247–8, 263, 275, 278–80, 292, 296, 299 GM 176 Google 22, 61, 121, 137, 293 gout 235 government 14, 18, 36, 63–95, 151 GPS 3, 15, 26, 50, 88, 138, 148, 209, 237, 262, 283 Grameen Bank 135 gravity tubes 57 green taxes 76 Greenpeace 172 GRIN technologies (genetics, robotics, internet, nanotechnology) 3, 10, 11 growing your own food 178, 192, 195 Gucci 221 Gulf States 125, 260, 268 H&M 217 habitual shopping 212 Handy, Charles 278 317 Happily 210 happiness 63–4, 71–2, 146, 260 health 15, 82, 178–9, 199 health monitoring 232, 236, 241 healthcare 2, 136, 144, 147–8, 154, 178–9, 183–4, 189–91, 228–53, 298; see also medicine trends 214–1534–7 Heinberg, Richard 74 Helm, Dieter 77 Heritage Foods 195 hikikomori 18 hive mind 45 holidays 31, 119; see also tourism holidays at home 255 cultural 259 dirt 236 Hollywood 33, 111–12 holographic displays 56 Home Equity Share 145 home baking 225 home-based microgeneration 64 home brewing 225 honesty 152 Hong Kong 267 hospitals 228, 241–3, 266 at home 228, 238, 240–42 hotels 19, 267 sleep 266 human cloning 23, 249 Hungary 247 hybrid humans 22 hydrogen power 64 hydrogen-powered cars 12, 31, 157, 173 Hyperactive Technologies 184 Hyundai 170 IBM 293 identities, multiple 35, 52 identity 64, 71 identity theft 88, 132 identity verification, two-way 132 immigration 151–2, 302 India 2, 10, 11, 70–72, 76, 78–9, 81, 92, 111, 125, 135, 139, 163, 174–5, 176, 247, 249–50, 254, 260, 270, 275, 279, 302 indirect taxation 86 318 individualism 36 Indonesia 2, 174 industrial robots 42 infinite content 96–7 inflation 151 information overlead 97, 120, 159, 285; see also too much information innovation 64, 81–2, 100, 175, 222, 238, 269, 277, 286–8, 291, 297, 299 innovation timeline 8 instant gratification 213 insurance 123, 138, 147–50, 154, 167, 191, 236, 250 pay-as-you-go 167 weather 264 intelligence 11 embedded 53, 154 implants 229 intelligent computers 23, 43 intelligent night vision 162–3 interaction, physical 22, 25, 97, 110, 118, 133–4, 215, 228, 243, 276, 304 interactive media 97, 105 intergenerational mortgages 140, 144–5 intermediaries 123, 135 internet 3, 10, 11, 17–18, 25, 68, 103, 108, 115–17, 124, 156, 240–41, 261, 270, 283, 289, 305 failure 301 impact on politics 93–4 sensory 56 interruption science 53 iPills 240 Iran 2, 69 Ishiguro, Hiroshi 55 Islamic fanaticism 16 Italy 92, 170, 198–9 iTunes 115, 130; see also Apple Japan 1, 18, 26, 28–9, 54–5, 63, 80–81, 114, 121, 128–9, 132, 140, 144–5, 147, 174, 186, 189, 192, 196, 198, 200, 209–10, 223, 240, 260, 264, 271, 279, 291 jetpacks 60 job security 292 journalism 96, 118 journalism, citizen 103–4, 107 joy-makers 57 FUTURE FILES Kaboodle 207 Kapor, Mitchell 45 Kenya 128 keys 28–9 Kindle 60, 121 Kramer, Peter 284 Kuhn, Thomas 281 Kurzweil, Ray 45 Kuwait 2 labor migration 290–91 labor shortages 3, 80–81, 289–90 Lanier, Jaron 47 laser shopping 212 leisure sickness 238 Let’s Dish 185 Lexus 157 libraries 121 Libya 73 life-caching 24, 107–8 lighting 158, 160 Like.com 216 limb farms 249 limited editions 216–17 live events 98, 110, 304 localization 10, 15–16, 116, 128, 170, 178, 189, 193, 195, 215, 220, 222–3, 224, 226, 255, 270, 297 location tagging 88 location-based marketing 116 longevity 188–9, 202 Longman, Philip 71 low cost 202, 219–22 luxury 202, 221, 225, 256, 260, 262, 265–6, 272 machinamas 112 machine-to-machine communication 56 marketing 115–16 location-based 116 now 116 prediction 116 Marks & Spencer 210 Maslow, Abraham 305–6 masstigue 223 materialism 37 Mayo Clinic 243 McDonald’s 130, 168, 180, 184 McKinsey 287 Index meaning, search for 16, 259, 282, 290, 305–6 MECU 132 media 96–122 democratization of 104, 108, 115 trends 96–8 medical outsourcing 247–8 medical tourism 2, 229, 247 medicine 188, 228–53; see also healthcare alternative 243–4 gendered 244–5 genomic 231 memory 229, 232, 239–40 memory loss 47 memory pills 231, 240 memory recovery 2, 228–9, 239 memory removal 29–30, 29, 240 Menicon 240 mental health 199 Meow Mix 216 Merriman, Jon 126 metabolomics 56 meta-materials 56 Metro 204–5 Mexico 2 micromedia 101 micro-payments 130, 150 Microsoft 137, 147, 293 Middle East 10, 11, 70, 81, 89, 119, 125, 129, 139, 174–5, 268, 301 migration 3, 11, 69–70, 78, 82, 234, 275, 290–91 boomerang 20 labor 290–91 Migros 215 military recruitment 69 military vehicles 158–9 mind-control toys 38 mindwipes 57 Mitsubishi 198, 279 mobile payments 123, 150 Modafinil 232 molecular biology 231 monetization 118 money 123–52 digital 12, 29, 123, 126–7, 129, 132, 138, 150, 191 monitoring, remote 154, 168, 228, 242 monolines 135, 137 319 mood sensitivity 41, 49, 154, 158, 164, 187–8 Morgan Stanley 127 mortality bonds 148 Mozilla Corp. 289 M-PESA 129 MTV 103 multigenerational families 20 multiple identities 35, 52 Murdoch, Rupert 109 muscular Christianity 16, 73 music industry 121 My-Food-Phone 242 MySpace 22, 25, 37, 46, 97, 107, 113 N11 nations (Bangladesh, Egypt, Indonesia, Iran, South Korea, Mexico, Nigeria, Pakistan, Philippines, Turkey, Vietnam) 2 nanoelectronics 56 nanomedicine 32 nanotechnology 3, 10, 23, 40, 44–5, 50, 157, 183, 232, 243, 286, 298 napcaps 56 narrowcasting 109 NASA 25, 53 nationalism 16, 70, 72–3, 139, 183, 298, 302 natural disasters 301 natural resources 2, 4, 11, 64, 298–9 Nearbynow 223 Nestlé 195 Netherlands 238 NetIntelligence 283 networkcar.com 154 networks 28, 166, 288 airborne 56 neural nets 49 neuronic whips 57 neuroscience 33, 48 Neville, Richard 58–9 New Economics Foundation 171 New Zealand 265, 269 newspapers 29, 102–9, 117, 119, 120 Nigeria 2, 73 Nike 23 nimbyism 63 no-frills 224 Nokia 61, 105 Norelift 189 320 Northern Rock 139–40 Norwich Union 167 nostalgia 16, 31–2, 51, 169–70, 179, 183, 199, 203, 225, 303 now marketing 116 nuclear annihilation 10, 91 nuclear energy 74 nutraceuticals 179, 182 Obama, Barack 92–3 obesity 75, 190–92, 199, 250–51 oceanic thermal converters 57 oil 69, 72–3, 93, 151, 174, 176, 272, 273, 301 Oman 2, 270 online relationships 38 organic computers 56 organic food 200, 226 osteoporosis 235 outsourcing 224, 292 Pakistan 2 pandemics 4, 10, 16, 59, 72, 128, 232, 234, 272, 295–7, 301 paper 37 parasite singles 145 passwords 52 pictorial 52 pathogens 233 patient simulators 247 patina 31 patriotism 63, 67, 299 pay-as-you-go cars 167–8 pay-as-you-go insurance 167 payments cellphone 129, 213 contactless 123, 150 micro- 130, 150 mobile 123, 150 pre- 123, 150 PayPal 124, 137 Pearson, Ian 44 performance-improving drugs 284–5 personal restraint 36 personal robots 42 personalization 19, 26, 56, 96–8, 100, 102–3, 106, 108–9, 120, 138, 149, 183, 205–6, 223, 244–5, 262, 267, 269 Peru 73 FUTURE FILES Peters, Tom 280 Pharmaca 244 pharmaceuticals 2, 33, 228, 237 Philippines 2, 212, 290 Philips 114 Philips, Michael 232–3 photographs 108 physical interaction 22, 25, 97, 110, 118, 133–4, 215, 228, 243, 276, 304 physicalization 96–7, 101–2, 106, 110, 120 pictorial passwords 52 piggy banks 151 Pink, Daniel 285 plagiarism 83 polarization 15–16, 285 politics 37, 63–95, 151–2 regional 63 trends 63–5 pop-up retail 216, 224 pornography 31 portability 178, 183–4 power shift eastwards 2, 10–11, 81, 252 Prada 205–6, 216 precision agriculture 181–2 precision healthcare 234–7 prediction marketing 116 predictions 37, 301–2 premiumization 223 pre-payments 123, 150 privacy 3, 15, 41, 50, 88, 154, 165–7, 205, 236, 249, 285, 295 digital 25, 97, 108 Procter & Gamble 105, 280 product sourcing 224 Prosper 124, 135 protectionism 67, 139, 156, 220, 226, 301 economic 10, 15, 72, 299 provenance 178, 193, 226 proximity indicators 32 PruHealth 149 psychological neoteny 52 public ownership 92 public transport 171 purposeful shopping 212 Qatar 2 quality 96–7, 98, 101, 109 Index quantum mechanics 56 quantum wires 56 quiet materials 56 radiation, EMF 251 radio 117 randominoes 57 ranking 34, 83, 109, 116, 134, 207 Ranking Ranqueen 186 reality mining 51 Really Cool Foods 185 rebalancing 37 recession 139–40, 202, 222 recognition 36, 304 refrigerators 197–8 refuge 121 regeneration 233 regional food 200 regional politics 63 regionality 178, 192–3 regulation 124, 137, 143 REI 207 Reid, Morris 90 relationships, online 38 religion 16, 58 remote diagnosis 228 remote monitoring 154, 168, 228, 242 renting 225 reputation 34–5 resistance to technology 51 resorts, enclosed 273 resource shortages 11, 15, 146, 155, 178, 194, 254, 300 resources, natural 2, 4, 11, 64, 73–4, 143, 298–9 respect 36, 304 restaurants 186–8 retail 20–21, 202–27, 298 pop-up 216, 224 stealth 215 theater 214 trends 202–3 Revkin, Andy 77 RFID 3, 24, 50, 121, 126, 149, 182, 185, 192, 196, 205 rickets 232 risk 15, 124, 134, 138, 141, 149–50, 162, 167, 172, 191, 265, 299–300, 303 Ritalin 232 321 road pricing 166 Robertson, Peter 49 robogoats 55 robot department store 209 Robot Rules 44 robotic assistants 54, 206 concierges 268 financial advisers 131–2 lobsters 55 pest control 57 soldiers 41, 55, 60 surgery 35, 41, 249 robotics 3, 10, 41, 44–5, 60, 238, 275, 285–6, 292, 297 robots 41, 54–5, 131, 237, 249 childcare 57 emotional capacity of 40, 60 industrial 42 personal 42 security 209 therapeutic 41, 54 Russia 2, 69, 72, 75, 80, 89, 92–3, 125, 174, 232, 254, 270, 295, 302 safety 32, 36, 151, 158–9, 172–3, 182, 192, 196 Sainsbury’s 215 Salt 187 sanctuary tourism 273 satellite tracking 166–7 Saudi Arabia 2, 69 Schwartz, Barry 186 science 13, 16, 40–62, 300 interruption 53 trends 40–42 scramble suits 57 scrapbooking 25, 108, 225 Sears Roebuck 137 seasonality 178, 193–4 second-hand goods 224 Second Life 133, 207–8 securitization 124, 140 security 16, 31, 151 security robots 209 self-driving cars 165 self-medication 242 self-publishing 103, 113–14 self-reliance 35, 75 self-repairing roads 57 322 self-replicating machines 23, 44 Selfridges 214 sensor motes 15, 50, 196 sensory internet 56 Sharia-based investment 125 Shop24 209 shopping 202–27 habitual 212 laser 212 malls 211–5 purposeful 212 slow 213 social 207 Shopping 2.0 224 short-wave scalpels 57 silicon photonics 56 simplicity 169–70, 179, 186, 202, 218, 224, 226, 272 Singapore 241 single-person households 19–20, 202–3, 208–9, 221, 244, 298, 304 skills shortage 293, 302 sky shields 57 sleep 159–60, 188, 228, 231, 246–7, 265 sleep debt 96, 266 sleep hotels 266 sleep surrogates 57 slow food 178, 193 slow shopping 213 slow travel 273 smart devices 26–7, 28, 32, 35, 44, 50, 56, 57, 164, 206, 207 smart dust 3, 15, 50, 196 smartisans 20 Smartmart 209 snakebots 55 social networks 97, 107, 110, 120, 133, 217, 261 social shopping 207 society 13, 15–16, 17–37 trends 15–16 Sodexho 193 solar energy 74 Sony 114, 121 South Africa 84, 149, 242 South America 82, 270 South Korea 2, 103, 128–9 space ladders 56 space mirrors 47 space tourism 271, 273 FUTURE FILES space tugs 57 speed 164, 202, 209, 245, 296–7 spirituality 16, 22, 282, 298, 306 spot knowledge 47 spray-on surgical gloves 57 St James’s Ethics Centre 282 stagflation 139 starch-based plastics 64 stealth retail 215 stealth taxation 86 Sterling, Bruce 55 storytelling 203 Strayer, David 161 street signs 162–3 stress 32, 96, 235, 243, 245–6, 258–9, 265, 257–9, 275, 277, 283–5 stress-control clothing 57 stupidity 151, 302 Stylehive 207 Sudan 73 suicide tourism 236 Super Suppers 185 supermarkets 135–6, 184–6, 188, 191–2, 194, 202–3, 212, 215, 218–19, 224, 229 surgery 2, 31 anti-ageing 2, 237 enhancement 249 Surowiecki, James 45 surveillance 35, 41 sustainability 4, 37, 74, 181, 193–5, 203, 281, 288, 298–9 Sweden 84 swine flu 38, 251, 272 Switzerland 168, 210, 215 synthetic biology 56 Taco Bell 184 Tactical Numerical Deterministic Model 49 tagging, location 86, 88 Taiwan 81 talent, war for 275, 279, 293; see also labor shortages Target 216 Tasmania 267 Tata Motors 174, 176 taxation 85–6, 92, 93 carbon 76, 172 conscientious objection 86 Index fat 190 flat 85–6 green 76 indirect 86 stealth 86 Tchibo 217 technology 3, 14–16, 18, 22, 26, 28, 32, 37, 40–62, 74–5, 82–3, 96, 119, 132, 147–8, 154, 157, 160, 162, 165–7, 178, 182, 195–8, 208, 221, 229, 237, 242–3, 249, 256, 261, 265–6, 268, 275–6, 280, 283–4, 292, 296–7, 300 refuseniks 30, 51, 97 trends 40–42 telemedicine 228, 238, 242 telepathy 29 teleportation 56 television 21, 96, 108, 117, 119 terrorism 67, 91, 108, 150, 262–3, 267, 272, 295–6, 301 Tesco 105, 135–6, 185, 206, 215, 219, 223 Thailand 247, 290 therapeutic robots 41, 54 thermal imaging 232 things that won’t change 10, 303–6 third spaces 224 ThisNext 207 thrift 224 Tik Tok Easy Shop 209 time scarcity 30, 96, 102, 178, 184–6, 218, 255 time shifting 96, 110, 116 time stamps 50 timeline, extinction 9 timeline, innovation 8 timelines 7 tired all the time 246 tobacco industry 251 tolerance 120 too much choice (TMC) 29, 202, 218–19 too much information (TMI) 29, 51, 53, 202, 229; see also information overload tourism 254–74 cultural 273 ethical 259 food 273 323 local 273 medical 2, 229, 247 sanctuary 273 space 271, 273 suicide 238 tribal 262 Tourism Concern 259 tourist quotas 254, 271 Toyota 48–9, 157 toys, mind-control 38 traceability 195 trading down 224 transparency 3, 15, 143, 152, 276, 282, 299 transport 15, 154–77, 298 public 155, 161 trends 154–6 transumerism 223 travel 2, 3, 11, 148, 254–74 economy 272 luxury 272 slow 273 trends 254–6 trend maps 6–7 trends 1, 5–7, 10, 13 financial services 123–5 food 178–80 healthcare 228–9 media 96–8 politics 63–5 retail 202–3 science and technology 40–42 society 15–16 transport 154–6 travel 254–6 work 275–7 tribal tourism 262 tribalism 15–16, 63, 127–8, 183, 192, 220, 260 trust 82, 133, 137, 139, 143, 192, 203, 276, 282–3 tunnels 171 Turing test 45 Turing, Alan 44 Turkey 2, 200, 247 Twitter 60, 120 two-way identity verification 132 UAE 2 UFOs 58 324 UK 19–20, 72, 76, 84, 86, 90–91, 100, 102–3, 105, 128–9, 132, 137, 139–42, 147–9, 150, 163, 167–8, 170–71, 175, 185, 195–6, 199, 200, 206, 210, 214–16, 238, 259, 267–8, 278–9, 284, 288 uncertainty 16, 30, 34, 52, 172, 199, 246, 263, 300, 303 unemployment 151 Unilever 195 University of Chicago 245–6 urban rental companies 176 urbanization 11, 18–19, 78, 84, 155, 233 Uruguay 200 US 1, 11, 19–21, 23, 55–6, 63, 67, 69, 72, 75, 77, 80–83, 86, 88–90, 92, 104–5, 106, 121, 129–33, 135, 139–42, 144, 147, 149, 150, 151, 162, 167, 169–71, 174, 185, 190–3, 195, 205–6, 209, 211, 213, 216, 218, 220, 222–3, 237–8, 240–8, 250, 260, 262, 267–8, 275, 279–80, 282–4, 287, 291 user-generated content (UGC) 46, 97, 104, 289 utility 224 values 36, 152 vending machines 209 Venezuela 69, 73 verbal signatures 132 VeriChip 126 video on demand 96 Vietnam 2, 290 Vino 100 113 Virgin Atlantic 261 virtual adultery 33 banks 134 economy 130–31 protests 65 reality 70 sex 32 stores 206–8 vacations 32, 261 worlds 157, 213, 255, 261, 270, 305 Vocation Vacations 259–60 Vodafone 137 voice recognition 41 voice-based internet search 56 voicelifts 2, 237 FUTURE FILES Volkswagen 175 voluntourism 259 Volvo 164 voting 3, 68, 90–91 Walgreens 244 Wal-Mart 105, 136–7, 215, 219–20, 223, 244, 282 war 68–9, 72 war for talent 275, 279; see also labor shortages war forecasting 49 water 69–70, 74, 77–9, 199 wearable computers 55 weather 64 weather insurance 264 Web 2.0 93, 224 Weinberg, Peter 125 wellbeing 2, 183, 188, 199 white flight 20 Wikipedia 46, 60, 104 wild swimming 273 Wilson, Edward O. 74 wind energy 74 wine producers 200 wisdom of idiots 47 Wizard 145 work 275–94 trends 275–94 work/life balance 64, 71, 260, 277, 289, 293 worldphone 19 xenophobia 16, 63 YouTube 46, 103, 107, 112 Zara 216–17 Zipcar 167 Zopa 124, 134


pages: 296 words: 86,610

The Bitcoin Guidebook: How to Obtain, Invest, and Spend the World's First Decentralized Cryptocurrency by Ian Demartino

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3D printing, AltaVista, altcoin, bitcoin, blockchain, buy low sell high, capital controls, cloud computing, corporate governance, crowdsourcing, cryptocurrency, distributed ledger, Edward Snowden, Elon Musk, ethereum blockchain, fiat currency, Firefox, forensic accounting, global village, GnuPG, Google Earth, Haight Ashbury, Jacob Appelbaum, Kevin Kelly, Kickstarter, litecoin, M-Pesa, Marshall McLuhan, Oculus Rift, peer-to-peer lending, Ponzi scheme, prediction markets, ransomware, Satoshi Nakamoto, self-driving car, Skype, smart contracts, Steven Levy, the medium is the message, underbanked, WikiLeaks, Zimmermann PGP

Apart from spambots and banks, another interesting idea is a social network that runs in a distributed fashion and turns ad revenue into a cryptocurrency that is distributed to all the users. It would be up to the owners of the network to put into the initial program how the network plans on turning a profit—but with a much lower overhead than traditional businesses, it might not be that difficult. Another idea is publicly run, self-driving cars acting as a decentralized, Uber-like platform. A group of people could raise money by selling a coin, then use that money to buy a fleet of self-driving cars, design an app that calls the cars, and let them loose on a metropolitan area. In the future, people might have multiple DACs that they own a small part of, and they might receive some residual income from them on a regular basis. This might help ease the employment issues that have been and will continue to be exacerbated by increasing automation.


pages: 372 words: 89,876

The Connected Company by Dave Gray, Thomas Vander Wal

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A Pattern Language, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, Atul Gawande, Berlin Wall, business process, call centre, Clayton Christensen, complexity theory, en.wikipedia.org, factory automation, Googley, index card, interchangeable parts, inventory management, Jeff Bezos, Kevin Kelly, loose coupling, market design, minimum viable product, more computing power than Apollo, profit maximization, Richard Florida, self-driving car, shareholder value, side project, Silicon Valley, skunkworks, software as a service, South of Market, San Francisco, Steve Jobs, Steven Levy, Stewart Brand, The Wealth of Nations by Adam Smith, Tony Hsieh, Toyota Production System, Vanguard fund, web application, WikiLeaks, Zipcar

Toy, The New York Times, July 12, 2007. FORD “Ford announced recently that they are creating an open platform that will allow tinkerers and developers to electronically ‘hot-rod’ their cars.” From “Hack your car with OpenXC, a platform for modding Ford car computers,” by Dean Takahashi, VentureBeat, September 12, 2011, http://www.venturebeat.com/2011/09/12/hack-your-car-with-openxc-platform-for-modding-ford-car-computers/. SELF-DRIVING CARS “Google is working on cars that will drive themselves.” From “Google Cars Drive Themselves, in Traffic,” by John Markoff, The New York Times, October 9, 2010. JOB GROWTH “Job growth will be led by health care…” From “Occupational Outlook Handbook: 2010-20 Projections,” Bureau of Labor Statistics, March 29, 2012, http://www.bls.gov/ooh/About/Projections-Overview.htm. Chapter 3. Everything is a service Our mission statement about treating people with respect and dignity is not just words but a creed we live by every day.

, How IBM Rediscovered Customers, How Profits Can Destroy Your Company American International Group (AIG), Too Much Autonomy–Failure to Invest in the Platform, Failure to Invest in the Platform ant colony, Strategy by Discovery AOL (company), The Platform Ashby’s Law, The Law of Requisite Variety Asplund, Jim, Promoters and Detractors ATM revolt (Bank of America), The ATM Revolt attractors, Attractors, Attractors attrition warfare, Three Types of Strategy authoritarian power structure, People Resist Being Controlled Autodesk software company, Disrupting Desktop Software at Autodesk–Network Weaving, Network Weaving automobiles, Urbanization–Urbanization, Urbanization, Urbanization, Urbanization, Urbanization, Urbanization self-driving cars, Urbanization services for, Urbanization urban populations and, Urbanization–Urbanization, Urbanization, Urbanization, Urbanization avatars, A Product is a Service Avatar products as, A Product is a Service Avatar B back stage, Front Stage and Back Stage–Balancing the Front Stage and the Back Stage, Balancing the Front Stage and the Back Stage–Making Platform Decisions, Balancing the Front Stage and the Back Stage, Balancing the Front Stage and the Back Stage, Making Platform Decisions about, Front Stage and Back Stage–Balancing the Front Stage and the Back Stage, Balancing the Front Stage and the Back Stage balancing with front stage, Balancing the Front Stage and the Back Stage–Making Platform Decisions, Balancing the Front Stage and the Back Stage, Making Platform Decisions Bain & Company, Promoters and Detractors, The Net Promoter Score Bank of America, The ATM Revolt Barabás, Albert-László, Scale-free Networks Basecamp application, You don’t have to be Big Bass, Carl, Disrupting Desktop Software at Autodesk Beer, Stafford, Purpose Sets the Context for Organizations to Learn behaviorist philosophy, The Company as a Machine Beinhocker, Eric D., Let a Thousand Flowers Bloom Best Buy (company), Whole Foods, an Agile Team of Agile Teams betweenness measure in networks, Power in Networks–Control, Control, Control Bezos, Jeff, Products as Job Descriptions, Amazon is Podular–A Podular System Trades Flexibility for Consistency, Amazon is Podular, Amazon is Podular, A Podular System Trades Flexibility for Consistency, Level One: How Entrepreneurs Learn, Balancing the Front Stage and the Back Stage, Small Bets: Set a Low Bar for Initial Experimentation, Moral Authority, Attractors Amazon and, Amazon is Podular–A Podular System Trades Flexibility for Consistency, Amazon is Podular, Amazon is Podular, A Podular System Trades Flexibility for Consistency appreciation shown by, Attractors as entrepreneur, Level One: How Entrepreneurs Learn moral authority and, Moral Authority on innovation, Small Bets: Set a Low Bar for Initial Experimentation on Kindle, Products as Job Descriptions on Zappos, Balancing the Front Stage and the Back Stage blind alleys, Small Bets: Set a Low Bar for Initial Experimentation blitzkrieg, Three Types of Strategy boiled frog effect, It Won’t be Easy boundary-setting in companies, Balance the Individual Freedom with the Common Good Boyd, John, Three Types of Strategy, Moral Authority, Customers First Brand, Stuart, Pace Layers Branson, Richard, Level One: How Entrepreneurs Learn Brickhouse innovation studio, Failure to Invest in the Platform Brogan, Chris, Network Weaving brokerage, defined, Small Worlds Brown, John Seely, Return on Assets is Dwindling Building Collaboration Services, Disrupting Desktop Software at Autodesk Bureau of Labor Statistics, Urbanization Burger King (company), Adaptive Moves Can Create Opportunities for Others Burt, Ron, Small Worlds Buzzsaw (company), Disrupting Desktop Software at Autodesk C Carlzon, Jan, Moments of Truth Carroll, Dave, Cascading Effects Can be Initiated by Customers–Cascading Effects Can be Initiated by Senior Executives, Cascading Effects Can be Initiated by Customers, Cascading Effects Can be Initiated by Senior Executives Carroll, Lewis, The Red Queen Race Castain, Eric, Be Connectable to Everything Channel Marketing Corp, Big Bets: The Responsibility of Senior Leaders chaos monkey, Netflix, a City of Services Christensen, Clayton, Purpose Sets the Context for Organizations to Learn Christian, Kristen, The ATM Revolt cities, Complex Adaptive Systems–The Long-lived Company, Complex Adaptive Systems, The Long-lived Company, What is a Platform?


pages: 421 words: 110,406

Platform Revolution: How Networked Markets Are Transforming the Economy--And How to Make Them Work for You by Sangeet Paul Choudary, Marshall W. van Alstyne, Geoffrey G. Parker

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3D printing, Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Andrei Shleifer, Apple's 1984 Super Bowl advert, autonomous vehicles, barriers to entry, big data - Walmart - Pop Tarts, bitcoin, blockchain, business process, buy low sell high, chief data officer, clean water, cloud computing, connected car, corporate governance, crowdsourcing, data acquisition, data is the new oil, discounted cash flows, disintermediation, Edward Glaeser, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, financial innovation, Haber-Bosch Process, High speed trading, Internet of things, inventory management, invisible hand, Jean Tirole, Jeff Bezos, jimmy wales, Khan Academy, Kickstarter, Lean Startup, Lyft, market design, multi-sided market, Network effects, new economy, payday loans, peer-to-peer lending, Peter Thiel, pets.com, pre–internet, price mechanism, recommendation engine, RFID, Richard Stallman, ride hailing / ride sharing, Ronald Coase, Satoshi Nakamoto, self-driving car, shareholder value, sharing economy, side project, Silicon Valley, Skype, smart contracts, smart grid, Snapchat, software is eating the world, Steve Jobs, TaskRabbit, The Chicago School, the payments system, Tim Cook: Apple, transaction costs, two-sided market, Uber and Lyft, Uber for X, winner-take-all economy, Zipcar

But for now, just let’s marvel at how swiftly and seemingly effortlessly a platform business is revolutionizing a once-secure industry. Even more remarkably, the changes that Uber has already brought are probably just the opening salvo in a barrage of further disruptions that may ultimately transform the entire transportation sector. Combining the platform model with another technology that is rapidly moving from the drawing board to the showroom—the self-driving car—will improve Uber’s already stellar economic model and could lead to a series of cascading impacts that extend beyond the taxi industry. One futurist foresees a time when millions of people will eschew car ownership altogether, instead relying on an instantly deployable fleet of driverless Uber vehicles to take them wherever they want to go at a cost of around fifty cents per mile. Uber cofounder and CEO Travis Kalanick comments, “We want to get to the point that using Uber is cheaper than owning a car.”

., 245–46 rate of conversion to sale, 197 ratings, 157–58, 265 razors-and-blades strategy, 109–10 Real Audio, 222 real estate market, 9, 12, 62, 124, 237, 277, 282 RealNetworks, 222 real-time processing, 247, 252–53 recipients, 100, 101, 104, 105 recruiters, 50, 51, 119, 218–19 redBus, 73, 95 Reddit, 5, 36, 47, 93, 173 Regulation 2.0, 253–56 regulatory capture, 235–37, 257 RelayRides, 9, 10, 67, 230 research and development (R & D), 14, 33, 275 reservations, 8–9, 90, 95, 101, 137, 142, 194 resources: allocation of, 6, 15, 70–71, 199, 200, 298–99 control of, 208–9, 212, 227 intensive use of, 263–64, 278, 289 model based on, 208–10, 213, 216 restaurants, 36, 37, 76, 90, 91, 95, 101, 113, 120, 142, 170, 194, 259 retail industry, 12, 63, 77, 82–83, 85, 89, 111, 123–24, 141, 145, 157–58, 204–7, 240–49, 251, 264 revenue grabs, 121, 157–58 rewards (incentives), 82, 101, 102, 166, 173–74, 182, 227 R/GA, 76 ride-sharing services, 2, 9, 12, 16–18, 25, 30, 36, 37, 49–50, 60–62, 67, 115, 175, 190, 227, 231, 233, 250–54, 258–59, 264, 278, 287, 297 Ries, Eric, 199, 201–2 Rifkin, Jeremy, 286 Roman military campaigns, 183, 237 Roth, Alvin, 164, 171 royalties, 72, 122 Rudder, Christian, 26–27 Sacks, David, 17, 18 Safaricom, 277–78 safety net, 280–81, 288 Saks Fifth Avenue, 275 sales conversion rate, 191–92 Salesforce, x, 55, 145, 245–46, 267 sales forces, 42–44, 73–74, 91, 125, 145 sales tax, 248–49 same-side effects, 29–32, 34, 298 Samsung, xi, 86, 137, 270–71, 295 San Francisco, 1–2, 18, 61, 233, 278, 281–83 SAP, vii, x, 155, 173–75, 216, 219, 241 scrapers (automated software), 91–92, 107 search engine optimization, 120–21, 145, 191, 297 search engines, 24–25, 40, 120–21, 145, 190, 191, 197–98, 215, 216, 242, 297 Sears, Roebuck, 207 seeding strategy, 18, 92–93, 105 self-driving cars, 62 self-governance, 176–80, 182, 246, 253–56 self-serve advertising, 131, 133–34 semiconductor industry, 225 senders, 100, 101–2, 105 sensor data, 246, 286 service interfaces, 176–78, 221 Shapiro, Carl, 19, 240–41 shared model, 137, 138, 140–41, 154–55 shareholders, 11, 164 sharing economy, 10, 298–99 Shleifer, Andrei, 236–37, 238 shopping malls, 123–24 ShopRunner, 206–7 ShopThis!


pages: 903 words: 235,753

The Stack: On Software and Sovereignty by Benjamin H. Bratton

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1960s counterculture, 3D printing, 4chan, Ada Lovelace, additive manufacturing, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, Berlin Wall, bioinformatics, bitcoin, blockchain, Buckminster Fuller, Burning Man, call centre, carbon footprint, carbon-based life, Cass Sunstein, Celebration, Florida, charter city, clean water, cloud computing, connected car, corporate governance, crowdsourcing, cryptocurrency, dark matter, David Graeber, deglobalization, dematerialisation, disintermediation, distributed generation, don't be evil, Douglas Engelbart, Edward Snowden, Elon Musk, en.wikipedia.org, Eratosthenes, ethereum blockchain, facts on the ground, Flash crash, Frank Gehry, Frederick Winslow Taylor, future of work, Georg Cantor, gig economy, global supply chain, Google Earth, Google Glasses, Guggenheim Bilbao, High speed trading, Hyperloop, illegal immigration, industrial robot, information retrieval, intermodal, Internet of things, invisible hand, Jacob Appelbaum, Jaron Lanier, Jony Ive, Julian Assange, Khan Academy, linked data, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, Masdar, McMansion, means of production, megacity, megastructure, Menlo Park, Minecraft, Monroe Doctrine, Network effects, new economy, offshore financial centre, oil shale / tar sands, packet switching, PageRank, pattern recognition, peak oil, performance metric, personalized medicine, Peter Thiel, phenotype, place-making, planetary scale, RAND corporation, recommendation engine, reserve currency, RFID, Sand Hill Road, self-driving car, semantic web, sharing economy, Silicon Valley, Silicon Valley ideology, Slavoj Žižek, smart cities, smart grid, smart meter, social graph, software studies, South China Sea, sovereign wealth fund, special economic zone, spectrum auction, Startup school, statistical arbitrage, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, Superbowl ad, supply-chain management, supply-chain management software, TaskRabbit, the built environment, The Chicago School, the scientific method, Torches of Freedom, transaction costs, Turing complete, Turing machine, Turing test, universal basic income, urban planning, Vernor Vinge, Washington Consensus, web application, WikiLeaks, working poor, Y Combinator

The project seems to enable a more open and diverse social machine than a hermetically sealed campus bubble might. It is still geared toward staging and accelerating cognitive accomplishment, or as Heatherwick says in the project's video, “what is the best possible environment we can make, to invent, engineer and most importantly, make ideas happen and go out into the world?”56 Strategies for that staging move past the open-plan faux warehouse, “our self-driving car team, for example, has very different needs when it comes to office space from our Search engineers,” Google executive, Daniel Radcliffe explains. Others are more circumspect about the “Googledome,” arguing that it is either a wasteful expenditure or that its success will further gentrify the area, making it all but unlivable for anyone but the elect.57 It is too early in the project to say anything definite about is success or failure on specific terms, but we can read in the choice to keep (at least parts of) the campus open to publics including retailers and non-employee pedestrians that Google wishes for its footprint to be more than a high-performance vitrine for its human resources, and more like a spatial platform that draws surplus value from and provides lesser surplus value to those who come.

For those who honestly don't know, the Google driverless car project is a research initiative to develop cars that can autonomously navigate all roads without human steerage (or much of it), using a combination of laser-guided mapping, video cameras, radar, motion sensors, on-board computing, and other tools. Prototypes to date have mostly used a customized Prius, though the company recently announced plans to work with auto manufacturers to build autonomous vehicles to Google's own specifications, and some early products could be commercially available in a few years, if some very wicked problems can be worked out first. On these see Lee Gomes, “Hidden Obstacles for Google's Self-Driving Cars,” MIT Technology Review, August 28, 2014. 58.  Levy again: “Why is OpenFlow so advantageous to a company like Google? In the traditional model you can think of routers as akin to taxicabs getting passengers from one place to another. If a street is blocked, the taxi driver takes another route—but the detour may be time-consuming. If the weather is lousy, the taxi driver has to go slower. In short, the taxi driver will get you there, but you don't want to bet the house on your exact arrival time.

It is interesting to note that the transposition of human labor into simple puzzle solving is taken by some as straightforward market efficiency and not as a transformation of humans into diminished automatons, whereas other Stack technologies that may ultimately allow for greater individual pleasure and safety are seen as affronts to the dignity of Creation. I recently heard Joi Ito, director of the MIT Media Lab say, “Google didn't just design a self-driving car. They designed a driver.” This focuses attention on the hardware-data-Cloud path dynamic that comes into play as the car navigates the City layer, partially or fully autonomous from human passenger intention. Among the most interesting features of what we call today the “driverless car” (“horseless carriage”) is how it decenters the agency and authority of the human pilot from the cockpit and disperses it into ambient networks operating at multiple scales.


pages: 565 words: 151,129

The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism by Jeremy Rifkin

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3D printing, additive manufacturing, Airbnb, autonomous vehicles, back-to-the-land, big-box store, bioinformatics, bitcoin, business process, Chris Urmson, clean water, cleantech, cloud computing, collaborative consumption, collaborative economy, Community Supported Agriculture, computer vision, crowdsourcing, demographic transition, distributed generation, en.wikipedia.org, Frederick Winslow Taylor, global supply chain, global village, Hacker Ethic, industrial robot, informal economy, intermodal, Internet of things, invisible hand, Isaac Newton, James Watt: steam engine, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Julian Assange, Kickstarter, knowledge worker, labour mobility, Mahatma Gandhi, manufacturing employment, Mark Zuckerberg, market design, means of production, meta analysis, meta-analysis, natural language processing, new economy, New Urbanism, nuclear winter, Occupy movement, oil shale / tar sands, pattern recognition, peer-to-peer lending, personalized medicine, phenotype, planetary scale, price discrimination, profit motive, RAND corporation, randomized controlled trial, Ray Kurzweil, RFID, Richard Stallman, risk/return, Ronald Coase, search inside the book, self-driving car, shareholder value, sharing economy, Silicon Valley, Skype, smart cities, smart grid, smart meter, social web, software as a service, spectrum auction, Steve Jobs, Stewart Brand, the built environment, The Nature of the Firm, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, too big to fail, transaction costs, urban planning, Watson beat the top human players on Jeopardy!, web application, Whole Earth Catalog, Whole Earth Review, WikiLeaks, working poor, Zipcar

Lawrence Burns, “A Vision of Our Transport Future,” Nature 497 (May 9, 2013): 181–82. 18. Ibid. 19. Joann Muller, “With Driverless Cars, Once Again It Is California Leading the Way,” Forbes, September 26, 2012, http://www.forbes.com/sites/joannmuller/2012/09/26/with-driverless-cars -once-again-it-is-california-leading-the-way/ (accessed June 2, 2013). 20. Chris Urmson, “The Self-Driving Car Logs More Miles on New Wheels,” Google Blog, August 7, 2012, http://googleblog.blogspot.com/2012/08/the-self-driving-car-logs-more-miles-on.html (accessed June 2, 2013). 21. Mary Slosson, “Google Gets First Self-Driven Car License in Nevada,” Reuters, May 8, 2012, http://www.reuters.com/article/2012/05/08/uk-usa-nevada-google-idUSLNE84701320120508 (accessed June 3, 2013). 22. Alex Hudson, “Will Driverless Cars Mean Computer Crashes?,” BBC News, October 1, 2012, http://news.bbc.co.uk/2/hi/programmes/9755210.stm (accessed June 2, 2013). 23.


pages: 742 words: 137,937

The Future of the Professions: How Technology Will Transform the Work of Human Experts by Richard Susskind, Daniel Susskind

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23andMe, 3D printing, additive manufacturing, AI winter, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, Andrew Keen, Atul Gawande, Automated Insights, autonomous vehicles, Big bang: deregulation of the City of London, big data - Walmart - Pop Tarts, Bill Joy: nanobots, business process, business process outsourcing, Cass Sunstein, Checklist Manifesto, Clapham omnibus, Clayton Christensen, clean water, cloud computing, computer age, computer vision, conceptual framework, corporate governance, crowdsourcing, Daniel Kahneman / Amos Tversky, death of newspapers, disintermediation, Douglas Hofstadter, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, Frank Levy and Richard Murnane: The New Division of Labor, full employment, future of work, Google Glasses, Google X / Alphabet X, Hacker Ethic, industrial robot, informal economy, information retrieval, interchangeable parts, Internet of things, Isaac Newton, James Hargreaves, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Khan Academy, knowledge economy, lump of labour, Marshall McLuhan, Narrative Science, natural language processing, Network effects, optical character recognition, personalized medicine, pre–internet, Ray Kurzweil, Richard Feynman, Richard Feynman, Second Machine Age, self-driving car, semantic web, Skype, social web, speech recognition, spinning jenny, strong AI, supply-chain management, telepresence, the market place, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, transaction costs, Turing test, Watson beat the top human players on Jeopardy!, young professional

One (not entirely uncontroversial) illustration is Google Flu Trends, a system that can identify outbreaks of flu earlier than was possible in the past, by identifying geographical clustering of users whose search requests are made up of similar symptoms. Another is provided by Walmart, which analysed the buying habits of its customers prior to hurricanes and found not just that flashlights were in greater demand but so too were Pop-Tarts; and this insight enabled them to stock up accordingly when the next storm came round. Natural language translation systems and self-driving cars are also said to operate on the back of Big Data techniques.41 While there are many ways in which Big Data is valuable,42 most specialists in the field would agree with Mayer-Schönberger and Cukier that, ‘[a]t its core, big data is about predictions … it’s about applying math to huge quantities of data in order to infer probabilities … these systems perform well because they are fed with lots of data on which to base their predictions’.43 More extravagantly, Eric Siegel, a computer scientist, goes further when he speaks of ‘computers automatically developing new knowledge and capabilities by furiously feeding on modern society’s greatest and most potent unnatural resource: data’.44 If we combine these views of Big Data, we can see its promise for the professions—as a way of making predictions and as a way of generating new knowledge.

They argued that computers had caused ‘a major upheaval in the nature of human work’, and that they would continue replacing people in ‘an ever widening range of tasks … the list becomes longer each year’.54 But they stopped short of declaring that computers would replace all jobs. One task that they thought was beyond their reach was driving. They said it was ‘hard to imagine’ that truck-drivers would ever be computerized. Is it not remarkable, therefore, that Google has developed a small fleet of self-driving cars just one decade later? In ten years robots have moved ‘from making cars to driving them’.55 By 2014, Google’s vehicles had travelled almost 700,000 miles, with only one incident (said to be caused by a car driven by a human being). In the United States legislation has been passed in four states and in Washington, DC, allowing driverless cars.56 By 2020 most major car manufacturers also expect to be selling autonomous vehicles.


pages: 437 words: 113,173

Age of Discovery: Navigating the Risks and Rewards of Our New Renaissance by Ian Goldin, Chris Kutarna

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2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, Airbnb, Albert Einstein, AltaVista, Asian financial crisis, asset-backed security, autonomous vehicles, banking crisis, barriers to entry, battle of ideas, Berlin Wall, bioinformatics, bitcoin, Bonfire of the Vanities, clean water, collective bargaining, Colonization of Mars, Credit Default Swap, crowdsourcing, cryptocurrency, Dava Sobel, demographic dividend, Deng Xiaoping, Doha Development Round, double helix, Edward Snowden, Elon Musk, en.wikipedia.org, epigenetics, experimental economics, failed state, Fall of the Berlin Wall, financial innovation, full employment, Galaxy Zoo, global supply chain, Hyperloop, immigration reform, income inequality, indoor plumbing, industrial robot, information retrieval, intermodal, Internet of things, invention of the printing press, Isaac Newton, Islamic Golden Age, Khan Academy, Kickstarter, labour market flexibility, low cost carrier, low skilled workers, Lyft, Malacca Straits, megacity, Mikhail Gorbachev, moral hazard, Network effects, New Urbanism, non-tariff barriers, Occupy movement, On the Revolutions of the Heavenly Spheres, open economy, Panamax, personalized medicine, Peter Thiel, post-Panamax, profit motive, rent-seeking, reshoring, Robert Gordon, Search for Extraterrestrial Intelligence, Second Machine Age, self-driving car, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Skype, smart grid, Snapchat, special economic zone, spice trade, statistical model, Stephen Hawking, Steve Jobs, Stuxnet, TaskRabbit, too big to fail, trade liberalization, trade route, transaction costs, transatlantic slave trade, uranium enrichment, We are the 99%, We wanted flying cars, instead we got 140 characters, working poor, working-age population, zero day

Now is the time to turn the tap back on, and get a leg up on over-cautious competitors who lack your insight into the bigger picture. Companies and entrepreneurs that are showing the way include: IBM, which in 2014 announced a five-year plan to bet 10 percent of its net income on post-silicon computer chips;30 Google (Alphabet), whose recent long-term bets include a new quantum artificial intelligence lab, self-driving cars and research into anti-aging drugs;31 and Elon Musk, a co-founder of PayPal whose moon shots include SpaceX (a space transport firm whose eventual goal is to colonize Mars) and Tesla (whose diverse aims include the mass-market adoption of electric cars, household battery packs to store renewable energy, and a 600-mile-per-hour hyperloop to transport people between Los Angeles and San Francisco).

See also trade Free Trade Area of the Association of Southeast Asian Nations (ASEAN), 25 Galaxy Zoo, 148 Galileo Galilei, 107, 134, 237 Gates, Bill, 36 Gemma, Reinerus, 61 genetics gene therapy, 119–20, 158 genome sequencing costs, 117 history of, 114 Human Genome Project, 117, 238 and paradigm shifts, 114–21, 129–31 and race, 236–7 synthetic biology, 120 and technology, 116–21, 129–31, 148, 161, 165, 183 genius challenges of, 163–8 collective genius, 132–9 and contact points, 137–9 and courage, 242–3 and diversity, 238 and doubt, 150–5 and economic data, 155–6 and education, 135, 263–6 embracing, 238–45 and failure, 240–2 and fast-flowing ideas, 134–9 formula for flourishing, 132 and hope, 155–63 impacts of, 155–9 mitigating risk, 251–2 and new maps, 251–2 and patronage, 239–51 and place, 245–51 and policy, 243–5, 259–62 and risk-taking, 135–6 and technology, 136–7 and virtue, 256–66 See also paradigm shifts al-Ghazali, 69 GitHub, 35 Giving Pledge, 262 globalization, 5, 195, 219, 231, 259 Google search trends, 5 Google (Alphabet), 59, 198 cloud storage, 33 Google Translate, 146 high-altitude balloons, 96 Quantum Artificial Intelligence Lab, 126, 243 search engine, 156, 159 self-driving car, 167, 243 Gorbachev, Mikhail, 21–2 Gordon, Robert, 151 Grove, Andy, 157 Guicciardini, Francesco, 164 gunpowder, 2, 10, 19, 60, 74, 164, 168, 194 Gutenberg, Johann, 1, 7, 11, 25–6, 39, 80, 133–4, 136, 143, 150, 156, 167, 229 Gutenberg moment, 30–3, 35 Hanny’s Voorwerp, 148 Hawking, Stephen, 141, 156 health and medicine aging, 7, 113, 119, 153, 162 child mortality, 84, 119 genetics, 114–21, 129–31, 148, 153, 157, 161, 163, 165, 183, 236–7 life expectancy, 4, 7, 76, 82, 84, 88, 101, 153, 158 life expectancy at birth vs.


pages: 559 words: 155,372

Chaos Monkeys: Obscene Fortune and Random Failure in Silicon Valley by Antonio Garcia Martinez

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Airbnb, airport security, Amazon Web Services, Burning Man, Celtic Tiger, centralized clearinghouse, cognitive dissonance, collective bargaining, corporate governance, Credit Default Swap, crowdsourcing, death of newspapers, El Camino Real, Elon Musk, Emanuel Derman, financial independence, global supply chain, Goldman Sachs: Vampire Squid, hive mind, income inequality, interest rate swap, intermodal, Jeff Bezos, Malcom McLean invented shipping containers, Mark Zuckerberg, Maui Hawaii, means of production, Menlo Park, minimum viable product, move fast and break things, Network effects, Paul Graham, performance metric, Peter Thiel, Ponzi scheme, pre–internet, Ralph Waldo Emerson, random walk, Sand Hill Road, Scientific racism, second-price auction, self-driving car, Silicon Valley, Silicon Valley startup, Skype, Snapchat, social graph, social web, Socratic dialogue, Steve Jobs, telemarketer, urban renewal, Y Combinator, éminence grise

Lung-cancer words, despite their superlative cost, are still a pretty niche market. What are the costliest Google keywords among relatively high-volume keywords? The ranking changes, but the top ten is always composed of some combination of “insurance,” “loans,” “mortgage,” “classes,” “credit,” “lawyer,” and so on. These are Google’s moneymakers, which pay for the Android phones, the Chrome browser, the self-driving cars, the flying Wi-Fi balloons, and whatever weird, geeky, philanthropic shit the company is up to recently. Think about this in the context of more traditional industries for a moment. Chain restaurants like McDonald’s have a best-performing outlet in a particularly busy high-rent district. Automakers have a particularly popular, bestselling model like the Ford Fusion or the Chevy Impala that makes their quarter.

Like “scuba,” “radar,” and “laser,” “Kitten” was originally an acronym, whose origins had been more or less forgotten; the name now simply referred to the current state of Facebook’s topic-extraction technology. Topic extraction is one of those critical but unsexy artificial-intelligence challenges that underlie huge pieces of Internet technology (e.g., Google Search), but never receive the attention of sexy initiatives like self-driving cars. In essence, it’s a programmatic way of mapping the convoluted parlance of human texts like messages, webpages, or social media posts into a dictionary of semantic categories. For example, your status update of “Tiger really managed to hit that birdie in the US Open” would be automatically mapped to the categories “Tiger Woods,” “Golf,” and “US Open.” In a world where human speech, rife with sarcasm, typos, slang, and double meanings, is devilishly difficult to understand, it’s a sophisticated hack to quickly categorize a piece of user-generated content.


pages: 204 words: 58,565

Keeping Up With the Quants: Your Guide to Understanding and Using Analytics by Thomas H. Davenport, Jinho Kim

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Black-Scholes formula, business intelligence, business process, call centre, computer age, correlation coefficient, correlation does not imply causation, Credit Default Swap, en.wikipedia.org, feminist movement, Florence Nightingale: pie chart, forensic accounting, global supply chain, Hans Rosling, hypertext link, invention of the telescope, inventory management, Jeff Bezos, margin call, Moneyball by Michael Lewis explains big data, Netflix Prize, p-value, performance metric, publish or perish, quantitative hedge fund, random walk, Renaissance Technologies, Robert Shiller, Robert Shiller, self-driving car, sentiment analysis, six sigma, Skype, statistical model, supply-chain management, text mining, the scientific method

The availability of all this data means that virtually every business or organizational activity can be viewed as a big data problem or initiative. Manufacturing, in which most machines already have one or more microprocessors, is increasingly a big-data environment. Consumer marketing, with myriad customer touchpoints and clickstreams, is already a big data problem. Google has even described its self-driving car as a big-data project. CEOs like Gary Loveman at Caesars Entertainment (he’s known for saying, “Do we think, or do we know?”), Jeff Bezos at Amazon (“We never throw away data”), and Reid Hoffman at LinkedIn (“Web 3.0 is about data”) are publicly on record that analytical thinking and decision making is a route to organizational success and personal fortune. All organizations in all industries will need to make sense of the onslaught of data.


pages: 238 words: 73,824

Makers by Chris Anderson

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3D printing, Airbnb, Any sufficiently advanced technology is indistinguishable from magic, Apple II, autonomous vehicles, barriers to entry, Buckminster Fuller, Build a better mousetrap, business process, crowdsourcing, dark matter, David Ricardo: comparative advantage, death of newspapers, dematerialisation, Elon Musk, factory automation, Firefox, future of work, global supply chain, global village, industrial robot, interchangeable parts, Internet of things, inventory management, James Hargreaves, James Watt: steam engine, Jeff Bezos, job automation, Joseph Schumpeter, Kickstarter, Lean Startup, manufacturing employment, Mark Zuckerberg, means of production, Menlo Park, Network effects, profit maximization, race to the bottom, Richard Feynman, Richard Feynman, Ronald Coase, self-driving car, side project, Silicon Valley, Silicon Valley startup, Skype, slashdot, South of Market, San Francisco, spinning jenny, Startup school, stem cell, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, supply-chain management, The Nature of the Firm, The Wealth of Nations by Adam Smith, transaction costs, trickle-down economics, Whole Earth Catalog, X Prize, Y Combinator

Many of them were actually designed by customers and simply reviewed and improved by Sparkfun engineers to make them easier to manufacture. It’s a classic community-centric company. The front of its website features not products but its blog, with chatty tutorials and videos from its employees. Its forums are full of customers helping one another. Every year Sparkfun throws an autonomous vehicle competition, featuring a live band playing robot-themed songs of its own composition, and lots of kids chasing self-driving cars (I’ve been competing in the aerial category every year since it started—no wins yet). At Maker festivals around the country, Sparkfun engineers teach people how to solder, which is actually a lot more fun than it may sound. Sparkfun’s employees are young, passionate, and appear to totally love their jobs. Dogs and hobbies are indulged at work (although not on the production floor); tattoos and indie punk rock reflect its culture.


pages: 252 words: 80,636

Bureaucracy by David Graeber

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3D printing, Affordable Care Act / Obamacare, airport security, Albert Einstein, banking crisis, barriers to entry, borderless world, Bretton Woods, British Empire, collateralized debt obligation, Columbine, conceptual framework, Corn Laws, David Graeber, George Gilder, High speed trading, hiring and firing, late capitalism, means of production, music of the spheres, new economy, obamacare, Occupy movement, Parkinson's law, Peter Thiel, planetary scale, price mechanism, Ronald Reagan, self-driving car, Silicon Valley, South Sea Bubble, transcontinental railway, union organizing, urban planning

Other, less bureaucratized parts of the world—or at least, parts of the world with bureaucracies that are not quite so hostile to creative thinking—will, slowly, inevitably, attain the resources required to pick up where the United States and its allies have left off. The Internet does provide opportunities for collaboration and dissemination that may eventually help break us through the wall, as well. Where will the breakthrough come? We can’t know. Over the last couple years, since the first version of this essay saw print, there has been a whole spate of new possibilities: 3-D printing, advances in materials technologies, self-driving cars, a new generation of robots, and as a result, a new spate of discussion of robot factories and the end of work. There are hints, too, of impending conceptual breakthroughs in physics, biology, and other sciences, made all the more difficult because of the absolute institutional lock of existing orthodoxies, but which might well have profound technological implications as well. At this point, the one thing I think we can be fairly confident about it is that invention and true innovation will not happen within the framework of contemporary corporate capitalism—or, most likely, any form of capitalism at all.


pages: 278 words: 70,416

Smartcuts: How Hackers, Innovators, and Icons Accelerate Success by Shane Snow

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3D printing, Airbnb, Albert Einstein, attribution theory, augmented reality, barriers to entry, conceptual framework, correlation does not imply causation, deliberate practice, Elon Musk, Fellow of the Royal Society, Filter Bubble, Google X / Alphabet X, hive mind, index card, index fund, Isaac Newton, job satisfaction, Khan Academy, Law of Accelerating Returns, Lean Startup, Mahatma Gandhi, meta analysis, meta-analysis, pattern recognition, Peter Thiel, popular electronics, Ray Kurzweil, Richard Florida, Ronald Reagan, Saturday Night Live, self-driving car, side project, Silicon Valley, Steve Jobs

The apostle of 10x Thinking is a man with perhaps the coolest name ever: Astro Teller. Teller is the goatee-and-ponytailed head of a rather secret Google laboratory in California called Google[x]. He holds a PhD in artificial intelligence. Teller’s job is to dream big. 10x big. Google’s founders have endowed him with an engineer-filled building and a mandate to blow their minds. His team has built self-driving cars, augmented reality glasses, and WiFi balloons meant to roam the stratosphere. He’s hired some brilliant minds onto his team, but that’s not the secret of their success. The secret sounds a bit crazy. Says Teller, “It’s often easier to make something 10 times better than it is to make it 10 percent better.” Hmm. Math would seem to suggest otherwise. Let’s let the man named Astro explain himself: “The way of going about trying to make something new or better often tends to polarize into one of two styles,” Teller says.


pages: 255 words: 78,207

Web Scraping With Python: Collecting Data From the Modern Web by Ryan Mitchell

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AltaVista, Amazon Web Services, cloud computing, en.wikipedia.org, Firefox, meta analysis, meta-analysis, natural language processing, optical character recognition, random walk, self-driving car, Turing test, web application

We can detect that redirect in a clever way by “watching” an element in the DOM when the page initially loads, then repeatedly calling that element until Selenium throws a StaleElementReferenceException; that is, the element is no longer attached to the page’s DOM and the site has redirected: from selenium import webdriver import time from selenium.webdriver.remote.webelement import WebElement from selenium.common.exceptions import StaleElementReferenceException def waitForLoad(driver): elem = driver.find_element_by_tag_name("html") count = 0 while True: count += 1 if count > 20: print("Timing out after 10 seconds and returning") return time.sleep(.5) try: elem == driver.find_element_by_tag_name("html") except StaleElementReferenceException: return driver = webdriver.PhantomJS(executable_path='<Path to Phantom JS>') driver.get("http://pythonscraping.com/pages/javascript/redirectDemo1.html") waitForLoad(driver) print(driver.page_source) 158 | Chapter 10: Scraping JavaScript This script checks the page every half second, with a timeout of 10 seconds, although the times used for the checking time and timeout can be easily adjusted up or down as needed. Handling Redirects | 159 CHAPTER 11 Image Processing and Text Recognition From Google’s self-driving cars to vending machines that recognize counterfeit cur‐ rency, machine vision is a huge field with far-reaching goals and implications. In this chapter, we will focus on one very small aspect of the field: text recognition, specifi‐ cally how to recognize and use text-based images found online by using a variety of Python libraries. Using an image in lieu of text is a common technique when you don’t want text to be found and read by bots.


pages: 271 words: 77,448

Humans Are Underrated: What High Achievers Know That Brilliant Machines Never Will by Geoff Colvin

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Ada Lovelace, autonomous vehicles, Baxter: Rethink Robotics, Black Swan, call centre, capital asset pricing model, computer age, corporate governance, deskilling, en.wikipedia.org, Freestyle chess, future of work, Google Glasses, Grace Hopper, industrial robot, interchangeable parts, job automation, knowledge worker, low skilled workers, meta analysis, meta-analysis, Narrative Science, new economy, rising living standards, self-driving car, sentiment analysis, Silicon Valley, Skype, Steve Jobs, Steve Wozniak, Steven Levy, Steven Pinker, theory of mind, Tim Cook: Apple, transaction costs

Even if it displaces some workers (substitutes for them), it also creates new, more productive jobs using that new capital so that, as Summers said, “if there’s more capital, the wage has to rise” (it complements workers). But now he and others began seeing a new possibility: Capital can substitute for labor, period. Summers explained, “That is, you can take some of the stock of machines and, by designing them appropriately, you can have them do exactly what labor did before.” The key word is “exactly.” A Google self-driving car doesn’t complement anybody’s work because nobody operates it at all. The company produced a version that doesn’t have a steering wheel, brake pedal, or accelerator, and it’s designed to transport even blind or other disabled people. So it doesn’t make drivers, even a shrunken population of them, more productive. It does exactly what they do and thus just replaces them. In a world like that, economic logic dictates that wage rates must fall, and the share of total income going to capital rather than labor must rise, which is indeed what has been happening.


pages: 294 words: 80,084

Tomorrowland: Our Journey From Science Fiction to Science Fact by Steven Kotler

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Albert Einstein, autonomous vehicles, barriers to entry, Burning Man, carbon footprint, Colonization of Mars, crowdsourcing, Dean Kamen, epigenetics, gravity well, haute couture, interchangeable parts, Kevin Kelly, life extension, Louis Pasteur, North Sea oil, Oculus Rift, oil shale / tar sands, peak oil, personalized medicine, Peter H. Diamandis: Planetary Resources, RAND corporation, Ray Kurzweil, Richard Feynman, Richard Feynman, Ronald Reagan, self-driving car, stem cell, Stephen Hawking, Stewart Brand, theory of mind, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, WikiLeaks

In the early 2000s, businesses started to realize that highly skilled jobs formerly performed in-house, by a single employee, could more efficiently be crowdsourced to a larger group via the Internet. Initially, offerings were simple. We crowdsourced the design of T-shirts (Threadless.com) and the writing of encyclopedias (Wikipedia.com), but it didn’t take long for the trend to start making inroads into the harder sciences. Pretty soon, the hunt for extraterrestrial life, the development of self-driving cars, and the folding of enzymes into new and novel proteins were being done this way. With the fundamental tools of genetic manipulation — tools that cost millions of dollars not ten years ago — dropping precipitously in price, the crowdsourced design of biological agents was just the next logical step. In 2008, casual DNA design competitions with small prizes arose; then, in 2011, with the launch of GE’s $100 million cancer challenge, the field moved onto serious contests.


pages: 293 words: 81,183

Doing Good Better: How Effective Altruism Can Help You Make a Difference by William MacAskill

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barriers to entry, Black Swan, Branko Milanovic, Cal Newport, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, clean water, corporate social responsibility, correlation does not imply causation, Daniel Kahneman / Amos Tversky, David Brooks, effective altruism, en.wikipedia.org, experimental subject, follow your passion, food miles, immigration reform, income inequality, index fund, Isaac Newton, job automation, job satisfaction, labour mobility, Lean Startup, M-Pesa, meta analysis, meta-analysis, microcredit, Nate Silver, Peter Singer: altruism, purchasing power parity, quantitative trading / quantitative finance, randomized controlled trial, self-driving car, Skype, Stanislav Petrov, Steve Jobs, Steve Wozniak, Steven Pinker, The Wealth of Nations by Adam Smith, universal basic income, women in the workforce

For example, before the advent of alarm clocks, people called knocker uppers were employed to knock on the windows of sleeping people in the morning, so they could get to work on time. Similarly, computers have decreased the need for jobs that involve basic number crunching; refrigerators have decreased the need for milkmen; robotic assemblers have decreased the need for assembly-line workers. The technology for self-driving cars is already here, so it may be unwise to become a taxi or a truck driver because there is a good chance that this industry will become automated over the next couple of decades. Improvements in technology are reducing demand for clerks and secretaries. In general, jobs that require social skills (like public relations), creativity (like fashion design), or precise perception and manipulation (like boilermaking) are the least likely to become automated.


pages: 268 words: 74,724

Who Needs the Fed?: What Taylor Swift, Uber, and Robots Tell Us About Money, Credit, and Why We Should Abolish America's Central Bank by John Tamny

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Airbnb, bank run, banks create money, Bernie Madoff, bitcoin, Bretton Woods, Carmen Reinhart, correlation does not imply causation, Credit Default Swap, crony capitalism, crowdsourcing, Donald Trump, Downton Abbey, fiat currency, financial innovation, Fractional reserve banking, full employment, George Gilder, Home mortgage interest deduction, Jeff Bezos, job automation, Joseph Schumpeter, Kenneth Rogoff, Kickstarter, liquidity trap, Mark Zuckerberg, market bubble, moral hazard, mortgage tax deduction, NetJets, offshore financial centre, oil shock, peak oil, Peter Thiel, price stability, profit motive, quantitative easing, race to the bottom, Ronald Reagan, self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Steve Jobs, The Wealth of Nations by Adam Smith, too big to fail, Uber for X, War on Poverty, yield curve

Arguably one of the more desirable luxuries the rich enjoy to the exclusion of the rest of us is access to private flight. This is particularly true in the age of the TSA. What’s exciting is that we don’t have to worry about how private flight will be turned into a common good; we can just wait for entrepreneurs to deliver it. Odds are the wait won’t be a long one. And with more and more talk about the promise of self-driving cars, can self-flying jets be too far off? There are many arguments for reducing government meddling in the economy and the allocation of credit, but private flight is perhaps one of the more visibly appealing of them. If government is consuming less of the economy’s resources, then entrepreneurs will have more credit to access and utilize in their attempts to turn the luxury that is private flight into a common good.


pages: 202 words: 72,857

The Wealth Dragon Way: The Why, the When and the How to Become Infinitely Wealthy by John Lee

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8-hour work day, Albert Einstein, barriers to entry, Bernie Madoff, butterfly effect, buy low sell high, California gold rush, Donald Trump, financial independence, high net worth, Mark Zuckerberg, passive income, payday loans, self-driving car, Snapchat, Stephen Hawking, Steve Jobs, Tony Hsieh, Y2K

Documentary filmmakers sit in boats in shark-infested seas or lie amongst ravenous lions in order to bring us never-before-seen footage. It's the same in business. People risk their money investing in technology, spending huge sums on research and development, and going through successes and failures, so that we can enjoy new innovations such as smartphones and (coming sooner than you think) self-driving cars. In Without Risk There's No Reward, Bob Mayer tells many anecdotes to show how his booming property business could not have been built without taking huge risks. Step two of your plan should be to create a passive income from rental income and through trading the money markets, with a possible long-term view to creating a passive income from a business. Business Creation and Brand Building Does building a business sound like hard work?


pages: 368 words: 96,825

Bold: How to Go Big, Create Wealth and Impact the World by Peter H. Diamandis, Steven Kotler

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3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, cloud computing, crowdsourcing, Daniel Kahneman / Amos Tversky, dematerialisation, deskilling, Elon Musk, en.wikipedia.org, Exxon Valdez, fear of failure, Firefox, Galaxy Zoo, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, industrial robot, Internet of things, Jeff Bezos, John Harrison: Longitude, Jono Bacon, Just-in-time delivery, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, loss aversion, Louis Pasteur, Mahatma Gandhi, Mark Zuckerberg, Mars Rover, meta analysis, meta-analysis, microbiome, minimum viable product, move fast and break things, Narrative Science, Netflix Prize, Network effects, Oculus Rift, optical character recognition, packet switching, PageRank, pattern recognition, performance metric, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, Ray Kurzweil, recommendation engine, Richard Feynman, Richard Feynman, ride hailing / ride sharing, risk tolerance, rolodex, self-driving car, sentiment analysis, shareholder value, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart grid, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, technoutopianism, telepresence, telepresence robot, Turing test, urban renewal, web application, X Prize, Y Combinator

Already, we’ve seen the first wave of this in the smart assembly lines and supply chains (what’s technically called process optimization) that have enabled things like just-in-time delivery. With the smart grid for energy and the smart grid for water—what’s technically called resource consumption optimization—we’re seeing the second wave. Next up is the automation and control of far more complex autonomous systems—such as self-driving cars. There are even further opportunities in finding simpler ways to connect decision makers to sensor data in real time. The aforementioned plants that tweet their owners when they need watering were an early (2010) iteration of this sector. A more contemporary example (2013) is the Washington, DC-based start-up SmartThings, a company that CNN called “a digital maestro for every object in the home.”21 SmartThings makes an interface that can recognize over a thousand smart household objects, from temperature sensors that control the thermostat to door and windows sensors that tell you if you left something unlocked to ways to have appliances automatically shut off before you go to bed.


pages: 294 words: 81,292

Our Final Invention: Artificial Intelligence and the End of the Human Era by James Barrat

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3D printing, AI winter, Amazon Web Services, artificial general intelligence, Automated Insights, Bernie Madoff, Bill Joy: nanobots, brain emulation, cellular automata, cloud computing, cognitive bias, computer vision, cuban missile crisis, Daniel Kahneman / Amos Tversky, Danny Hillis, data acquisition, don't be evil, Extropian, finite state, Flash crash, friendly AI, friendly fire, Google Glasses, Google X / Alphabet X, Isaac Newton, Jaron Lanier, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, Loebner Prize, lone genius, mutually assured destruction, natural language processing, Nicholas Carr, optical character recognition, PageRank, pattern recognition, Peter Thiel, prisoner's dilemma, Ray Kurzweil, Rodney Brooks, Search for Extraterrestrial Intelligence, self-driving car, semantic web, Silicon Valley, Singularitarianism, Skype, smart grid, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, superintelligent machines, technological singularity, The Coming Technological Singularity, traveling salesman, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, zero day

It’s clear they’ve put once loyal customers in our place, and it’s not first place. So it seemed inconceivable that Google did not have AGI in mind. Then, about a month after my last correspondence with Freidenfelds, The New York Times broke a story about Google X. Google X was a stealth company. The secret Silicon Valley laboratory was initially headed by AI expert and developer of Google’s self-driving car, Sebastian Thrun. It is focused on one hundred “moon-shot” projects such as the Space Elevator, which is essentially a scaffolding that would reach into space and facilitate the exploration of our solar system. Also onboard at the stealth facility is Andrew Ng, former director of Stanford University’s Artificial Intelligence Lab, and a world-class roboticist. Finally, late in 2012, Google hired esteemed inventor and author Ray Kurzweil to be its director of engineering.


pages: 292 words: 85,151

Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It) by Salim Ismail, Yuri van Geest

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23andMe, 3D printing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, bioinformatics, bitcoin, Black Swan, blockchain, Burning Man, business intelligence, business process, call centre, chief data officer, Clayton Christensen, clean water, cloud computing, cognitive bias, collaborative consumption, collaborative economy, corporate social responsibility, cross-subsidies, crowdsourcing, cryptocurrency, dark matter, Dean Kamen, dematerialisation, discounted cash flows, distributed ledger, Edward Snowden, Elon Musk, en.wikipedia.org, ethereum blockchain, Galaxy Zoo, game design, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, hiring and firing, Hyperloop, industrial robot, Innovator's Dilemma, Internet of things, Iridium satellite, Isaac Newton, Jeff Bezos, Kevin Kelly, Kickstarter, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, loose coupling, loss aversion, Lyft, Mark Zuckerberg, market design, means of production, minimum viable product, natural language processing, Netflix Prize, Network effects, new economy, Oculus Rift, offshore financial centre, p-value, PageRank, pattern recognition, Paul Graham, Peter H. Diamandis: Planetary Resources, Peter Thiel, prediction markets, profit motive, publish or perish, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Ronald Coase, Second Machine Age, self-driving car, sharing economy, Silicon Valley, skunkworks, Skype, smart contracts, Snapchat, social software, software is eating the world, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, subscription business, supply-chain management, TaskRabbit, telepresence, telepresence robot, Tony Hsieh, transaction costs, Tyler Cowen: Great Stagnation, urban planning, WikiLeaks, winner-take-all economy, X Prize, Y Combinator

Sustainable production and logistics Greener and more self-sufficient production driven by robo-transport, sensors, AI, flexible solar panels and perovskite solar cells. Nanomaterials (graphene) that can be added to buildings, vehicles, machines and equipment. Transformation in Logistics (road, water and air transport). Autonomous transport and delivery Leveraging autonomous vehicles (e.g., Google’s self-driving car) and drones (e.g., Matternet) for the transport and delivery of supplies and products, especially in remote areas. Full supply chain tracking/monitoring Internet of Things sensors used to monitor the entire supply chain. Location, status, preservation and safety of most substances can be monitored (chemical substance traces, pollution, quality of life). Biological production Biology has the unique trait of being software that can create its own hardware.


pages: 379 words: 108,129

An Optimist's Tour of the Future by Mark Stevenson

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23andMe, Albert Einstein, Andy Kessler, augmented reality, bank run, carbon footprint, carbon-based life, clean water, computer age, decarbonisation, double helix, Douglas Hofstadter, Elon Musk, flex fuel, Gödel, Escher, Bach, Hans Rosling, Internet of things, invention of agriculture, Isaac Newton, Jeff Bezos, Kevin Kelly, Law of Accelerating Returns, life extension, Louis Pasteur, mutually assured destruction, Naomi Klein, packet switching, peak oil, pre–internet, Ray Kurzweil, Richard Feynman, Richard Feynman, Rodney Brooks, self-driving car, Silicon Valley, smart cities, stem cell, Stephen Hawking, Steven Pinker, Stewart Brand, strong AI, the scientific method, Wall-E, X Prize

Vicki was the mayor of this city (Christchurch) for nine years, popular for her initiatives in tackling unemployment, invigorating the town’s cultural life and addressing housing and social care issues. Today she’s leading a charge for the renewable energy industry in New Zealand that could have international repercussions – initiatives that are undoubtedly good for the planet whether you worry about climate change or not. The guard takes my blog address and promises to buy the book. ‘Don’t forget to mention those self-driving cars!’ he says. Vicki meets me at my hotel the next morning to drive us to breakfast. Although to say one ‘meets’ Vicki is something of an understatement. Vicki meets me in the same way a tornado ‘meets’ the air. You are instantly swept up, and it’s invigorating. The first thing you notice is she cannot stop laughing. She laughs at everything. It’s not the laughter that comes from a good joke, more an all-consuming joie de vivre.


pages: 391 words: 105,382

Utopia Is Creepy: And Other Provocations by Nicholas Carr

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Air France Flight 447, Airbnb, AltaVista, Amazon Mechanical Turk, augmented reality, autonomous vehicles, Bernie Sanders, book scanning, Brewster Kahle, Buckminster Fuller, Burning Man, Captain Sullenberger Hudson, centralized clearinghouse, cloud computing, cognitive bias, collaborative consumption, computer age, corporate governance, crowdsourcing, Danny Hillis, deskilling, Donald Trump, Elon Musk, factory automation, failed state, feminist movement, Frederick Winslow Taylor, friendly fire, game design, global village, Google bus, Google Glasses, Google X / Alphabet X, Googley, hive mind, impulse control, indoor plumbing, interchangeable parts, Internet Archive, invention of movable type, invention of the steam engine, invisible hand, Isaac Newton, Jeff Bezos, jimmy wales, job automation, Kevin Kelly, low skilled workers, Mark Zuckerberg, Marshall McLuhan, means of production, Menlo Park, mental accounting, natural language processing, Network effects, new economy, Nicholas Carr, oil shale / tar sands, Peter Thiel, Plutocrats, plutocrats, profit motive, Ralph Waldo Emerson, Ray Kurzweil, recommendation engine, Republic of Letters, robot derives from the Czech word robota Czech, meaning slave, Ronald Reagan, self-driving car, SETI@home, side project, Silicon Valley, Silicon Valley ideology, Singularitarianism, Snapchat, social graph, social web, speech recognition, Startup school, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, technoutopianism, the medium is the message, theory of mind, Turing test, Whole Earth Catalog, Y Combinator

Over the next year, as the presidential campaign careens toward its conclusion, all of us—the public, the press, the candidates—will get an education in how national elections work in the age of social media. We may discover that the gates maintained by our new gatekeepers are narrower than ever. From Politico Magazine 2015 WHY ROBOTS WILL ALWAYS NEED US “HUMAN BEINGS ARE ASHAMED to have been born instead of made,” wrote the philosopher Günther Anders in 1956. Our shame has only deepened as our machines have grown more adept. Every day we’re reminded of the superiority of computers. Self-driving cars are immune to distractions and road rage. Automatic trains don’t speed out of control. Factory robots don’t goof off. Algorithms don’t suffer the cognitive biases that can cloud the judgments of doctors, accountants, and lawyers. Computers work with a speed and precision that make us look like bumbling slackers. It seems obvious: The best way to get rid of human error is to get rid of humans.

Pandora's Brain by Calum Chace

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3D printing, AI winter, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, brain emulation, Extropian, friendly AI, hive mind, Ray Kurzweil, self-driving car, Silicon Valley, Singularitarianism, Skype, speech recognition, stealth mode startup, Stephen Hawking, strong AI, technological singularity, theory of mind, Turing test, Wall-E

Maybe we will create an artificial consciousness some time in the future, but not for thousands of years. When you watch those videos of the latest military-grade robots on YouTube, or when you ask your smartphone a question, do you detect anything like a conscious mind? The AI community said we would have them by now, along with flying cars and personal jetpacks.’ Matt raised his hands. ‘Come off it, Carl. It’s ridiculous to say that AI has made no progress. Self-driving cars are legal on public roads in parts of the US, and they will be legal over here soon too. Computers can recognise faces as well as you and I can: a lot of people said that would be in the ‘too-hard’ box for decades. Real-time machine translation is getting seriously impressive. This is all driven by the hugely increased processing power at researchers’ disposal, so they are going back to their original goal of developing a human-level intelligence which will pass a robust version of the Turing Test.


pages: 374 words: 89,725

A More Beautiful Question: The Power of Inquiry to Spark Breakthrough Ideas by Warren Berger

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3D printing, Airbnb, carbon footprint, Clayton Christensen, clean water, fear of failure, Google X / Alphabet X, Isaac Newton, Jeff Bezos, jimmy wales, Kickstarter, late fees, Lean Startup, Mark Zuckerberg, minimum viable product, new economy, Paul Graham, Peter Thiel, Ray Kurzweil, self-driving car, sharing economy, side project, Silicon Valley, Silicon Valley startup, Stephen Hawking, Steve Jobs, Steven Levy, Thomas L Friedman, Toyota Production System, Watson beat the top human players on Jeopardy!, Y Combinator, Zipcar

Teachers and professors, we think our authority rests on having answers. But students find it really liberating to have a teacher say, ‘I don’t know the answer—so let’s figure this out together.’” Is it possible the kind of Socratic teaching that Deresiewicz’s professor did could make a comeback in the online world? That’s what Sebastian Thrun is hoping. Thrun, known for developing Google’s self-driving car and other tech breakthroughs, says he was never comfortable asking disruptive questions in his native Germany but found a much more receptive environment in Silicon Valley. While working at Google he also taught at Stanford University; in 2011, an artificial intelligence course he co-taught was offered online, and Thrun was surprised to see that tens of thousands signed up for it. Soon after, he made the jump from self-directed cars to self-directed learning.


pages: 371 words: 108,317

The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future by Kevin Kelly

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3D printing, A Declaration of the Independence of Cyberspace, AI winter, Airbnb, Albert Einstein, Amazon Web Services, augmented reality, bank run, barriers to entry, Baxter: Rethink Robotics, bitcoin, blockchain, book scanning, Brewster Kahle, Burning Man, cloud computing, computer age, connected car, crowdsourcing, dark matter, dematerialisation, Downton Abbey, Edward Snowden, Elon Musk, Filter Bubble, Freestyle chess, game design, Google Glasses, hive mind, Howard Rheingold, index card, indoor plumbing, industrial robot, Internet Archive, Internet of things, invention of movable type, invisible hand, Jaron Lanier, Jeff Bezos, job automation, Kevin Kelly, Kickstarter, linked data, Lyft, M-Pesa, Marshall McLuhan, means of production, megacity, Minecraft, multi-sided market, natural language processing, Netflix Prize, Network effects, new economy, Nicholas Carr, peer-to-peer lending, personalized medicine, placebo effect, planetary scale, postindustrial economy, recommendation engine, RFID, ride hailing / ride sharing, Rodney Brooks, self-driving car, sharing economy, Silicon Valley, slashdot, Snapchat, social graph, social web, software is eating the world, speech recognition, Stephen Hawking, Steven Levy, Ted Nelson, the scientific method, transport as a service, two-sided market, Uber for X, Watson beat the top human players on Jeopardy!, Whole Earth Review

Or recall every pixel of every video on YouTube, but not anticipate your work routines. In the next 10 years, 99 percent of the artificial intelligence that you will interact with, directly or indirectly, will be nerdly narrow, supersmart specialists. In fact, robust intelligence may be a liability—especially if by “intelligence” we mean our peculiar self-awareness, all our frantic loops of introspection and messy currents of self-consciousness. We want our self-driving car to be inhumanly focused on the road, not obsessing over an argument it had with the garage. The synthetic Dr. Watson at our hospital should be maniacal in its work, never wondering whether it should have majored in finance instead. What we want instead of conscious intelligence is artificial smartness. As AIs develop, we might have to engineer ways to prevent consciousness in them. Our most premium AI services will likely be advertised as consciousness-free.


pages: 375 words: 88,306

The Sharing Economy: The End of Employment and the Rise of Crowd-Based Capitalism by Arun Sundararajan

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3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, autonomous vehicles, barriers to entry, bitcoin, blockchain, Burning Man, call centre, collaborative consumption, collaborative economy, collective bargaining, corporate social responsibility, cryptocurrency, David Graeber, distributed ledger, employer provided health coverage, Erik Brynjolfsson, ethereum blockchain, Frank Levy and Richard Murnane: The New Division of Labor, future of work, George Akerlof, gig economy, housing crisis, Howard Rheingold, Internet of things, inventory management, invisible hand, job automation, job-hopping, Kickstarter, knowledge worker, Kula ring, Lyft, megacity, minimum wage unemployment, moral hazard, Network effects, new economy, Oculus Rift, pattern recognition, peer-to-peer lending, profit motive, purchasing power parity, race to the bottom, recommendation engine, regulatory arbitrage, rent control, Richard Florida, ride hailing / ride sharing, Robert Gordon, Ronald Coase, Second Machine Age, self-driving car, sharing economy, Silicon Valley, smart contracts, Snapchat, social software, supply-chain management, TaskRabbit, The Nature of the Firm, total factor productivity, transaction costs, transportation-network company, two-sided market, Uber and Lyft, Uber for X, universal basic income, Zipcar

As illustrated, most of the population actually uses an owned car less than 10% of its life, which means that a vast majority of cars are parked on the street or in a garage for over 90% of their lives. (As I point out in the Introduction, this seems true in Manhattan as well.) And Californians actually use their cars more intensively than the average US resident does. Figure 5.1 Vehicle usage in the United States (compiled from NHTS data as of 2009). We may not need to wait for self-driving cars to see a digitally induced economic revolution in the auto and transportation sector. The range of new peer-to-peer models—Uber to get a driven car on-demand, Lyft to see who else is driving your route, Getaround to see whose car in your neighborhood might be available for you to drive by yourself, BlaBlaCar to get a ride to another city—have already started to increase the impact of the global automobile stock.


pages: 330 words: 91,805

Peers Inc: How People and Platforms Are Inventing the Collaborative Economy and Reinventing Capitalism by Robin Chase

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3D printing, Airbnb, Amazon Web Services, Andy Kessler, banking crisis, barriers to entry, bitcoin, blockchain, Burning Man, business climate, call centre, car-free, cloud computing, collaborative consumption, collaborative economy, collective bargaining, congestion charging, crowdsourcing, cryptocurrency, decarbonisation, don't be evil, Elon Musk, en.wikipedia.org, ethereum blockchain, Ferguson, Missouri, Firefox, frictionless, Gini coefficient, hive mind, income inequality, index fund, informal economy, Internet of things, Jane Jacobs, Jeff Bezos, jimmy wales, job satisfaction, Kickstarter, Lean Startup, Lyft, means of production, megacity, Minecraft, minimum viable product, Network effects, new economy, Oculus Rift, openstreetmap, optical character recognition, pattern recognition, peer-to-peer lending, Richard Stallman, ride hailing / ride sharing, Ronald Coase, Ronald Reagan, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, self-driving car, shareholder value, sharing economy, Silicon Valley, six sigma, Skype, smart cities, smart grid, Snapchat, sovereign wealth fund, Steve Crocker, Steve Jobs, Steven Levy, TaskRabbit, The Death and Life of Great American Cities, The Nature of the Firm, transaction costs, Turing test, Uber and Lyft, Zipcar

I realized that in cities, where 50 percent of the world lives now and most will be living in the future, the vast bulk of vehicles on the street will necessarily be shared. There is no economic rationale to have cars that are used 5 percent of the time taking up valuable and heavily subsidized real estate for parking the other 95 percent of the time. Once vehicles are shared, we only need about a tenth of the number we currently have. The move to shared-cars-only cities is almost certainly inevitable, particularly when the self-driving car arrives. Technology makes it simple, the economics of pay per use are preferable to consumers, and cities will increasingly require it because of parking space constraints. Back in the mid-2000s, I couldn’t figure out why the car companies didn’t see this (today, they all do). Why weren’t they adapting faster? In part for all the reasons I’ve outlined in this chapter, and in part for a reason I hadn’t appreciated.


pages: 283 words: 85,824

The People's Platform: Taking Back Power and Culture in the Digital Age by Astra Taylor

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A Declaration of the Independence of Cyberspace, Andrew Keen, barriers to entry, Berlin Wall, big-box store, Brewster Kahle, citizen journalism, cloud computing, collateralized debt obligation, Community Supported Agriculture, conceptual framework, corporate social responsibility, cross-subsidies, crowdsourcing, David Brooks, digital Maoism, disintermediation, don't be evil, Donald Trump, Edward Snowden, Fall of the Berlin Wall, Filter Bubble, future of journalism, George Gilder, Google Chrome, Google Glasses, hive mind, income inequality, informal economy, Internet Archive, Internet of things, invisible hand, Jane Jacobs, Jaron Lanier, Jeff Bezos, job automation, Julian Assange, Kevin Kelly, Kickstarter, knowledge worker, Mark Zuckerberg, means of production, Naomi Klein, Narrative Science, Network effects, new economy, New Journalism, New Urbanism, Nicholas Carr, oil rush, Peter Thiel, Plutocrats, plutocrats, pre–internet, profit motive, recommendation engine, Richard Florida, Richard Stallman, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley ideology, slashdot, Slavoj Žižek, Snapchat, social graph, Steve Jobs, Stewart Brand, technoutopianism, trade route, Whole Earth Catalog, WikiLeaks, winner-take-all economy, Works Progress Administration, young professional

This is the contradiction at the center of the new information system: the more customized and user friendly our computers and mobile devices are, the more connected we are to an extensive and opaque circuit of machines that coordinate and keep tabs on our activities; everything is accessible and individualized, but only through companies that control the network from the bottom up.31 Amazon strives to control both the bookshelf and the book and everything in between. It makes devices, offers cloud computing services, and has begun to produce its own content, starting various publishing imprints before expanding to feature film production.32 Google is taking a similar approach, having expanded from search into content, operating system design, gadget manufacturing, retail, “smart” appliances, robotics, self-driving cars, debit cards, and fiber broadband service in select communities.” More troublingly, at least for those who believed the Internet upstarts would inevitably vanquish the establishment dinosaurs, are the ways the new and old players have melded. Condé Nast bought Reddit, Fox has a stake in Vice Media, Time Warner bet on Maker Studios (which is behind some of YouTube’s biggest stars), Apple works intimately with Hollywood and AT&T, Facebook joined forces with Microsoft and the major-label-backed Spotify, and Twitter is trumpeting its utility to television programmers.

Frugal Innovation: How to Do Better With Less by Jaideep Prabhu Navi Radjou

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3D printing, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, barriers to entry, Baxter: Rethink Robotics, Bretton Woods, business climate, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, cloud computing, collaborative consumption, collaborative economy, connected car, corporate social responsibility, crowdsourcing, Elon Musk, financial innovation, global supply chain, income inequality, industrial robot, Internet of things, job satisfaction, Khan Academy, Kickstarter, late fees, Lean Startup, low cost carrier, M-Pesa, Mahatma Gandhi, megacity, minimum viable product, more computing power than Apollo, new economy, payday loans, peer-to-peer lending, Peter H. Diamandis: Planetary Resources, precision agriculture, race to the bottom, reshoring, ride hailing / ride sharing, risk tolerance, Ronald Coase, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley startup, six sigma, smart grid, smart meter, software as a service, Steve Jobs, supply-chain management, TaskRabbit, The Fortune at the Bottom of the Pyramid, The Nature of the Firm, transaction costs, unbanked and underbanked, underbanked, women in the workforce, X Prize, yield management, Zipcar

Contributors could either tinker with concept designs available on Volkswagen’s website or submit their own original designs. The winning idea came from Wan Jia, a student from Chengdu, who dreamed up a two-person, emissions-free hover car that uses magnetic levitation. Volkswagen then brought to life Jia’s imaginary car by making a short video that shows her ecstatic parents proudly flying in the self-driving car that their daughter designed (the video has been viewed over 7.5 million times on YouTube). Validators Validators are customers who do not create anything themselves but help to validate new product ideas and prototypes. These customers can reduce thousands of potential options and features to a few that matter most to the majority of customers. For example, Hasbro, a US multinational toy and board-game company, invited thousands of customers on Facebook to vote on new house rules for its Monopoly game, such as “freezing your assets so you can’t collect rent from other players while in jail”.


pages: 481 words: 120,693

Plutocrats: The Rise of the New Global Super-Rich and the Fall of Everyone Else by Chrystia Freeland

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Albert Einstein, algorithmic trading, banking crisis, barriers to entry, Basel III, battle of ideas, Bernie Madoff, Big bang: deregulation of the City of London, Black Swan, Branko Milanovic, Bretton Woods, BRICs, business climate, call centre, carried interest, Cass Sunstein, Clayton Christensen, collapse of Lehman Brothers, conceptual framework, corporate governance, credit crunch, Credit Default Swap, crony capitalism, Deng Xiaoping, don't be evil, double helix, energy security, estate planning, experimental subject, financial deregulation, financial innovation, Flash crash, Frank Gehry, Gini coefficient, global village, Goldman Sachs: Vampire Squid, Gordon Gekko, Guggenheim Bilbao, haute couture, high net worth, income inequality, invention of the steam engine, job automation, joint-stock company, Joseph Schumpeter, knowledge economy, knowledge worker, linear programming, London Whale, low skilled workers, manufacturing employment, Mark Zuckerberg, Martin Wolf, Mikhail Gorbachev, Moneyball by Michael Lewis explains big data, NetJets, new economy, Occupy movement, open economy, Peter Thiel, place-making, Plutocrats, plutocrats, Plutonomy: Buying Luxury, Explaining Global Imbalances, postindustrial economy, Potemkin village, profit motive, purchasing power parity, race to the bottom, rent-seeking, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, self-driving car, short selling, Silicon Valley, Silicon Valley startup, Simon Kuznets, Solar eclipse in 1919, sovereign wealth fund, stem cell, Steve Jobs, The Spirit Level, The Wealth of Nations by Adam Smith, Tony Hsieh, too big to fail, trade route, trickle-down economics, Tyler Cowen: Great Stagnation, wage slave, Washington Consensus, winner-take-all economy

Google’s company motto is “Don’t be evil,” and at a recent company conference, Larry Page, Google’s cofounder and now its CEO, said earnestly that one of Google’s greatest accomplishments was to save lives—thanks to the search engine, for instance, people can type in their symptoms, learn immediately they are having a heart attack, and get life-saving help sooner than they would have otherwise. The self-driving car, one of Page’s pet projects, would eventually, he argued, save more lives than any political, social, or humanitarian effort. “It’s not possible in tech to frame your ambitions aside from those who are making the world a better place,” Eric Schmidt, former CEO of Google, told me. “I think it has a lot to do with the way Silicon Valley was formed and the university culture. The egalitarian culture.


pages: 497 words: 150,205

European Spring: Why Our Economies and Politics Are in a Mess - and How to Put Them Right by Philippe Legrain

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3D printing, Airbnb, Asian financial crisis, bank run, banking crisis, barriers to entry, Basel III, battle of ideas, Berlin Wall, Big bang: deregulation of the City of London, Bretton Woods, BRICs, British Empire, business process, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, Celtic Tiger, central bank independence, centre right, cleantech, collaborative consumption, collapse of Lehman Brothers, collective bargaining, corporate governance, credit crunch, Credit Default Swap, crony capitalism, currency manipulation / currency intervention, currency peg, debt deflation, Diane Coyle, Downton Abbey, Edward Glaeser, Elon Musk, en.wikipedia.org, energy transition, eurozone crisis, fear of failure, financial deregulation, first-past-the-post, forward guidance, full employment, Gini coefficient, global supply chain, Growth in a Time of Debt, hiring and firing, hydraulic fracturing, Hyman Minsky, Hyperloop, immigration reform, income inequality, interest rate derivative, Irish property bubble, James Dyson, Jane Jacobs, job satisfaction, Joseph Schumpeter, Kenneth Rogoff, labour market flexibility, labour mobility, liquidity trap, margin call, Martin Wolf, mittelstand, moral hazard, mortgage debt, mortgage tax deduction, North Sea oil, Northern Rock, offshore financial centre, oil shale / tar sands, oil shock, open economy, price stability, private sector deleveraging, pushing on a string, quantitative easing, Richard Florida, rising living standards, risk-adjusted returns, Robert Gordon, savings glut, school vouchers, self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart grid, smart meter, software patent, sovereign wealth fund, Steve Jobs, The Death and Life of Great American Cities, The Wealth of Nations by Adam Smith, too big to fail, total factor productivity, Tyler Cowen: Great Stagnation, working-age population, Zipcar

Using nitrates as fertilisers helped us feed billions but their overuse pollutes the water; perhaps the solution will be genetic engineering to enable plants to fix more of their own nitrates or bacteria that convert nitrates into nitrogen at more efficient rates. Fossil fuels have made possible all the comforts of modern life; now we need clean energy to limit climate change. Above all, we don’t know what we don’t know and have no idea what the future holds. Only a decade ago, even technologists thought self-driving cars were scarcely on the horizon, yet Google has successfully developed them. Future advances in computing, biotech, nanotechnology or something else entirely are likely to surprise us.710 We have surely only scratched the surface of what is possible. Astronomy, nanochemistry and genetic engineering are advancing in leaps and bounds. New materials and digital manufacturing techniques such as 3D printing are leading to what some are dubbing a New Industrial Revolution.


pages: 494 words: 116,739

Geek Heresy: Rescuing Social Change From the Cult of Technology by Kentaro Toyama

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Albert Einstein, Berlin Wall, Bernie Madoff, blood diamonds, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, cognitive dissonance, computer vision, conceptual framework, delayed gratification, Edward Glaeser, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Francis Fukuyama: the end of history, fundamental attribution error, germ theory of disease, global village, Hans Rosling, happiness index / gross national happiness, income inequality, invention of the printing press, invisible hand, Isaac Newton, Khan Academy, Kibera, knowledge worker, libertarian paternalism, M-Pesa, Mahatma Gandhi, Mark Zuckerberg, means of production, microcredit, mobile money, Nicholas Carr, North Sea oil, pattern recognition, Peter Singer: altruism, Peter Thiel, post-industrial society, randomized controlled trial, rent-seeking, RFID, Richard Florida, Richard Thaler, school vouchers, self-driving car, Silicon Valley, Simon Kuznets, Steve Jobs, Steven Pinker, technoutopianism, The Fortune at the Bottom of the Pyramid, Upton Sinclair, Walter Mischel, War on Poverty, winner-take-all economy, World Values Survey, Y2K

Yet in clinging to this belief, we are renouncing our potential and our responsibility to save ourselves. The flaw is not in either technology or technocracy, per se, but in our misguided, overly optimistic beliefs about what kinds of social change they will accomplish. It hasn’t yet been a century since Asimov imagined his first fictional robots, but robots are already current news: Google has prototyped a self-driving car; software bots manipulate online product ratings; Amazon proposes delivery by automated quad-copter. These robots are designed for profit, not human betterment. Technology doesn’t bootstrap an ethical outlook on its own. Ultimately, people govern technology. Any progress worthy of the name requires progress in human heart, mind, and will. In spite of his optimism, Asimov – who served in the military during World War II and lived through the height of the Cold War – knew intimately that powerful technologies don’t trump Stone Age emotions.17 He worried that critics would see through his robot paternalism and pan him for painting human beings as a species in need of chaperoning.


pages: 382 words: 120,064

Bank 3.0: Why Banking Is No Longer Somewhere You Go but Something You Do by Brett King

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3D printing, additive manufacturing, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, asset-backed security, augmented reality, barriers to entry, bitcoin, bounce rate, business intelligence, business process, business process outsourcing, call centre, capital controls, citizen journalism, Clayton Christensen, cloud computing, credit crunch, crowdsourcing, disintermediation, en.wikipedia.org, George Gilder, Google Glasses, high net worth, I think there is a world market for maybe five computers, Infrastructure as a Service, invention of the printing press, Jeff Bezos, jimmy wales, London Interbank Offered Rate, M-Pesa, Mark Zuckerberg, mass affluent, microcredit, mobile money, more computing power than Apollo, Northern Rock, Occupy movement, optical character recognition, performance metric, platform as a service, QWERTY keyboard, Ray Kurzweil, recommendation engine, RFID, risk tolerance, self-driving car, Skype, speech recognition, stem cell, telepresence, Tim Cook: Apple, transaction costs, underbanked, web application

So think about the implications of this. A computing device with the power of our current iPhone would fit inside a “nano-robot” computer the size of a blood cell in two or three decades’ time. What does that mean for medical sciences? What will it mean when the device we carry around in our pocket is more powerful than the most advanced supercomputer available today? How will such technologies impact our life? Self-driving cars, computer-based personal assistants that can predict and anticipate our needs or manage our calendar without needing to ask us any questions; holographic telepresence when we’re away from our loved ones; computers built into everything, from the paint we put on our walls, the clothes to jewellery we wear, to sensors in our bathrooms that can monitor our health based on our morning’s ablutions . . .


pages: 288 words: 16,556

Finance and the Good Society by Robert J. Shiller

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bank run, banking crisis, barriers to entry, Bernie Madoff, capital asset pricing model, capital controls, Carmen Reinhart, Cass Sunstein, cognitive dissonance, collateralized debt obligation, collective bargaining, computer age, corporate governance, Daniel Kahneman / Amos Tversky, Deng Xiaoping, diversification, diversified portfolio, Donald Trump, Edward Glaeser, eurozone crisis, experimental economics, financial innovation, full employment, fundamental attribution error, George Akerlof, income inequality, invisible hand, joint-stock company, Joseph Schumpeter, Kenneth Rogoff, land reform, loss aversion, Louis Bachelier, Mahatma Gandhi, Mark Zuckerberg, market bubble, market design, means of production, microcredit, moral hazard, mortgage debt, Occupy movement, passive investing, Ponzi scheme, prediction markets, profit maximization, quantitative easing, random walk, regulatory arbitrage, Richard Thaler, road to serfdom, Robert Shiller, Robert Shiller, Ronald Reagan, self-driving car, shareholder value, Sharpe ratio, short selling, Simon Kuznets, Skype, Steven Pinker, telemarketer, The Market for Lemons, The Wealth of Nations by Adam Smith, Thorstein Veblen, too big to fail, Vanguard fund, young professional, Zipcar

Financial Crisis Inquiry Commission, in its nal 2011 report, described the boom as “madness,”2 but, whatever it was, it was not for the most part criminal. And, pursuing this highway metaphor a bit further, we may suggest that automotive designers would best stay focused on how new technology can help us better manage vehicular tra c, with improved cruise control, external electronic feedback to cars, and ultimately even self-driving cars—complex new systems that will enable people to reach their travel destinations more easily and more safely. If that’s the future for our highways, something like it should be the future for our financial institutions as well. All of these protest movements are only the most manifest signs of discontent that have been discernible in conversations and blogs ever since the nancial crisis began.


pages: 483 words: 143,123

The Frackers: The Outrageous Inside Story of the New Billionaire Wildcatters by Gregory Zuckerman

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American energy revolution, Asian financial crisis, Bakken shale, Bernie Sanders, Buckminster Fuller, corporate governance, credit crunch, energy security, Exxon Valdez, housing crisis, hydraulic fracturing, LNG terminal, margin call, Maui Hawaii, North Sea oil, oil rush, oil shale / tar sands, oil shock, peak oil, Peter Thiel, reshoring, self-driving car, Silicon Valley, sovereign wealth fund, Steve Jobs, urban decay

Edward Morse and other analysts at Citigroup also note slowing oil demand in China. Any slowing of global energy demand will bring benefits to the environment and put pressure on prices. Alternative-fuel vehicles, such as all-electric cars and hybrids, are also gaining popularity. Brokerage firm Raymond James says electric vehicles could claim 1 percent of the market in 2013, a share likely to keep rising. Any widespread embrace of self-driving cars could cripple oil demand. George Mitchell’s son, Todd, says his father’s work will have had a negative impact on the world if it forestalls progress on renewable energy, instead of giving innovators time to improve wind, solar, and other cleaner energy sources. “I think that it will be clear in decades or more that extracting hydrocarbons from tight shale formations blew up all previous assumptions about the availability and economics of oil and gas development,” Mitchell says.


pages: 433 words: 127,171

The Grid: The Fraying Wires Between Americans and Our Energy Future by Gretchen Bakke

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Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, back-to-the-land, big-box store, Buckminster Fuller, demand response, dematerialisation, distributed generation, energy security, energy transition, full employment, illegal immigration, indoor plumbing, Internet of things, laissez-faire capitalism, Menlo Park, Negawatt, new economy, post-oil, profit motive, Ronald Reagan, self-driving car, Silicon Valley, smart grid, smart meter, the built environment, too big to fail, washing machines reduced drudgery, Whole Earth Catalog

A second, increasingly popular means for translating interests of different kinds into a single system is to rely upon a platform—an integrative computer program rather than a gadget. In order to help ensure that our grid is wrenched out of its current workings, this platform would need to be open to all the strange sorts of things people are dreaming up and building today (from vehicle to grid-enabled self-driving car pods to real live nanogrids) and to the boring old stuff we’re stuck with for the moment (like natural gas combustion plants and old coal or nuclear), and also to the desires and activities of regular people. All without letting the basic structures of the grid get too rotten or out of date. A platform is an interesting tool to think with in part because it moves us into a domain where computing, or “digital” systems, becomes the means for solving mechanical or “analog” problems.


pages: 405 words: 117,219

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

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3D printing, Ada Lovelace, agricultural Revolution, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, anthropic principle, Asperger Syndrome, autonomous vehicles, barriers to entry, battle of ideas, Berlin Wall, bioinformatics, British Empire, business process, carbon-based life, cellular automata, Claude Shannon: information theory, combinatorial explosion, complexity theory, continuous integration, Conway's Game of Life, cosmological principle, dark matter, dematerialisation, double helix, Douglas Hofstadter, Edward Snowden, epigenetics, Flash crash, Google Glasses, Gödel, Escher, Bach, income inequality, index card, industrial robot, Internet of things, invention of agriculture, invention of the steam engine, invisible hand, Isaac Newton, Jacquard loom, Jacquard loom, Jacques de Vaucanson, James Watt: steam engine, job automation, John von Neumann, Joseph-Marie Jacquard, millennium bug, natural language processing, Norbert Wiener, On the Economy of Machinery and Manufactures, packet switching, pattern recognition, Paul Erdős, post-industrial society, prediction markets, Ray Kurzweil, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, speech recognition, stem cell, Stephen Hawking, Steven Pinker, strong AI, technological singularity, The Coming Technological Singularity, the scientific method, theory of mind, Turing complete, Turing machine, Turing test, Tyler Cowen: Great Stagnation, Vernor Vinge, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Y2K

Dick. 1989: Tim Berners-Lee invents the World Wide Web. 1990: Seiji Ogawa presents the first fMRI machine. 1993: Rodney Brooks and others start the MIT Cog Project, an attempt to build a humanoid robot child in five years. 1997: Deep Blue defeats Garry Kasparov at chess. 2000: Cynthia Breazeal at MIT describes Kismet, a robot with a face that simulates expressions. 2004: DARPA launches the Grand Challenge for autonomous vehicles. 2009: Google builds the self-driving car. 2011: IBM’s Watson wins the TV game show Jeopardy!. 2014: Google buys UK company Deep Mind for $650 million. 2014: Eugene Goostman, a computer program that simulates a thirteen-year-old boy, passes the Turing Test. 2014: Estimated number of robots in the world reaches 8.6 million.1 2015: Estimated number of PCs in the world reaches two billion.2 NOTES Introduction 1PCs (‘Personal computers’) started becoming widely available in the early 1980s: IBM 5150 in 1981, Commodore PET in 1983.


pages: 523 words: 143,139

Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian, Tom Griffiths

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4chan, Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, algorithmic trading, anthropic principle, asset allocation, autonomous vehicles, Berlin Wall, Bill Duvall, bitcoin, Community Supported Agriculture, complexity theory, constrained optimization, cosmological principle, cryptocurrency, Danny Hillis, delayed gratification, dematerialisation, diversification, double helix, Elon Musk, fault tolerance, Fellow of the Royal Society, Firefox, first-price auction, Flash crash, Frederick Winslow Taylor, George Akerlof, global supply chain, Google Chrome, Henri Poincaré, information retrieval, Internet Archive, Jeff Bezos, John Nash: game theory, John von Neumann, knapsack problem, Lao Tzu, linear programming, martingale, Nash equilibrium, natural language processing, NP-complete, P = NP, packet switching, prediction markets, race to the bottom, RAND corporation, RFC: Request For Comment, Robert X Cringely, sealed-bid auction, second-price auction, self-driving car, Silicon Valley, Skype, sorting algorithm, spectrum auction, Steve Jobs, stochastic process, Thomas Malthus, traveling salesman, Turing machine, urban planning, Vickrey auction, Walter Mischel, Y Combinator

When it comes to traffic of the human kind, the low price of anarchy cuts both ways. The good news is that the lack of centralized coordination is making your commute at most only 33% worse. On the other hand, if you’re hoping that networked, self-driving autonomous cars will bring us a future of traffic utopia, it may be disheartening to learn that today’s selfish, uncoordinated drivers are already pretty close to optimal. It’s true that self-driving cars should reduce the number of road accidents and may be able to drive more closely together, both of which would speed up traffic. But from a congestion standpoint, the fact that anarchy is only 4/3 as congested as perfect coordination means that perfectly coordinated commutes will only be 3/4 as congested as they are now. It’s a bit like the famous line by James Branch Cabell: “The optimist proclaims that we live in the best of all possible worlds; and the pessimist fears this is true.”


pages: 626 words: 181,434

I Am a Strange Loop by Douglas R. Hofstadter

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Albert Einstein, Andrew Wiles, Benoit Mandelbrot, Brownian motion, double helix, Douglas Hofstadter, Georg Cantor, Gödel, Escher, Bach, Isaac Newton, James Watt: steam engine, John Conway, John von Neumann, mandelbrot fractal, pattern recognition, Paul Erdős, place-making, probability theory / Blaise Pascal / Pierre de Fermat, publish or perish, random walk, Ronald Reagan, self-driving car, Silicon Valley, telepresence, Turing machine

If we once again postulate the idea of obtaining nutrition by carrying out certain remote actions, and if we add back the ability to control distant motion by means of a joystick or even by certain brain events, then things really start to shimmer in uncertainty. For surely a mobile robot is not where the radio-connected computer that is controlling it happens to be sitting. A robot might be strolling about on the moon while its computerized guidance system was in some earthbound laboratory. Or a self-driving car like Stanley could be crossing the Nevada desert, and its computer control system might be on board or might be located in a lab in California, connected by radio. But would we even care where the computer was? Why should we care where it is located? A robot, we feel, is where its body is. And so when my brain can switch at will (using the fancy glasses described above) between inhabiting any one of a hundred different bodies — or worse yet, when it can inhabit several bodies at the same time, processing different kinds of input from all of them at once (perhaps visual input from one, sonic from another, tactile from a third) — then where I am becomes extremely ill-defined.


pages: 348 words: 39,850

Data Scientists at Work by Sebastian Gutierrez

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Albert Einstein, algorithmic trading, bioinformatics, bitcoin, business intelligence, chief data officer, clean water, cloud computing, computer vision, continuous integration, correlation does not imply causation, crowdsourcing, data is the new oil, DevOps, domain-specific language, follow your passion, full text search, informal economy, information retrieval, Infrastructure as a Service, inventory management, iterative process, linked data, Mark Zuckerberg, microbiome, Moneyball by Michael Lewis explains big data, move fast and break things, natural language processing, Network effects, nuclear winter, optical character recognition, pattern recognition, Paul Graham, personalized medicine, Peter Thiel, pre–internet, quantitative hedge fund, quantitative trading / quantitative finance, recommendation engine, Renaissance Technologies, Richard Feynman, Richard Feynman, self-driving car, side project, Silicon Valley, Skype, software as a service, speech recognition, statistical model, Steve Jobs, stochastic process, technology bubble, text mining, the scientific method, web application

Models are judged by their empirical utility, not by some elusive Platonic rationalist ideal. Now for the final question: Why is data science sexy? It has something to do with all that grasping. And the begetting: so many new applications and entire new industries come into being from the judicious use of copious amounts of data. Examples include speech recognition, object recognition in computer vision, robots and self-driving cars, bioinformatics, neuroscience, the discovery of exoplanets and an understanding of the origins of the universe, and the assembling of inexpensive but winning baseball teams. In each of these instances, the data scientist is central to the whole enterprise. He or she must combine knowledge of the application area with statistical expertise and implement it all using the latest in computer science ideas.