autonomous vehicles

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Autonomous Driving: How the Driverless Revolution Will Change the World by Andreas Herrmann, Walter Brenner, Rupert Stadler

Airbnb, Airbus A320, augmented reality, autonomous vehicles, blockchain, call centre, carbon footprint, cleantech, computer vision, conceptual framework, connected car, crowdsourcing, cyber-physical system, DARPA: Urban Challenge, data acquisition, demand response, digital map, disruptive innovation, Elon Musk, fault tolerance, fear of failure, global supply chain, industrial cluster, intermodal, Internet of things, Jeff Bezos, Lyft, manufacturing employment, market fundamentalism, Mars Rover, Masdar, megacity, Pearl River Delta, peer-to-peer rental, precision agriculture, QWERTY keyboard, RAND corporation, ride hailing / ride sharing, self-driving car, sensor fusion, sharing economy, Silicon Valley, smart cities, smart grid, smart meter, Steve Jobs, Tesla Model S, Tim Cook: Apple, uber lyft, upwardly mobile, urban planning, Zipcar

Some concept cars are being developed that look like rolling lounges, bedrooms or offices, and are equipped with the best communications and entertainment technology. Others focus on urban use and the integration of autonomous cars with public transport. Autonomous mobility offers the opportunity to link up various modes of transportation intelligently (see Figure 1.4). One application of self-driving vehicles could be to transport travellers on the last mile from the train station to their homes, for example. It is to be expected, however, that not just one, but three types of autonomous vehicles will emerge in the coming years [24, 113, 115]. Robo-cars are revolutionary because they have been conceived as autonomous vehicles right from the start. These vehicles will operate in cities at low speeds, in exactly defined areas and on previously programmed routes. They will operate in fleets, managed by taxi companies, railroad companies or municipalities.

Apart from controlling the traffic flow, the traffic management centre could also intervene when the control system of an autonomous vehicle reaches its limits. Imagine that an autonomous car approaches another vehicle that has broken down and blocks the road. The continuous line in the middle tells the control system that the defective vehicle cannot be overtaken. Obviously, the car has a traffic problem that cannot be solved by the system. The Sharing Economy 347 In such a case, the traffic management centre could instruct the vehicle to overtake the defective car despite the continuous line. The car would still be autonomous; the centre would merely provide an alternative in order to enable the car to drive on. Robo-cars could be integrated into a traffic concept that is based on the interaction of public and private modes of transportation.

This page intentionally left blank INDEX A9 autobahn in Germany, 134, 135, 407 ACCEL, 324 Accelerating, 8, 22, 27, 59, 78, 91, 122, 295, 296 Access Economy, 344 Acoustic signals, 108 Ad-hoc mobility solutions, 354 Ad-hoc networks, 133 Adaptive cruise control, 4, 51, 72 74, 78, 86, 96, 113, 116, 289, 297, 333 Aerospace industry, 153 Agenda for auto industry culture change, 396 increasing speed, 398 service-oriented business model, 397 398 V-to-home and V-to-business applications, 399 Agile operating models, 330 Agriculture, 154 productivity, 155 sector, 154 157 Air pollution, 27 AirBnB, 311 Airplane electronics, 144 Aisin, 9 Albert (head of design at Yahoo), 228 Alexandra (founder and owner of Powerful Minds), 228 Alibaba Alipay payment system, 372 Alternative fuels, autonomous vehicles enabling use of, 305 Altruistic mode (a-drive mode), 252 Amazon, 138, 141, 311 American Trucking Association, 68 Android operating system, 327 Anthropomorphise products, 290 Appel Logistics transports, 167 Apple, 9, 138, 327 CarPlay, 285 Apple Mac OS, 247 Apple-type model, 323 Application layer, 119 software, 118 Artificial intelligence, 115, 255, 291, 332 333 Artificial neuronal networks, 114 115 Asia projects, 371 374 Assembly Row, 386 Assessment of Safety Standards for Automotive Electronic Control Systems, 144 Assistance systems, 71 77 Audi, 5, 130, 134, 137, 179, 211, 301, 318, 322, 398 Driverless Race Car, 5 piloted driving, 286 piloted-parking technology, 386 387 Audi A7, 44, 198, 282 427 428 Audi A8 series-car, 79, 180 Audi AI traffic jam pilot, 79 Audi Fit Driver service, 318 319 Audi piloted driving lab, 227, 229 Audi Q7, 74 assistance systems in, 75 Audi RS7, 43, 44, 79 autonomous racing car, 179 driverless, 227 Audi TTS, 43 Audi Urban Future Initiative, 384 386, 406 Augmented reality, 279 vision and example, 279 280 Authorities and cities, 171 173 Auto ISAC, 146 Autolib, 317, 344 Autoliv, 285 Automakers’ bug-bounty programs, 146 Automated car, 233, 246, 264, 289, 384 Automated driving division of labour between driver and driving system, 48 examples, 51 53 image, 177 levels of, 47 51 scenarios for making use of travelling time, 52 strategies, 53 56 technology, 160 Automated vehicles, 9, 174, 246 Automated Vehicles Index, 367 368 Automatic car, 233, 244 Automatic pedestrian highlighting, 78 Automation ironies of, 76 responsibility with increasing, 235 Automobile, 3, 21 locations, 405 manufacturers, 311 Index Automotive design, 265 266 Automotive Ethernet, 126 Automotive incumbents operate, 330 Automotive industry, 332 335, 367, 379, 397 Automotive technology, 327 328 AutoNet2030 project, 369 Autonomous buses, 14, 81, 158, 159, 175, 302 Autonomous cars, 25, 126, 197, 205 206, 233, 244, 270 expected worldwide sales of, 85 savings effects from, 67 68 Autonomous driving, 3, 8, 39, 62, 94, 111, 116, 120, 121 123, 141, 160 162, 171, 173, 207 208, 217, 247, 252, 266, 332 333, 379 applications, 10 12, 160 aspects for, 93 Audi car, 5 autonomous Audi TTS on Way to Pikes Peak, 43 in combination with autonomous loading hubs, 166 driving to hub, 213 ecosystem, 18 20, 131 element, 243 facts about, 306 functions, 74 impression, 40 industry, 16 18 living room in Autonomous Mercedes F015, 44 milestones of automotive development, 4 NuTonomy, 6 projects, 41 45 real-world model of, 92 scenarios, 211 215 science fiction, 39 41 technology, 9 10, 92 Index time management, 215 218 vehicles, 12 16 See also Human driving Autonomous driving failure, 221 consequence, 221 222 decision conflict in autonomous car, 223 design options, 222 223 influencer, 223 224 Autonomous Mercedes F015, living room in, 44 Autonomous mobility, 12, 13, 16 17, 172, 405 establishment as industry of future, 404 405 resistance to, 171 172 Autonomous Robocars, 81 Autonomous sharp, 274 ‘Autonomous soft’ mode, 274 Autonomous trucks, 161 from Daimler, 163 savings effects from, 68 69 Autonomous vehicles, 26, 81, 99, 138, 155, 182, 221, 238, 249, 255, 353 354 enabling use of alternative fuels, 305 integration in cities, 406 promoting tests with, 407 uses, 153 AutoVots fleet, 350 Backup levels, 127 Baidu apps, 338, 372 Base layer, 119 Becker, Jan, 42 43 Behavioural law, 234 Being driven, 61, 63, 78, 342 343 Ben-Noon, Ofer, 142, 143, 145 Benz, Carl, 3, 4 Bertha (autonomous research vehicle), 42 Big data, 313, 332 333 BlaBlaCar, 359 429 Blackfriars bridge, lidar print cloud of, 104 Blind-spot detection, 78 Bloggers, 225 227 Blonde Salad, The, 226 Bluetooth, 130, 142, 154 BMW, 6, 130, 137, 174, 180, 316, 320, 322, 332 333, 372, 398 3-series cars, 338 BMW i3, 27 holoactive touch, 285 Boeing 777 development, 243 Boeing, 787, 261 Bosch, 9, 181 182 Bosch, Robert, 333 Bosch suppliers, 315 BosWash, metropolitan region, 384 Budii car, 272 273 Business models, 311, 353 355 automobile manufacturers, 311 content creators, 319 320 data creators, 320 322 examples, 312 hardware creators, 314 315 options, 312 314 passenger looks for new products, 321 passenger visits website, 321 service creators, 316 319 software creators, 315 316 strategic mix, 322 323 Business vehicle, 15 Business-to-consumer car sharing, 342 343 Cadillac, 180 California PATH Research Reports, 298 299 Cambot, 290 Cameras, 111, 126 CAN bus, 126, 143 Capsule, 33 Car and ride sharing, studies on, 348 430 Car dealers, repair shops and insurance companies, 173 174 Car manufacturers, 328, 396 397 business model, 312 Car-pooling efforts, 364 365 Car-sharing programs, 364 365 service, 383 Car-sharing, 206 Car2Go, 317, 345 Casey Neistat, 226 Castillo, Jose, 364 365 Celebrities and bloggers, 225 227 Central driver assistance control unit, 124 Central processing unit, 96, 124 zFAS, 125 Centre for Economic and Business Research in London, 189 Chevrolet, 40 app from General Motors, 316 Spark EV, 27 Cisco, 41 CityMobil project, 369, 406 CityMobil2, 14, 157 Cognitive distraction, 287 Coherent European framework, 246 Committee on Autonomous Road Transport for Singapore, 347 Communication, 198 200 investing in communication infrastructure, 403 404 technology, 261 Community, 341 detection algorithms, 389 Companion app, 316 Compelling force, 223 Competitiveness Iain Forbes, 368 369 projects in Asia, 371 374 Index projects in Europe and United States, 369 371 projects in Israel, 374 375 Computer operating systems, 247 Computer-driven driving, 108 Computerised information processing, 109 Congestion pricing, 296 Connected car, 129 ad-hoc networks, 133 connected driving, 137 138 connected mobility, 138 development of mobile communication networks, 130 digital ecosystems, 138 eCall, 136 137 online services, 136 137 permanent networks, 130 statement by telecommunications experts, 132 133 V-to-I communication, 134 135 V-to-V communication, 133 134 V-to-X communication, 135 136 See also Digitised car Connected mobility, 129, 138 Connected vehicles, 138 vulnerability of, 142 Connected-car services, 313 Connectivity of vehicles, 147 Consumer-electronics companies, 285 Container Terminal, 159 Content creators, 319 320 Continental (automotive suppliers), 9, 284, 315 Continuous feedback, 281 Convenience, 302 304, 306 Conventional breakthrough approach, 332 Index Conventional broadband applications, 132 Conventional car manufacturing, 10 Cook, Tim, 182 Cooperative intelligent transport system (C-ITS), 369 370 Corporate Average Fuel Economy standard, 297 Cost(s), 187 192, 295 autonomous vehicles enabling use of alternative fuels, 305 fuel economy, 297 299 intelligent infrastructures, 299 301 land use, 304 operating costs, 301 302 relationship between road speed and road throughput, 296 vehicle throughput, 295 297 Croove app, 318 Culture, 330 change, 396 differences, 195 197 and organisational transformation, 395 Curtatone, Joseph, 387 Customers’ expectations attitudes, 204 207 incidents, 203 204 interview with 14 car dealers, 207 persuasion, 207 208 statements by two early adopters, 205 Cyber attacks, 141 Cyber hacking or failures in algorithms, 354 Cyber security, 141 146 Cyber-physical systems, 9 Daimler, 130 Data, 121 categories in vehicle, 147 creators, 320 322 431 from passengers, 94 95 privacy, 147 148 processing, 91 protection principles, 148 recorders, 239 Data-capturing technology, 103 Data-protection issues, 239 Database, 98 Decelerating, 91, 122 Decision-making mechanism, 369 Declaration of Amsterdam, 246 247 Deep learning, 115 Deep neural networks, 115 116 Deere, John, 154, 155 Deere, John, 154, 155, 263 Defense Advanced Research Project Agency (DARPA), 41 Degree of autonomous driving, 53 Degree of autonomy, 262 Degree of market penetration, 84 Degree of not-invented-here arrogance, 332 Degree of vehicle’s automation, 233 234 Delhi municipal government, 21 22 Delphi, 9, 181 Delphi Automotive Systems, 6 Demise of Kodak, 111 Denner, Volkmar, 333 334 Denso, 9 Depreciation, 345 Destination control, 299, 300 Digital company development, 395 396 Digital economy, 225 Digital ecosystems, 138 Digital light-processing technology, 277, 279 Digital maps, 101 Digital products, 267 Digitised car algorithms, 113 117 432 backup levels, 127 car as digitised product, 111 112 data, 121 drive recorder, 125 126 drive-by-wire, 122 over-provisioning, 127 processor, 122 125 software, 117 121 See also Connected car Digitising and design of vehicle, 265 267 Dilemma situations, 61 Direct attacks, 141 Direct connectivity of vehicle, 130 Disruptions in mobility, 31, 34 arguments, 34 35 history, 32 33 OICA, 34 Disruptive technologies, 221, 223, 402 Document operation-relevant data, 263 Doll, Claus, 166 Dongles, 142 Drees, Joachim, 165 ‘Drive boost’ mode, 274 “Drive me” project, 370 Drive recorder, 125 126 ‘Drive relax’ mode, 274 Drive-by-wire, 122 DriveNow, 317, 345 Driver, 235 role, 235 238 Driver distraction, 55 causes and consequences, 278 Driver-assistance systems, 53, 71, 160, 174, 222, 298, 333, 353 Driverless cars, 3, 7, 27 28, 222, 233, 244 taxis, 302 vans, 406 vehicles, 168 Index Driverless Audi RS7, 227 229 Driverless Race Car of Audi, 5 Driving manoeuvres, 91 modes, 107 oneself, 342 343 Drunk driving, 303 Dvorak keyboard, 242 Dynamic patterns of movement in city of London, 390 eCall.


pages: 472 words: 80,835

Life as a Passenger: How Driverless Cars Will Change the World by David Kerrigan

3D printing, Airbnb, airport security, Albert Einstein, autonomous vehicles, big-box store, butterfly effect, call centre, car-free, Cesare Marchetti: Marchetti’s constant, Chris Urmson, commoditize, computer vision, congestion charging, connected car, DARPA: Urban Challenge, deskilling, disruptive innovation, edge city, Elon Musk, en.wikipedia.org, future of work, invention of the wheel, Just-in-time delivery, loss aversion, Lyft, Marchetti’s constant, Mars Rover, megacity, Menlo Park, Metcalfe’s law, Minecraft, Nash equilibrium, New Urbanism, QWERTY keyboard, Ralph Nader, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Rodney Brooks, Sam Peltzman, self-driving car, sensor fusion, Silicon Valley, Simon Kuznets, smart cities, Snapchat, Stanford marshmallow experiment, Steve Jobs, technoutopianism, the built environment, Thorstein Veblen, traffic fines, transit-oriented development, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, Unsafe at Any Speed, urban planning, urban sprawl, Yogi Berra, young professional, zero-sum game, Zipcar

You need to tread carefully about this because if in writing some article that is negative you are effectively dissuading people from using autonomous vehicles, you are killing people” Elon Musk, CEO, Tesla Media coverage is under the spotlight worldwide as never before after allegations of fake news, echo bubbles and social media complicity in spreading misleading information. It is against this backdrop that the emergence of driverless cars is taking place. What I’ve seen so far regarding driverless cars has often been sensationalist (both negative and positive) rather than genuinely analytical or even just informative. I don’t think the fact that the robocars (which are by all accounts still years away from use) needed occasional intervention is worthy of a headline like the BBC used: “Google’s self-drive cars had to be stopped from crashing” as they reported[352] in January 2016 on Google’s publication of data related to disengagements.

utm_term=.4d4d2f0dad4c http://philosophicaldisquisitions.blogspot.ie/2017/04/the-ethics-of-crash-optimisation.html https://www.wired.com/2017/03/make-us-safer-robocars-will-sometimes-kill/ http://www.wired.com/2016/06/self-driving-cars-will-power-kill-wont-conscience/?mbid=nl_6916 Tumbleweeds example coverage: http://www.ft.com/intl/cms/s/0/e698c396-8d61-11e5-8be4-3506bf20cc2b.html?siteedition=uk#axzz3rtZYwi9N http://www.express.co.uk/life-style/cars/620296/self-driving-cars-could-be-stopped-by-tumbleweed-technology-problems Employment: https://techcrunch.com/2016/08/18/dropping-off-drivers/ https://www.wired.com/2017/01/nissans-self-driving-teleoperation/ https://www.bloomberg.com/news/articles/2017-04-17/will-autonomous-driving-kill-the-sports-car http://www.wired.com/2016/03/self-driving-cars-wont-work-change-roads-attitudes/?mbid=nl_31516 http://www.nlc.org/article/new-autonomous-vehicle-guide-helps-cities-prepare-for-a-driverless-future http://www.nctr.usf.edu/wp-content/uploads/2016/11/Implications-for-Public-Transit-of-Emerging-Technologies-11-1-16.pdf http://globalpolicysolutions.org/wp-content/uploads/2017/03/Stick-Shift-Autonomous-Vehicles.pdf https://www.technologyreview.com/s/607841/a-single-autonomous-car-has-a-huge-impact-on-alleviating-traffic/ Chapter 7 - Regulation & Acceptance https://www.transportation.gov/AV/federal-automated-vehicles-policy-september-2016 https://www.scientificamerican.com/article/when-it-comes-to-safety-autonomous-cars-are-still-teen-drivers1/# http://www.newsweek.com/when-will-we-know-self-driving-cars-are-safe-501270 http://www.huffingtonpost.com/entry/how-safe-are-self-driving-cars_us_5908ba48e4b03b105b44bc6b?

MOD=AJPERES [348] https://www.automotiveisac.com/best-practices/ [349] https://techcrunch.com/2015/10/23/connected-car-security-separating-fear-from-fact/ [350] http://www.raymondloewy.com/about.html [351] https://www.ft.com/content/97a04f76-3494-11e7-99bd-13beb0903fa3 [352] http://www.bbc.com/news/technology-35301279 [353] https://static.nhtsa.gov/odi/inv/2016/INCLA-PE16007-7876.PDF [354] http://www.reuters.com/investigates/special-report/autos-driverless/ [355] https://en.wikipedia.org/wiki/Stanford_marshmallow_experiment [356] https://www.scribd.com/document/333075344/Apple-Comments-on-Federal-Automated-Vehicles-Policy [357] Remarks at Infrastructure Week, May 2017 [358] Technological Revolutions and Financial Capital, Carlota Perez, 2002 [359] https://www.washingtonpost.com/news/innovations/wp/2014/10/14/move-over-humans-the-robocars-are-coming/ [360] https://www.cnet.com/uk/news/a-brief-history-of-the-qwerty-keyboard/ [361] http://www.wsj.com/articles/could-self-driving-cars-spell-the-end-of-ownership-1448986572 [362] https://www.morganstanley.com/articles/autonomous-cars-the-future-is-now [363] https://www.wired.com/2016/04/american-cities-nowhere-near-ready-self-driving-cars/ [364] Machiavelli, The Prince, Chapter 6 [365] http://time.com/4236980/against-human-driving/ [366] https://www.ft.com/content/e961f914-6ba3-11e6-ae5b-a7cc5dd5a28c [367] Paul Roberts, The Impulse Society: What's Wrong With Getting What We Want, 2014 [368] http://content.time.com/time/magazine/article/0,9171,2033076,00.html [369] http://www.ft.com/intl/cms/s/0/da5d033c-8e1c-11e1-bf8f-00144feab49a.html#axzz1t4qPww6r [370] http://www.wbur.org/bostonomix/2016/04/29/traffic-future-driverless-cars


pages: 245 words: 83,272

Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard

1960s counterculture, A Declaration of the Independence of Cyberspace, Ada Lovelace, AI winter, Airbnb, Amazon Web Services, autonomous vehicles, availability heuristic, barriers to entry, Bernie Sanders, bitcoin, Buckminster Fuller, Chris Urmson, Clayton Christensen, cloud computing, cognitive bias, complexity theory, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, digital map, disruptive innovation, Donald Trump, Douglas Engelbart, easy for humans, difficult for computers, Electric Kool-Aid Acid Test, Elon Musk, Firefox, gig economy, global supply chain, Google Glasses, Google X / Alphabet X, Hacker Ethic, Jaron Lanier, Jeff Bezos, John von Neumann, Joi Ito, Joseph-Marie Jacquard, life extension, Lyft, Mark Zuckerberg, mass incarceration, Minecraft, minimum viable product, Mother of all demos, move fast and break things, move fast and break things, Nate Silver, natural language processing, PageRank, payday loans, paypal mafia, performance metric, Peter Thiel, price discrimination, Ray Kurzweil, ride hailing / ride sharing, Ross Ulbricht, Saturday Night Live, school choice, self-driving car, Silicon Valley, speech recognition, statistical model, Steve Jobs, Steven Levy, Stewart Brand, Tesla Model S, the High Line, The Signal and the Noise by Nate Silver, theory of mind, Travis Kalanick, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, women in the workforce

Programmers don’t even like to type; it’s hard to imagine them being so detail-oriented that they voluntarily comply with fifty-plus different state traffic schemas and then manage to communicate these different operating procedures to each customer who buys an autonomous car. The communication problem surfaces again when we talk about self-driving cars. The National Highway Traffic Safety Association (NHTSA), the government agency in charge of motor vehicle and highway safety, had to come up with a complex scale to describe autonomous driving so we could talk about it. For a long time, programmers and executives used the term self-driving car without defining specifically what they meant. Again—normal for language, problematic for policy. In an effort to wrangle the Wild West of autonomous vehicles, the NHTSA published a set of categories for autonomous vehicles. The September 2016 Federal Automated Vehicles Policy reads as follows: There are multiple definitions for various levels of automation and for some time there has been need for standardization to aid clarity and consistency.

The lidar guidance system in an autonomous car works by bouncing laser beams off nearby objects. It estimates how far the objects are by measuring the reflection time. In the rain or snow or dust, the beams bounce off the particles in the air instead of bouncing off obstacles like bicyclists. One self-driving car was spotted going the wrong way down a one-way street. The software apparently didn’t reflect that the street was one way. The cars are easy to confuse because they rely on the same mediocre image recognition algorithms that mislabel pictures of black people as gorillas.13 Most autonomous vehicles use algorithms called deep neural networks, which can be confused by simply putting a sticker or graffiti on a stop sign.14 GPS hacking is a very real danger for autonomous vehicles as well. Pocket-size GPS jammers are illegal, but they are easy to order online for about $50.

It turned out that perhaps not as much has changed as I might have thought. The story of the race to build a self-driving car is a story about the fundamental limits of computing. Looking at what worked—and what didn’t—during the first decade of autonomous vehicles is a cautionary tale about how technochauvinism can lead to magical thinking about technology and can create a public health hazard. My first ride happened on an autonomous vehicle test track: the weekend-empty parking lot of the Boeing factory in South Philadelphia. The Ben Franklin Racing Team, a group of engineering students at the University of Pennsylvania, was building an autonomous vehicle for a competition. I was writing a story about them for the University of Pennsylvania alumni magazine. I met the members of the Ben Franklin Racing Team on campus at dawn on a Sunday morning, and I followed them down the highway for self-driving-car racing practice.


pages: 265 words: 74,807

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

Air France Flight 447, autonomous vehicles, Captain Sullenberger Hudson, Charles Lindbergh, Chris Urmson, digital map, disruptive innovation, drone strike, en.wikipedia.org, Erik Brynjolfsson, fudge factor, index card, John Markoff, low earth orbit, Mars Rover, ride hailing / ride sharing, Ronald Reagan, self-driving car, Silicon Valley, telepresence, telerobotics, trade route, US Airways Flight 1549, William Langewiesche, zero-sum game

John Markoff, “Google Cars Drive Themselves, in Traffic,” New York Times, October 9, 2010, http://www.nytimes.com/2010/10/10/science/10google.html. The Google car’s successful driving tests: Mark Harris, “How Google’s Autonomous Car Passed the First U.S. State Self-Driving Test,” IEEE Spectrum Online, September 10, 2014, http://spectrum.iee.org. Idem., “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.

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. Google discovered that “people are lazy”: Tom Simonite, “Lazy Humans Shaped Google’s New Autonomous Car,” MIT Technology Review (May 30, 2014), http://www.technologyreview.com/news/527756/lazy-humans-shaped-googles-new-autonomous-car/.

Or a person on the surface might even teleoperate the vehicle when it’s in optical range, then let it do more on its own when out of range or if the optical link is lost. Autonomy then becomes a function of position and bandwidth. Overall, the lines between the human, remote, and autonomous vehicles undersea are blurring. Engineers now envision an ocean with many vehicles working in concert. Some may contain people, others will be remote or autonomous, all will be capable of shifting modes at different times. The recently upgraded Alvin has software originally designed for autonomous vehicles; one day it may connect to the surface with an optical fiber. One day it might even operate unmanned. The challenges are to coordinate all of these machines, keep the humans informed, and ensure the robots’ actions reflect human intentions.


pages: 386 words: 113,709

Why We Drive: Toward a Philosophy of the Open Road by Matthew B. Crawford

1960s counterculture, Airbus A320, airport security, augmented reality, autonomous vehicles, Bernie Sanders, Boeing 737 MAX, British Empire, Burning Man, call centre, collective bargaining, crony capitalism, deskilling, digital map, don't be evil, Donald Trump, Elon Musk, en.wikipedia.org, Fellow of the Royal Society, gig economy, Google Earth, hive mind, income inequality, informal economy, Internet of things, Jane Jacobs, labour mobility, Lyft, Network effects, New Journalism, New Urbanism, Nicholas Carr, Ponzi scheme, Ralph Nader, ride hailing / ride sharing, Ronald Reagan, Sam Peltzman, security theater, self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, smart cities, social graph, social intelligence, Stephen Hawking, technoutopianism, the built environment, The Death and Life of Great American Cities, the High Line, too big to fail, traffic fines, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, Unsafe at Any Speed, urban planning, Wall-E, Works Progress Administration

Ian Bogost offers a convincing thought experiment about the terms on which the public will have access to roads as public infrastructure comes to be financed and planned by partnerships between municipalities and tech companies. “It’s easy to imagine that cross-town transit might soon require acceptance of non-negotiable terms of service that would allow your robocar provider to aggregate and sell where you go, when, with whom, and for what purpose.” One can imagine the removal of street signs, those eyesores that aren’t needed by autonomous vehicles, tipping us further into dependence on the cartel. Bogost writes that “other, stranger realities are possible. Imagine if walking across the street required a microtransaction to insure safe passage. Violations might be subject to tickets or fines—although more likely, your local transit vendor would already know where you are thanks to your smartphone, and just debit your metered service plan accordingly.”7 Something about this picture sits ill with our liberal political traditions, but its wrongness goes deeper still.

In November 2017 in Automotive News, Bob Lutz, the head of GM, suggested that driving will be outlawed in twenty years. Bob Lutz, “Kiss the Good Times Goodbye,” Automotive News, November 5, 2017, http://www.autonews.com/apps/pbcs.dll/article?AID=/20171105/INDUSTRY_REDESIGNED/171109944/industry-redesigned-bob-lutz. 7.Ian Bogost, “Will Robocars Kick Humans off City Streets?” Atlantic, June 23, 2016, https://www.theatlantic.com/technology/archive/2016/06/robocars-only/488129/. 8.A. M. Glenberg and J. Hayes, “Contribution of Embodiment to Solving the Riddle of Infantile Amnesia,” Frontiers in Psychology 7 (2016), as characterized in M. R. O’Connor, “For Kids, Learning Is Moving,” Nautilus, September 22, 2016, http://nautil.us/issue/40/learning/for-kids-learning-is-moving. There have been experiments giving infants with severe motor impairments motorized carts to enable them to explore their environment, resulting in accelerated cognitive and language development compared to those without the benefit of self-locomotion, M.

According to the Times, “Experimental designs for autonomous cars incorporate as many as 16 video cameras, 12 radar sensors, half a dozen ultrasonic sensors, and four or five lidar detectors. And still more sensors and scanners might be necessary to make self-driving cars impervious to exigencies like blinding blizzards and soaking downpours.” The chief executive of one of the leading lidar makers is quoted as saying, “You have to have ridiculous, superhuman sensors to make up for the fact that computers aren’t nearly as smart as humans—and won’t be for a very, very long time.”5 It is estimated that an autonomous car will need a computer capable of 300 trillion operations per second. RADICAL MONOPOLY If, at some late date in the future, a highly coordinated system of autonomous cars were to achieve the level of efficiency that prevails today at an intersection in the old country, it would be counted a smashing success.


Driverless: Intelligent Cars and the Road Ahead by Hod Lipson, Melba Kurman

AI winter, Air France Flight 447, Amazon Mechanical Turk, autonomous vehicles, barriers to entry, butterfly effect, carbon footprint, Chris Urmson, cloud computing, computer vision, connected car, creative destruction, crowdsourcing, DARPA: Urban Challenge, digital map, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Google Earth, Google X / Alphabet X, high net worth, hive mind, ImageNet competition, income inequality, industrial robot, intermodal, Internet of things, job automation, Joseph Schumpeter, lone genius, Lyft, megacity, Network effects, New Urbanism, Oculus Rift, pattern recognition, performance metric, precision agriculture, RFID, ride hailing / ride sharing, Second Machine Age, self-driving car, Silicon Valley, smart cities, speech recognition, statistical model, Steve Jobs, technoutopianism, Tesla Model S, Travis Kalanick, Uber and Lyft, uber lyft, Unsafe at Any Speed

Proposes Spending $4 Billion on Self-Driving Cars,” New York Times, January 14, 2016, http://www.nytimes.com/2016/01/15/business/us-proposes-spending-4-billion-on-self-driving-cars.html?_r=0 17. Brad Templeton “California DMV Regulations May Kill the State’s Robocar Lead,” 4brad.com, December 17, 2015, http://ideas.4brad.com/california-dmv-regulations-may-kill-states-robocar-lead 18. Grace Meng, “H.R.3876—Autonomous Vehicle Privacy Protection Act of 2015,” Congress.gov, https://www.congress.gov/bill/114th-congress/house-bill/3876/text 8 Rise of the Robots Modern driverless cars began to emerge from the labs of robotics researchers in the final decades of the twentieth century. Throughout the 1980s and 1990s, German autonomous-vehicle pioneer Ernst Dickmanns built several prototypes that used sensors and intelligent software to steer themselves. In Italy, Professor Alberto Broggi created a car that used machine vision software to follow painted lane markers.

United Nations Office on Drugs and Crime, World Drug Report 2014 (United Nations publication, Sales No. E. 14.XI.7). 11. Daniel Fagnant and Kara Kockelman, “Preparing a National for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations,” Eno Center for Transportation. October 2013. 12. Todd Litman, “Autonomous Vehicle Implementation Predictions: Implications for Transport Planning,” Victoria Transport Policy Institute, December 2015. 13. National Highway Traffic Safety Administration, Department of Transportation (US). Traffic Safety Facts 2012: Older Population, Washington, D.C.: NHTSA; 2012. 14. IHS Automotive report, Autonomous Driving: Question Is When, Not If, 2015. 15. Litman, “Autonomous Vehicle Implementation Predictions.” 16. Ravi Shanker et al., “Autonomous Cars: Self-Driving the New Auto Industry Paradigm,” Morgan Stanley report, November 2013. 2 A Driverless World If the billion cars that roam the world’s roads were magically transformed into reliable, driverless vehicles, the first thing you would notice would be the silence.

If an annual driverless-car competition sounds familiar, that’s because it is. The DARPA Grand Challenges of 2004, 2005, and 2007 showcased the best autonomous vehicle technology of the day, catalyzing the careers of dozens of bright minds who have since helped spark today’s renaissance in driverless technologies. States and cities need federal guidance. The AVA should mandate that all states offer a standard driver’s license for autonomous vehicles provided the technology passes a certain minimal safety record. In 2015, only four states had a driver’s license for autonomous vehicles: California, Nevada, Michigan, and Florida. In addition, states with long empty stretches of highway should be encouraged to create designated lanes for testing and validation of fully autonomous vehicles and trucks. None of these goals will be easy to accomplish and writing good technology policy is notoriously difficult.


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The Driver in the Driverless Car: How Our Technology Choices Will Create the Future by Vivek Wadhwa, Alex Salkever

23andMe, 3D printing, Airbnb, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, Bernie Sanders, bitcoin, blockchain, clean water, correlation does not imply causation, distributed ledger, Donald Trump, double helix, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Google bus, Hyperloop, income inequality, Internet of things, job automation, Kevin Kelly, Khan Academy, Kickstarter, Law of Accelerating Returns, license plate recognition, life extension, longitudinal study, Lyft, M-Pesa, Menlo Park, microbiome, mobile money, new economy, personalized medicine, phenotype, precision agriculture, RAND corporation, Ray Kurzweil, recommendation engine, Ronald Reagan, Second Machine Age, self-driving car, Silicon Valley, Skype, smart grid, stem cell, Stephen Hawking, Steve Wozniak, Stuxnet, supercomputer in your pocket, Tesla Model S, The Future of Employment, Thomas Davenport, Travis Kalanick, Turing test, Uber and Lyft, Uber for X, uber lyft, uranium enrichment, Watson beat the top human players on Jeopardy!, zero day

A small vehicle that looks like a cross between a golf cart and a Jetsonesque bubble-topped spaceship glides to a stop at an intersection. Someone is sitting in the passenger seat, but no one seems to be sitting in the driver seat. How odd, I think. And then I realize I am looking at a Google car. The technology giant is headquartered in Mountain View, and the company is road-testing its diminutive autonomous cars there. This is my first encounter with a fully autonomous vehicle on a public road in an unstructured setting. The Google car waits patiently as a pedestrian passes in front of it. Another car across the intersection signals a left-hand turn, but the Google car has the right of way. The automated vehicle takes the initiative and smoothly accelerates through the intersection. The passenger, I notice, appears preternaturally calm.

Soon the public conversation will be about whether humans should be allowed to take control of the wheel at all. This paradigm shift will not be without costs or controversies. For sure, widespread adoption of autonomous vehicles will eliminate the jobs of the millions of Americans whose living comes of driving cars, trucks, and buses (and eventually all those who pilot planes and ships). We will begin sharing our cars, in a logical extension of Uber and Lyft. But how will we handle the inevitable software faults that result in human casualties? And how will we program the machines to make the right decisions when faced with impossible choices—such as whether an autonomous car should drive off a cliff to spare a busload of children at the cost of killing the car’s human passenger? I was surprised, upon my first sight of a Google car on the street, at how mixed my emotions were.

Beyond this revisionist take on automobile autonomy, I see self-driving cars as opening up entirely new vistas. When parents can call a Google car and put their children in the back seat for a ride to soccer practice, that increases autonomy. When an elderly person who can no longer drive can call an autonomous vehicle for a lift to the supermarket or to the art museum, that increases autonomy. When all of this is affordable—so affordable that anyone can pay for it—it will have brought about a massive net increase in autonomy for all and an important increase in equity. Yes, we will be dependent on autonomous cars, but we have always been dependent. Here, the dependency is actually replaced with something more reliable. The child always needs to get to soccer practice, whether a parent or neighbor or a Google car is providing the transportation. As you can tell, when it comes to self-driving cars, I’m a starry-eyed optimist.


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The End of Traffic and the Future of Transport: Second Edition by David Levinson, Kevin Krizek

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, American Society of Civil Engineers: Report Card, autonomous vehicles, barriers to entry, Bay Area Rapid Transit, big-box store, Chris Urmson, collaborative consumption, commoditize, crowdsourcing, DARPA: Urban Challenge, dematerialisation, Elon Musk, en.wikipedia.org, Google Hangouts, Induced demand, intermodal, invention of the printing press, jitney, John Markoff, labor-force participation, lifelogging, Lyft, means of production, megacity, Menlo Park, Network effects, Occam's razor, oil shock, place-making, post-work, Ray Kurzweil, rent-seeking, ride hailing / ride sharing, Robert Gordon, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, technological singularity, Tesla Model S, the built environment, Thomas Kuhn: the structure of scientific revolutions, transaction costs, transportation-network company, Uber and Lyft, Uber for X, uber lyft, urban renewal, women in the workforce, working-age population, Yom Kippur War, zero-sum game, Zipcar

Just as owning a car was once a class signifier in the US, and remains so elsewhere in the world, and as owning a particular model of car (like a Prius or a BMW) persists as a signifier, we can expect that during the transition period owning an autonomous car will be a class-signifier. It indicates at once that you are wealthy enough to own a new car, and technologically sophisticated enough to trust your life to it. While eventually we expect this to be uniform, early adopters will have very different economic and social characteristics from the population at large. During the long transition, those who cannot afford such cars may come to be vilified as the cause of crashes. —— While people, animals, weather, larger cargo needs, and so on are still potential confounding factors, autonomous vehicles interacting with only autonomous vehicles should be much easier to design and manage than autonomous vehicles in mixed traffic. The next chapter considers how Transport Network Companies such as Lyft and Uber compete with taxis.

There likely will remain debate about how old a child must be before she is placed alone in an autonomous cars, but the consensus is likely to be, if they are in kindergarten, they can ride alone, as with school buses. This is a similar argument with ridesharing services today that offer rides, but that is, to date, a small phenomenon. Human travel will be much more point-to-point, with far fewer pick-up and drop-off passenger trips required, as those can be off-loaded to the vehicle without a driver. Deadheading autonomous vehicles, driving around without a passenger to pick up their next family member may become common, though logistics and shared vehicles can minimize the amount of this. Costs. The capital costs for autonomous vehicles are likely to be higher than traditional cars, at least at first, until driver-facing technologies (like the steering wheel, brake and accelerator pedals, and so on) can be removed for cost savings, as the sensors and computers add some cost compared to existing systems.

Widely called the 'sharing economy' or 'collaborative consumption,' its manifestations in transport: carsharing and ridesharing are viable if not widespread. Couple these technologies with autonomous vehicles discussed in the previous chapter, and one arrives at what we term 'cloud commuting' — the convergence of ridesharing, carsharing, and autonomous vehicles.211 More formally, this range of options can be termed Mobility-as-a-Service (MaaS). While nascent today, clearly big players are placing big bets that this will be a big change in how people travel. It is this which explains Uber's $62.5 Billion valuation.212 A vehicle from a giant pool of autonomous cars operated by organizations based 'in the cloud' would be dispatched to a customer on-demand and in short order, and then would deliver the customer to her destination (be it work or otherwise) before moving on to the next customer.


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Robot Rules: Regulating Artificial Intelligence by Jacob Turner

Ada Lovelace, Affordable Care Act / Obamacare, AI winter, algorithmic trading, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, autonomous vehicles, Basel III, bitcoin, blockchain, brain emulation, Clapham omnibus, cognitive dissonance, corporate governance, corporate social responsibility, correlation does not imply causation, crowdsourcing, distributed ledger, don't be evil, Donald Trump, easy for humans, difficult for computers, effective altruism, Elon Musk, financial exclusion, financial innovation, friendly fire, future of work, hive mind, Internet of things, iterative process, job automation, John Markoff, John von Neumann, Loebner Prize, medical malpractice, Nate Silver, natural language processing, nudge unit, obamacare, off grid, pattern recognition, Peace of Westphalia, race to the bottom, Ray Kurzweil, Rodney Brooks, self-driving car, Silicon Valley, Stanislav Petrov, Stephen Hawking, Steve Wozniak, strong AI, technological singularity, Tesla Model S, The Coming Technological Singularity, The Future of Employment, The Signal and the Noise by Nate Silver, Turing test, Vernor Vinge

Ngo, “Redesign of the Vehicle Bonnet Structure for Pedestrian Safety”, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, Vol. 226, No. 1 (2012), 70–84. 109Many commentators have pointed out the applicability of the Trolley Problem to self-driving cars, but beyond articulating the issue, few have actually suggested a legal or moral answer. See, for example, Matt Simon, “To Make Us All Safer, Robocars Will Sometimes Have to Kill”, Wired, 17 March 2017, https://​www.​wired.​com/​2017/​03/​make-us-safer-robocars-will-sometimes-kill/​, accessed 1 June 2018; Alex Hern, “Self-Driving Cars Don’t Care About Your Moral Dilemmas”, The Guardian, 22 August 2016, https://​www.​theguardian.​com/​technology/​2016/​aug/​22/​self-driving-cars-moral-dilemmas, accessed 1 June 2018; Jean-François Bonnefon, Azim Shariff, and Iyad Rahwan, “The Social Dilemma of Autonomous Vehicles”, Science, Vol. 352, No. 6293 (2016), 1573–1576; Noah J. Goodall, “Machine Ethics and Automated Vehicles”, in Road Vehicle Automation, edited by Gereon Meyer and Sven Beiker (New York: Springer, 2014), 93–102. 110“Ethics Commission at the German Ministry of Transport and Digital Infrastructure”, 5 June 2017, https://​www.​bmvi.​de/​SharedDocs/​EN/​Documents/​G/​ethic-commission-report.​pdf?​

The term does not include an active safety system or a system for driver assistance, including without limitation, a system to provide electronic blind spot detection, crash avoidance, emergency braking, parking assistance, adaptive cruise control, lane keeping assistance, lane departure warning, or traffic jam and queuing assistance, unless any such system, alone or in combination with any other system, enables the vehicle on which the system is installed to be driven without the active control or monitoring of a human operator (Added to NRS by 2013, 2009). Chapter 482A—Autonomous Vehicles, https://​www.​leg.​state.​nv.​us/​NRS/​NRS-482A.​html, accessed 1 June 2018. 45Ryan Calo, “Nevada Bill Would Pave the Road to Autonomous Cars”, Centre for Internet and Society Blog, 27 April 2011, http://​cyberlaw.​stanford.​edu/​blog/​2011/​04/​nevada-bill-would-pave-road-autonomous-cars, accessed 1 June 2018. 46Will Knight, “Alpha Zero’s “Alien” Chess Shows the Power, and the Peculiarity, of AI”, MIT Technology Review, https://​www.​technologyreview​.​com/​s/​609736/​alpha-zeros-alien-chess-shows-the-power-and-the-peculiarity-of-ai/​, accessed 1 June 2018.

Kazuo Yano, “Enterprises of the Future Will Need Multi-purpose AIs”, Hitachi Website, http://​www.​hitachi.​co.​jp/​products/​it/​it-pf/​mag/​special/​2016_​02th_​e/​interview_​ky_​02.​pdf, accessed 1 June 2018. 41UK Department of Transport, “The Pathway to Driverless Cars: Detailed Review of Regulations for Automated Vehicle Technologies”, UK Government Website, February 2015, https://​www.​gov.​uk/​government/​uploads/​system/​uploads/​attachment_​data/​file/​401565/​pathway-driverless-cars-main.​pdf, accessed 1 June 2018. 42When in 2017 the UK’s House of Lords Science and Technology Select Committee published a report entitled “Connected and Autonomous Vehicles: The Future?”, it concentrated solely on land-based vehicles. House of Lords, Science and Technology Select Committee, “Connected and Autonomous Vehicles: The Future?”, 2nd Report of Session 2016–17, HL Paper 115 (15 March 2017). The Report expressly says at para. 23: “We have not considered remote control vehicles (RCV) or drones (unmanned aerial vehicles) in this report”. The US Department of Transport published its Federal Automated Vehicles Policy in September 2016.


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

Affordable Care Act / Obamacare, Amazon Web Services, asset allocation, autonomous vehicles, bank run, bitcoin, Bob Noyce, Brian Krebs, business cycle, 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, information asymmetry, invention of agriculture, Jaron Lanier, Jeff Bezos, job automation, John Markoff, 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, The Future of Employment, Turing test, Watson beat the top human players on Jeopardy!, winner-take-all economy, women in the workforce, working poor, Works Progress Administration

See also David Begg, “A 2050 Vision for London: What Are the Implications of Driverless Transport?” Transport Times, June, 2014, http://www.transporttimes.co.uk/Admin/uploads/64165-Transport-Times_A-2050-Vision-for-London_AW-WEB-READY.pdf; http://emarketing.pwc.com/reaction/images/AutofactsAnalystNoteUS(Feb2013)FINAL.pdf 7. According to Brad Templeton, autonomous car consultant to Google, “In Los Angeles, it is estimated that over half of all real estate is devoted to cars (roads and environs, driveways, parking),” personal blog, accessed November 29, 2014, http://www.templetons.com/brad/robocars/numbers.html. 8. Transportation Energy Data Book, table 8.5, Center for Transportation Analysis, Oak Ridge National Laboratory, accessed November 29, 2014, http://cta.ornl.gov/data/chapter8.shtml. 9. Lawrence D. Burns, William C. Jordan, and Bonnie A. Scarborough, “Transforming Personal Mobility,” the Earth Institute, Columbia University, January 27, 2013, http://sustainablemobility.ei.columbia.edu/files/2012/12/Transforming-Personal-Mobility-Jan-27-20132.pdf. 10.

See shipping depth perception, 42–43 D. E. Shaw and Company, 53, 95, 96, 97 DIDO (distributed input, distributed output), 127 digital recording, 193 Dijkstra, Edgar, 3 dishwashing, 145 Disneyland VIP tour option, 165 doctors. See medical care Dow Jones Industrial Average, 8–9, 61–63 driverless cars. See autonomous vehicles drones, 43, 44 duty-based normative ethics, 82 “Easterlin Paradox,” 225n31 economic system, 7, 10–15 absolute vs. reactive needs and, 186 asset-based, 14–15, 175–87 autonomous vehicles’ effects on, 195, 196 class and, 115, 116, 118 competitive advantage and, 102, 103, 106, 161–65, 181, 186, 187 expansion of, 15, 165 incentives and, 176, 177 inequality and, 12–15, 117–18, 165–66, 174–76 inflation rate, 173, 175 innovation and, 158, 161–64, 186–87 Silicon Valley disruption of, 16 synthetic intellect takeover of, 201–2.

., 200 Rocket Fuel, 64–65, 67–71, 136 founding/current worth of, 72 Rolling Stone (magazine), 170 Roosevelt, Franklin Delano, 170 Rosenblatt, Frank, 24 Rothschild, Nathan Mayer, 58 R202 (mechanical factotum), 40 Rutter, Brad, 150 SAAS (software-as-a-service), 43 safety: autonomous vehicles, 89, 142, 195 commercial pilots, 151 highway, 44–45, 142, 178 traffic, 195 workplace, 37–38, 44–45 salaries, 116, 120, 145, 172 salespeople, 139 S&P 500 E-mini, 62 San Francisco State University, 121, 158 sanitation, 169 savings. See assets ownership Scheinman, Victor, 35 schools. See education system Schrodinger’s cat, 213n9 science, ix, 114 science fiction, ix–x, xii Seattle, 114 SEC (Securities and Exchange Commission), 8, 61–62, 63 segregation, 168, 222n10 self-driving vehicles. See autonomous vehicles self stocking, 40 sensors, 194, 205 applications of, 4, 5 network of, 42–43, 44 recognition by, 39 sex workers and toys, 144–45 Shaw, Dave (King Quant), 51–53, 58, 95, 96, 97, 103 shipping, 39 costs of, 100, 101 delivery and, 141–42, 177 “free,” 101 warehouse stacking and, 144 ShotSpotter system, 43 Silicon Valley startups, x–xi, 64–65, 95–96, 127, 144, 223–24n15 disruption of industries by, 16 personal wealth from, 109 restricted stock vesting by, 184 Simon, Paul, 112 simulated intelligence.


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Hello World: Being Human in the Age of Algorithms by Hannah Fry

23andMe, 3D printing, Air France Flight 447, Airbnb, airport security, augmented reality, autonomous vehicles, Brixton riot, chief data officer, computer vision, crowdsourcing, DARPA: Urban Challenge, Douglas Hofstadter, Elon Musk, Firefox, Google Chrome, Gödel, Escher, Bach, Ignaz Semmelweis: hand washing, John Markoff, Mark Zuckerberg, meta analysis, meta-analysis, pattern recognition, Peter Thiel, RAND corporation, ransomware, recommendation engine, ride hailing / ride sharing, selection bias, self-driving car, Shai Danziger, Silicon Valley, Silicon Valley startup, Snapchat, speech recognition, Stanislav Petrov, statistical model, Stephen Hawking, Steven Levy, Tesla Model S, The Wisdom of Crowds, Thomas Bayes, Watson beat the top human players on Jeopardy!, web of trust, William Langewiesche

‘The rhetoric of autonomy and transport is all about not changing the world,’ Stilgoe tells me. ‘It’s about keeping the world as it is but making and allowing a robot to just be as good as and then better than a human at navigating it. And I think that’s stupid.’ But hang on, some of you may be thinking. Hasn’t this problem already been cracked? Hasn’t Waymo, Google’s autonomous car, driven millions of miles already? Aren’t Waymo’s fully autonomous cars (or at least, close to fully autonomous cars) currently driving around on the roads of Phoenix, Arizona? Well, yes. That’s true. But not every mile of road is created equally. Most miles are so easy to drive, you can do it while daydreaming. Others are far more challenging. At the time of writing, Waymo cars aren’t allowed to go just anywhere: they’re ‘geo-fenced’ into a small, pre-defined area.

But I’m also sympathetic to the aloof reaction it receives in the driverless car community. They, more than anyone, know how far away we are from having to worry about the trolley problem as a reality. Breaking the rules of the road Bayes’ theorem and the power of probability have driven much of the innovation in autonomous vehicles ever since the DARPA challenge. I asked Paul Newman, professor of robotics at the University of Oxford and founder of Oxbotica, a company that builds driverless cars and tests them on the streets of Britain, how his latest autonomous vehicles worked, and he explained as follows: ‘It’s many, many millions of lines of code, but I could frame the entire thing as probabilistic inference. All of it.’36 But while Bayesian inference goes some way towards explaining how driverless cars are possible, it also explains how full autonomy, free from any input by a human driver, is a very, very difficult nut to crack.

Pomerleau, Neural Network Perception for Mobile Robot Guidance (New York: Springer, 2012), p. 52. 27. A. Filgueira, H. González-Jorge, S. Lagüela, L. Diaz-Vilariño and P. Arias, ‘Quantifying the influence of rain in LiDAR performance’, Measurement, vol. 95, Jan. 2017, pp. 143–8, DOI: https://doi.org/10.1016/j.measurement.2016.10.009; https://www.sciencedirect.com/science/article/pii/S0263224116305577. 28. Chris Williams, ‘Stop lights, sunsets, junctions are tough work for Google’s robo-cars’, The Register, 24 Aug. 2016, https://www.theregister.co.uk/2016/08/24/google_self_driving_car_problems/. 29. Novatel, IMU Errors and Their Effects, https://www.novatel.com/assets/Documents/Bulletins/APN064.pdf. 30. The theorem itself is just an equation, linking the probability of a hypothesis, given some observed pieces of evidence, and the probability of that evidence, given the hypothesis.


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Insane Mode: How Elon Musk's Tesla Sparked an Electric Revolution to End the Age of Oil by Hamish McKenzie

Airbnb, Albert Einstein, augmented reality, autonomous vehicles, barriers to entry, basic income, Bay Area Rapid Transit, Ben Horowitz, business climate, car-free, carbon footprint, Chris Urmson, Clayton Christensen, cleantech, Colonization of Mars, connected car, crony capitalism, Deng Xiaoping, disruptive innovation, Donald Trump, Elon Musk, Google Glasses, Hyperloop, Internet of things, Jeff Bezos, John Markoff, low earth orbit, Lyft, Marc Andreessen, margin call, Mark Zuckerberg, megacity, Menlo Park, Nikolai Kondratiev, oil shale / tar sands, paypal mafia, Peter Thiel, ride hailing / ride sharing, Ronald Reagan, self-driving car, Shenzhen was a fishing village, short selling, side project, Silicon Valley, Silicon Valley startup, Snapchat, South China Sea, special economic zone, stealth mode startup, Steve Jobs, Tesla Model S, Tim Cook: Apple, Uber and Lyft, uber lyft, universal basic income, urban planning, urban sprawl, Zipcar

At that point, you could read a book, take a nap, or watch a movie while the car drove itself. Google has tested fully autonomous vehicles to a Level 5 designation, meaning the cars could perform all “safety-critical driving functions and monitor roadway conditions for an entire trip,” but they haven’t yet left the test circuit. The development of autonomous vehicles goes hand in hand with the development of electric vehicles, because self-driving cars are best controlled by drive-by-wire systems, in which electrical signals and digital controls, rather than mechanical functions, operate a car’s core systems, such as steering, acceleration, and braking. The absence of a large engine block, too, opens up more design possibilities for electric vehicles, so autonomous cars could come in more varied shapes and sizes, as small as a covered Segway or as large as a double-decker bus.

At the South by Southwest tech conference in March 2016, Chris Urmson, who was then the head of Google’s self-driving car program, said that in some places autonomous vehicles won’t be on the roads for as many as thirty years. At the same time, Elon Musk, ever the optimist, has said that he thinks Tesla’s cars will be ready for “complete autonomy” by 2018, but that the regulatory process will add another year to the rollout. In October 2016, Tesla said that all its new cars would be equipped with hardware that would allow full self-driving at some point in the future. The company said it would “further calibrate the system using millions of miles of real-world driving.” A Morgan Stanley analyst has predicted that complete autonomy will be ready by 2022, with massive market penetration coming by 2026. In bad weather, it’s harder for an autonomous car’s cameras to make out markings on the road and see other vehicles in front of them.

Or, faced with a choice between crashing into two elderly citizens on the left side of the road or one infant on the right, what should an autonomous car do? What if, in a case where the system determines that fatalities are certain to occur, a car’s computer knows it can ensure a victim has a more humane death if it accelerates toward him? Answering such questions could keep regulatory debates running in circles for years. Lobby groups will keep the regulators busy. In April 2016, as NHTSA held public hearings about self-driving cars, a group that included Ford, Google, Uber, Lyft, and Volvo announced the formation of the Self-Driving Coalition for Safer Streets, led by David Strickland, a former NHTSA administrator. The group has been advocating for a clear set of federal standards for autonomous vehicles in the United States. In June 2016, the National Association of City Transportation Officials, a coalition of officials from dozens of large North American cities, published a policy statement that included a series of safety- and civic-minded recommendations, such as capping inner-city speeds for autonomous vehicles at twenty-five miles an hour and offering federal and state incentives to cities that prioritize self-driving electric cars that can be shared.


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Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

Ada Lovelace, AI winter, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, artificial general intelligence, autonomous vehicles, Bernie Sanders, Claude Shannon: information theory, cognitive dissonance, computer age, computer vision, dark matter, Douglas Hofstadter, Elon Musk, en.wikipedia.org, Gödel, Escher, Bach, I think there is a world market for maybe five computers, ImageNet competition, Jaron Lanier, job automation, John Markoff, John von Neumann, Kevin Kelly, Kickstarter, license plate recognition, Mark Zuckerberg, natural language processing, Norbert Wiener, ought to be enough for anybody, pattern recognition, performance metric, RAND corporation, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Rodney Brooks, self-driving car, sentiment analysis, Silicon Valley, Singularitarianism, Skype, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, theory of mind, There's no reason for any individual to have a computer in his home - Ken Olsen, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!

Sizing up a complex situation and making split-second assessments of who is likely to jaywalk, dart across the street to run for a bus, turn abruptly without signaling, or stop in a crosswalk to adjust a broken high-heeled shoe—this is second nature to most human drivers, but not yet to self-driving cars. Another looming problem for autonomous vehicles is the potential for malicious attacks of various kinds. Computer-security experts have shown that even many of the nonautonomous cars we drive today—which are increasingly controlled by software—are vulnerable to hacking via their connection to wireless networks, including Bluetooth, cell phone networks, and internet connections.2 Because autonomous cars will be completely controlled by software, they will potentially be even more vulnerable to malicious hacking. In addition, as I described in chapter 6, machine-learning researchers have demonstrated possible “adversarial attacks” on computer-vision systems of self-driving cars—some as simple as putting inconspicuous stickers on stop signs that make the car classify them as speed-limit signs.

Achieving full autonomy in driving essentially requires general AI, which likely won’t be achieved anytime soon. Cars with partial autonomy exist now, but are dangerous because the humans driving them don’t always pay attention. The most likely solution to this dilemma is to change the definition of full autonomy: allowing autonomous cars to drive only in specific areas—those that have created the infrastructure to ensure that the cars will be safe. A common version of this solution goes by the name “geofencing.” Jackie DiMarco, former chief engineer for autonomous vehicles at Ford Motor Company, explained geofencing this way: When we talk about level 4 autonomy, it’s fully autonomous within a geofence, so within an area where we have a defined high definition map. Once you have that map you can understand your environment. You can understand where the lamp posts are, where the crosswalks are, what the rules of the road are, speed limit and so on.

Several fatal accidents caused by self-driving cars, including the experimental ones, have occurred when a human was supposed to have been ready to take over but was not paying attention. The self-driving car industry desperately wants to produce and sell fully autonomous vehicles (that is, level 5); indeed, full autonomy is what we, the consumers, have long been promised in all the buzz around self-driving cars. What are the obstacles to getting to true autonomy in our cars? The primary obstacles are the kinds of long-tail situations (“edge cases”) that I described in chapter 6: situations that the vehicle was not trained on, and that might individually occur rarely, but that, taken together, will occur frequently when autonomous vehicles are widespread. As I described, human drivers deal with these events by using common sense—particularly the ability to understand and make predictions about novel situations by analogy to situations the driver already understands.


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Speculative Everything: Design, Fiction, and Social Dreaming by Anthony Dunne, Fiona Raby

3D printing, augmented reality, autonomous vehicles, Berlin Wall, Buckminster Fuller, Cass Sunstein, computer age, corporate governance, David Attenborough, en.wikipedia.org, Fall of the Berlin Wall, game design, global village, Google X / Alphabet X, haute couture, life extension, Mark Zuckerberg, mouse model, New Urbanism, Peter Eisenman, RAND corporation, Richard Thaler, Ronald Reagan, self-driving car, Silicon Valley, social software, technoutopianism, Wall-E

For other fictional Englands, see Rupert Thomson, Divided Kingdom (London: Bloomsbury, 2006); and Julian Barnes, England England (London: Picador, 2005 [19881). 26. Timothy Mitchell, "Hydrocarbon Utopia," in Utopia/Dystopia: Conditions of Historical Possibility, ed. Michael D.Gordin, Helen Tilley, and Gyan Prakish (Princeton, NJ: Princeton University Press, 2010), 118. 27. For more about how the design of cars can change to due to robocars, see Brad Templeton, "New Design Factors for Robot Cars." Available at http:// www.templetons.com/brad/robocars/design-change.html. Accessed December 23, 2012. 28. Each micro-kingdom represents a different combination of technology and political ideology. For example, if biotechnology were part of the digitarian world, it would be shaped by market mechanisms and lead to a "bio-tarian" culture. Likewise, if the bioliberals embraced digital technology, it would result in a "digi-liberal" culture.


pages: 717 words: 150,288

Cities Under Siege: The New Military Urbanism by Stephen Graham

addicted to oil, airport security, anti-communist, autonomous vehicles, Berlin Wall, call centre, carbon footprint, clean water, congestion charging, creative destruction, credit crunch, DARPA: Urban Challenge, defense in depth, deindustrialization, digital map, edge city, energy security, European colonialism, failed state, Food sovereignty, Gini coefficient, global supply chain, Google Earth, illegal immigration, income inequality, knowledge economy, late capitalism, loose coupling, market fundamentalism, mass incarceration, McMansion, megacity, moral panic, mutually assured destruction, Naomi Klein, New Urbanism, offshore financial centre, one-state solution, pattern recognition, peak oil, planetary scale, private military company, Project for a New American Century, RAND corporation, RFID, Richard Florida, Scramble for Africa, Silicon Valley, smart transportation, surplus humans, The Bell Curve by Richard Herrnstein and Charles Murray, urban decay, urban planning, urban renewal, urban sprawl, Washington Consensus, white flight, white picket fence

The agency stressed that the aim of the 2007 competition, called ‘Urban Challenge’, was to develop ‘technology that will keep warfighters off the battlefield and out of harm’s way’.127 It was ‘the first time in history that truly autonomous vehicles met and (mostly) avoided each other on the open road’.128 The event required that competing teams build vehicles capable of driving autonomously in traffic, relying entirely on on-board sensors, cameras, radars, computers and GPS systems. These vehicles had to perform turns, mergers, overtaking, and passing, and had to negotiate junctions within a cordoned-off sixty-mile ‘urban’ course in and around a former military base in Victorville, California. To ramp up the challenge, thirty manned vehicles also roamed the course. Urban Challenge was truly groundbreaking, declared DARPA, as it was ‘the first time autonomous vehicles have interacted with both manned and unmanned vehicle traffic in an urban environment’.129 Thirty-five teams from twenty-two US states entered the competition, involving consortia linked to every major high-tech US university, defence company, and computing corporation.

Eleven fully robotized SUVs and other cars had to navigate a simulated urban course completely autonomously. 9.10 Estimates for the future introduction of fully autonomous military and civilian vehicles from the Urban Challenge presentations of Stanford University’s entry. Whilst driverless cars are unlikely to become available to consumers until 2030 at the earliest, the Urban Challenge robocars are already being displayed at car shows, billed as a way to ‘fortify road safety and eliminate driver error as the most common cause of crashes’.131 The already strong links between militarized robotic combat vehicles (Figure 9.10) and an increasingly militarized society where cars become increasingly automated and surveilled, will likely intensify. One team of Italian military scientists working on these cross-overs said in 2006 that ‘the Urban Challenge will provide some feel of how long it will be before we sit in our own automatic cars’.132 It is also becoming clear that Urban Challenge is a way for the Pentagon to capture the latest civilian technology in robotic vehicles and apply it to its own huge Future Combat Systems programme for the partial robotization of US Army vehicles within urban operating environments.

Widespread campaigns, drawing on a long history of such activism, have targeted the militarized R&D that is carried on in US universities and so firmly underpins securocratic war, ubiquitous bordering, and the Long War.57 Two of the main centres for work on the robotization of weapons–the Robotics Institute and its commercial arm, the National Robotics Engineering Center (NREC) – are at Carnegie Mellon University in Pittsburgh, and both have been the target of a jamming campaign (Figure 10.13). (In Chapter 9 we already encountered NREC: its ‘robocar’ was the winner of DARPA’s 2007 Urban Challenge competition.) The Carnegie Mellon campaign, labelled ‘Barricade the War Machine’, is challenging the take-over of engineering sciences in the university and the local economy by military-robotics research in the service of the military-industrial-academic complex. It is also raising the key ethical question forced by the shift to fully autonomous weapons systems (see Chapter 5): ‘Who bears moral responsibility for outcomes that are caused by autonomous robotic systems?’


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

3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, Charles Lindbergh, cloud computing, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, dematerialisation, deskilling, disruptive innovation, Elon Musk, en.wikipedia.org, Exxon Valdez, fear of failure, Firefox, Galaxy Zoo, Google Glasses, Google Hangouts, gravity well, ImageNet competition, industrial robot, Internet of things, Jeff Bezos, John Harrison: Longitude, John Markoff, Jono Bacon, Just-in-time delivery, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, loss aversion, Louis Pasteur, low earth orbit, Mahatma Gandhi, Marc Andreessen, 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, 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, superconnector, technoutopianism, telepresence, telepresence robot, Turing test, urban renewal, web application, X Prize, Y Combinator, zero-sum game

To look at this from a more expansive angle, consider that we now live in a world where Google’s autonomous car can cruise our streets safely because of a rooftop sensor called LIDAR—a laser-based sensing device that uses sixty-four eye-safe lasers to scan 360 degrees while concurrently generating 750 megabytes of image data per second to help with navigation.5 Pretty soon, though, we’ll live in a world with, say, two million autonomous cars on our roads (not much of a stretch, as that’s less than one percent of cars currently registered in the United States),6 seeing and recording nearly everything they encounter, giving us near-perfect knowledge of the environment they observe. What’s more, ubiquitous imaging doesn’t stop there. 360-degree LIDAR imaging in Google’s driverless car Source: http://people.bath.ac.uk/as2152/cars/lidar.jpg In addition to these autonomous cars scanning the roadside, by 2020, an estimated five privately owned low-Earth-orbiting satellite constellations will be imaging every square meter of the Earth’s surface in resolutions ranging from 0.5 to 2 meters.7 Simultaneously, we’re also about to see an explosion of AI-operated microdrones buzzing around our cities and taking images down in the centimeter range.

Chapter Three: Five to Change the World 1 Adrian Kingsley-Hughes, “Mobile gadgets driving massive growth in touch sensors,” ZDNet, June 18, 2013, http://www.zdnet.com/mobile-gadgets-driving-massive-growth-in-touch-sensors-7000016954/. 2 Peter Kelly-Detwiler, “Machine to Machine Connections—The Internet of Things—And Energy,” Forbes, August 6, 2013, http://www.forbes.com/sites/peterdetwiler/2013/08/06/machine-to-machine-connections-the-internet-of-things-and-energy/. 3 See http://www.shotspotter.com. 4 Clive Thompson, “No Longer Vaporware: The Internet of Things Is Finally Talking,” Wired, December 6, 2012, http://www.wired.com/2012/12/20-12-st_thompson/. 5 Brad Templeton, “Cameras or Lasers?,” Templetons, http://www.templetons.com/brad/robocars/cameras-lasers.html. 6 See http://en.wikipedia.org/wiki/Passenger_vehicles_in_the_United_States. 7 Commercial satellite players include: PlanetLabs (already launched), Skybox (launched and acquired by Google), Urthecast (launched), and two still-confidential companies still under development (about which Peter Diamandis has firsthand knowledge). 8 Stanford University, “Need for a Trillion Sensors Roadmap,” Tsensorsummit.org, 2013, http://www.tsensorssummit.org/Resources/Why%20TSensors%20Roadmap.pdf. 9 Rickie Fleming, “The battle of the G networks,” NCDS.com blog, June 28, 2014, http://www.ncds.com/ncds-business-technology-blog/the-battle-of-the-g-networks. 10 AI with Dan Hesse, 2013–14. 11 Unless otherwise noted, all IoT information and Padma Warrior quotes come from an AI with Padma, 2013. 12 Cisco, “2013 IoE Value Index,” Cisco.com, 2013, http://internetofeverything.cisco.com/learn/2013-ioe-value-index-whitepaper. 13 NAVTEQ, “NAVTEQ Traffic Patterns,” Navmart.com, 2008, http://www.navmart.com/pdf/NAVmart_TrafficPatterns.pdf. 14 Juho Erkheikki, “Nokia to Buy Navteq for $8.1 Billion, Take on TomTom (Update 7),” Bloomberg, October 1, 2007, http://www.bloomberg.com/apps/news?

In the aftermath of these reports, Foxconn’s president, Terry Gou, said he intended to replace one million workers with robots over the next three years.54 Besides replacing our blue-collar workforce, over the next three to five years, robots will invade a much wider assortment of fields. “Already,” says Dan Barry, “we’re seeing telepresence robots transport our eyes, ears, arms, and legs to conferences and meetings. Autonomous cars, which are, after all, just robots, will [start to] chauffeur people around and deliver goods and services. Over the next decade, robots will also move into health care, replacing doctors for routine surgeries and supplementing nurses for eldercare. If I were an exponential entrepreneur looking to create tremendous value, I’d look for those jobs that are least enjoyable for humans to do. . . .


pages: 235 words: 62,862

Utopia for Realists: The Case for a Universal Basic Income, Open Borders, and a 15-Hour Workweek by Rutger Bregman

autonomous vehicles, banking crisis, Bartolomé de las Casas, basic income, Berlin Wall, Bertrand Russell: In Praise of Idleness, Branko Milanovic, cognitive dissonance, computer age, conceptual framework, credit crunch, David Graeber, Diane Coyle, Erik Brynjolfsson, everywhere but in the productivity statistics, Fall of the Berlin Wall, Francis Fukuyama: the end of history, Frank Levy and Richard Murnane: The New Division of Labor, full employment, George Gilder, George Santayana, happiness index / gross national happiness, Henry Ford's grandson gave labor union leader Walter Reuther a tour of the company’s new, automated factory…, income inequality, invention of gunpowder, James Watt: steam engine, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, Kodak vs Instagram, low skilled workers, means of production, megacity, meta analysis, meta-analysis, microcredit, minimum wage unemployment, Mont Pelerin Society, Nathan Meyer Rothschild: antibiotics, Occupy movement, offshore financial centre, Paul Samuelson, Peter Thiel, post-industrial society, precariat, RAND corporation, randomized controlled trial, Ray Kurzweil, Ronald Reagan, Second Machine Age, Silicon Valley, Simon Kuznets, Skype, stem cell, Steven Pinker, telemarketer, The Future of Employment, The Spirit Level, The Wealth of Nations by Adam Smith, Thomas Malthus, Thorstein Veblen, Tyler Cowen: Great Stagnation, universal basic income, wage slave, War on Poverty, We wanted flying cars, instead we got 140 characters, wikimedia commons, women in the workforce, working poor, World Values Survey

Or compare it to electricity: All the major technological innovations happened in the 1870s, but it wasn’t until around 1920 that most factories actually switched to electric power.25 Fast forward to today, and chips are doing things that even ten years ago were still deemed impossible. In 2004 two prominent scientists authored a chapter suggestively titled “Why People Still Matter.”26 Their argument? Driving a car is something that could never be automated. Six years later, Google’s robo-cars had already covered a million miles without a mishap. Okay, one mishap – when a human decided to take the wheel. Futurologist Ray Kurzweil is convinced that by 2029 computers will be just as intelligent as people. In 2045 they might even be a billion times smarter than all human brains put together. According to the techno-prophets, there simply is no limit to the exponential growth of machine computing power.


pages: 309 words: 91,581

The Great Divergence: America's Growing Inequality Crisis and What We Can Do About It by Timothy Noah

assortative mating, autonomous vehicles, blue-collar work, Bonfire of the Vanities, Branko Milanovic, business cycle, call centre, collective bargaining, computer age, corporate governance, Credit Default Swap, David Ricardo: comparative advantage, Deng Xiaoping, easy for humans, difficult for computers, Erik Brynjolfsson, Everybody Ought to Be Rich, feminist movement, Frank Levy and Richard Murnane: The New Division of Labor, Gini coefficient, Gunnar Myrdal, income inequality, industrial robot, invisible hand, job automation, Joseph Schumpeter, longitudinal study, low skilled workers, lump of labour, manufacturing employment, moral hazard, oil shock, pattern recognition, Paul Samuelson, performance metric, positional goods, post-industrial society, postindustrial economy, purchasing power parity, refrigerator car, rent control, Richard Feynman, Ronald Reagan, shareholder value, Silicon Valley, Simon Kuznets, Stephen Hawking, Steve Jobs, The Spirit Level, too big to fail, trickle-down economics, Tyler Cowen: Great Stagnation, union organizing, upwardly mobile, very high income, Vilfredo Pareto, War on Poverty, We are the 99%, women in the workforce, Works Progress Administration, Yom Kippur War

All this is well beyond the ability of a computer.8 Or so it seemed when Levy and Murnane wrote their book. In 2011 their MIT colleagues Erik Brynjolfsson and Andrew McAfee of the Sloan School of Management wrote that this conclusion had become obsolete by the end of 2010. In October of that year Google automated a fleet of Toyota Priuses and put them on the road (with human drivers behind the wheel as safety backups). The robocars navigated from Google’s Mountain View, California, headquarters to its Santa Monica office, taking a detour along the way to wind down San Francisco’s Lombard Street (“the crookedest street in the world”). The cars made the 350-mile trip with only a few minor human interventions. “Levy and Murnane were correct that automatic driving on populated roads is an enormously difficult task,” Brynjolfsson and McAfee conclude, “and it’s not easy to build a computer that can substitute for human perception and pattern matching in this domain.


pages: 412 words: 128,042

Extreme Economies: Survival, Failure, Future – Lessons From the World’s Limits by Richard Davies

agricultural Revolution, air freight, Anton Chekhov, artificial general intelligence, autonomous vehicles, barriers to entry, big-box store, cashless society, clean water, complexity theory, deindustrialization, eurozone crisis, failed state, financial innovation, illegal immigration, income inequality, informal economy, James Hargreaves, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, joint-stock company, large denomination, Livingstone, I presume, Malacca Straits, mandatory minimum, manufacturing employment, means of production, megacity, meta analysis, meta-analysis, new economy, off grid, oil shale / tar sands, pension reform, profit motive, randomized controlled trial, school choice, school vouchers, Scramble for Africa, side project, Silicon Valley, Simon Kuznets, Skype, spinning jenny, The Chicago School, the payments system, trade route, Travis Kalanick, uranium enrichment, urban planning, wealth creators, white picket fence, working-age population, Y Combinator, young professional

At Las Vegas day-care the carers have the usual, unglamorous duties – for instance, helping the elderly clients visit the bathroom – but they also spend a good chunk of their day playing games, and the young blackjack croupier seems to be having a great time at his work. The staff carefully match players of a similar cognitive function and on many tables the players are so focused that the carers are hardly needed at all. Kazuko Kikuchi’s table is bubbling away and as I say goodbye the octogenarian mah-jong master summons me to her table, looking over her glasses: ‘Tell me: if the elderly in the UK don’t have Las Vegas, what on earth do they do?’ ROBO-CARER Life enters its last phases eventually, even in Japan, leaving many unable to attend day-care, and needing full-time personal nursing and observation instead. Here, the country faces another crunch. Late-stage care often requires one-on-one tasks such as feeding patients and lifting them from bed to bath. This is another 3K job, hard for recruiters to fill, and even if it were not it is hard to see how the numbers will add up.


Driverless Cars: On a Road to Nowhere by Christian Wolmar

Airbnb, autonomous vehicles, Beeching cuts, bitcoin, Boris Johnson, BRICs, carbon footprint, Chris Urmson, cognitive dissonance, congestion charging, connected car, deskilling, Diane Coyle, don't be evil, Elon Musk, high net worth, RAND corporation, ride hailing / ride sharing, self-driving car, Silicon Valley, smart cities, Tesla Model S, Travis Kalanick, wikimedia commons, Zipcar

Without proper security systems in place, it is feasible that people could commandeer self-driving vehicles to do their bidding, which could be malicious or simply just for the thrill. This issue has been of great concern to developers of autonomous cars, and an article in the MIT Technology Review outlined details of various forms of hacking that could disrupt autonomous vehicle use: [Autonomous] vehicles will have to anticipate and defend against a full spectrum of malicious attackers wielding both traditional cyberattacks and a new generation of attacks based on so-called adversarial machine learning. 42 The author points out that one possible motive, apart from terrorism, for cyber attacks on autonomous cars would 72 Bumps in the road be anger over the widespread loss of jobs that would result from their introduction (an issue that we discuss in the next chapter).

The vision, as Tillemann wrote, was tantalizingly close except, as he pointed out, the visitors re-emerged into the exhaust-laden smog swamps of Shanghai, where ‘real life meant navigating manic waves of oil-burning SAICs, VWs, Audis and Buicks’.8 If autonomous car heaven is the carrot, the stick is safety. The propagandists for autonomous vehicle technology invariably start their presentations with accounts of the terrible death toll on the roads. And terrible it certainly is, especially in the United States where the main drive for autonomy is coming from. Moreover, the toll is increasing, thanks to cheaper fuel prices – which leads to increased traffic – and a failure to clamp down on people using mobile phones while driving. Google’s main selling point for the driverless car is safety. The mission statement of Waymo, the name now being used for Google’s autonomous car project, is: ‘We are a self-driving technology company with a mission to make it safe and easy for people and things to move around.’

Google and Tesla are pushing for a legal framework for their vehicles to ensure that once the technology is developed, it will be possible to sell them straight away. 88 Bumps in the road However, the regulators may be far more cautious than the tech companies want because of public opinion and the reluctance of the companies to provide information. Volvo has attempted to pre-empt the situation by accepting liability for any collisions involving its autonomous vehicles. This is an easy promise to make when there are no cars on the road but it might be far more difficult if there were a series of incidents that could bankrupt the company. Hacking could also cause accidents, adding complexity to the question of liability. Will autonomous cars require regular ‘patches’ to make up for security or other flaws? And if so, whose fault will it be if the patch is not installed? Even a small-scale trial involving three autonomous vehicles (with a ‘driver’ aboard) on fifteen miles of road in an older citizens’ residential village in San Jose, California was nearly killed off because of insurance concerns.


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

"Robert Solow", A Declaration of the Independence of Cyberspace, AI winter, airport security, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, basic income, 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 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, Gunnar Myrdal, 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 Markoff, 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, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, Mitch Kapor, 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, zero-sum game

A young Google engineer, Anthony Levandowski, routinely commuted from Berkeley to Mountain View, a distance of fifty miles, in one of the Priuses, and Thrun himself would let a Google car drive him from Mountain View to his vacation home in Lake Tahoe on weekends. Today, partially autonomous cars are already appearing on the market, and they offer two paths toward the future of transportation—one with smarter and safer human drivers and one in which humans will become passengers. Google had not disclosed how it planned to commercialize its research, but by the end of 2013 more than a half-dozen automakers had already publicly stated their intent to offer autonomous vehicles. Indeed, 2014 was the year that the line was first crossed commercially when a handful of European car manufacturers including BMW, Mercedes, Volvo, and Audi announced an optional feature—traffic jam assist, the first baby step toward autonomous driving.

After a half-dozen miles, the robotic meanderings of the Touareg felt anticlimactic. Stanley wasn’t driving down the freeway, so as the desert scenery slid by, it seemed increasingly unnecessary to wear crash helmets for what was more or less a Sunday drive in the country. The car was in training to compete in the Pentagon’s second Grand Challenge, an ambitious autonomous vehicle contest intended to jump-start technology planned for future robotic military vehicles. At the beginning of the twenty-first century, Congress instructed the U.S. military to begin designing autonomous vehicles. Congress even gave the Pentagon a specific goal: by 2015, one-third of the army’s vehicles were supposed to go places without human drivers present. The directive wasn’t clear as to whether both autonomous and remotely teleoperated vehicles would satisfy the requirement. In either case the idea was that smart vehicles would save both money and soldiers’ lives.

Software could generally be designed to choose the lesser evil; however, the framing of the question seems wrong on other levels. Because 90 percent of road accidents result from driver error, it is likely that a transition to autonomous vehicles will result in a dramatic drop in the overall number of injuries and deaths. So, clearly the greater good would be served even though there will still be a small number of accidents purely due to technological failures. In some respects, the automobile industry has already agreed with this logic. Air bags, for example, save more lives than are lost due to faulty air bag deployments. Secondly, the narrow focus of the question ignores how autonomous vehicles will probably operate in the future, when it is highly likely that road workers, cops, emergency vehicles, cars, pedestrians, and cyclists will electronically signal their presence to each other, a feature that even without complete automation should dramatically increase safety.


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The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives by Peter H. Diamandis, Steven Kotler

Ada Lovelace, additive manufacturing, Airbnb, Albert Einstein, Amazon Mechanical Turk, augmented reality, autonomous vehicles, barriers to entry, bitcoin, blockchain, blood diamonds, Burning Man, call centre, cashless society, Charles Lindbergh, Clayton Christensen, clean water, cloud computing, Colonization of Mars, computer vision, creative destruction, crowdsourcing, cryptocurrency, Dean Kamen, delayed gratification, dematerialisation, digital twin, disruptive innovation, Edward Glaeser, Edward Lloyd's coffeehouse, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Ethereum, ethereum blockchain, experimental economics, food miles, game design, Geoffrey West, Santa Fe Institute, gig economy, Google X / Alphabet X, gravity well, hive mind, housing crisis, Hyperloop, indoor plumbing, industrial robot, informal economy, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the telegraph, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Kickstarter, late fees, Law of Accelerating Returns, life extension, lifelogging, loss aversion, Lyft, M-Pesa, Mary Lou Jepsen, mass immigration, megacity, meta analysis, meta-analysis, microbiome, mobile money, multiplanetary species, Narrative Science, natural language processing, Network effects, new economy, New Urbanism, Oculus Rift, out of africa, packet switching, peer-to-peer lending, Peter H. Diamandis: Planetary Resources, Peter Thiel, QR code, RAND corporation, Ray Kurzweil, RFID, Richard Feynman, Richard Florida, ride hailing / ride sharing, risk tolerance, Satoshi Nakamoto, Second Machine Age, self-driving car, Silicon Valley, Skype, smart cities, smart contracts, smart grid, Snapchat, sovereign wealth fund, special economic zone, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, supercomputer in your pocket, supply-chain management, technoutopianism, Tesla Model S, Tim Cook: Apple, transaction costs, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, urban planning, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, X Prize

It means that our autonomous car transition hasn’t really begun and already traditional insurance companies are years behind the curve. When we combine autonomous vehicle technology with smart traffic systems and sensor-embedded roads—two developments that have already begun rolling out—transit risks don’t just plummet, they mutate. For instance, if the LIDAR sensor that’s helping steer an autonomous car goes on the blink and causes an accident, who do you blame? Not the passenger. Maybe the carmaker. Maybe the LIDAR supplier. Or, who’s fault is it if your Waymo loses its 5G connection and suddenly can’t drive? Is it Alphabet, who owns the car; Verizon, who manages the connection; or OneWeb, who owns the satellite that provides that connection? What if an autonomous vehicle gets hacked or stolen?

With this many cars, Waymo intends to deliver a million trips per day in 2020 (this might be ambitious but Uber currently delivers 15 million rides a day). To understand the importance of this figure or anything close to it, consider that the more miles an autonomous car drives, the more data it gathers—and data is the gasoline of the driverless world. Since 2009, Waymo’s vehicles have logged over 10 million miles. By 2020, with twenty thousand Jaguars doing hundreds of thousands of daily trips, they’ll be adding an extra million miles or so every day. All of those miles matter. As autonomous vehicles drive, they gather information: positions of traffic signs, road conditions, and the like. More information equals smarter algorithms equals safer cars—and this combination is the very edge needed for market domination. To compete with Waymo, General Motors is making up for lost time with big dollars.

But let’s start with a simple question: If you’re riding in an autonomous car as a service, and there is no driver, do you need insurance? The Car That Doesn’t Crash Insurance is a game of averages. The industry’s basic business model is assess risk and set premiums—or, covering this much risk will cost this much money. With a large enough number of customers and long enough stretches of time, this averages out to a profit for the underwriter. Car insurance premiums, for example, are currently calculated according to the age and history of the driver, traits of the car itself, and where the driver and that car live. Get enough drivers involved, stay in business long enough, and the result is massive profit. But what happens over the next decade, when autonomous vehicles take to the road and change every aspect of that calculation?


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

"Robert Solow", 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, basic income, Baxter: Rethink Robotics, British Empire, business cycle, business intelligence, business process, call centre, Charles Lindbergh, Chuck Templeton: OpenTable:, clean water, combinatorial explosion, computer age, computer vision, congestion charging, corporate governance, creative destruction, crowdsourcing, David Ricardo: comparative advantage, digital map, 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, G4S, game design, global village, happiness index / gross national happiness, illegal immigration, immigration reform, income inequality, income per capita, indoor plumbing, industrial robot, informal economy, intangible asset, inventory management, James Watt: steam engine, Jeff Bezos, jimmy wales, job automation, John Markoff, 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, Marc Andreessen, Mark Zuckerberg, Mars Rover, mass immigration, 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, Paul Samuelson, payday loans, post-work, 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

“Microsoft’s KinectFusion Research Project Offers Real-time 3D Reconstruction, Wild AR Possibilities,” Engadget, August 9, 2011, http://www.engadget.com/2011/08/09/microsofts-kinectfusion-research-project-offers-real-time-3d-re/ (accessed June 26, 2013). 23. Thomas Whelan et al., “Kintinuous: Spatially Extended KinectFusion,” n.d., http://dspace.mit.edu/bitstream/handle/1721.1/71756/MIT-CSAIL-TR-2012-020.pdf?sequence=1. 24. Brett Solomon, “Velodyne Creating Sensors for China Autonomous Vehicle Market,” Technology Tell, July 5, 2013, http://www.technologytell.com/in-car-tech/4283/velodyne-creating-sensors-for-china-autonomous-vehicle-market/. Chapter 4 THE DIGITIZATION OF JUST ABOUT EVERYTHING 1. Nick Wingfield and Brian X. Chen, “Apple Keeps Loyalty of Mobile App Developers,” New York Times, June 10, 2012, http://www.nytimes.com/2012/06/11/technology/apple-keeps-loyalty-of-mobile-app-developers.html. 2. “How Was the Idea for Waze Created?,” http://www.waze.com/faq/ (accessed June 27, 2013). 3.

This same period is called by others the Second Industrial Revolution, which is how we’ll refer to it in later chapters. “Any sufficiently advanced technology is indistinguishable from magic.” —Arthur C. Clarke IN THE SUMMER OF 2012, we went for a drive in a car that had no driver. During a research visit to Google’s Silicon Valley headquarters, we got to ride in one of the company’s autonomous vehicles, developed as part of its Chauffeur project. Initially we had visions of cruising in the back seat of a car that had no one in the front seat, but Google is understandably skittish about putting obviously autonomous autos on the road. Doing so might freak out pedestrians and other drivers, or attract the attention of the police. So we sat in the back while two members of the Chauffeur team rode up front.

We were further convinced that year by the initial results of the DARPA Grand Challenge for driverless cars. DARPA, the Defense Advanced Research Projects Agency, was founded in 1958 (in response to the Soviet Union’s launch of the Sputnik satellite) and tasked with spurring technological progress that might have military applications. In 2002 the agency announced its first Grand Challenge, which was to build a completely autonomous vehicle that could complete a 150-mile course through California’s Mojave Desert. Fifteen entrants performed well enough in a qualifying run to compete in the main event, which was held on March 13, 2004. The results were less than encouraging. Two vehicles didn’t make it to the starting area, one flipped over in the starting area, and three hours into the race only four cars were still operational.


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

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, Induced demand, intermodal, invention of the wheel, lake wobegon effect, Loma Prieta earthquake, longitudinal study, 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, uber lyft, Unsafe at Any Speed, urban decay, urban planning, urban renewal, walkable city, Wall-E, white flight, white picket fence, Works Progress Administration, Yogi Berra, Zipcar

Also, virtually all the really dramatic predictions about the benefits of driverless cars assume an entirely driverless network—one in which no one drives, and for which virtually driving is done autonomously. This is a nontrivial point: a system that is “only” driverless on expressways, for example, isn’t going to change behavior in large ways, since most trips are less than ten miles in length. And don’t get me started on trying to figure out who gets sued in the event of a collision between autonomous cars. Maybe more plausibly, others have wondered whether autonomous cars, by reducing the pain and misery associated with driving, will therefore make it more appealing—so appealing, in fact, as to reverse the centripetal phenomenon that is now drawing more and more people back into densely populated cities from the sprawling suburbs that attracted their parents and grandparents after the Second World War. In that scenario, a new generation of commuters will be so happy to enter a driverless vehicle—one that allows them to watch movies, read books, or catch up on e-mail without ever having to worry about other drivers, traffic jams, or even missing that exit on Route 124—that they will be quite content to accept commutes that run into hours each day.

They will prevent the driver from . . . turning out into traffic except when he should. They will aid him in passing through intersections without slowing down or causing anyone else to do so and without endangering himself or others. For the next five decades, companies like RCA, General Motors, Mercedes-Benz, and others worked to bring Bel Geddes’s vision to life. For most of that time, autonomous vehicles were conceived as part of a system that traveled on dedicated roads or tracks, rather than streets, and went by the name of Personal Rapid Transit, or PRT. PRT is generally used to describe a network of small, driverless electrical vehicles—pod cars—traveling on elevated guidewaysh containing sensors and switches that can, in combination, offer point-to-point travel nearly as flexibly as an automobile, but as safely and efficiently as a subway or streetcar.

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. It has so far succeeded in a dozen different road tests, comprising more than seven hundred thousand autonomous miles without a single self-caused problem (one car did get rear-ended; not, one hopes, by another autonomous vehicle). Though the company admits to a number of limitations to the existing technology, including bad weather, the Google car has done a spectacular job promoting the potential of autonomous driving. For people who believe in the never-ending bounty of digital improvement it seems only a matter of a few years before Google solves the remaining technical obstacles in the path of truly autonomous driving.j (At that point, Google, which invested more than $250 million in Uber back in 2013, will be able to launch its new subsidiary, which I call Goober.)


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Better Buses, Better Cities by Steven Higashide

Affordable Care Act / Obamacare, autonomous vehicles, business process, congestion charging, decarbonisation, Elon Musk, Hyperloop, income inequality, intermodal, jitney, Lyft, mass incarceration, Pareto efficiency, performance metric, place-making, self-driving car, Silicon Valley, six sigma, smart cities, transportation-network company, Uber and Lyft, Uber for X, uber lyft, urban planning, urban sprawl, walkable city, white flight, young professional

Medium, October 16, 2017. https://​medium.com/​self-driven/​a-decade-after-darpa-our-view-on-the-state-of-the-art-in-self-driving-cars-3e8698e6afe8 4. Aarian Marshall, “After Peak Hype, Self-Driving Cars Face the Trough of Disillusionment.” WIRED, December 29, 2017. https://​www.wired.com/​story/​self-driving-cars-challenges/ 5. Shara Tibken, “Waymo CEO: Autonomous Cars Won’t Ever Be Able to Drive in All Conditions.” CNET, November 13, 2018. https://​www.cnet.com/​news/​alphabet-google-waymo-ceo-john-krafcik-autonomous-cars-wont-ever-be-able-to-drive-in-all-conditions/ 6. The Digit Group, “The Digit Group Plays Key Role in Nashville Transit Proposal Centered around Autonomous Vehicles and Double-Decker Highways” (press release). Accessed February 11, 2019 at https://​docs.wixstatic.com/​ugd/​feb19d_​cbf20b35c1334ee39ab4eedbd113b2c8.pdf 7. Randal O’Toole, “Transit Death Watch: April Ridership Declines 2.3 Percent.”

Across the country, Swope’s counterparts are claiming that technologies such as autonomous vehicles, microtransit, and Uber and Lyft will inevitably replace trains and buses. The evidence suggests that will happen only if cities buy the snake oil. Distraction and Hype Those who are closest to autonomous vehicle technology say it will be a long time, if ever, before fully autonomous vehicles can coexist on streets with pedestrians and human drivers. In a 2017 blog post, the CEO of Argo AI, which has received $1 billion from Ford to work on self-driving technology, wrote that “those who think self-driving cars will be ubiquitous on city streets . . . in a few years are not well connected to the state of the art or committed to the safe deployment of the technology.”3 That same year, a prominent investor in autonomous vehicle startups concluded that “autonomous technology is currently where computing was in the 60s.”4 Waymo, a subsidiary of Alphabet, Google’s parent company, is considered (along with General Motors) one of the leaders in the autonomous vehicle industry.

Mark Fisher, the executive who led the Indy Chamber’s transit campaign, told me that throughout the 2010s, as advocates lobbied the Indiana legislature to grant cities the ability to tax themselves for transit, many legislators argued that “‘we don’t need transit because autonomous vehicles are going to be ubiquitous [within] the next three or four years.’ . . . It was way more widespread than I would’ve guessed.” Microtransit, Uber and Lyft, and autonomous vehicles cater perfectly to the futurist instinct. They are cloaked in Silicon Valley mystique, often with big-talking founders and attention paid to branding. The launch event for Swope’s “Intelligent Transit” plan included the CEO of a “smart cities” company, who appeared via teleconference because he was on a business trip in Tokyo, where he had met with the Japanese prime minister. The press release for the plan announced that it included “the world’s first carbon-positive electric vehicles (EV) and autonomous vehicles (AV) for mass transportation on a global basis.”6 Although this tactic looks to the future, it is quite old.


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Augmented: Life in the Smart Lane by Brett King

23andMe, 3D printing, additive manufacturing, Affordable Care Act / Obamacare, agricultural Revolution, Airbnb, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, Apple II, artificial general intelligence, asset allocation, augmented reality, autonomous vehicles, barriers to entry, bitcoin, blockchain, business intelligence, business process, call centre, chief data officer, Chris Urmson, Clayton Christensen, clean water, congestion charging, crowdsourcing, cryptocurrency, deskilling, different worldview, disruptive innovation, distributed generation, distributed ledger, double helix, drone strike, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, fiat currency, financial exclusion, Flash crash, Flynn Effect, future of work, gig economy, Google Glasses, Google X / Alphabet X, Hans Lippershey, Hyperloop, income inequality, industrial robot, information asymmetry, Internet of things, invention of movable type, invention of the printing press, invention of the telephone, invention of the wheel, James Dyson, Jeff Bezos, job automation, job-hopping, John Markoff, John von Neumann, Kevin Kelly, Kickstarter, Kodak vs Instagram, Leonard Kleinrock, lifelogging, low earth orbit, low skilled workers, Lyft, M-Pesa, Mark Zuckerberg, Marshall McLuhan, megacity, Metcalfe’s law, Minecraft, mobile money, money market fund, more computing power than Apollo, Network effects, new economy, obamacare, Occupy movement, Oculus Rift, off grid, packet switching, pattern recognition, peer-to-peer, Ray Kurzweil, RFID, ride hailing / ride sharing, Robert Metcalfe, Satoshi Nakamoto, Second Machine Age, selective serotonin reuptake inhibitor (SSRI), self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart grid, smart transportation, Snapchat, social graph, software as a service, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, TaskRabbit, technological singularity, telemarketer, telepresence, telepresence robot, Tesla Model S, The Future of Employment, Tim Cook: Apple, trade route, Travis Kalanick, Turing complete, Turing test, uber lyft, undersea cable, urban sprawl, V2 rocket, Watson beat the top human players on Jeopardy!, white picket fence, WikiLeaks

However, once manufacturers demonstrate the safety of self-driving car systems, it is far more likely that both passengers and legislators will start to opt in voluntarily. Some legislators will insist that autonomous vehicles must have the option for a human to “take over”, and there will no doubt be purists who try to hack around autonomous routines in some way. Moreover, we can expect lobby groups of manufacturers who fall behind in autonomous technology to attempt to muddy the waters with figures around safety. The first death of a passenger or a pedestrian by an autonomous vehicle will be a watershed moment. It is unlikely, however, to stop autonomous cars from dominating our future. Interestingly, the CEO of Volvo, Håkan Samuelsson, has already said that Volvo will accept liability when a self-driving car is involved in an accident.7 That is a big deal!

Other data showed that the autonomous software was much better and more consistent at maintaining a safe distance from the vehicle ahead. “We’re spending less time in near-collision states,” said Chris Urmson, the leader of Google’s autonomous car project at a robotics conference in 2013. “Our car is driving more smoothly and more safely than our trained professional drivers.” Google’s self-driving car has the most data publicly available about this incredible autonomous capability, but other car manufacturers like Tesla, Audi, BMW, Mercedes and Volvo all say similar things about the future of driving. Autonomous vehicles will most likely be significantly safer than those driven by humans within a decade or so. Giving further insight into this technology, Google has disclosed that the sensors on the Google self-driving car capture nearly 1 gigabyte (GB) of sensor data every second, and subsequently process that information to identify risks or anticipate issues that it may need to react to.

It is estimated that iRobot sold more than 100,000 home robots in 2015, with the Roomba 800/900 vacuum cleaner being the most popular.6 The Federal Aviation Administration (FAA) estimated that over 1 million drones were sold over the 2015 Christmas period alone.7 It is likely that we added close to 10 million robots to the global robot population just in 2015, if you include industrial robots, household robots and military application. But there are some big outliers coming in the next five to ten years, including autonomous vehicles. By 2025, it is estimated that between 15 and 20 million autonomous vehicles could be sold annually.8 By 2025, more than 1.5 billion robots will be operating on the planet, and we’ll be seeing that exponential growth curve exhibited with that number doubling every few years. By the early 2030s, robots are likely to outnumber humans. Figure 4.5: Global robot population growth (Credit: Stuart Staniford Early Warning Blog 2012) Robots can be very small, and will eventually be self-replicating.


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Radical Technologies: The Design of Everyday Life by Adam Greenfield

3D printing, Airbnb, augmented reality, autonomous vehicles, bank run, barriers to entry, basic income, bitcoin, blockchain, business intelligence, business process, call centre, cellular automata, centralized clearinghouse, centre right, Chuck Templeton: OpenTable:, cloud computing, collective bargaining, combinatorial explosion, Computer Numeric Control, computer vision, Conway's Game of Life, cryptocurrency, David Graeber, dematerialisation, digital map, disruptive innovation, distributed ledger, drone strike, Elon Musk, Ethereum, ethereum blockchain, facts on the ground, fiat currency, global supply chain, global village, Google Glasses, IBM and the Holocaust, industrial robot, informal economy, information retrieval, Internet of things, James Watt: steam engine, Jane Jacobs, Jeff Bezos, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, joint-stock company, Kevin Kelly, Kickstarter, late capitalism, license plate recognition, lifelogging, M-Pesa, Mark Zuckerberg, means of production, megacity, megastructure, minimum viable product, money: store of value / unit of account / medium of exchange, natural language processing, Network effects, New Urbanism, Occupy movement, Oculus Rift, Pareto efficiency, pattern recognition, Pearl River Delta, performance metric, Peter Eisenman, Peter Thiel, planetary scale, Ponzi scheme, post scarcity, post-work, RAND corporation, recommendation engine, RFID, rolodex, Satoshi Nakamoto, self-driving car, sentiment analysis, shareholder value, sharing economy, Silicon Valley, smart cities, smart contracts, social intelligence, sorting algorithm, special economic zone, speech recognition, stakhanovite, statistical model, stem cell, technoutopianism, Tesla Model S, the built environment, The Death and Life of Great American Cities, The Future of Employment, transaction costs, Uber for X, undersea cable, universal basic income, urban planning, urban sprawl, Whole Earth Review, WikiLeaks, women in the workforce

Consider the set of arguments put forth by Florida state senator Jeff Brandes. In his successful 2014 attempt to eliminate subsidies for mass transit in his district, Brandes argued that it was futile to invest in mass transit when an age of autonomous vehicles was dawning upon us: “It’s like they’re designing the Pony Express in the world of the telegraph.”77 Never mind that Pony Express riders historically delivered mail, packages and other things the telegraph could not have; the argument from technological inevitability is a vivid and compelling one, especially for Americans nurtured practically from birth on the belief in a gleaming technological future. If autonomous cars really are just a year or two away, why invest in modes of public transit that would surely be rendered obsolete before they even entered service? This sentiment carried the day, and the light-rail line Brandes opposed was never built.

The software controlling a moving vehicle must integrate in real time a highly unstable environment, engine conditions, changes in weather, and the inherently unpredictable behavior of animals, pedestrians, bicyclists, other drivers and random objects it may encounter.8 (Now the significance of those reports you may have encountered of Google pre-driving nominally autonomous vehicles through the backstreets of its Peninsular domain becomes clearer: its engineers are training their guidance algorithm in what to expect from its first environment.) For autonomous vehicles, drones, robots and other systems intended to reckon with the real world in this way, then, the grail is unsupervised deep learning. As the name implies, the algorithms involved are neither prompted nor guided, but are simply set loose on vast fields of data. Order simply emerges. The equivalent of classification for unsupervised learning is clustering, in which an algorithm starts to develop a sense for what is significant in its environment via a process of accretion.

We are equally free to envision the weaving women of rural Balochistan organizing themselves into a networked cooperative, hiving off dozens of self-directing enterprises and raining plenty upon their villages. Or a global collective of human journalists and autonomous search agents working stories together, linked in a comradely fashion by a collaboration platform that lets them render findings vital to the public interest permanently visible. We might even join Bitcoin core developer Mike Hearn in dreaming a transhuman future in which sovereign autonomous vehicles own themselves, lease themselves to users, and transact with a marketized grid for the power they need.37 It’s not unreasonable to be intrigued by these possibilities, whatever your own politics. But if the collapse of The DAO holds any lesson for us, it’s that any envelope of potentials in which these things are possible must also necessarily contain monsters. If Ethereum’s vision proves out in any way, we need to prepare for densely layered Ponzi schemes whose true beneficiaries are ultimately obscured by cryptographic means, for vampiric rent-extraction syndicates and fully autonomous nonhuman grifters.


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Wild Ride: Inside Uber's Quest for World Domination by Adam Lashinsky

"side hustle", Airbnb, always be closing, Amazon Web Services, autonomous vehicles, Ayatollah Khomeini, business process, Chuck Templeton: OpenTable:, cognitive dissonance, corporate governance, DARPA: Urban Challenge, Donald Trump, Elon Musk, gig economy, Golden Gate Park, Google X / Alphabet X, information retrieval, Jeff Bezos, Lyft, Marc Andreessen, Mark Zuckerberg, megacity, Menlo Park, new economy, pattern recognition, price mechanism, ride hailing / ride sharing, Sand Hill Road, self-driving car, Silicon Valley, Silicon Valley startup, Skype, Snapchat, South of Market, San Francisco, sovereign wealth fund, statistical model, Steve Jobs, TaskRabbit, Tony Hsieh, transportation-network company, Travis Kalanick, turn-by-turn navigation, Uber and Lyft, Uber for X, uber lyft, ubercab, young professional

And in the one area where humans were critical to deliver service, Uber barely needed to manage its volunteer army of drivers. In the summer of 2013, with his business booming yet barely three years old, Travis Kalanick confronted Uber’s own potential disruption: autonomous vehicles, also known as self-driving cars. The development was so potentially game-changing that it could eliminate the one area where Uber still relied on people. These vehicles would use rapid advancement of sensors and artificial intelligence to “see” all the obstacles and guideposts that drivers need to navigate. In theory, autonomous vehicles would be far safer, given that robots aren’t susceptible to drowsiness or distraction. While radical, there was ample precedent for the technology. In aviation, the autopilot for decades had played a role in dramatically reducing airplane fatality rates.

By now we are discussing driverless cars, and Kalanick suggests big moves are ahead that he can’t yet discuss. He lets on that this six-mile walk, always including the In-N-Out Burger stop, has become a summer-evening routine and that he typically walks it with one person he won’t identify. I later learn his walking partner is Anthony Levandowski, the ex-Google autonomous vehicles engineer who went on to found Otto, the self-driving truck company. Uber will purchase Otto just a few weeks after our walk, and Kalanick tells me he used his time with Levandowski to absorb the technology and business-plan vision for autonomous vehicles. Having spent so much time discussing Kalanick’s entrepreneurial days, I want to know how he views the bigger, more established company Uber has become. His answers betray a reluctance to think of the company that way. He doesn’t know everyone at the company anymore, but he still conducts hours-long interviews with top prospects, something he did when the company was smaller.

Where the company has sufficient scale, in all its major cities, the system is so efficient that power users have begun to think the unthinkable: car ownership isn’t so necessary anymore. As well, Uber’s carpooling aspirations portend to eliminate congestion itself. If people don’t own cars and ride in them with other people more, at least in theory there would be fewer cars on the street. Should the vision of autonomous vehicles become a reality—and Uber is investing heavily in the technology—roads may become less crowded for the first time since the invention of roads. That said, it’s easy to get carried away with Uber’s promise—and Uber frequently does. Car ownership hasn’t yet declined in the United States as the result of the advent of ridesharing or for any other reason. According to U.S. Census data, the percentage of households with no vehicles declined from 21.5 percent in 1960 to 9.1 percent in 2010, the year Uber started.


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AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee

AI winter, Airbnb, Albert Einstein, algorithmic trading, artificial general intelligence, autonomous vehicles, barriers to entry, basic income, business cycle, cloud computing, commoditize, computer vision, corporate social responsibility, creative destruction, crony capitalism, Deng Xiaoping, deskilling, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, full employment, future of work, gig economy, Google Chrome, happiness index / gross national happiness, if you build it, they will come, ImageNet competition, income inequality, informal economy, Internet of things, invention of the telegraph, Jeff Bezos, job automation, John Markoff, Kickstarter, knowledge worker, Lean Startup, low skilled workers, Lyft, mandatory minimum, Mark Zuckerberg, Menlo Park, minimum viable product, natural language processing, new economy, pattern recognition, pirate software, profit maximization, QR code, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, risk tolerance, Robert Mercer, Rodney Brooks, Rubik’s Cube, Sam Altman, Second Machine Age, self-driving car, sentiment analysis, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, special economic zone, speech recognition, Stephen Hawking, Steve Jobs, strong AI, The Future of Employment, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, urban planning, Y Combinator

The World Health Organization estimates that there are around 260,000 annual road fatalities in China and 1.25 million around the globe. Autonomous vehicles are on the path to eventually being far safer than human-driven vehicles, and widespread deployment of the technology will dramatically decrease these fatalities. It will also lead to huge increases in efficiency of transportation and logistics networks, gains that will echo throughout the entire economy. But alongside the lives saved and productivity gained, there will be other instances in which jobs or even lives are lost due to the very same technology. For starters, taxi, truck, bus, and delivery drivers will be largely out of luck in a self-driving world. There will also inevitably be malfunctions in autonomous vehicles that cause crashes. There will be circumstances that force an autonomous vehicle to make agonizing ethical decisions, like whether to veer right and have a 55 percent chance of killing two people or veer left and have a 100 percent chance of killing one person.

Every one of these downside risks presents thorny ethical questions. How should we balance the livelihoods of millions of truck drivers against the billions of dollars and millions of hours saved by autonomous vehicles? What should a self-driving car “optimize for” in situations where it is forced to choose which car to crash into? How should an autonomous vehicle’s algorithm weigh the life of its owner? Should your self-driving car sacrifice your own life to save the lives of three other people? These are the questions that keep ethicists up at night. They’re also questions that could hold up the legislation needed for autonomous-vehicle deployment and tie up AI companies in years of lawsuits. They may well lead American politicians, ever fearful of interest groups and attack ads, to pump the brakes on widespread self-driving vehicle deployment.

By 2016, Google had taken six years to accumulate 1.5 million miles of real-world driving data. In just six months, Tesla had accumulated 47 million miles. Google and Tesla are now inching toward one another in terms of approach. Google—perhaps feeling the heat from Tesla and other rivals—accelerated deployment of fully autonomous vehicles, piloting a program with taxi-like vehicles in the Phoenix metropolitan area. Meanwhile, Tesla appears to have pumped the brakes on its rapid rollout of fully autonomous vehicles, a deceleration that followed a May 2016 crash that killed a Tesla owner who was using autopilot. But the fundamental difference in approach remains, and it presents a real tradeoff. Google is aiming for impeccable safety, but in the process it has delayed deployment of systems that could likely already save lives.


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The Fourth Industrial Revolution by Klaus Schwab

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, commoditize, conceptual framework, continuous integration, crowdsourcing, digital twin, disintermediation, disruptive innovation, 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, mass immigration, 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, Sam Altman, 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, supercomputer in your pocket, TaskRabbit, The Future of Employment, The Spirit Level, total factor productivity, transaction costs, Uber and Lyft, uber lyft, Watson beat the top human players on Jeopardy!, WikiLeaks, winner-take-all economy, women in the workforce, working-age population, Y Combinator, Zipcar

To identify the megatrends and convey the broad landscape of technological drivers of the fourth industrial revolution, I have organized the list into three clusters: physical, digital and biological. All three are deeply interrelated and the various technologies benefit from each other based on the discoveries and progress each makes. 2.1.1 Physical There are four main physical manifestations of the technological megatrends, which are the easiest to see because of their tangible nature: – autonomous vehicles – 3D printing – advanced robotics – new materials Autonomous vehicles The driverless car dominates the news but there are now many other autonomous vehicles including trucks, drones, aircrafts and boats. As technologies such as sensors and artificial intelligence progress, the capabilities of all these autonomous machines improve at a rapid pace. It is only a question of a few years before low-cost, commercially available drones, together with submersibles, are used in different applications.

We have yet to grasp fully the speed and breadth of this new revolution. Consider the unlimited possibilities of having billions of people connected by mobile devices, giving rise to unprecedented processing power, storage capabilities and knowledge access. Or think about the staggering confluence of emerging technology breakthroughs, covering wide-ranging fields such as artificial intelligence (AI), robotics, the internet of things (IoT), autonomous vehicles, 3D printing, nanotechnology, biotechnology, materials science, energy storage and quantum computing, to name a few. Many of these innovations are in their infancy, but they are already reaching an inflection point in their development as they build on and amplify each other in a fusion of technologies across the physical, digital and biological worlds. We are witnessing profound shifts across all industries, marked by the emergence of new business models, the disruption1 of incumbents and the reshaping of production, consumption, transportation and delivery systems.

The scale and scope of change explain why disruption and innovation feel so acute today. The speed of innovation in terms of both its development and diffusion is faster than ever. Today’s disruptors – Airbnb, Uber, Alibaba and the like – now household names - were relatively unknown just a few years ago. The ubiquitous iPhone was first launched in 2007. Yet there were as many as 2 billion smart phones at the end of 2015. In 2010 Google announced its first fully autonomous car. Such vehicles could soon become a widespread reality on the road. One could go on. But it is not only speed; returns to scale are equally staggering. Digitization means automation, which in turn means that companies do not incur diminishing returns to scale (or less of them, at least). To give a sense of what this means at the aggregate level, compare Detroit in 1990 (then a major centre of traditional industries) with Silicon Valley in 2014.


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The Great Race: The Global Quest for the Car of the Future by Levi Tillemann

Affordable Care Act / Obamacare, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, banking crisis, car-free, carbon footprint, cleantech, creative destruction, decarbonisation, deindustrialization, demand response, Deng Xiaoping, Donald Trump, Elon Musk, en.wikipedia.org, energy security, factory automation, global value chain, hydrogen economy, index card, Intergovernmental Panel on Climate Change (IPCC), joint-stock company, Joseph Schumpeter, Kickstarter, manufacturing employment, market design, megacity, Nixon shock, obamacare, oil shock, Ralph Nader, RFID, rolodex, Ronald Reagan, Rubik’s Cube, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, skunkworks, smart cities, sovereign wealth fund, special economic zone, Steve Jobs, Tesla Model S, too big to fail, Unsafe at Any Speed, zero-sum game, Zipcar

But it was also an enormous opportunity. An autonomous vehicle could be worth a lot to the consumer and there was palpable public excitement surrounding the issue. Automakers wanted to harness that power. There was no turning back. For academics and policy makers it was suddenly legitimate, reasonable, and even necessary to start pondering the implications of a future with cars that drove themselves and what this might mean for automakers, cities, and countries around the world. Different people used different terminologies with various meanings to describe these robotic cars—self-driving cars, robot cars, automated vehicles, and autonomous vehicles, to name a few. However, the ultimate goal was generally understood to be a car that could drive itself. Although theirs was not the first autonomous car, Google broke the logjam.

How many lives have been lost to mindless texting, distraction, or drunk driving? With autonomous vehicles, such concerns may disappear within decades. On a day-to-day level, autonomous vehicles will ease the stress and monotony of commuting in heavy traffic. Perhaps they will prove to be the ultimate cure for “road rage.” Rather than wasting countless hours steering cars around town as the herd lurches from place to place, autonomous vehicle passengers will be able to surf the Web, watch TV, read the newspaper, or just sit back, relax and enjoy the ride as their car safely and smoothly delivers them to their desired destination. Cars that don’t crash could also be much smaller and lighter, with fewer safety features. Rather than merely cushion the impact of a crash, autonomous vehicles should be able to avoid them altogether in all but the most extreme circumstances.

But as the car crept onto the lower level of the bridge, its GPS failed—the steel and concrete structure above it had blocked the signal. Now the robot was solely dependent on its onboard sensors. It soldiered on. But as it approached the sharp off-ramp toward Treasure Island, Levandowski’s autonomous car slowly smooshed into a concrete barrier. The car ground to a halt, unable to move. Levandowski jumped into the car, disengaged the twitching steering column, and set it back on course for delivery. There were high-fives all around, but in reality it was a mixed success. Driving Blind But five years later, the awkward robot had transformed into a sleek, formidable, and truly autonomous driving machine. In the interim, Google had acquired the autonomous vehicles company, and management at the cash-flush Silicon Valley giant had been so impressed by the team’s results that they eventually gave them a virtually unlimited development budget.


pages: 416 words: 112,268

Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell

3D printing, Ada Lovelace, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Alfred Russel Wallace, Andrew Wiles, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, basic income, blockchain, brain emulation, Cass Sunstein, Claude Shannon: information theory, complexity theory, computer vision, connected car, crowdsourcing, Daniel Kahneman / Amos Tversky, delayed gratification, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ernest Rutherford, Flash crash, full employment, future of work, Gerolamo Cardano, ImageNet competition, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the wheel, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Nash: game theory, John von Neumann, Kenneth Arrow, Kevin Kelly, Law of Accelerating Returns, Mark Zuckerberg, Nash equilibrium, Norbert Wiener, NP-complete, openstreetmap, P = NP, Pareto efficiency, Paul Samuelson, Pierre-Simon Laplace, positional goods, probability theory / Blaise Pascal / Pierre de Fermat, profit maximization, RAND corporation, random walk, Ray Kurzweil, recommendation engine, RFID, Richard Thaler, ride hailing / ride sharing, Robert Shiller, Robert Shiller, Rodney Brooks, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, smart cities, smart contracts, social intelligence, speech recognition, Stephen Hawking, Steven Pinker, superintelligent machines, Thales of Miletus, The Future of Employment, Thomas Bayes, Thorstein Veblen, transport as a service, Turing machine, Turing test, universal basic income, uranium enrichment, Von Neumann architecture, Wall-E, Watson beat the top human players on Jeopardy!, web application, zero-sum game

In 1987, Ernst Dickmanns demonstrated a self-driving Mercedes van on the autobahn in Germany; it was capable of staying in lane, following another car, changing lanes, and overtaking.3 More than thirty years later, we still don’t have a fully autonomous car, but it’s getting much closer. The focus of development has long since moved from academic research labs to large corporations. As of 2019, the best-performing test vehicles have logged millions of miles of driving on public roads (and billions of miles in driving simulators) without serious incident.4 Unfortunately, other autonomous and semi-autonomous vehicles have killed several people.5 Why has it taken so long to achieve safe autonomous driving? The first reason is that the performance requirements are exacting. Human drivers in the United States suffer roughly one fatal accident per one hundred million miles traveled, which sets a high bar. Autonomous vehicles, to be accepted, will need to be much better than that: perhaps one fatal accident per billion miles, or twenty-five thousand years of driving forty hours per week.

The Society of Automotive Engineers (SAE) defines six levels of automation, where Level 0 is none at all and Level 5 is full automation: “The full-time performance by an automatic driving system of all aspects of the dynamic driving task under all roadway and environmental conditions that can be managed by a human driver.” 7. Forecast of economic effects of automation on transportation costs: Adele Peters, “It could be 10 times cheaper to take electric robo-taxis than to own a car by 2030,” Fast Company, May 30, 2017. 8. The impact of accidents on the prospects for regulatory action on autonomous vehicles: Richard Waters, “Self-driving car death poses dilemma for regulators,” Financial Times, March 20, 2018. 9. The impact of accidents on public perception of autonomous vehicles: Cox Automotive, “Autonomous vehicle awareness rising, acceptance declining, according to Cox Automotive mobility study,” August 16, 2018. 10. The original chatbot: Joseph Weizenbaum, “ELIZA—a computer program for the study of natural language communication between man and machine,” Communications of the ACM 9 (1966): 36–45. 11.

Then, using lookahead search, the vehicle has to find a trajectory that optimizes some combination of safety and progress. Some projects are trying more direct approaches based on reinforcement learning (mainly in simulation, of course) and supervised learning from recordings of hundreds of human drivers, but these approaches seem unlikely to reach the required level of safety. The potential benefits of fully autonomous vehicles are immense. Every year, 1.2 million people die in car accidents worldwide and tens of millions suffer serious injuries. A reasonable target for autonomous vehicles would be to reduce these numbers by a factor of ten. Some analyses also predict a vast reduction in transportation costs, parking structures, congestion, and pollution. Cities will shift from personal cars and large buses to ubiquitous shared-ride, autonomous electric vehicles, providing door-to-door service and feeding high-speed mass-transit connections between hubs.7 With costs as low as three cents per passenger mile, most cities would probably opt to provide the service for free—while subjecting riders to interminable barrages of advertising.


pages: 586 words: 186,548

Architects of Intelligence by Martin Ford

3D printing, agricultural Revolution, AI winter, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, bitcoin, business intelligence, business process, call centre, cloud computing, cognitive bias, Colonization of Mars, computer vision, correlation does not imply causation, crowdsourcing, DARPA: Urban Challenge, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Fellow of the Royal Society, Flash crash, future of work, gig economy, Google X / Alphabet X, Gödel, Escher, Bach, Hans Rosling, ImageNet competition, income inequality, industrial robot, information retrieval, job automation, John von Neumann, Law of Accelerating Returns, life extension, Loebner Prize, Mark Zuckerberg, Mars Rover, means of production, Mitch Kapor, natural language processing, new economy, optical character recognition, pattern recognition, phenotype, Productivity paradox, Ray Kurzweil, recommendation engine, Robert Gordon, Rodney Brooks, Sam Altman, self-driving car, sensor fusion, sentiment analysis, Silicon Valley, smart cities, social intelligence, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, Ted Kaczynski, The Rise and Fall of American Growth, theory of mind, Thomas Bayes, Travis Kalanick, Turing test, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, women in the workforce, working-age population, zero-sum game, Zipcar

MARTIN FORD: Let’s talk about one of the highest-profile applications of AI: self-driving cars. How far off are they really? Imagine you’re in a city and you’re going to call for a fully autonomous car that will take you from one random location to another. What’s the time frame for when you think that becomes a widely available service? ANDREW NG: I think that self-driving cars in geofenced regions will come relatively soon, possibly by the end of this year, but that self-driving cars in more general circumstances will be a long way off, possibly multiple decades. MARTIN FORD: By geofenced, you mean autonomous cars that are running essentially on virtual trolley tracks, or in other words only on routes that have been intensively mapped? ANDREW NG: Exactly! A while back I co-authored a Wired article talking about Train Terrain (https://www.wired.com/2016/03/self-driving-cars-wont-work-change-roads-attitudes/) about how I think self-driving cars might roll out.

I think there’s also a reasonable question about how much those game-theoretic ideas in poker extend into the real world. We’re not aware of doing much randomization in our normal day-to-day lives, even though—for sure—the world is full of agents; so it ought to be game-theoretic, and yet we’re not aware of randomizing very much in our day-to-day lives. MARTIN FORD: Self-driving cars are one of the highest-profile applications of AI. What is your estimate for when fully autonomous vehicles will become a truly practical technology? Imagine you’re in a random place in Manhattan, and you call up an Uber, and it’s going to arrive with no one in it, and then it will take you to another random place that you specify. How far off is that realistically, do you think? STUART J. RUSSELL: Yes, the timeline for self-driving cars is a concrete question, and it’s also an economically important question because companies are investing a great deal in these projects.

RANA EL KALIOUBY: Absolutely. In fact in the last year we’ve started to get a ton of inbound interest from the automotive industry. It’s really exciting because it’s a major market opportunity for Affectiva and we’re solving two interesting problems for the car industry. In the cars of today, where there is an active driver, safety is a huge issue. And safety will continue to be an issue, even when we have semi-autonomous vehicles like Tesla that can drive themselves for a while but do still need a co-pilot to be paying attention. Using Affectiva software, we’re able to monitor the driver or the co-pilot for things like drowsiness, distraction, fatigue and even intoxication. In the case of intoxication, we would alert the driver or also even potentially have the car intervene. Intervention could be anything from changing the music to blasting a little bit of cold air, or tightening the seat belt, all the way to potentially saying, “You know what?


pages: 304 words: 90,084

Net Zero: How We Stop Causing Climate Change by Dieter Helm

3D printing, autonomous vehicles, Berlin Wall, blockchain, Boris Johnson, carbon footprint, clean water, congestion charging, coronavirus, COVID-19, Covid-19, decarbonisation, deindustrialization, demand response, Deng Xiaoping, Donald Trump, fixed income, food miles, Francis Fukuyama: the end of history, Haber-Bosch Process, hydrogen economy, Intergovernmental Panel on Climate Change (IPCC), Internet of things, market design, means of production, North Sea oil, off grid, oil shale / tar sands, oil shock, peak oil, planetary scale, price mechanism, quantitative easing, remote working, reshoring, Ronald Reagan, smart meter, South China Sea, sovereign wealth fund, statistical model, Thomas Malthus

An autonomous vehicle provider (and not you) will own the car and will have powerful incentives to standardise the technologies of its fleet. It may see an advantage in battery swapping back at the autonomous car depot, or perhaps as part of the optimised journey timing and use. This is what the car companies fear. They may not be able to extract the economic rents that go with advertising and branding. They may simply sell thousands of identical cars to a car pool company, with the capital provided by an infrastructure fund, and then the economic value comes in the convenience and frequency of the autonomous vehicle’s availability, and not from packing it with all sorts of extras that the customer might not need. Why, for example, does the car need a sophisticated sound system when you can get it all directly via an app on your phone? Travellers do all this for themselves on the bus and the train.

It looks like we are going down the route of a mix of home charging and rapid charging, and the result will be a much greater cost to decarbonisation. Choice comes at a price. It might improve as autonomous vehicles come onto the road systems. If the car is autonomous and guided by smart systems, it may be that there is a shift in our relationship with the car, from the huge variety of styles, engines, colours, designs and interiors, towards seeing it as merely a way of getting from A to B. People rarely worry what type of car a taxi or Uber driver uses when summoning one, and when calling up an autonomous vehicle on an app, programming it to take you from A to B, you may not really care what sort of battery it has got. The market may respond to this. Suppose what matters to you for this A to B trip is the price. An autonomous vehicle provider (and not you) will own the car and will have powerful incentives to standardise the technologies of its fleet.

The reason this smart technology is not in place is because the communications infrastructure is not up to the job, and nor will it be for the whole country for perhaps another decade. You cannot run a smart meter or enable your smart devices unless you have good internet and mobile connectivity. The road system is designed entirely around petrol and diesel vehicles. It is anything but smart, and incapable of supporting the roll-out of smart cars and autonomous vehicles. Charging points for electric vehicles are still notable by their absence even in major conurbations. Where they are available, the roads are often so congested that getting to a charge point can be a challenge in itself. The oil companies have not developed a retail petrol and diesel network designed around the electricity grid, for the very good reason that it has been irrelevant. Much of the railway network still relies on diesel, and when there are power cuts the electric trains grind to a halt.


pages: 389 words: 119,487

21 Lessons for the 21st Century by Yuval Noah Harari

1960s counterculture, accounting loophole / creative accounting, affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, algorithmic trading, augmented reality, autonomous vehicles, Ayatollah Khomeini, basic income, Bernie Sanders, bitcoin, blockchain, Boris Johnson, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon-based life, cognitive dissonance, computer age, computer vision, cryptocurrency, cuban missile crisis, decarbonisation, deglobalization, Donald Trump, failed state, Filter Bubble, Francis Fukuyama: the end of history, Freestyle chess, gig economy, glass ceiling, Google Glasses, illegal immigration, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invisible hand, job automation, knowledge economy, liberation theology, Louis Pasteur, low skilled workers, Mahatma Gandhi, Mark Zuckerberg, mass immigration, means of production, Menlo Park, meta analysis, meta-analysis, Mohammed Bouazizi, mutually assured destruction, Naomi Klein, obamacare, pattern recognition, post-work, purchasing power parity, race to the bottom, RAND corporation, Ronald Reagan, Rosa Parks, Scramble for Africa, self-driving car, Silicon Valley, Silicon Valley startup, transatlantic slave trade, Tyler Cowen: Great Stagnation, universal basic income, uranium enrichment, Watson beat the top human players on Jeopardy!, zero-sum game

Fagnant and Kara Kockelman, ‘Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations’, Transportation Research Part A: Policy and Practice 77 (2015), 167–81; Jean-François Bonnefon, Azim Shariff and Iyad Rahwan, ‘Autonomous Vehicles Need Experimental Ethics: Are We Ready for Utilitarian Cars?’, arXiv (2015), 1–15. For suggestions for inter-vehicle networks to prevent collision, see: Seyed R. Azimi et al., ‘Vehicular Networks for Collision Avoidance at Intersections’, SAE International Journal of Passenger Cars – Mechanical Systems 4:1 (2011), 406–16; Swarun Kumar et al., ‘CarSpeak: A Content-Centric Network for Autonomous Driving’, SIGCOM Computer Communication Review 42:4 (2012), 259–70; Mihail L. Sichitiu and Maria Kihl, ‘Inter-Vehicle Communication Systems: A Survey’, IEEE Communications Surveys & Tutorials 10:2 (2008); Mario Gerla et al., ‘Internet of Vehicles: From Intelligent Grid to Autonomous Cars and Vehicular Clouds’, 2014 IEEE World Forum on Internet of Things (WF-IoT) (2014), 241–6. 9 Michael Chui, James Manyika and Mehdi Miremadi, ‘Where Machines Could Replace Humans – and Where They Can’t (Yet)’, McKinsey Quarterly, July 2016. 10 Wu Youyou, Michal Kosinski and David Stillwell, ‘Computer-based personality judgments are more accurate than those made by humans’, PANS, vol. 112 (2014), 1036–8. 11 Stuart Dredge, ‘AI and music: will we be slaves to the algorithm?’

Fagnant and Kara Kockelman, ‘Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations’, Transportation Research Part A: Policy and Practice 77 (2015), 167–81; for a general worldwide survey, see, for example: OECD/ITF, Road Safety Annual Report 2016 (Paris: OECD, 2016). 8 Kristofer D. Kusano and Hampton C. Gabler, ‘Safety Benefits of Forward Collision Warning, Brake Assist, and Autonomous Braking Systems in Rear-End Collisions’, IEEE Transactions on Intelligent Transportation Systems 13:4 (2012), 1546–55; James M. Anderson et al., Autonomous Vehicle Technology: A Guide for Policymakers (Santa Monica: RAND Corporation, 2014), esp. 13–15; Daniel J. Fagnant and Kara Kockelman, ‘Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations’, Transportation Research Part A: Policy and Practice 77 (2015), 167–81; Jean-François Bonnefon, Azim Shariff and Iyad Rahwan, ‘Autonomous Vehicles Need Experimental Ethics: Are We Ready for Utilitarian Cars?’

, NPR, 8 February 2017. 16 Jean-François Bonnefon, Azim Shariff and Iyad Rahwan, ‘The Social Dilemma of Autonomous Vehicles’, Science 352:6293 (2016), 1573–6. 17 Christopher W. Bauman et al., ‘Revisiting External Validity: Concerns about Trolley Problems and Other Sacrificial Dilemmas in Moral Psychology’, Social and Personality Psychology Compass 8:9 (2014), 536–54. 18 John M. Darley and Daniel C. Batson, ‘“From Jerusalem to Jericho”: A Study of Situational and Dispositional Variables in Helping Behavior’, Journal of Personality and Social Psychology 27:1 (1973), 100–8. 19 Kristofer D. Kusano and Hampton C. Gabler, ‘Safety Benefits of Forward Collision Warning, Brake Assist, and Autonomous Braking Systems in Rear-End Collisions’, IEEE Transactions on Intelligent Transportation Systems 13:4 (2012), 1546–55; James M. Anderson et al., Autonomous Vehicle Technology: A Guide for Policymakers (Santa Monica: RAND Corporation, 2014), esp. 13–15; Daniel J.


pages: 477 words: 75,408

The Economic Singularity: Artificial Intelligence and the Death of Capitalism by Calum Chace

3D printing, additive manufacturing, agricultural Revolution, AI winter, Airbnb, artificial general intelligence, augmented reality, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Berlin Wall, Bernie Sanders, bitcoin, blockchain, call centre, Chris Urmson, congestion charging, credit crunch, David Ricardo: comparative advantage, Douglas Engelbart, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Flynn Effect, full employment, future of work, gender pay gap, gig economy, Google Glasses, Google X / Alphabet X, ImageNet competition, income inequality, industrial robot, Internet of things, invention of the telephone, invisible hand, James Watt: steam engine, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, knowledge worker, lifelogging, lump of labour, Lyft, Marc Andreessen, Mark Zuckerberg, Martin Wolf, McJob, means of production, Milgram experiment, Narrative Science, natural language processing, new economy, Occupy movement, Oculus Rift, PageRank, pattern recognition, post scarcity, post-industrial society, post-work, precariat, prediction markets, QWERTY keyboard, railway mania, RAND corporation, Ray Kurzweil, RFID, Rodney Brooks, Sam Altman, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, software is eating the world, speech recognition, Stephen Hawking, Steve Jobs, TaskRabbit, technological singularity, The Future of Employment, Thomas Malthus, transaction costs, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, universal basic income, Vernor Vinge, working-age population, Y Combinator, young professional

[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.

utm_source=Twitter&utm_medium=tweet&utm_campaign=@KyleSGibson [clxxvi] The Flynn Effect: http://www.bbc.co.uk/news/magazine-31556802 [clxxvii] WHO "Global Status Report on Road Safety 2013: supporting a decade of action [clxxviii] http://www.japantimes.co.jp/news/2015/11/15/business/tech/human-drivers-biggest-threat-developing-self-driving-cars/#.Vo7D5fmLRD8 [clxxix] http://www.theatlantic.com/business/archive/2013/02/the-american-commuter-spends-38-hours-a-year-stuck-in-traffic/272905/ [clxxx] http://www.reinventingparking.org/2013/02/cars-are-parked-95-of-time-lets-check.html [clxxxi] http://www.etymonline.com/index.php?term=autocar [clxxxii] http://www.digitaltrends.com/cars/audi-autonomous-car-prototype-starts-550-mile-trip-to-ces/ [clxxxiii] http://www.nhtsa.gov/About+NHTSA/Press+Releases/U.S.+Department+of+Transportation+Releases+Policy+on+Automated+Vehicle+Development [clxxxiv] http://www.reuters.com/investigates/special-report/autos-driverless/ [clxxxv] http://www.wired.com/2015/04/delphi-autonomous-car-cross-country/ [clxxxvi] http://recode.net/2015/03/17/google-self-driving-car-chief-wants-tech-on-the-market-within-five-years/ [clxxxvii] http://techcrunch.com/2015/12/22/a-new-system-lets-self-driving-cars-learn-streets-on-the-fly/ [clxxxviii] http://cleantechnica.com/2015/10/12/autonomous-buses-being-tested-in-greek-city-of-trikala/ [clxxxix] http://www.bloomberg.com/news/articles/2015-12-16/google-said-to-make-driverless-cars-an-alphabet-company-in-2016 [cxc] http://electrek.co/2015/12/21/tesla-ceo-elon-musk-drops-prediction-full-autonomous-driving-from-3-years-to-2/ [cxci] http://venturebeat.com/2016/01/10/elon-musk-youll-be-able-to-summon-your-tesla-from-anywhere-in-2018/ [cxcii] https://www.washingtonpost.com/news/the-switch/wp/2016/01/11/elon-musk-says-teslas-autopilot-is-already-probably-better-than-human-drivers/ [cxciii] http://electrek.co/2016/04/24/tesla-autopilot-probability-accident/ [cxciv] http://www.bbc.co.uk/news/technology-35280632 [cxcv] http://www.zdnet.com/article/ford-self-driving-cars-are-five-years-away-from-changing-the-world/ [cxcvi] http://www.reuters.com/investigates/special-report/autos-driverless/ [cxcvii] http://www.wired.com/2015/12/californias-new-self-driving-car-rules-are-great-for-texas/ [cxcviii] http://www.reuters.com/investigates/special-report/autos-driverless/ [cxcix] It has been suggested that electric cars should make noises so that people don’t step off the pavement in front of them.

[cxcii] In April 2016 he went further, claiming that Tesla’s autopilot system was already reducing the number of accidents by 50% - where an accident meant an incident where an airbag was deployed.[cxciii] Ford reported success in January 2016 with tests of its self-driving car in snowy conditions. Unable to determine its location by the obscured road markings, it navigates by using buildings and other above-ground features.[cxciv] In May 2016 an executive in Ford’s autonomous vehicle team estimated that the remaining technological hurdles would be overcome within five years, although adoption would of course take longer. He said the amount of computing power each car currently required was “about the equivalent of five decent laptops.”[cxcv] At the time of writing, the only accident which a Google self-driving car might be blamed for happened in February 2016. The car was trying to merge into a line of traffic and expected that a bus which was approaching from behind would give way.


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Rush Hour: How 500 Million Commuters Survive the Daily Journey to Work by Iain Gately

Albert Einstein, autonomous vehicles, Beeching cuts, blue-collar work, Boris Johnson, British Empire, business intelligence, business process, business process outsourcing, call centre, car-free, Cesare Marchetti: Marchetti’s constant, Clapham omnibus, cognitive dissonance, congestion charging, connected car, corporate raider, DARPA: Urban Challenge, Dean Kamen, decarbonisation, Deng Xiaoping, Detroit bankruptcy, don't be evil, Elon Musk, extreme commuting, global pandemic, Google bus, Henri Poincaré, Hyperloop, Jeff Bezos, lateral thinking, low skilled workers, Marchetti’s constant, 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

Nissan has promised the world affordable autonomous cars by 2020. Google will have a hundred of its Google-bugs on Californian test tracks, and possibly roads in 2014; the University of Michigan is building a 32-acre model village to test self-driving ‘connected cars’; Volvo, in conjunction with the city of Gothenburg, has announced a pilot scheme scheduled to commence in 2017, in which a fleet of a hundred of its driverless cars will be set loose on an initial thirty miles of public roads described as ‘typical commuter arteries’. Meanwhile the British city of Milton Keynes has plans to introduce a fleet of self-directed ‘pods’ in 2015, which will run along a dedicated track from its centre to its railway station. Even accountants are getting fired up over autonomous vehicles: the multinational firm KPMG has quantified the potential benefits that might flow from letting computers and servomotors do the driving.

If motorcars could detect each other, could communicate among themselves, and might be programmed to avoid collisions, then rush hours would be far safer. Google, which is leading research in autonomous vehicles, is also motivated by safety. Its informal corporate motto is ‘Don’t be Evil’, and it believes that driverless cars will end the global carnage on the roads that claims more victims each year than warfare. In the same speech in which CFO Patrick Pichette dismissed telecommuting, he also stated that, in an ideal world, ‘nobody should be driving cars… Look at factorial math and probabilities of everything that could go wrong, times the number of cars out there… That’s why you have gridlock… It makes no sense to make people drive cars.’ Google’s ambitions for autonomous vehicles reach beyond safety: Its lead developer Sebastian Thrun, a veteran of the DARPA ’05 Grand Challenge, sums them up as: (1) We can reduce traffic accidents by 90%

According to its twelfth five-year plan for transportation, it will spend US$787.4 billion on building roads between 2011 and 2015, or about the same as the GDP of Holland. While commuting by motorcar looks set to stay for the foreseeable future, there may be significant changes in the way it’s carried out. Although more and more people will be using cars to get to work, it may be as passengers rather than drivers. Driverless or autonomous vehicles have been on futurologists’ radars for longer than telecommuting. The first recorded example was the Achen Motor Company’s ‘phantom motor car’, which it promised to drive around the streets of Milwaukee by radio control in December 1926. The Milwaukee Sentinel waxed lyrical over the ‘ghost’: ‘Driverless, it will start its own motor, throw its clutch, twist its steering wheel, toot its horn, and it may even “sass” the policeman at the corner.’


The New Map: Energy, Climate, and the Clash of Nations by Daniel Yergin

3D printing, 9 dash line, activist fund / activist shareholder / activist investor, addicted to oil, Admiral Zheng, Albert Einstein, American energy revolution, Asian financial crisis, autonomous vehicles, Ayatollah Khomeini, Bakken shale, Bernie Sanders, BRICs, British Empire, coronavirus, COVID-19, Covid-19, decarbonisation, Deng Xiaoping, disruptive innovation, distributed generation, Donald Trump, Edward Snowden, Elon Musk, energy security, energy transition, failed state, gig economy, global pandemic, global supply chain, hydraulic fracturing, Indoor air pollution, Intergovernmental Panel on Climate Change (IPCC), inventory management, James Watt: steam engine, Kickstarter, LNG terminal, Lyft, Malacca Straits, Malcom McLean invented shipping containers, Masdar, mass incarceration, megacity, Mikhail Gorbachev, mutually assured destruction, new economy, off grid, oil rush, oil shale / tar sands, oil shock, open economy, paypal mafia, peak oil, pension reform, price mechanism, purchasing power parity, RAND corporation, rent-seeking, ride hailing / ride sharing, Ronald Reagan, self-driving car, Silicon Valley, smart cities, South China Sea, sovereign wealth fund, supply-chain management, trade route, Travis Kalanick, Uber and Lyft, uber lyft, ubercab, UNCLOS, UNCLOS, uranium enrichment, women in the workforce

But there were others, across the country in Silicon Valley, who were not short of money. Google was already at work on autonomous vehicles, with Sebastian Thrun in the lead. Google’s effort—“Project Chauffeur”—was housed in a separate building. “No one at Google had a clue we existed for a year and a half,” said Thrun. As part of their work on autonomous vehicles, they posted 360-degree cameras on the roofs of the cars. This generated the idea for Google Street View, with the ambition of photographing every street in the world. In 2010, with a blog post from Sebastian Thrun—“we have developed technology for cars that can drive themselves”—Google went public with the stunning news that it was working on the autonomous car. “Our automated cars” had just driven from Silicon Valley in northern California “to our Santa Monica office and on to Hollywood Boulevard,” Thrun reported.

Wagoner asked Burns. “If the automobile were being invented today, then what form would it take?” Burns reflected that “there haven’t really been any disruptive innovations in that time.” And he kept thinking, “That’s true of very few industries.” Autonomous cars could be a big part of the answer to Wagoner’s question—if they could work. Burns was also gripped by what he saw as the “most important sustainability issue faced by automobiles”—not energy or emissions, but a deadly epidemic in which 1.2 million people a year globally died in auto accidents. Autonomous vehicles might be able to virtually eliminate crashes. That was one of the main reasons why Burns hooked GM up with Whittaker and the robotics group at Carnegie Mellon.6 And in this third Grand Challenge, held on the deserted air base in Victorville, Carnegie Mellon won—by twenty minutes.

And regardless of who owns the data, who can access it—and for what purposes? In short, the prospect of autonomous vehicles is already creating major new regulatory puzzles. A host of issues will have to be addressed by both regulators and legislators—including who will be the regulators and who will “own” which part of the puzzle. Disputes continue over safety, security, and privacy, as well as the role of the federal government versus the states. Meanwhile, within the U.S. federal government, a number of agencies—as many as thirty-eight—are trying to figure out their varying roles in regulating different aspects of autonomous vehicles—safety, privacy, and connectivity—while about thirty states have already passed legislation related to autonomous vehicles. The likely result will be some mix of federal and state regulations.12 And then there are the social impacts.


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Give People Money by Annie Lowrey

"Robert Solow", affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, Airbnb, airport security, autonomous vehicles, barriers to entry, basic income, Bernie Sanders, bitcoin, clean water, collective bargaining, computer age, crowdsourcing, cryptocurrency, deindustrialization, desegregation, Donald Trump, Edward Glaeser, Elon Musk, ending welfare as we know it, everywhere but in the productivity statistics, full employment, gender pay gap, gig economy, Google Earth, Home mortgage interest deduction, income inequality, indoor plumbing, information asymmetry, Jaron Lanier, jitney, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kickstarter, Kodak vs Instagram, labor-force participation, late capitalism, Lyft, M-Pesa, Mahatma Gandhi, Mark Zuckerberg, mass incarceration, McMansion, Menlo Park, mobile money, mortgage tax deduction, new economy, obamacare, Peter Thiel, post scarcity, post-work, Potemkin village, precariat, randomized controlled trial, ride hailing / ride sharing, Robert Bork, Ronald Reagan, Sam Altman, self-driving car, Silicon Valley, single-payer health, Steve Jobs, TaskRabbit, The Future of Employment, theory of mind, total factor productivity, Turing test, two tier labour market, Uber and Lyft, uber lyft, universal basic income, uranium enrichment, War on Poverty, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, women in the workforce, working poor, World Values Survey, Y Combinator

Many cars already offer numerous features to assist with driving, including fancy cruise controls, backup warnings, lane-keeping technology, emergency braking, automatic parking, and so on. Add in enough of those options, along with some advanced sensors and thousands of lines of code, and you end up with an autonomous car that can pilot itself from origin to destination. Soon enough, cars, trucks, and taxis might be able to do so without a driver in the vehicle at all. This technology has gone from zero to sixty—forgive me—in only a decade and a half. Back in 2002, the Defense Advanced Research Projects Agency, part of the Department of Defense and better known as DARPA, announced a “grand challenge,” an invitation for teams to build autonomous vehicles and race one another on a 142-mile desert course from Barstow, California, to Primm, Nevada. The winner would take home a cool million. At the marquee event, none of the competitors made it through the course, or anywhere close.

The domestic auto industry nearly died during the Great Recession, and despite its strong rebound in the years following, Americans were still not buying as many cars as they did back in the 1990s and early aughts—in part because Americans were driving less, and in part because the young folks who tend to be the most avid new car consumers were still so cash-strapped. Analysts have thus excitedly described this new technological frontier as a “gold rush” for the industry. Autonomous cars are expected to considerably expand the global market, with automakers anticipating selling 12 million vehicles a year by 2035 for some $80 billion in revenue. Yet to many, the driverless car boom does not seem like a stimulus, or the arrival of a long-awaited future. It seems like an extinction-level threat. Consider the fate of workers on industrial sites already using driverless and autonomous vehicles, watching as robots start to replace their colleagues. “Trucks don’t get pensions, they don’t take vacations. It’s purely dollars and cents,” Ken Smith, the president of a local union chapter representing workers on the Canadian oil sands, said in an interview with the Canadian Broadcasting Corporation.

“growing exponentially”: Sandy Lobenstein, Speech at the 2017 North American International Auto Show, Jan. 10, 2017, http://corporatenews.pressroom.toyota.com/​releases/​2017-naias-lobenstein-entune.htm. in part because Americans were driving less: Elisabeth Rosenthal, “The End of Car Culture,” New York Times, June 29, 2013. young folks…were still so cash-strapped: Jordan Weissmann, “Why Don’t Young Americans Buy Cars?,” Atlantic, Mar. 25, 2012. 12 million vehicles a year by 2035: “Autonomous Vehicle Adoption Study,” Boston Consulting Group, Jan. 2015, https://www.bcg.com/​en-us/​industries/​automotive/​autonomous-vehicle-adoption-study.aspx. “Trucks don’t get pensions”: Kim Trynacity, “Oilsands Workers Worry Driverless Trucks Will Haul Away Their Jobs,” CBC News, Nov. 3, 2016. between 2.2 and 3.1 million jobs: Executive Office of the President, Artificial Intelligence, Automation, and the Economy (Washington, DC, Dec. 2016). “Twenty years is a short period of time”: Ryan Bort, “Elon Musk Says Governments Will Have to Introduce ‘Universal Basic Income’ for Unemployed,” Newsweek, Feb. 13, 2017.


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The Glass Cage: Automation and Us by Nicholas Carr

Airbnb, Airbus A320, Andy Kessler, Atul Gawande, autonomous vehicles, Bernard Ziegler, business process, call centre, Captain Sullenberger Hudson, Charles Lindbergh, Checklist Manifesto, cloud computing, computerized trading, David Brooks, deliberate practice, deskilling, digital map, Douglas Engelbart, drone strike, Elon Musk, Erik Brynjolfsson, Flash crash, Frank Gehry, Frank Levy and Richard Murnane: The New Division of Labor, Frederick Winslow Taylor, future of work, global supply chain, Google Glasses, Google Hangouts, High speed trading, indoor plumbing, industrial robot, Internet of things, Jacquard loom, James Watt: steam engine, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Kelly, knowledge worker, Lyft, Marc Andreessen, Mark Zuckerberg, means of production, natural language processing, new economy, Nicholas Carr, Norbert Wiener, Oculus Rift, pattern recognition, Peter Thiel, place-making, plutocrats, Plutocrats, profit motive, Ralph Waldo Emerson, RAND corporation, randomized controlled trial, Ray Kurzweil, recommendation engine, robot derives from the Czech word robota Czech, meaning slave, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley ideology, software is eating the world, Stephen Hawking, Steve Jobs, TaskRabbit, technoutopianism, The Wealth of Nations by Adam Smith, turn-by-turn navigation, US Airways Flight 1549, Watson beat the top human players on Jeopardy!, William Langewiesche

With the manufacturer that installed the self-driving system? With the programmers who wrote the software? Until such thorny questions get sorted out, fully automated cars are unlikely to grace dealer showrooms. Progress will sprint forward nonetheless. Much of the Google test cars’ hardware and software will come to be incorporated into future generations of cars and trucks. Since the company went public with its autonomous vehicle program, most of the world’s major carmakers have let it be known that they have similar efforts under way. The goal, for the time being, is not so much to create an immaculate robot-on-wheels as to continue to invent and refine automated features that enhance safety and convenience in ways that get people to buy new cars. Since I first turned the key in my Subaru’s ignition, the automation of driving has already come a long way.

Would the program make a different choice if it knew that one of your own children was riding with you, strapped into a sensor-equipped car seat in the back? What if there was an oncoming vehicle in the other lane? What if that vehicle was a school bus? Isaac Asimov’s first law of robot ethics—“a robot may not injure a human being, or, through inaction, allow a human being to come to harm”1—sounds reasonable and reassuring, but it assumes a world far simpler than our own. The arrival of autonomous vehicles, says Gary Marcus, the NYU psychology professor, would do more than “signal the end of one more human niche.” It would mark the start of a new era in which machines will have to have “ethical systems.”2 Some would argue that we’re already there. In small but ominous ways, we have started handing off moral decisions to computers. Consider Roomba, the much-publicized robotic vacuum cleaner.

The company ended up losing almost half a billion dollars, putting it on the verge of bankruptcy. Within a week, a consortium of other Wall Street firms bailed Knight out to avoid yet another disaster in the financial industry. Technology improves, of course, and bugs get fixed. Flawlessness, though, remains an ideal that can never be achieved. Even if a perfect automated system could be designed and built, it would still operate in an imperfect world. Autonomous cars don’t drive the streets of utopia. Robots don’t ply their trades in Elysian factories. Geese flock. Lightning strikes. The conviction that we can build an entirely self-sufficient, entirely reliable automated system is itself a manifestation of automation bias. Unfortunately, that conviction is common not only among technology pundits but also among engineers and software programmers—the very people who design the systems.


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

3D printing, AI winter, anthropic principle, artificial general intelligence, Asilomar, augmented reality, Automated Insights, autonomous vehicles, availability heuristic, blue-collar work, brain emulation, call centre, cognitive bias, combinatorial explosion, computer vision, create, read, update, delete, cuban missile crisis, David Attenborough, Elon Musk, en.wikipedia.org, epigenetics, Ernest Rutherford, factory automation, feminist movement, finite state, Flynn Effect, friendly AI, general-purpose programming language, Google Glasses, Google X / Alphabet X, Gödel, Escher, Bach, industrial robot, Isaac Newton, job automation, John von Neumann, Law of Accelerating Returns, license plate recognition, Mahatma Gandhi, mandelbrot fractal, natural language processing, Parkinson's law, patent troll, patient HM, pattern recognition, phenotype, ransomware, Ray Kurzweil, self-driving car, semantic web, Silicon Valley, Singularitarianism, Skype, sorting algorithm, speech recognition, statistical model, stem cell, Stephen Hawking, Stuxnet, superintelligent machines, technological singularity, Thomas Malthus, Turing machine, Turing test, uranium enrichment, Von Neumann architecture, Watson beat the top human players on Jeopardy!, wikimedia commons, zero day

Factory robots have become much more sophisticated and widely available, so fully automated production lines are now commonplace. This is one reason why manufactured goods have become much less expensive in recent years. Over the next few years we will see robots begin to leave the factories and enter less structured, more natural environments. An important and recent achievement is the development of cars that that can effectively drive themselves. The 2005 DARPA Grand Challenge had fully autonomous vehicles slowly drive for 11 km over a very rough and winding desert track. More recently, Google and others have successfully driven fully automated vehicles on ordinary roads. Negotiating suburban roads with normal traffic and pedestrians is much more difficult than driving down a freeway or traversing a Martian landscape. It requires excellent, real time vision and other sensory analysis, combined with sophisticated models of how other vehicles move and react.

To understand what the hard problems are, and what might be required to solve them. There is no easy road to defining intelligence based on a few cute phrases. Robotic vs cognitive intelligence In order to discuss these issues, it is useful to roughly classify intelligent programs as being either robotic or cognitive. Robotic programs are concerned with sensing the world using techniques such as vision, and then interacting with it by mechanical means. Autonomous vehicles mainly use robotic intelligence. Cognitive intelligence involves higher-level thinking that is abstracted from the real world. Watson and chess programs are examples of cognitive applications. Currently these are normally built using quite different technologies. Robotic intelligence requires many floating point calculations that measure and predict their environment, whereas cognitive applications tend to work with higher-level symbol manipulation.

As our surveillance systems become more powerful and integrated, many of these attacks will be able to be prevented. Robotic surveillance and control is also becoming more sophisticated. Having large numbers of troops in places like Afghanistan, where they can be picked off by snipers or blown up by mines, is grossly inefficient and politically unpalatable. So armies are keen to augment and perhaps ultimately replace human soldiers with small semi-autonomous vehicles that can be conveniently controlled from far away. As the machines become more intelligent they will need fewer people to control them. And computer-based monitoring systems will make it easier for the authorities to control the controllers. This means that a smaller number of active personnel could more effectively control a large civilian population, even in rugged country such as Afghanistan.


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Possible Minds: Twenty-Five Ways of Looking at AI by John Brockman

AI winter, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, Asilomar, autonomous vehicles, basic income, Benoit Mandelbrot, Bill Joy: nanobots, Buckminster Fuller, cellular automata, Claude Shannon: information theory, Daniel Kahneman / Amos Tversky, Danny Hillis, David Graeber, easy for humans, difficult for computers, Elon Musk, Eratosthenes, Ernest Rutherford, finite state, friendly AI, future of work, Geoffrey West, Santa Fe Institute, gig economy, income inequality, industrial robot, information retrieval, invention of writing, James Watt: steam engine, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, Kickstarter, Laplace demon, Loebner Prize, market fundamentalism, Marshall McLuhan, Menlo Park, Norbert Wiener, optical character recognition, pattern recognition, personalized medicine, Picturephone, profit maximization, profit motive, RAND corporation, random walk, Ray Kurzweil, Richard Feynman, Rodney Brooks, self-driving car, sexual politics, Silicon Valley, Skype, social graph, speech recognition, statistical model, Stephen Hawking, Steven Pinker, Stewart Brand, strong AI, superintelligent machines, supervolcano, technological singularity, technoutopianism, telemarketer, telerobotics, the scientific method, theory of mind, Turing machine, Turing test, universal basic income, Upton Sinclair, Von Neumann architecture, Whole Earth Catalog, Y2K, zero-sum game

We can make robots try to align with a person’s internal values, but there’s more than one person involved here. The robot has an end user (or perhaps a few, like a personal robot caring for a family, a car driving a few passengers to different destinations, or an office assistant for an entire team); it has a designer (or perhaps a few); and it interacts with society—the autonomous car shares the road with pedestrians, human-driven vehicles, and other autonomous cars. How to combine these people’s values when they might be in conflict is an important problem we need to solve. AI research can give us the tools to combine values in any way we decide but can’t make the necessary decision for us. In short, we need to enable robots to reason about us—to see us as something more than obstacles or perfect game players.

They learn to control their own behavior, and they learn to control their environment to the extent that they can. Computer science has a long history—going back to before there even was computer science—of implementing neural networks, but for the most part these have been simulations of neural networks by digital computers, not neural networks as evolved in the wild by nature herself. This is starting to change: from the bottom up, as the threefold drivers of drone warfare, autonomous vehicles, and cell phones push the development of neuromorphic microprocessors that implement actual neural networks, rather than simulations of neural networks, directly in silicon (and other potential substrates); and from the top down, as our largest and most successful enterprises increasingly turn to analog computation in their infiltration and control of the world. While we argue about the intelligence of digital computers, analog computing is quietly supervening upon the digital, in the same way that analog components like vacuum tubes were repurposed to build digital computers in the aftermath of World War II.

Even that rosy vision will depend on a radical shake-up of education and lifelong learning. The Industrial Revolution did trigger enormous social change of this kind, including a shift to universal education. But it will not happen unless we make it happen: This is essentially about power, agency, and control. What’s next for, say, the forty-year-old taxi driver or truck driver in an era of autonomous vehicles? One idea that has been touted is that of a universal basic income, which will allow citizens to pursue their interests, retrain for new occupations, and generally be free to live a decent life. However, market economies, which are predicated on growing consumer demand over all else, may not tolerate this innovation. There is also a feeling among many that meaningful work is essential to human dignity and fulfillment.


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

"Robert Solow", Amazon Mechanical Turk, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, business cycle, business process, call centre, combinatorial explosion, corporate governance, creative destruction, 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, intangible asset, job automation, John Markoff, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Khan Academy, Kickstarter, knowledge worker, Loebner Prize, low skilled workers, minimum wage unemployment, patent troll, pattern recognition, Paul Samuelson, 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!, wealth creators, winner-take-all economy, zero-sum game

The GeoFluent offering from Lionbridge has brought instantaneous machine translation to customer service interactions. IBM is working with Columbia University Medical Center and the University of Maryland School of Medicine to adapt Watson to the work of medical diagnosis, announcing a partnership in that area with voice recognition software maker Nuance. And the Nevada state legislature directed its Department of Motor Vehicles to come up with regulations covering autonomous vehicles on the state’s roads. Of course, these are only a small sample of the myriad IT-enabled innovations that are transforming manufacturing, distribution, retailing, media, finance, law, medicine, research, management, marketing, and almost every other economic sector and business function. Where People Still Win (at Least for Now) Although computers are encroaching into territory that used to be occupied by people alone, like advanced pattern recognition and complex communication, for now humans still hold the high ground in each of these areas.

The “winning” vehicle couldn’t even make it eight miles into the course and took hours to go even that far. In Domain After Domain, Computers Race Ahead Just six years later, however, real-world driving went from being an example of a task that couldn’t be automated to an example of one that had. In October of 2010, Google announced on its official blog that it had modified a fleet of Toyota Priuses to the point that they were fully autonomous cars, ones that had driven more than 1,000 miles on American roads without any human involvement at all, and more than 140,000 miles with only minor inputs from the person behind the wheel. (To comply with driving laws, Google felt that it had to have a person sitting behind the steering wheel at all times). Levy and Murnane were correct that automatic driving on populated roads is an enormously difficult task, and it’s not easy to build a computer that can substitute for human perception and pattern matching in this domain.

This is an impossible question to answer precisely, of course, but a reasonable estimate yields an intriguing conclusion. The U.S. Bureau of Economic Analysis added “Information Technology” as a category of business investment in 1958, so let’s use that as our starting year. And let’s take the standard 18 months as the Moore’s Law doubling period. Thirty-two doublings then take us to 2006 and to the second half of the chessboard. Advances like the Google autonomous car, Watson the Jeopardy! champion supercomputer, and high-quality instantaneous machine translation, then, can be seen as the first examples of the kinds of digital innovations we’ll see as we move further into the second half—into the phase where exponential growth yields jaw-dropping results. Computing the Economy: The Economic Power of General Purpose Technologies These results will be felt across virtually every task, job, and industry.


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

affirmative action, Air France Flight 447, Airbnb, airport security, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, assortative mating, 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, industrial cluster, Internet of things, Jane Jacobs, Jeff Bezos, Loebner Prize, Louis Pasteur, Marc Andreessen, 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, William Langewiesche

No matter how many years of experience a driver has, her skills will slowly erode if she lets the computer take over. Prajan’s proposal gives us the worst of both worlds: we let teenage drivers loose in manual cars, when they are most likely to have accidents. And even when they’ve learned some road craft, it won’t take long being a passenger in a usually reliable autonomous car before their skills begin to fade. Recall that Earl Wiener said, “Digital devices tune out small errors while creating opportunities for large errors.”21 In the case of autopilots and autonomous vehicles, we might add that it’s because digital devices tidily tune out small errors that they create the opportunities for large ones. Deprived of any awkward feedback, any modest challenges that might allow us to maintain our skills, when the crisis arrives we find ourselves lamentably unprepared

(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. Raj Rajkumar, an autonomous-driving expert at Carnegie Mellon University, thinks completely autonomous vehicles are ten to twenty years away. Until then, we can look forward to a more gradual process of letting the car drive itself in easier conditions, while humans take over at more challenging moments. “The number of scenarios that are automatable will increase over time, and one fine day, the vehicle is able to control itself completely, but that last step will be a minor, incremental step and one will barely notice this actually happened,” Rajkumar told the 99% Invisible podcast.

Pradhan of the University of Michigan.20 It seems likely that we’ll react by playing a computer game or chatting on a video phone, rather than watching like a hawk how the computer is driving—maybe not on our first trip in an autonomous car, but certainly on our hundredth. And when the computer gives control back to the driver, it may well do so in the most extreme and challenging situations. The three Air France pilots had two or three minutes to work out what to do when their autopilot asked them to take over an A330; what chance would you or I have when the computer in our car says “Automatic Mode Disengaged” and we look up from our smartphone screen to see a bus careening toward us? Anuj Prajan has floated the idea that humans should have to acquire several years of manual experience before they are allowed to supervise an autonomous car. But it is hard to see how this solves the problem. No matter how many years of experience a driver has, her skills will slowly erode if she lets the computer take over.


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Army of None: Autonomous Weapons and the Future of War by Paul Scharre

active measures, Air France Flight 447, algorithmic trading, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, brain emulation, Brian Krebs, cognitive bias, computer vision, cuban missile crisis, dark matter, DARPA: Urban Challenge, DevOps, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, fault tolerance, Flash crash, Freestyle chess, friendly fire, IFF: identification friend or foe, ImageNet competition, Internet of things, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Loebner Prize, loose coupling, Mark Zuckerberg, moral hazard, mutually assured destruction, Nate Silver, pattern recognition, Rodney Brooks, Rubik’s Cube, self-driving car, sensor fusion, South China Sea, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Ballmer, Steve Wozniak, Stuxnet, superintelligent machines, Tesla Model S, The Signal and the Noise by Nate Silver, theory of mind, Turing test, universal basic income, Valery Gerasimov, Wall-E, William Langewiesche, Y2K, zero day

The first DARPA Grand Challenge was held in 2004, on autonomous vehicles. Twenty-one research teams competed to build a fully autonomous vehicle that could navigate a 142-mile course across the Mojave Desert. It was truly a “DARPA hard” problem. The day ended with every single vehicle broken down, overturned, or stuck. The furthest any car got was 7.4 miles, only 5 percent of the way through the course. The organization kept at it, sponsoring a follow-up Grand Challenge the next year. This time, it was a resounding success. Twenty-two vehicles beat the previous year’s distance record and five cars finished the entire course. In 2007, DARPA hosted an Urban Challenge for self-driving cars on a closed, urban course complete with traffic and stop signs. These Grand Challenges matured autonomous vehicle technology in leaps and bounds, laying the seeds for the self-driving cars now in development at companies like Google and Tesla.

Effective human intervention may be even more challenging in supervised autonomous systems, where the system does not pause to wait for human input. The human’s ability to actually regain control of the system in real time depends heavily on the speed of operations, the amount of information available to the human, and any time delays between the human’s actions and the system’s response. Giving a driver the ability to grab the wheel of an autonomous vehicle traveling at highway speeds in dense traffic, for example, is merely the illusion of control, particularly if the operator is not paying attention. This appears to have been the case in a 2016 fatality involving a Tesla Model S that crashed while driving on autopilot. For fully autonomous systems, the human is out of the loop and cannot intervene at all, at least for some period of time.

In 2014, the Navy’s Autonomous Aerial Cargo/Utility System (AACUS) helicopter autonomously scouted out an improvised landing area and executed a successful landing on its own. Then in 2015, the X-47B drone again made history by conducting the first autonomous aerial refueling, taking gas from another aircraft while in flight. These are key milestones in building more fully combat-capable uninhabited aircraft. Just as autonomous cars will allow a vehicle to drive from point A to point B without manual human control, the ability to takeoff, land, navigate, and refuel autonomously will allow robots to perform tasks under human direction and supervision, but without humans controlling each movement. This can begin to break the paradigm of humans manually controlling the robot, shifting humans into a supervisory role. Humans will command the robot what action to take, and it will execute the task on its own.


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Human + Machine: Reimagining Work in the Age of AI by Paul R. Daugherty, H. James Wilson

3D printing, AI winter, algorithmic trading, Amazon Mechanical Turk, augmented reality, autonomous vehicles, blockchain, business process, call centre, carbon footprint, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, digital twin, disintermediation, Douglas Hofstadter, en.wikipedia.org, Erik Brynjolfsson, friendly AI, future of work, industrial robot, Internet of things, inventory management, iterative process, Jeff Bezos, job automation, job satisfaction, knowledge worker, Lyft, natural language processing, personalized medicine, precision agriculture, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Rodney Brooks, Second Machine Age, self-driving car, sensor fusion, sentiment analysis, Shoshana Zuboff, Silicon Valley, software as a service, speech recognition, telepresence, telepresence robot, text mining, the scientific method, uber lyft

FUSION SKILL #2: Responsible Normalizing Definition: The act of responsibly shaping the purpose and perception of human-machine interaction as it relates to individuals, businesses, and society. It’s surprising how quickly you can get used to riding in an autonomous car. The first time you see its wheel turn on its own, you might shudder, but by the second right turn, it all starts to feel normal. Many people who have ridden in autonomous cars quickly conclude that driving is far too complex and dangerous a task for people to do. Unfortunately, autonomous cars are not yet widely distributed, and in many places, they’re still misunderstood. There’s a gap between the use of AI technologies and the wide acceptance and understanding of them. Bridging this gap is where the skill of normalizing comes into play.

The skills of trainers, explainers, and sustainers are absolutely crucial, as we’ll see later. But just as important is fostering positive experiences with AI augmentation. Make it clear to employees that you are using AI to replace tasks and reimagine processes. Demonstrate that AI tools can augment employees and make their day-to-day work less tedious and more engaging. Meanwhile, though, here’s what businesses are facing. When discussing the safety of autonomous vehicles, Gill Pratt, chief executive of the Toyota Research Institute, told lawmakers on Capitol Hill in 2017 that people are more inclined to forgive mistakes that humans make than those by machines.7 Research confirms the inconsistency and ambiguity with which we trust machines. A 2009 paper reported that when people thought their stock reports were coming from a human expert, their price estimates were more likely to be swayed than if they thought the information came from a statistical forecasting tool.

Tesla is training its AI platform in a distributed test bed with the best data around—its own drivers in real-life conditions. In this case, people’s driving skills—at scale—are crucial in the training of the system. AI has allowed Tesla to rethink its fundamental R&D processes and, along the way, speed up the development of its system. This reconsideration of how it conducts R&D is positioning Tesla to be a leader in autonomous cars. Tesla isn’t the only one using AI to rethink its R&D processes, using both machines and people in new, innovative ways. This chapter explores the way that AI enables experimentation within companies and how it’s shaking up business processes, especially those that involve customers, medical patients, and others who provide useful data. You’ll see how AI is boosting R&D in the pharmaceutical and life sciences industries, augmenting researchers’ intuition and ability to test theories, and speeding up the product-design cycle by orders of magnitude.


pages: 269 words: 70,543

Tech Titans of China: How China's Tech Sector Is Challenging the World by Innovating Faster, Working Harder, and Going Global by Rebecca Fannin

Airbnb, augmented reality, autonomous vehicles, blockchain, call centre, cashless society, Chuck Templeton: OpenTable:, cloud computing, computer vision, connected car, corporate governance, cryptocurrency, data is the new oil, Deng Xiaoping, digital map, disruptive innovation, Donald Trump, El Camino Real, Elon Musk, family office, fear of failure, glass ceiling, global supply chain, income inequality, industrial robot, Internet of things, invention of movable type, Jeff Bezos, Kickstarter, knowledge worker, Lyft, Mark Zuckerberg, megacity, Menlo Park, money market fund, Network effects, new economy, peer-to-peer lending, personalized medicine, Peter Thiel, QR code, RFID, ride hailing / ride sharing, Sand Hill Road, self-driving car, sharing economy, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart transportation, Snapchat, social graph, software as a service, South China Sea, sovereign wealth fund, speech recognition, stealth mode startup, Steve Jobs, supply-chain management, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, urban planning, winner-take-all economy, Y Combinator, young professional

Michael Dunne, “Driving the Future of US-China Relations: China’s Global Automotive Push,” Asia Society Northern California, February 27, 2019; asiasociety.org/video/driving-future-us-china-relations-chinas-global-automotive-push. 2. China Passenger Car Association, broker reports. Accessed February 28, 2019. 3. Luca Pizzuto et al., “How China Will Help Fuel the Revolution in Autonomous Vehicles,” McKinsey & Co., January 2019; mckinsey.com/industries/automotive-and-assembly/our-insights/how-china-will-help-fuel-the-revolution-in-autonomous-vehicles?reload. 4. Dana Hull and Peter Blumberg, “Tesla Joins Apple in Trade Secret Cases Tied to Xpeng, Bloomberg, March 21, 2019; bloomberg.com/news/articles/2019-03-21/tesla-sues-rival-zoox-claiming-ex-workers-stole-trade-secrets. 5. Trefor Moss, “Chinese Annual Car Sales Slip for First Time in Decades,” Wall Street Journal, January 14, 2019; wsj.com/articles/chinese-annual-car-sales-slip-for-first-time-in-decades-11547465112.

Now back in the driver’s seat at Baidu, the challenge for Li is keeping a big-picture vision while running day-to-day operations and juggling both AI and search businesses. Next for Baidu is making money from its suite of AI products, branded Baidu Brain; voice-assisted DuerOS lights, speakers, and smartphone chargers, which have surpassed 200 million users; and self-driving technology Apollo, which has 50 municipal licenses in China to test autonomous vehicles on open roads. “If anyone starts to be able to generate meaningful revenue (in AI), we will be the first to achieve that goal,” Li told analysts on a recent earnings call, while acknowledging that Apollo is at a “very early stage.”8 Raymond Feng, an analyst at market research company Pacific Epoch in Shanghai, predicts that Baidu will start making money on driverless vehicle technology by 2020, providing improved AI services to vehicle manufacturers and drivers.

The DNA of their companies is tech, not auto making. Since the early 1980s, China has wanted to have its own powerful automobile industry, but it’s not been until now that the nation can realize that goal through its leading technology companies, notes Michael Dunne, CEO of Hong Kong–based auto tech advisory firm ZoZo Go. Now these homegrown tech-oriented startups are powering up with electric and autonomous vehicles that can make China proud. With no legacy of gas-powered engines or Chinese state-owned, auto-making enterprises, they have an open road to roam in China. “China has the potential to make world-class vehicles with their smart and cashed-up tech companies,” said consultant Dunne, speaking at an Asia Society meeting in Northern California. “By relying on tech companies instead of the entrenched automakers, they have found their point of leverage.


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Robots Will Steal Your Job, But That's OK: How to Survive the Economic Collapse and Be Happy by Pistono, Federico

3D printing, Albert Einstein, autonomous vehicles, bioinformatics, Buckminster Fuller, cloud computing, computer vision, correlation does not imply causation, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Firefox, future of work, George Santayana, global village, Google Chrome, happiness index / gross national happiness, hedonic treadmill, illegal immigration, income inequality, information retrieval, Internet of things, invention of the printing press, jimmy wales, job automation, John Markoff, Kevin Kelly, Khan Academy, Kickstarter, knowledge worker, labor-force participation, Lao Tzu, Law of Accelerating Returns, life extension, Loebner Prize, longitudinal study, means of production, Narrative Science, natural language processing, new economy, Occupy movement, patent troll, pattern recognition, peak oil, post scarcity, QR code, race to the bottom, Ray Kurzweil, recommendation engine, RFID, Rodney Brooks, selection bias, self-driving car, slashdot, smart cities, software as a service, software is eating the world, speech recognition, Steven Pinker, strong AI, technological singularity, Turing test, Vernor Vinge, women in the workforce

The initial investment is very low, and the distributed nature of computation allows costs to increase incrementally as the business expands. We are about to experience tremendous changes in such technologies, the consequences of which are unthinkable for us at the moment. Just as cavemen could not think of the complex cities and societies we live in today, so do we compared to what is about to come. 7.7 Autonomous Vehicles Often people say that something is either obvious and everything will change, or that it will never happen. It turns out that things are not quite that simple. Societies are multi-faceted, complex, evolving organisms, with many variables, and a certain degree of unpredictability. Technicians often fail to take into account the human factor, the psychology of the masses, and how events unfold accordingly.

Seats could now move in any direction, all four people could face each other if they liked, in circle. Being in a car now became a whole different experience; it could be a truly social event. Given the situation, one would expect every car, bus, truck, and taxi to run autonomously by now. It would certainly have been the right choice: more efficient, less accidents, no traffic jams, cheaper and more reliable than human drivers…having autonomous vehicles would be logical. But things do not always go according to what is logical. They follow complex dynamics that have to do with society, group thinking and complex dynamics that have little to do with technology and what is good; and a lot to do with politics, marketing, emotional attachments, old habit, delusions, beliefs, and what appears to be good. The invention and creation of a technology may be a hard problem, but sometimes social acceptance of that technology is a much harder one.

The classical “Turing test approach” has been largely abandoned as a realistic research goal, and is now just an intellectual curiosity (the annual Loebner prize for realistic chattiest81), but helped spawn the two dominant themes of modern cognition and artificial intelligence: calculating probabilities and producing complex behaviour from the interaction of many small, simple processes. As of today (2012), we believe these represent more closely what the human brain does, and they have been used in a variety of real-world applications: Google’s autonomous cars, search results, recommendation systems, automated language translation, personal assistants, cybernetic computational search engines, and IBM’s newest super brain Watson. Natural language processing was believed to be a task that only humans could accomplish. A word can have different meanings depending on the context, a phrase could not mean what it says if it is a joke or a pun. One may infer a subtext implicitly, make cultural references specific to a geographical or cultural area, the possibilities are truly endless.


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

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

.*) Whatever happened to the carpal tunnel syndrome epidemic? (Once journalists stopped getting it, they stopped writing about it—but the problem persists, especially among blue-collar workers.) Some questions were existential: What makes people truly happy? Is income inequality as dangerous as it seems? Would a diet high in omega-3 lead to world peace? People wanted to know the pros and cons of: autonomous vehicles, breast-feeding, chemotherapy, estate taxes, fracking, lotteries, “medicinal prayer,” online dating, patent reform, rhino poaching, using an iron off the tee, and virtual currencies. One minute we’d get an e-mail asking us to “solve the obesity epidemic” and then, five minutes later, one urging us to “wipe out famine, right now!” Readers seemed to think no riddle was too tricky, no problem too hard, that it couldn’t be sorted out.

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.

And since roughly 90 percent of the world’s 1.2 million traffic deaths each year—yes, 1.2 million deaths, every year!—are the result of driver error, the driverless car may be one of the biggest lifesavers in recent history. Unlike humans, a driverless car won’t drive drowsy or drunk, or while texting or applying mascara; it won’t change lanes while putting ketchup on french fries or turn around to smack its kids in the backseat. Google has already driven its fleet of autonomous cars more than 500,000 miles on real roads throughout the United States without causing an accident.* But safety isn’t the only benefit. Elderly and handicapped people wouldn’t have to drive themselves to the doctor (or, if they prefer, to the beach). Parents wouldn’t have to worry about their reckless teenagers getting behind the wheel. People could drink without hesitation when they go out at night—good news for restaurants, bars, and the alcohol industry.


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It's Better Than It Looks: Reasons for Optimism in an Age of Fear by Gregg Easterbrook

affirmative action, Affordable Care Act / Obamacare, air freight, autonomous vehicles, basic income, Bernie Madoff, Bernie Sanders, Branko Milanovic, business cycle, Capital in the Twenty-First Century by Thomas Piketty, clean water, coronavirus, David Brooks, David Ricardo: comparative advantage, deindustrialization, Dissolution of the Soviet Union, Donald Trump, Elon Musk, Exxon Valdez, factory automation, failed state, full employment, Gini coefficient, Google Earth, Home mortgage interest deduction, hydraulic fracturing, Hyperloop, illegal immigration, impulse control, income inequality, Indoor air pollution, interchangeable parts, Intergovernmental Panel on Climate Change (IPCC), invisible hand, James Watt: steam engine, labor-force participation, liberal capitalism, longitudinal study, Lyft, mandatory minimum, manufacturing employment, Mikhail Gorbachev, minimum wage unemployment, obamacare, oil shale / tar sands, Paul Samuelson, peak oil, plutocrats, Plutocrats, Ponzi scheme, post scarcity, purchasing power parity, quantitative easing, reserve currency, rising living standards, Robert Gordon, Ronald Reagan, self-driving car, short selling, Silicon Valley, Simon Kuznets, Slavoj Žižek, South China Sea, Steve Wozniak, Steven Pinker, supervolcano, The Chicago School, The Rise and Fall of American Growth, the scientific method, There's no reason for any individual to have a computer in his home - Ken Olsen, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, transaction costs, uber lyft, universal basic income, War on Poverty, Washington Consensus, WikiLeaks, working poor, Works Progress Administration

Hacking into a computer-driven vehicle’s software will create a precision-guided urban weapon; no degree of car-code security can rule this out. If future autonomous cars really don’t have pedals and steering, just seats for passengers, a cyberattack that cripples GPS signals will bring society to a halt. Buses and trucks will be driven by computers too. Long-distance truck drivers often are overworked and sleepy; far more people die in collisions caused by trucks than in airline crashes, and the rate of truck-caused fatalities has been rising in recent years, even as other forms of unnatural death moderate. For most families, the chief benefit of autonomous vehicles will be convenience. For trucking companies, the autonomous vehicle will replace wages, benefits, and workplace litigation with a capital cost that can be depreciated. Already the truck division of Mercedes-Benz has prototype autonomous trucks operating on German roads.

But the fact that Americans like to think there exists some kind of right to overpowered vehicles is a barrier to reducing traffic death risk, petroleum consumption, and driving stress. IF PEOPLE ARE RESISTING MORE sensible cars, let’s get people out of the loop. Before too long, the autonomous car is likely. Computer-driven cars will reduce accidents: the computer won’t get drowsy or try to cut another computer off. They will reduce traffic jams: traffic would flow more smoothly if cars employed uniform speeds and didn’t make needless lane changes, jockeying to get a few seconds ahead. Studies by researchers at the Massachusetts Institute of Technology suggest that an all-autonomous-vehicle system would reduce traffic jams 80 percent—traffic would flow freely even in the Manhattan tunnels. The three-car suburban family—about one-third of American households own at least three vehicles—will become a one-car family, the vehicle driving itself to wherever the next pickup or drop-off is required by a family member.

Groups of friends will get together to purchase one shared self-driving car, rather than each person owning a car: all members of the group will save on car and insurance expenses, resulting in another increase in standards of living. The horsepower arms race will end, since a car that refuses to violate the speed limit—this is going to please some people while driving others to distraction—would not benefit from prodigious power output. Automakers have opened research offices in and around Palo Alto, California, seeking techie-wizard input into self-driving designs. Ford Motors expects by 2021 to be selling fully autonomous cars—no steering wheel—designed for group ownership. This would seem the fulfillment of a Summer-of-Love hippie whimsy were it not the marketing strategy of a Fortune 500 firm. Once cars become safer through computer control, more affordable through group ownership, cleaner through lower oil consumption, and less of a source of urban headaches through the end of the rush-hour traffic jam—then we’ll never be rid of car culture, which will exist for decades or centuries to come, if not until the sun explodes.


Robot Futures by Illah Reza Nourbakhsh

3D printing, autonomous vehicles, Burning Man, commoditize, computer vision, Mars Rover, Menlo Park, phenotype, Skype, social intelligence, software as a service, stealth mode startup, strong AI, telepresence, telepresence robot, Therac-25, Turing test, Vernor Vinge

The legal code would be a particularly bad place to look for hints regarding people’s interactions with autonomous robots. Last year, following encouragement from Google, the State of Nevada enacted legislation to soon make it legal for autonomous vehicles to drive on the state’s highway system (State of Nevada 2011). To date Google’s autonomous driving machines have already covered more than 200,000 miles in California, where there are no laws explicitly forbidding robotically driven vehicles. And yet the diversity of ways in which legal boundaries, human behavior, and robot cars on the road will intersect are not predicted by Nevada’s legislation or by existing bodies of law. In August 2011 the automotive blog jalopnik broke the news of a fender bender caused by one of Google’s autonomous cars. Google issued a statement explaining that the accident was caused by the human in the Google car, since he was driving manually at the time.

., 55 Digital walls, 14 Disempowerment, 110 Do-it-yourself (DIY), 25–27 Driverless vehicle, 49–51, 59, 60, Drone, 76, 102, 103 132 Electric motor. See Motor Empathy, 54, 55, 114 Ethics, 25, 26, 55, 60–62, 101, 117, 118 Euclid Elements, 12 Index human, xvi, market, 6, 12, 43 Joints, 27, 29, 31, 32, 95, 123 Kinect, 36 Face tracking, 39 Fan out, 76–78, 81, 82 Flash, 26 Flying robots, xv, 29, 30, 47 Funding, 95, 96, 111–113 Fundraising. See Funding Gladwell, Malcolm, 82 Google Android operating system, 40 autonomous vehicles (see Driverless vehicle) Robo-Google, 43 Hacking, 22–24, 37 Human–robot interaction (HRI) adjustable autonomy, 45, 46, 77, 80, 102, 103, 121 ethics of, 54, 101, 104, 110 nanorobot coupling, 97–100 peer-to-peer, xix, 44, 45 psychological experiments, 54 Hyperactive Bob, 11 Identity, 62, 100, 103–107, 117 Intel IETF, 38 OpenCV, 39–41 Intelligence artificial (see Artificial Intelligence) Laser cutting, 28, 122 Machine learning, 98, 122 Manipulation, 29, 33, 40, 41, 123 Microsoft Kinect (see Kinect) robotics studio, 39 Moore’s Law, 31, 33 Motor, xv, xvii, 28, 29, 31–33, 38 Nanorobot, 89–94, 97–99, 106 NASA, 44, 45, 113 National Science Foundation, 112, 113 Nielsen, 5, 13 O’Terrill’s, 24, 25 Particulates.


pages: 285 words: 58,517

The Network Imperative: How to Survive and Grow in the Age of Digital Business Models by Barry Libert, Megan Beck

active measures, Airbnb, Amazon Web Services, asset allocation, autonomous vehicles, big data - Walmart - Pop Tarts, business intelligence, call centre, Clayton Christensen, cloud computing, commoditize, crowdsourcing, disintermediation, diversification, Douglas Engelbart, Douglas Engelbart, future of work, Google Glasses, Google X / Alphabet X, Infrastructure as a Service, intangible asset, Internet of things, invention of writing, inventory management, iterative process, Jeff Bezos, job satisfaction, Kevin Kelly, Kickstarter, late fees, Lyft, Mark Zuckerberg, Oculus Rift, pirate software, ride hailing / ride sharing, self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, six sigma, software as a service, software patent, Steve Jobs, subscription business, TaskRabbit, Travis Kalanick, uber lyft, Wall-E, women in the workforce, Zipcar

General Motors (GM) is coming enthusiastically, albeit a bit late, to the innovative ride-sharing market with a $500 million investment in Lyft as part of Lyft’s latest $1 billion venture financing round. Although a shift from car ownership to car sharing, and even further to autonomous vehicles, could be a risky disruption to their market, GM’s leaders have decided to embrace the changing business model landscape in transportation and innovate what they do and how they do it. Daniel Ammann, GM’s president, said, “We think there’s going to be more change in the world of mobility in the next five years than there has been in the last 50,” and GM is getting ready for that change.1 From that perspective, Lyft is an excellent partner who will help GM turn their views of the market upside down. Lyft’s president John Zimmer stated, “We strongly believe that autonomous vehicle go-to-market strategy is through a network, not through individual car ownership.” According to executives at both GM and Lyft, they will start work on developing a network of self-driving vehicles—a challenge to Google, Tesla, and Uber, which are also devoting resources to this innovation.2 Openness Makes Space for Ongoing Change Will GM’s self-driving-car aspiration create value for the firm?

Founder of Angie’s List Angie Hicks connected homeowners to share reviews of local businesses and service providers, creating enormous value over traditional listings like the Yellow Pages. But don’t assume that only start-ups can embrace new mental models and core beliefs. General Motors is demonstrating that it too can shift its core beliefs about value with a $500 million investment in the ride-sharing start-up Lyft and a commitment to build digital networks and autonomous vehicles. Take It Outside Your Mind and into the Real World As you update your mental model, you need to take reinforcing actions to help realize the change. Here’s what we recommend. TAKE IN NEW INFORMATION, NEW DATA, AND NEW IDEAS. As a leader in business, you probably already keep up with the latest news and the ways others are thinking, but, as we’ve said, all of us naturally have a bias toward perspectives like our own.

Google invests judiciously in its moonshots. Former spokeswoman Jill Hazelbaker notes, “The sums involved are very small by comparison to the investments we make in our core businesses.”1 The right talent. X projects benefit from passionate leaders having relevant experience. For example, when embarking on the self-driving car project, the team brought on Sebastian Thrun, who previously sent an autonomous car through a seven-mile obstacle course in the Mojave Desert. The external network. Google leverages external experts to support its projects. It has partnered with at least sixteen other companies so far, ranging from Silicon Valley start-ups to established chip manufacturers. All these criteria—separation, low investment, the right talent, and the external network—make for an open space in which teams can be innovative and almost instantly responsive to market feedback.


pages: 411 words: 98,128

Bezonomics: How Amazon Is Changing Our Lives and What the World's Best Companies Are Learning From It by Brian Dumaine

activist fund / activist shareholder / activist investor, AI winter, Airbnb, Amazon Web Services, Atul Gawande, autonomous vehicles, basic income, Bernie Sanders, Black Swan, call centre, Chris Urmson, cloud computing, corporate raider, creative destruction, Danny Hillis, Donald Trump, Elon Musk, Erik Brynjolfsson, future of work, gig economy, Google Glasses, Google X / Alphabet X, income inequality, industrial robot, Internet of things, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Lyft, Marc Andreessen, Mark Zuckerberg, money market fund, natural language processing, pets.com, plutocrats, Plutocrats, race to the bottom, ride hailing / ride sharing, Sand Hill Road, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Snapchat, speech recognition, Steve Jobs, Stewart Brand, supply-chain management, Tim Cook: Apple, too big to fail, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, wealth creators, web application, Whole Earth Catalog

Bezos will in all likelihood use the savings to drop prices for his customers, which will in turn attract more sellers, which will lower costs and attract more customers. And his AI flywheel will spin faster and faster. With visions of those savings dangling before him, Bezos has jumped headlong into the autonomous vehicle race. Amazon’s vast computing power and machine-learning expertise make it a potentially formidable player in the field. In 2016, the company earned a patent for a system that helps autonomous cars figure out which direction traffic is traveling in any particular lane to help a vehicle safely enter the proper lane. In its partnership with Toyota, Amazon is developing a self-driving concept vehicle called the e-Palette, a minivan that can move people or packages, and the two companies plan to unveil it at the 2020 summer Olympic games in Tokyo.

Aurora will not build cars but is developing the AI brains behind autonomous vehicles and plans to partner with retailers like Amazon and major automakers to create state-of-the-art autonomous vehicles. Amazon is far from alone in the race for self-driving vehicles. According to the research firm CB Insights, at least forty-six companies around the world are working on self-driving vehicle technology. The ranks include major automakers such as GM, Ford, BMW, and Audi; tech companies such as Alphabet, Baidu, Microsoft, and Cisco; Internet car services such as Uber and Didi in China; retailers such as Walmart, Kroger, and Alibaba; and a slew of start-ups like Aurora and Udelv. One thing that’s almost certain is that when autonomous vehicles do first appear in significant numbers, they’ll be delivery vans.

This is where the retail industry is headed, and Amazon is using its technological might to change the rules of the game in a fundamental way. By morphing into a hybrid retailer, Amazon will not only find growth in new markets such as groceries but will discover new efficiencies that will unlock more capital for investment. The $10 billion–plus that McKinsey says Amazon will save by moving to autonomous vehicles is a case in point. Those savings and ones like them will give Amazon even more capital to drive down prices for its customers and build and buy more brick-and-mortar stores. Perhaps, as rumor has it, they’ll even acquire Target—which would keep Bezos’s AI flywheel spinning faster and faster. So far, only one company in the U.S. is big enough and smart enough to compete on Amazon’s scale.


pages: 444 words: 127,259

Super Pumped: The Battle for Uber by Mike Isaac

"side hustle", activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, always be closing, Amazon Web Services, Andy Kessler, autonomous vehicles, Ayatollah Khomeini, barriers to entry, Bay Area Rapid Transit, Burning Man, call centre, Chris Urmson, Chuck Templeton: OpenTable:, citizen journalism, Clayton Christensen, cloud computing, corporate governance, creative destruction, don't be evil, Donald Trump, Elon Musk, family office, gig economy, Google Glasses, Google X / Alphabet X, high net worth, Jeff Bezos, John Markoff, Kickstarter, Lyft, Marc Andreessen, Mark Zuckerberg, mass immigration, Menlo Park, Mitch Kapor, money market fund, moral hazard, move fast and break things, move fast and break things, Network effects, new economy, off grid, peer-to-peer, pets.com, Richard Florida, ride hailing / ride sharing, Sand Hill Road, self-driving car, shareholder value, side project, Silicon Valley, Silicon Valley startup, skunkworks, Snapchat, software as a service, software is eating the world, South China Sea, South of Market, San Francisco, sovereign wealth fund, special economic zone, Steve Jobs, TaskRabbit, the payments system, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, ubercab, union organizing, upwardly mobile, Y Combinator

He spent his undergrad years in the East Bay, at the University of California, Berkeley, where as an industrial engineer he built one of his first robots—a Lego-constructed machine that could pick up and sort Monopoly money. Soon he convinced his classmates to enter the DARPA Robotics Challenge with him, a program put on by the Department of Defense in which competitors would build autonomous cars and race them across the Mojave Desert. They entered with high hopes, but the autonomous vehicle they built, a motorcycle nicknamed “Ghostrider,” ended up crashing within seconds of beginning the race.†††† The loss deflated him; Levandowski liked to win almost as much as he liked building his robots. He scored a post-collegiate job at Google working on the company’s Street View project. Levandowski was the exact type of engineer Google loved to hire; curious, brilliant, and harboring a wide array of interests outside of his main duties at Google.

He had come to Google to build self-driving cars and upend the world of transportation. But Google, for all its foresight, was proving skittish. Google was terrified to approve what Levandowski really wanted; true, open-road testing of autonomous vehicles. Aside from the ever-present concern about negative public opinion, the nonsensical design of San Francisco’s traffic-clogged grid presented an absurdly thorny engineering problem. The smallest error risked a dangerous accident. Naysayers imagined a video of a Google-branded SUV wrapped around the mangled chassis of another car—or worse, the mangled body of a pedestrian. But Levandowski knew Google needed real-world testing to get autonomous vehicles out of the conceptual phase. Levandowski imagined a future without automobile deaths or congestion, where carpooling was automatic and simple. And here was Google, dragging its feet because it was too scared to break a few rules.

He had sharp elbows, was pushy, tough on people, sneering when someone disagreed with him. While Google was careful and methodical, employees saw Levandowski as corner-cutting and occasionally reckless. Without telling his bosses, Levandowski hired an outside lobbyist in Nevada to write a new law that allowed autonomous vehicles to operate in the state without a backup safety driver. Google executives were furious, yet the law passed statewide in 2011. Levandowski’s divisive methods earned him enemies. When he made a play to become leader of the Google X autonomous vehicle unit, a group of employees staged a mutiny, requiring Page himself to step in and name Chris Urmson, a rival of Levandowski’s, the head of the self-driving division. Levandowski was crushed and made no attempt to hide it; at one point, he stopped coming into work entirely.


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Don't Be Evil: How Big Tech Betrayed Its Founding Principles--And All of US by Rana Foroohar

"side hustle", accounting loophole / creative accounting, Airbnb, AltaVista, autonomous vehicles, banking crisis, barriers to entry, Bernie Madoff, Bernie Sanders, bitcoin, book scanning, Brewster Kahle, Burning Man, call centre, cashless society, cleantech, cloud computing, cognitive dissonance, Colonization of Mars, computer age, corporate governance, creative destruction, Credit Default Swap, cryptocurrency, data is the new oil, death of newspapers, Deng Xiaoping, disintermediation, don't be evil, Donald Trump, drone strike, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Etonian, Filter Bubble, future of work, game design, gig economy, global supply chain, Gordon Gekko, greed is good, income inequality, informal economy, information asymmetry, intangible asset, Internet Archive, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, Kenneth Rogoff, life extension, light touch regulation, Lyft, Mark Zuckerberg, Marshall McLuhan, Martin Wolf, Menlo Park, move fast and break things, move fast and break things, Network effects, new economy, offshore financial centre, PageRank, patent troll, paypal mafia, Peter Thiel, pets.com, price discrimination, profit maximization, race to the bottom, recommendation engine, ride hailing / ride sharing, Robert Bork, Sand Hill Road, search engine result page, self-driving car, shareholder value, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, smart cities, Snapchat, South China Sea, sovereign wealth fund, Steve Jobs, Steven Levy, subscription business, supply-chain management, TaskRabbit, Telecommunications Act of 1996, The Chicago School, the new new thing, Tim Cook: Apple, too big to fail, Travis Kalanick, trickle-down economics, Uber and Lyft, Uber for X, uber lyft, Upton Sinclair, WikiLeaks, zero-sum game

Currently, about 90 percent of the value of an automobile lives in the hardware. But as autonomous driving and digital apps become a bigger deal, that ratio is expected to shift dramatically. Morgan Stanley predicts that in autonomous vehicles, 40 percent of the value of an automobile will come from hardware, 40 percent from software, and 20 percent from the content that streams into the vehicle.35 That would include things like games, advertisements, and news enabled by the software. This shift is partly driven—no pun intended—by the fact that millennials want their cars souped-up with all their favorite apps. But it also reflects another idea: When you are in an autonomous vehicle, brand identity disappears. “If you take away control of the steering wheel, consumers are much less likely to care what type of car they are in,” says Nick Johnson, principal at the consultancy Applico, who has advised major automakers on the shift.

The ubiquitous ride-hailing business he founded had been drawing criticism from municipal lawmakers and union activists—particularly in large cities like New York and San Francisco—for years, but their PR crisis reached a boiling point following a series of scandals that started with a blog post from a former engineer, Susan Fowler, alleging harassment and rampant sexism at the company. That news went viral in the same month that Waymo, an autonomous vehicle unit owned by Google’s parent company, Alphabet, filed a federal lawsuit against the ridesharing company alleging that a software engineer had stolen its trade secrets and taken them to Uber, which is developing its own autonomous vehicles. This was followed only five days later by a shocking video showing the CEO himself blowing up at an Uber driver who deigned to complain about the company’s payment system.1 Uber’s own dashcam recorded the interaction, in which the driver claimed to have gone bankrupt after investing $97,000 in a high-end car in order to drive for uberBLACK, only to find that rates began falling and the service was being dropped in favor of cheaper cars.

It’s a question that faces any number of industries, from retail (which has already been decimated by Amazon) to healthcare (under competitive threat from both Amazon and Google), finance (which is under threat from fintech, the merging of tech platform technology and banking), manufacturing, and so on. The businesses that have the best technology in automotive software and apps are, unsurprisingly, technology companies such as Google, Apple, and China’s Baidu, all of which are pouring money into autonomous vehicle technology and platforms to support it. Right now, motorists can mainly stream music, GPS information, and whatever other data they can access on their phone via such systems. But once the platforms are embedded more deeply in vehicles, customers will be able to tap into everything from fluid levels and engine temperatures to safety information. Those are all currently the domain of carmakers.


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The War on Normal People: The Truth About America's Disappearing Jobs and Why Universal Basic Income Is Our Future by Andrew Yang

3D printing, Airbnb, assortative mating, augmented reality, autonomous vehicles, basic income, Ben Horowitz, Bernie Sanders, call centre, corporate governance, cryptocurrency, David Brooks, Donald Trump, Elon Musk, falling living standards, financial deregulation, full employment, future of work, global reserve currency, income inequality, Internet of things, invisible hand, Jeff Bezos, job automation, John Maynard Keynes: technological unemployment, Khan Academy, labor-force participation, longitudinal study, low skilled workers, Lyft, manufacturing employment, Mark Zuckerberg, megacity, Narrative Science, new economy, passive income, performance metric, post-work, quantitative easing, reserve currency, Richard Florida, ride hailing / ride sharing, risk tolerance, Ronald Reagan, Sam Altman, self-driving car, shareholder value, Silicon Valley, Simon Kuznets, single-payer health, Stephen Hawking, Steve Ballmer, supercomputer in your pocket, technoutopianism, telemarketer, The Wealth of Nations by Adam Smith, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, unemployed young men, universal basic income, urban renewal, white flight, winner-take-all economy, Y Combinator

“Your car will drop you off at work, and then it will pick other people up and make you money all day until it’s time to pick you up again,” Musk proclaimed. “This will 100 percent happen.” It is obvious that Tesla trucks will eventually have the same self-driving capabilities as their cars. Other autonomous vehicle companies report similar timelines, with 2020 being the first year of mass adoption. And it’s not just those driving trucks who are at risk. A senior official at one of the major ride-sharing companies told me that their internal projections are that half of their rides will be given by autonomous vehicles by 2022. This has the potential to affect about 300,000 Uber and Lyft drivers in the United States. The replacement of drivers will be one of the most dramatic, visible battlegrounds between automation and the human worker. Companies can eliminate the jobs of call center workers, retail clerks, fast food workers, and the like with minimal violence and fuss.

Companies can eliminate the jobs of call center workers, retail clerks, fast food workers, and the like with minimal violence and fuss. Truck drivers will be different. Right now, the federal government has said that it will allow autonomous vehicles in any states that permit them. One industry report noted that “the [U.S.] Department of Transportation is throwing its full support behind development of autonomous vehicles as a way to improve safety on our roadways.” In 2016 the trucking industry spent $9.1 million on lobbying, and the Ohio government has already committed $15 million to set up a 35-mile stretch of highway outside Columbus for testing self-driving trucks. Arizona, California, and Nevada have begun allowing self-driving car trials in their states, and others will follow. Will truckers and the industry fight back?

… about half of the 310,000 residents who left the workforce in Michigan between 2003 and 2013 went on disability: Chad Halcon, “Disability Rolls Surge in State: One in 10 Workers in Michigan Collecting Checks,” Crain’s Detroit Business, June 26, 2015. The average age of truck drivers is 49…: Sean Kilcarr, “Demographics Are Changing Truck Driver Management,” FleetOwner, September 20, 2017. Morgan Stanley estimated the savings of automated freight delivery…: Autonomous Cars: Self-Driving the New Auto Industry Paradigm, Morgan Stanley Blue Paper, November 6, 2013. Crashes involving large trucks killed 3,903 people…: Olivia Solon, “Self-Driving Trucks: What’s the Future for America’s 3.5 Million Truckers?” The Guardian, June 17, 2016. … 88 percent of drivers have at least one risk factor for chronic disease: W. Karl Sieber et al., “Obesity and Other Risk Factors: The National Survey of U.S.


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

3D printing, Airbnb, algorithmic trading, Amazon Web Services, anti-work, artificial general intelligence, autonomous vehicles, basic income, business cycle, cloud computing, collective bargaining, correlation does not imply causation, creative destruction, data is the new oil, David Graeber, David Ricardo: comparative advantage, deindustrialization, deskilling, disintermediation, Donald Trump, Erik Brynjolfsson, feminist movement, Frederick Winslow Taylor, future of work, gig economy, global supply chain, income inequality, informal economy, Internet of things, Jarndyce and Jarndyce, Jarndyce and Jarndyce, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, knowledge economy, Loebner Prize, low skilled workers, Lyft, Mark Zuckerberg, means of production, moral panic, Network effects, new economy, off grid, pattern recognition, post-work, Ronald Coase, Second Machine Age, self-driving car, sharing economy, Steve Jobs, strong AI, technoutopianism, The Chicago School, The Future of Employment, the market place, The Nature of the Firm, The Wealth of Nations by Adam Smith, Thorstein Veblen, Turing test, Uber for X, uber lyft, universal basic income, wealth creators, working poor

There’s a Worldwide Shortage of the Board Game Go after Google’s Computer Beat the World Champ. Business Insider. Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T., & Hassabis, D. (2018). Mastering the Game of Go Without Human Knowledge. Nature, 550, 354–359. Tschangho, J. K. (2018). Automated Autonomous Vehicles: Prospects and Impacts on Society. Journal of Transportation Technologies, 8, 137–150. Walsh, T. (2018). Machines That Think: The Future of Artificial Intelligence. Prometheus Books. Part V Work in the Digital Economy 13 Work in the Digital Economy Daniel Susskind In this talk, I want to do two things. First, I want to offer a very brief intellectual history of the way in which many economists have thought about technological change and its impact on the labour market in recent decades.

Colton To summarize so far, I believe that any technical limitations on the abilities of AI systems to become intelligent enough for employment in workplaces instead of people are not in terms of fundamental, theoretical issues, but rather in terms of the speed to make scientific discoveries about computational intelligence, and in terms of engineering systems to take advantage of these breakthroughs. While it would not surprise me if we saw autonomous cars routinely on our streets in 10 years’ time, it would also not surprise me if it took another 50 years for this to happen (Tschangho 2018). The other limitations on the usage of AI in the workplace, will I hope, be self-imposed, as society in general responds to automation. Slowly but surely, AI systems will gain abilities to take on the duties of people undertaking intelligent tasks. To highlight what I see as the main issue with this, I use a thought experiment called: ‘a new kind of lucky’, as follows.

We could, of course, campaign as a society for ethical roll-out of automation, and press our politicians to enshrine this in legislation. However, quite the opposite seems to be happening. Some taxi companies for example seem to be using human labour purely as a stop-­ gap to raise venture capital for research and development, so that in the 12 Possibilities and Limitations for AI: What Can’t Machines Do? 115 longer-term they can roll-out a fleet of autonomous cars, eventually putting their entire human workforce out of a job (Price 2019). I hold quite a utopian view that automation can free humanity from the drudgery of meaningless toil—which, taking a sincere look at the world of work—is what many people in paid employment are asked, or indeed forced via circumstance, to do. This may be a naive position to hold, and certainly has numerous issues. In particular, it is clear that long-term benefits to society from automation will bring short/medium-­ term difficulties in job losses, with accompanying loss of earnings, self-­ esteem and well-being.


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

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

If it senses that a driver is highly stressed, it can suggest relaxing music and give the GPS guidance a more soothing tone of voice. If the driver is spacing out, it could jar him back to attention by making the steering wheel vibrate. Most intriguingly (or hilariously), the researchers even used special thermochromatic paint to make a car change its external color based on the driver’s emotional state, as a signal to other drivers. Whether this technology has time to be commercialized before the arrival of autonomous vehicles makes it irrelevant is a separate question. The power of computers to sense human emotions means, inevitably, that a machine can outdo us even in detecting our own emotions. It’s surely tempting to suppose that I possess a sense of my own emotional state that no entity standing outside of me, human or electronic, could ever reach. Yet of course it isn’t true. We’ve all had the experience of asking someone why they’re in a bad mood and having that person, eyes flaming, roar, “I’m not in a bad mood!”

Watson could do it at light speed with an electronic signal, so the developers interposed a delay to level the playing field. Otherwise I’d never have a prayer of winning, even if we both knew the correct response. But, of course, even with the delay, I lost. So let’s confront reality: Watson is smarter than I am. In fact, I’m surrounded by technology that’s better than I am at sophisticated tasks. Google’s autonomous car is a better driver than I am. The company has a whole fleet of vehicles that have driven hundreds of thousands of miles with only one accident while in autonomous mode, when one of the cars was rear-ended by a human driver at a stoplight. Computers are better than humans at screening documents for relevance in the discovery phase of litigation, an activity for which young lawyers used to bill at an impressive hourly rate.

This is the kind of work that computers for decades could hardly do at all. An example illustrates the gap in abilities: In 1997 a computer could beat the world’s greatest chess player yet could not physically move the pieces on the board. But again the technology needed only time, a few more doublings of power. The skills of physical work are also not immune to the advance of infotech. Google’s autonomous cars are an obvious and significant example—significant because the number one job among American men is truck driver. Many more examples are appearing. You can train a Baxter robot (from Rethink Robotics) to do all kinds of things—pack or unpack boxes, take items to or from a conveyor belt, fold a T-shirt, carry things around, count them, inspect them—just by moving its arms and hands (“end-effectors”) in the desired way.


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Subscribed: Why the Subscription Model Will Be Your Company's Future - and What to Do About It by Tien Tzuo, Gabe Weisert

3D printing, Airbnb, airport security, Amazon Web Services, augmented reality, autonomous vehicles, blockchain, Build a better mousetrap, business cycle, business intelligence, business process, call centre, cloud computing, cognitive dissonance, connected car, death of newspapers, digital twin, double entry bookkeeping, Elon Musk, factory automation, fiat currency, Internet of things, inventory management, iterative process, Jeff Bezos, Kevin Kelly, Lean Startup, Lyft, manufacturing employment, minimum viable product, natural language processing, Network effects, Nicholas Carr, nuclear winter, pets.com, profit maximization, race to the bottom, ride hailing / ride sharing, Sand Hill Road, shareholder value, Silicon Valley, skunkworks, smart meter, social graph, software as a service, spice trade, Steve Ballmer, Steve Jobs, subscription business, Tim Cook: Apple, transport as a service, Uber and Lyft, uber lyft, Y2K, Zipcar

Companies need to invest billions of dollars in factories and distribution channels to make the whole production process work. Also, the three largest automakers’ financial resources are huge. Since GM and Chrysler emerged from bankruptcy in 2009, the Big Three have invested more than $30 billion in new jobs and facilities. The American automobile industry spends $18 billion a year on research and development, focusing on fuel-efficient, electric, and autonomous vehicles. GM CEO Mary Barra says that her company is “quarters, not years” away from deploying fully autonomous vehicles at scale. These car companies have spent decades crafting their vehicles and their brands, and as a result enjoy some forbidding advantages, but there is a red flag—if they don’t know their drivers by the time autonomy and access-based consumption roll around, they will lose out to a competitor. Finally, the Big Three are currently in the midst of reimagining themselves as not just car manufacturers, but transportation solutions.

All due respect to other potential ecommerce vendors, but Amazon has my business, in no small part due to Amazon Prime—they hooked me with the free shipping, and now I’ve got music, movies, and all sorts of other services. I’m not going anywhere. Uber and Lyft are both vying for that same lock-in effect by offering discounted services around consistent consumption patterns—in other words, they’re going after my commute. As Lyft president John Zimmer, anticipating fully autonomous vehicles, told The New York Times: “The cost of owning a car is $9,000 a year. Let’s say we offer a $500 monthly plan in which you can tap a button and get access to transportation whenever you want it, and you get to choose your room-on-wheels experience. Maybe you want a cup of coffee on your way to work, or you want to watch the Warriors game later, so you’re in what’s basically a sports bar, with a bartender.”

get a car subscription for two months Christina Bonnington, “You Will No Longer Lease a Car. You Will Subscribe to It,”Slate, December 2, 2017, www.slate.com/articles/technology/technology/2017/12/car_subscriptions_ford_volvo_porsche_and_cadillac_offer_lease_alternative.html. a sports bar, with a bartender “The Rev-Up: Imagining a 20% Self Driving World,” The New York Times, November 8, 2017, www.nytimes.com/interactive/2017/11/08/magazine/tech-design-future-autonomous-car-20-percent-sex-death-liability.html?_r=0. 250 million connected cars on the road by 2020 “Gartner Says by 2020, a Quarter Billion Connected Vehicles Will Enable New In-Vehicle Services and Automated Driving Capabilities,” January 26, 2015, www.gartner.com/newsroom/id/2970017. Without control over the platform, PC hardware Horace Dediu, “IBM and Apple: Catharsis,” July 15, 2014, www.asymco.com/2014/07/15/catharsis.


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Always Day One: How the Tech Titans Plan to Stay on Top Forever by Alex Kantrowitz

accounting loophole / creative accounting, Albert Einstein, AltaVista, Amazon Web Services, augmented reality, Automated Insights, autonomous vehicles, Bernie Sanders, Clayton Christensen, cloud computing, collective bargaining, computer vision, Donald Trump, drone strike, Elon Musk, Firefox, Google Chrome, hive mind, income inequality, Infrastructure as a Service, inventory management, iterative process, Jeff Bezos, job automation, Jony Ive, knowledge economy, Lyft, Mark Zuckerberg, Menlo Park, new economy, Peter Thiel, QR code, ride hailing / ride sharing, self-driving car, Silicon Valley, Skype, Snapchat, Steve Ballmer, Steve Jobs, Steve Wozniak, Tim Cook: Apple, uber lyft, wealth creators, zero-sum game

“Apple Delaying HomePod Smart Speaker Launch until next Year.” 9to5Mac, November 17, 2017. https://9to5mac.com/2017/11/17/homepad-delay/. Apple moved two hundred employees off its struggling Project Titan: Kolodny, Lora, Christina Farr, and Paul A. Eisenstein. “Apple Just Dismissed More than 200 Employees from Project Titan, Its Autonomous Vehicle Group.” CNBC. CNBC, January 24, 2019. https://www.cnbc.com/2019/01/24/apple-lays-off-over-200-from-project-titan-autonomous-vehicle-group.html. “How is the work culture”: “How Is the Work Culture at the IS&T Division of Apple?” Quora. https://www.quora.com/How-is-the-work-culture-at-the-IS-T-division-of-Apple. fifteen-dollar-per-hour wage floor: Salinas, Sara. “Amazon Raises Minimum Wage to $15 for All US Employees.” CNBC. CNBC, October 2, 2018. https://www.cnbc.com/2018/10/02/amazon-raises-minimum-wage-to-15-for-all-us-employees.html

As our call wound down, Cowie noted the discussion was getting dark. “You’re bringing me down,” he said. When you look into the Black Mirror, happy endings are hard to come by. From Doomsday to Disneyland? Linda, an accountant at a midsize financial services firm, gets made fun of by her husband and two kids as she makes them breakfast and sees them off for the day. A tear runs down her cheek as she takes an autonomous vehicle to work. When Linda arrives at the office, she’s approached by a consultant who tells her she’s going to wear a recording device at all times. The company has already automated her entire department, so she understands what’s coming. One month later, after the device has fully recorded her work, Linda’s self-driving car plunges into a lake. Dealing with the loss, her family sits down to review the recording device’s footage, and they’re struck by what they see.

It’s made them more useful with wearables like the Apple Watch (a watch for iPhone owners) and AirPods (earbuds for iPhone owners). And it’s made daily life better for iPhone owners with features like Face ID and Apple Pay (both delightful). Few companies get more out of their existing assets than Apple. Inventing beyond these devices, however, is another story. Apple’s bets to create ambitious new products—like the HomePod and its own autonomous car—are failing. And Apple’s refinement culture, a relic of the Jobs era, is to blame. The Refiner’s Mindset In place of Jobs, six Apple executives drive the company today, delivering ideas that the rest of the company executes. They are: Tim Cook, the unassuming CEO with an operations background. Eddy Cue, the colorful senior vice president of software and services. Phil Schiller, the deceptively powerful head of product marketing.


pages: 424 words: 114,905

Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again by Eric Topol

23andMe, Affordable Care Act / Obamacare, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, augmented reality, autonomous vehicles, bioinformatics, blockchain, cloud computing, cognitive bias, Colonization of Mars, computer age, computer vision, conceptual framework, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, dark matter, David Brooks, digital twin, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, fault tolerance, George Santayana, Google Glasses, ImageNet competition, Jeff Bezos, job automation, job satisfaction, Joi Ito, Mark Zuckerberg, medical residency, meta analysis, meta-analysis, microbiome, natural language processing, new economy, Nicholas Carr, nudge unit, pattern recognition, performance metric, personalized medicine, phenotype, placebo effect, randomized controlled trial, recommendation engine, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, speech recognition, Stephen Hawking, text mining, the scientific method, Tim Cook: Apple, War on Poverty, Watson beat the top human players on Jeopardy!, working-age population

Goodfellow, “Privacy and Machine Learning: Two Unexpected Allies?,” cleverhans-blog. 2018. 58. Etzioni, O., “How to Regulate Artificial Intelligence,” New York Times. 2017; Simonite, T., “Do We Need a Speedometer for Artificial Intelligence?” Wired. 2017. 59. Bonnefon, J. F., A. Shariff, and I. Rahwan, “The Social Dilemma of Autonomous Vehicles.” Science, 2016. 352(6293): pp. 1573–1576. 60. Bonnefon, Shariff, and Rahwan, “The Social Dilemma of Autonomous Vehicles.” 61. Bonnefon, Shariff, and Rahwan, “The Social Dilemma of Autonomous Vehicles.” 62. Road traffic injuries, ed. World Health Organization. 2018. 63. Howard, B., “Fatal Arizona Crash: Uber Car Saw Woman, Called It a False Positive,” Extreme Tech. 2018. 64. AI for Healthcare: Balancing Efficiency and Ethics, ed. Infosys. 2017. https://www.infosys.com/smart-automation/Documents/ai-healthcare.pdf. 65.

There is no correct answer, with the conflicts of moral values, cultural norms, and personal self-interest, but the majority of respondents did not go for the “greater good” choice of sacrificing themselves. Clearly, trying to deal with these issues in the design of an algorithm to control an autonomous vehicle will be formidable60 and has been labeled as one of “the thorniest challenges in artificial intelligence today.”61 Another layer of this dilemma is who should be involved in algorithmic design—consumers, manufacturers, government? As you might anticipate, companies are not enthusiastic about government regulation; many firms, including Microsoft and Google, have set up their own internal ethics boards, arguing that regulatory involvement might be counterproductive, delaying the adoption of self-driving cars over fringe issues when it already seems clear that autonomous vehicles will reduce traffic fatalities overall. But we don’t think of it in the big picture way. More than 1.25 million people are killed by human drivers each year, most by human error, but we as a society don’t bat an eye at the situation.62 The introduction of computers into the mix sets up a cognitive bias, not acknowledging the net benefit.

Giving a green light to an algorithm that is not properly validated, or that is easily hacked, could have disastrous implications. FIGURE 5.1: The three self-driving car traffic situations that result in imminent, unavoidable harm. The car must decide whether to (A) kill several pedestrians or one passerby, (B) kill one pedestrian or its own passenger, or (C) kill several pedestrians or its own passenger. Source: Adapted from J. F. Bonnefon et al., “The Social Dilemma of Autonomous Vehicles,” Science (2016): 352(6293), 1573–1576. The concern for ethical breaches and harm led not only to the formation of the AI Now Institute but to many other efforts across the globe to promote the ethics and safety of AI, including OpenAI, Pervade, Partnership on AI, the Future of Life Institute, the AI for Good Summit, and academic efforts at UC Berkeley, Harvard, the University of Oxford, and Cambridge.


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

Ada Lovelace, agricultural Revolution, AI winter, Albert Einstein, Alexey Pajitnov wrote Tetris, algorithmic trading, Amazon Mechanical Turk, Apple II, artificial general intelligence, Automated Insights, autonomous vehicles, book scanning, borderless world, call centre, cellular automata, Claude Shannon: information theory, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, drone strike, Elon Musk, Flash crash, friendly AI, game design, global village, Google X / Alphabet X, hive mind, industrial robot, information retrieval, Internet of things, iterative process, Jaron Lanier, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, life extension, Loebner Prize, Marc Andreessen, Mark Zuckerberg, Menlo Park, natural language processing, Norbert Wiener, out of africa, PageRank, pattern recognition, Ray Kurzweil, recommendation engine, remote working, RFID, self-driving car, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, social intelligence, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, technological singularity, The Coming Technological Singularity, The Future of Employment, Tim Cook: Apple, too big to fail, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!

Not coincidentally, the finance sector quickly joined the video game business as an industry ready to throw money at AI researchers. The age of algorithmic trading had begun. Another eye-catching application of neural nets during this time was the invention of the self-driving car. Autonomous vehicles had been a long-time dream of technologists. In 1925, the inventor Francis Houdina demonstrated a radio-controlled car, which he drove through the streets of Manhattan without anyone at the steering wheel. Later, autonomous vehicle tests used guidewires and on-board sensors to follow painted white lines on the road or seek out the alternating current of buried cables. In 1969, John McCarthy came closest to describing modern self-driving vehicles when he wrote an essay with the provocative title, ‘Computer-Controlled Cars’.

In shops, bars, theme parks and museums, bluetooth beacons will transmit user-relevant information to your smartphone or wearable device depending on your location and personal preferences. On the street, by far the biggest visible change likely to happen in the next several decades will be the mass arrival of self-driving cars. Following on from the work of Dean Pomerleau, as described in the last chapter, both Google and Apple have invested in this field and look set to play a key role in bringing autonomous vehicles to the mainstream. Self-driving cars won’t only affect us on an individual level, but also collectively by helping to reduce traffic congestion in cities. The data that they gather will be vital to town planners as cities continue to expand. We are already starting to see how this may work. In early 2015, the Google-owned traffic app Waze teamed up with the city of Boston to reduce local traffic.

Imagine, for instance, a robot medic that is transporting an injured soldier to a field hospital encounters another soldier with an injured leg. Weighing up the pros and cons of stopping its mission to administer aid, potentially administering pain relief by applying traction in the field, and other conundrums are all complex issues for a human to navigate – let alone a machine. Issues like this will become ever more prevalent. Consider what would happen if a company that builds autonomous cars decides, in order to protect its driver, that it will make its vehicles swerve out of the way if they detect an imminent collision. This makes perfect sense, and is exactly what most of us would do if we were driving. However, what if your car is stopped at a red traffic light when it detects another vehicle coming up fast behind you? Knowing that there is almost certainly going to be a rear-end collision, your vehicle then makes the decision to swerve out of the way … and right into a group of school children walking home at the end of the day.


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The Technology Trap: Capital, Labor, and Power in the Age of Automation by Carl Benedikt Frey

"Robert Solow", 3D printing, autonomous vehicles, basic income, Bernie Sanders, Branko Milanovic, British Empire, business cycle, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, Clayton Christensen, collective bargaining, computer age, computer vision, Corn Laws, creative destruction, David Graeber, David Ricardo: comparative advantage, deindustrialization, demographic transition, desegregation, deskilling, Donald Trump, easy for humans, difficult for computers, Edward Glaeser, Elon Musk, Erik Brynjolfsson, everywhere but in the productivity statistics, factory automation, falling living standards, first square of the chessboard / second half of the chessboard, Ford paid five dollars a day, Frank Levy and Richard Murnane: The New Division of Labor, full employment, future of work, game design, Gini coefficient, Hyperloop, income inequality, income per capita, industrial cluster, industrial robot, intangible asset, interchangeable parts, Internet of things, invention of agriculture, invention of movable type, invention of the steam engine, invention of the wheel, Isaac Newton, James Hargreaves, James Watt: steam engine, job automation, job satisfaction, job-hopping, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kickstarter, knowledge economy, knowledge worker, labor-force participation, labour mobility, Loebner Prize, low skilled workers, Malcom McLean invented shipping containers, manufacturing employment, mass immigration, means of production, Menlo Park, minimum wage unemployment, natural language processing, new economy, New Urbanism, Norbert Wiener, oil shock, On the Economy of Machinery and Manufactures, Pareto efficiency, pattern recognition, pink-collar, Productivity paradox, profit maximization, Renaissance Technologies, rent-seeking, rising living standards, Robert Gordon, robot derives from the Czech word robota Czech, meaning slave, Second Machine Age, secular stagnation, self-driving car, Silicon Valley, Simon Kuznets, social intelligence, speech recognition, spinning jenny, Stephen Hawking, The Future of Employment, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Malthus, total factor productivity, trade route, Triangle Shirtwaist Factory, Turing test, union organizing, universal basic income, washing machines reduced drudgery, wealth creators, women in the workforce, working poor, zero-sum game

And in 1931—just before tractor adoption accelerated—the New York Times reported that, in Somerville, New Jersey, a tractor had crushed a four-year old boy to death, and one tractor was reported to have exploded, killing several people.28 It is also worth recalling that as engineers are pushing autonomous driving forward, accidents involving human drivers are happening every minute. A survey prepared for the National Highway Transportation Safety Administration of car crashes found that human error was responsible for 92.6 percent of them.29 And the number of casualties are many: just in 2013, 1.25 million people died in car accidents globally and 32,000 in the United States alone.30 Thus, autonomous cars do not need to be perfect to be justifiable. Human drivers are certainly not. There are still situations that autonomous vehicles struggle to handle, especially in crowded cities where pedestrians and cyclists provide additional complicating elements. In Singapore, autonomous taxis have a safety driver in them who takes over in emergencies, to minimize the possibility of accidents. But while self-driving cars are still at an experimental stage, successful trips in city traffic have already been accomplished.

But by storing records from the last time that snow fell, AI can now handle this problem.21 AI researchers have shown that algorithmic drivers now are able to identify major changes in the environment in which they operate, such as roadwork.22 In a major study, my Oxford engineering colleagues Bonolo Mathibela, Paul Newman, and Ingmar Posner concluded: “A vehicle can therefore prepare for the possibility of encountering humans on the road, or areas where [the vehicle] may not be stationary—thus gaining a dynamic sense of situational awareness, like a human.”23 While it is still early days, autonomous vehicles are being deployed in a number of settings. Some agricultural vehicles, forklifts, and cargo-handling vehicles are already autonomous; and in recent years hospitals have begun to use autonomous robots to transport food, prescriptions, and samples.24 In 2017, Rio Tinto, an Anglo-Australian metals and mining giant, announced that it will expand its fleet of autonomous hauling trucks in its Pilbara mine by 50 percent by 2019, making operations fully autonomous.25 But so far, the adoption of autonomous vehicles has mostly been limited to relatively structured environments like warehouses, hospitals, factories, and mines. When computer programs can better anticipate the range of objects and scenarios a vehicle may encounter, automation is relatively straightforward.

., 2018, IPUMS USA, version 8.0 (dataset), https://usa.ipums.org/usa/. Today, the largest single occupation in most American states is that of the truck driver (figure 20). It is true, as the economist Austan Goolsbee has pointed out, that if all 3.5 million truck, bus, and taxi drivers lose their jobs to autonomous vehicles over a fifteen-year period, that would amount to just over nineteen thousand per month: in 2017, 5.1 million Americans were separated from their jobs on a monthly basis, while 5.3 million jobs were generated on average. In this scenario, autonomous vehicles would increase the separation rate by less than four-tenths of a percent.111 And this would be very unlikely to happen over a fifteen-year period. Technology adoption is never frictionless, and it will take much longer for taxis to become fully autonomous than long-haul trucks.


pages: 374 words: 111,284

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

"Robert Solow", 3D printing, agricultural Revolution, AI winter, Albert Einstein, anti-work, autonomous vehicles, basic income, Ben Bernanke: helicopter money, Bernie Sanders, blockchain, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chris Urmson, computer age, conceptual framework, corporate governance, correlation does not imply causation, creative destruction, David Ricardo: comparative advantage, deindustrialization, deskilling, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, facts on the ground, financial intermediation, full employment, future of work, income inequality, income per capita, industrial robot, Internet of things, invention of the wheel, Isaac Newton, James Watt: steam engine, Jeff Bezos, job automation, job satisfaction, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, Kevin Kelly, license plate recognition, Marc Andreessen, Mark Zuckerberg, market bubble, mega-rich, natural language processing, Network effects, new economy, Nicholas Carr, Paul Samuelson, Peter Thiel, positional goods, quantitative easing, RAND corporation, Ray Kurzweil, Richard Florida, ride hailing / ride sharing, rising living standards, road to serfdom, Robert Gordon, Robert Shiller, Robert Shiller, Second Machine Age, secular stagnation, self-driving car, Silicon Valley, Simon Kuznets, Skype, social intelligence, spinning jenny, Stanislav Petrov, Stephen Hawking, Steven Pinker, technological singularity, The Future of Employment, The Wealth of Nations by Adam Smith, Thomas Malthus, trade route, universal basic income, US Airways Flight 1549, Vernor Vinge, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, wealth creators, winner-take-all economy, Y2K, Yogi Berra

The problem seems to be that human drivers find it difficult to interact with other vehicles when the latter are driverless. This is such a problem that some tech companies are trying to make driverless cars less robotic, even inducing them to cut corners, be aggressive, and inch forward at junctions. In fact, things aren’t quite so simple as even this might seem to imply. For all the boasts about what their autonomous vehicles can do and the reports that their vehicles have passed so many tests with flying colors, the claims of the manufacturers and developers of autonomous vehicles cannot be taken seriously. For these tests are usually conducted in secret and without independent verification. We do not know – and we are not allowed to know – the road and weather conditions that the vehicles were subjected to, nor how far they were dependent on any human intervention. It is significant that so much of the experience with driverless vehicles so far has been in locations like Phoenix, Arizona, a place blessed with a predictable and attractive climate and good driving conditions.

The state of California has recently approved new rules allowing driverless cars to operate without a human driver sitting behind the wheel. In the UK the Chancellor of the Exchequer, Philip Hammond, told the BBC that he aimed to have “fully driverless cars” in use by 2021. About 50 companies, including Alphabet, Apple, Ford, GM, Toyota, and Uber, are already testing self-driving cars in California. Indeed, more than a hundred trials of autonomous vehicles are currently taking place around the world. Moreover, according to the companies developing them, the performance of self-driven cars is already impressive and is improving all the time. All these companies have invested huge sums and clearly believe that driverless vehicles are the future. But, of course, this does not necessarily mean that they are right – or that they will get a good return on their money.

AI and terrorism Terrorists are also starting to use AI for encryption purposes. Extremist groups have started to employ “virtual planner” models of terrorism, as a way of managing lone attackers. Top operatives are able to recruit members, coordinate the target and timing of attacks, and provide assistance on topics like bomb-making, without being detected. Furthermore, there are fears that terrorists will acquire autonomous vehicles and drones to perform attacks. Naturally, just as in conventional warfare, these dangers have led to a corresponding increase in defensive activities. Facebook has counterterrorism detection systems that operate using AI. It says that it removed or flagged 1.9 million pieces of content linked to terrorism in the first part of 2018, nearly a twofold increase over the previous quarter. Nevertheless, its defensive moves may still be inadequate.


Innovation and Its Enemies by Calestous Juma

3D printing, additive manufacturing, agricultural Revolution, Asilomar, Asilomar Conference on Recombinant DNA, autonomous vehicles, big-box store, business cycle, Cass Sunstein, clean water, collective bargaining, colonial rule, computer age, creative destruction, Daniel Kahneman / Amos Tversky, deskilling, disruptive innovation, energy transition, Erik Brynjolfsson, financial innovation, global value chain, Honoré de Balzac, illegal immigration, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of movable type, invention of the printing press, Joseph Schumpeter, knowledge economy, loss aversion, Marc Andreessen, means of production, Menlo Park, mobile money, New Urbanism, Nicholas Carr, pensions crisis, phenotype, Ray Kurzweil, refrigerator car, Second Machine Age, self-driving car, smart grid, smart meter, stem cell, Steve Jobs, technological singularity, The Future of Employment, Thomas Kuhn: the structure of scientific revolutions, Travis Kalanick

For a discussion on ideas, see Bernard Barber, “Resistance by Scientists to Scientific Discovery,” Science 134, no. 3479 (1961): 596–602. 29. The bill read, “Autonomous vehicle operators must be a licensed driver who possesses an autonomous vehicle operator certificate issued by the DMV. The operator will be responsible for monitoring the safe operation of the vehicle at all times, and must be capable of taking over immediate control in the event of an autonomous technology failure or other emergency. In addition, operators will be responsible for all traffic violations that occur while operating the autonomous vehicle. These operator requirements create the safeguard of a driver who is capable of taking control of the vehicle when needed.” California Department of Motor Vehicles, “Summary of Draft Autonomous Vehicles Deployment Regulations,” December 16, 2015: 2. 30. Bob Sorokanich, “California Proposes Tightened Regulations on Autonomous Cars,” Roadandtrack.com, December 17, 2015. 31.

Self-driving cars will restructure transportation through new ownership patterns, insurance arrangements, and business models. Computer-aided diagnosis, robotic surgery, and myriad medical devices are already changing the role of doctors and how medical care is provided.7 Artificial intelligence and computer algorithms are influencing the way basic decisions are made. Battlefields are being automated with drones and other autonomous vehicles doing the work that used to be performed by a wide range of military personnel. Some of the advances are already shifting the locus of product development. Data-based firms such as Google and IBM are moving into pharmaceutical research. Sharing services such as Uber are acquiring robotics and other engineering capabilities. The implications of exponential growth will continue to elude political leaders if they persist in operating with linear worldviews.

Bob Sorokanich, “California Proposes Tightened Regulations on Autonomous Cars,” Roadandtrack.com, December 17, 2015. 31. R. Rycroft and D. Kash, “Path Dependence and the Modernization of Agriculture: A Case Study of Aragon, 1955–1985,” Technology Analysis and Strategic Management 14, no. 1 (2002): 21–35. 32. Marie-Laure Djelic and Sigird Quack, “Overcoming Path Dependency: Path Generation in Open Systems,” Theory and Society 36, no. 2 (2007): 161–186. 33. Bennett Alan Weinberg and Bonnie K. Bealer, The World of Caffeine: The Science and Culture of the World’s Most Popular Drug (London: Routledge, 2002), 77. 34. Selma Akyazici Özkoçak, “Coffeehouses: Rethinking the Public and Private in Early Modern Istanbul,” Journal of Urban History 33 (2007): 965–986; Brian W. Beeley, “The Turkish Village Coffeehouse as a Social Institution,” Geographical Review 60, no. 4 (1970): 475–493. 35.


pages: 667 words: 149,811

Economic Dignity by Gene Sperling

active measures, Affordable Care Act / Obamacare, autonomous vehicles, basic income, Bernie Sanders, Cass Sunstein, collective bargaining, corporate governance, David Brooks, desegregation, Detroit bankruptcy, Donald Trump, Double Irish / Dutch Sandwich, Elon Musk, employer provided health coverage, Erik Brynjolfsson, Ferguson, Missouri, full employment, gender pay gap, ghettoisation, gig economy, Gini coefficient, guest worker program, Gunnar Myrdal, housing crisis, income inequality, invisible hand, job automation, job satisfaction, labor-force participation, late fees, liberal world order, longitudinal study, low skilled workers, Lyft, Mark Zuckerberg, market fundamentalism, mass incarceration, mental accounting, meta analysis, meta-analysis, minimum wage unemployment, obamacare, offshore financial centre, payday loans, price discrimination, profit motive, race to the bottom, RAND corporation, randomized controlled trial, Richard Thaler, ride hailing / ride sharing, Ronald Reagan, Rosa Parks, Second Machine Age, secular stagnation, shareholder value, Silicon Valley, single-payer health, speech recognition, The Chicago School, The Future of Employment, The Wealth of Nations by Adam Smith, Toyota Production System, traffic fines, Triangle Shirtwaist Factory, Uber and Lyft, uber lyft, union organizing, universal basic income, War on Poverty, working poor, young professional, zero-sum game

Erik Brynjolfsson and Andrew McAfee also highlight 99Degrees Custom, an apparel maker in Lawrence, Massachusetts, as an example of how technology can generate jobs. “99Degrees Custom embraces a highly engineered, partially automated production line to make highly customized textile products.”70 That approach has allowed 99Degrees Custom to create new jobs that are “more varied, more highly skilled, and better paid” than “the old [textile] factory jobs.”71 The Massachusetts Executive Office of Housing and Economic Development gave the company a $2.8 million tax credit provided that the company hire 350 additional workers by 2023.72 Why shouldn’t all states provide tax credits for such companies that marry dignified labor and new technology? While most of the focus on autonomous vehicles and job loss has been on truck drivers and taxicab drivers, about six hundred thousand bus driver jobs—more than twice the number of taxi driver jobs—are at risk.73 Bus drivers typically work for the government and are disproportionately people of color and women.74 Instead of simply pocketing a reduction in labor costs due to autonomous vehicles, local governments could use extra tax dollars to reimagine how public transportation works for its citizens. Bus drivers are already “highly skilled customer-service” workers who manage conflicts and help people with directions, among other things.75 If we come to a day when fewer bus drivers are needed full-time, rather than just eliminate jobs, cities could train them and future “bus managers” to monitor and address bullying among schoolchildren, help older citizens explore their city, and better accommodate those with disabilities.

Millions worked under such harsh conditions that the poet William Blake called them “dark Satanic Mills.”5 Frey notes that for the Luddite generation, they and their children and grandchildren were worse off for three generations.6 As Frey writes, “most economists will acknowledge that technological progress will cause some adjustment problems in the short run. What is rarely noted is that the short run can be a lifetime.”7 Or three. Today, there are new looming threats that could cause even bigger upheavals than the automobile in the twentieth century—and they are occurring during an era of globalization that puts more pressure on jobs in the United States. Artificial intelligence (AI), robots, and autonomous vehicle technology are among the new technological advances that threaten major economic disruption. A quarter of American adults say the possibility that robots and computers could do many of the jobs done by humans makes them feel “very worried.”8 A widely cited study by Frey put the number of U.S. jobs at high risk of being automated in the next decade or two due to advances in AI and robots at 47 percent.9 According to a Brookings Institution study, thirty-six million jobs “will face high exposure to automation in the coming decades.”10 Some experts project up to three million jobs could be at risk due to self-driving trucks and cars.11 Martin Ford, author of Rise of the Robots, believes that artificial intelligence “could very well end up in a future with significant unemployment . . . maybe even declining wages . . .

Yet, as currently designed, these reforms turn back the clock from the protections for negative dignity in the private sector that grew out of the Progressive Era and were designed to ensure that the most vulnerable workers were not overwhelmed by the market power of employers. A NARROW VISION OF PEOPLE SUFFERING Some Dignity of Work Conservatives focus on the real policy neglect that many—particularly working-class white males—have felt in especially hard-hit manufacturing and coal communities, or could feel through technological disruptions like autonomous vehicles. They are certainly correct to draw attention to such neglect. But too often, their deployment of the need for “dignity” seems reserved for this limited segment of the population. And, having raised the sense of neglect, they call for precious few tangible policies that could remedy such dignity gaps. Much of the conservative rhetorical focus on dignity emerged in the shadow of Trump’s victory.


Industry 4.0: The Industrial Internet of Things by Alasdair Gilchrist

3D printing, additive manufacturing, Amazon Web Services, augmented reality, autonomous vehicles, barriers to entry, business intelligence, business process, chief data officer, cloud computing, connected car, cyber-physical system, deindustrialization, DevOps, digital twin, fault tolerance, global value chain, Google Glasses, hiring and firing, industrial robot, inflight wifi, Infrastructure as a Service, Internet of things, inventory management, job automation, low cost airline, low skilled workers, microservices, millennium bug, pattern recognition, peer-to-peer, platform as a service, pre–internet, race to the bottom, RFID, Skype, smart cities, smart grid, smart meter, smart transportation, software as a service, stealth mode startup, supply-chain management, trade route, undersea cable, web application, WebRTC, Y2K

It uses the IIoT for remote asset management and predictive maintenance. By using a strategy of sensors, remote communication, and Big Data analytics, Thames Water can anticipate equipment failures and respond quicker to any critical situation that may arise due to inclement weather. However, other industries have other tactical priorities when deploying IIoT, one being health and safety. Here we have seen some innovative projects from using drones and autonomous vehicles to inspect Oil and Gas lines in inhospitable areas to using autonomous mining equipment. Indeed Schlumberger is currently using an autonomous underwater vehicle to inspect sub-sea conditions. The unmanned vehicle travels around the ocean floor and monitors conditions for anything up to a year powered only by wave motion, which makes deployment in remote ocean locations possible, as they are both autonomous and self-sufficient requiring no local team support.

Ideally, a forklift would communicate with other forklifts, ensuring they were aware of one another to take avoiding action, such as slowing or stopping at blind intersections if another forklift is detected in the immediate vicinity. However, in the developed world it is still far more common to pick-by-paper, which is the term applied to the manual human picking of goods from a shelf. Forklifts, autonomous vehicles, and robots are great for heavy lifting of large pallets, but not much use for picking small intricate articles out of a stock bin. This is where human workers are in their element. Remember all those pedestrians being injured in the warehouse by forklifts? Well those pedestrians are most likely to be the pick-by-paper workforce. These are workers employed to collect individual stock items from a list.

The Control Domain A representation of the control domain is typically a collection of functional units that perform tasks such as reading data from sensors then logic units apply rules, logic, and subsequently applying feedback to the machines in order to exercise control over the process. In an industrial scenario, accuracy and resolution are both critical components of the control functions, and as such, the logic, the compute element, is usually situated as close to the sensors as is technically feasible. Examples of control domains may be in a large IIS system, for example, a control room in a nuclear plant or in smaller IISs a microprocessor in an autonomous vehicle, which controls temperature in a smart office. The control domain is made up of a set of common functions, which may well vary in their complexity. An example could be that the IIS will require sensors and therefore the control domain will require a function to be able to read sensor activity. This could require not just hardware, software but also analytics, as there can be a requirement for recursive sensing, which is a complex feedback mechanism that requires real-time linkage to the IIS.


pages: 323 words: 90,868

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

"Robert Solow", 3D printing, Airbnb, American energy revolution, assortative mating, autonomous vehicles, Bakken shale, barriers to entry, basic income, Bernie Sanders, BRICs, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, Clayton Christensen, cloud computing, collective bargaining, computer age, creative destruction, dark matter, David Ricardo: comparative advantage, deindustrialization, dematerialisation, Deng Xiaoping, deskilling, disruptive innovation, 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, intangible asset, interchangeable parts, Internet of things, inventory management, invisible hand, 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, Marc Andreessen, mass immigration, means of production, new economy, performance metric, pets.com, post-work, 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 Future of Employment, The Nature of the Firm, The Rise and Fall of American Growth, The Spirit Level, The Wealth of Nations by Adam Smith, trade liberalization, transaction costs, Tyler Cowen: Great Stagnation, Uber and Lyft, Uber for X, uber lyft, very high income, working-age population

About five million Americans work providing ‘transportation services’, including about half a million cab drivers and nearly one and a half million drivers of freight trucks.13 Autonomous vehicles could eliminate all of that work. But that would only be the beginning. Driverless vehicles might double as nannies, picking up youngsters from school and delivering them to a parent’s office or an after-school activity. They could facilitate the near-complete automation of massive amounts of retail; many grocery shops might vanish as consumers could instead get into the habit of mentioning to their smartphone when a bottle of wine is needed, which could then be ferried from a nearby warehouse by autonomous car. Car ownership might itself become obsolete, since vehicles of any sort could be hailed instantly. Traffic might vanish in the space of a few years, while the massive tracts of land given over to parking lots could suddenly be used more productively.

With a few keystrokes they can see whether rearranging the enormous machines will save time or leave robots banging their metal arms together. Today, automobile manufacturing is first and foremost a software business, as opposed to an industrial operation. The value of the code in the machines becomes relatively more important as cars get smarter; Volvo, like many manufacturers, is working to get autonomous vehicles in regular operation on Swedish streets within the next few years. Already the cars are smart enough to do much of the brainwork involved in driving, from plotting routes to keeping a safe distance from the car ahead. Driverless cars are not yet generating discomfort among the men who drive cabs around central Gothenburg, many of whom are immigrants or the children of immigrants. The hollowing out of the industrial workforce is, however.

Karabarbounis, Loukas, and Neiman, Brent, ‘The Global Decline of the Labor Share’, Quarterly Journal of Economics; Elsby, Michael, Hobijn, Bart, and Sahin, Aysegul, ‘The Decline of the US Labor Share’, Brookings Papers on Economic Activity, Fall 2013. 3. In Search of a Better Sponge   1. EIA (http://www.eia.gov/beta/international).   2. BLS, State and Metro Area Employment, hours and earnings.   3. Logan, Bryan, ‘Mercedes-Benz’s Self-driving Big-rig Proves that Autonomous Vehicles are Coming Sooner than We Think’, Tech Insider, 5 October 2015.   4. Crooks, Ed, and Hornby, Lucy, ‘Sunshine Revolution: The Age of Solar Power’, Financial Times, 5 November 2015.   5. BLS, Current Employment Statistics.   6. From the author’s own conversations with Michael Mandel.   7. BLS, ibid.   8. ‘The Digital Degree’, The Economist, 28 June 2014.   9. ‘Wealth by Degrees’, ibid. 10. 


pages: 561 words: 157,589

WTF?: What's the Future and Why It's Up to Us by Tim O'Reilly

4chan, Affordable Care Act / Obamacare, Airbnb, Alvin Roth, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, basic income, Bernie Madoff, Bernie Sanders, Bill Joy: nanobots, bitcoin, blockchain, Bretton Woods, Brewster Kahle, British Empire, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, Chuck Templeton: OpenTable:, Clayton Christensen, clean water, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, computer vision, corporate governance, corporate raider, creative destruction, crowdsourcing, Danny Hillis, data acquisition, deskilling, DevOps, Donald Davies, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, Firefox, Flash crash, full employment, future of work, George Akerlof, gig economy, glass ceiling, Google Glasses, Gordon Gekko, gravity well, greed is good, Guido van Rossum, High speed trading, hiring and firing, Home mortgage interest deduction, Hyperloop, income inequality, index fund, informal economy, information asymmetry, Internet Archive, Internet of things, invention of movable type, invisible hand, iterative process, Jaron Lanier, Jeff Bezos, jitney, job automation, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Kelly, Khan Academy, Kickstarter, knowledge worker, Kodak vs Instagram, Lao Tzu, Larry Wall, Lean Startup, Leonard Kleinrock, Lyft, Marc Andreessen, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, McMansion, microbiome, microservices, minimum viable product, mortgage tax deduction, move fast and break things, move fast and break things, Network effects, new economy, Nicholas Carr, obamacare, Oculus Rift, packet switching, PageRank, pattern recognition, Paul Buchheit, peer-to-peer, peer-to-peer model, Ponzi scheme, race to the bottom, Ralph Nader, randomized controlled trial, RFC: Request For Comment, Richard Feynman, Richard Stallman, ride hailing / ride sharing, Robert Gordon, Robert Metcalfe, Ronald Coase, Sam Altman, school choice, Second Machine Age, secular stagnation, self-driving car, SETI@home, shareholder value, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart contracts, Snapchat, Social Responsibility of Business Is to Increase Its Profits, social web, software as a service, software patent, spectrum auction, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, strong AI, TaskRabbit, telepresence, the built environment, The Future of Employment, the map is not the territory, The Nature of the Firm, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Davenport, transaction costs, transcontinental railway, transportation-network company, Travis Kalanick, trickle-down economics, Uber and Lyft, Uber for X, uber lyft, ubercab, universal basic income, US Airways Flight 1549, VA Linux, Watson beat the top human players on Jeopardy!, We are the 99%, web application, Whole Earth Catalog, winner-take-all economy, women in the workforce, Y Combinator, yellow journalism, zero-sum game, Zipcar

In order to maintain the benefits of the marketplace model, rather than deploying self-driving cars itself, Uber or Lyft might instead create incentives for its drivers to purchase them and make them available to the company. In many ways, this would change their business model to one closer to that of Airbnb, in which the participants in the marketplace provide an asset they own rather than their labor. But for this plan to work, Uber or Lyft would not need to develop their own autonomous vehicles, but instead could promote interoperability between different autonomous vehicle vendors. If the plan is something like “Buy your autonomous Tesla, drive it to work, and then let us use it for the rest of the day,” it would imply a mixed fleet of vehicles, requiring investments in interoperable control and dispatch. (Tesla seems to have other plans, though, forbidding their drivers from using their cars for Uber and Lyft, with the intention of rolling out its own competing service.

When people asked me what came after Web 2.0, I was quick to answer “collective intelligence applications driven by data from sensors rather than from people typing on keyboards.” Sure enough, advances in areas like speech recognition and image recognition, real-time traffic and self-driving cars, all depend on massive amounts of data harvested from sensors on connected devices. The current race in autonomous vehicles is a race not just to develop new algorithms, but to collect larger and larger amounts of data from human drivers about road conditions, and ever-more-detailed maps of the world created by millions of unwitting contributors. It’s easy to forget that in 2007, when Stanford won the DARPA Grand Challenge for self-driving vehicles, they did so by completing a seven-mile course in seven hours.

Ensuring interoperability of self-driving cars is as important as was the original interoperability that drove the Internet revolution. Open standards in this area will help ordinary people, not just big companies, to reap the benefits of the next wave of automation. Betsy Masiello, who works in public policy at Uber, responded to my questions on how the peer-to-peer model might mix with autonomous vehicles by saying that right now, people think of Uber as a replacement for taxis; perhaps instead, it will end up closer to peer-to-peer fractional car rental. It is likely that the reality will be a mix of both. Finally, if the augmented worker is indeed central to Uber and Lyft’s business model, perhaps the right way to think about self-driving cars is as a further augmentation, enabling new kinds of services.


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Machine, Platform, Crowd: Harnessing Our Digital Future by Andrew McAfee, Erik Brynjolfsson

"Robert Solow", 3D printing, additive manufacturing, AI winter, Airbnb, airline deregulation, airport security, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, backtesting, barriers to entry, bitcoin, blockchain, British Empire, business cycle, business process, carbon footprint, Cass Sunstein, centralized clearinghouse, Chris Urmson, cloud computing, cognitive bias, commoditize, complexity theory, computer age, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, Dean Kamen, discovery of DNA, disintermediation, disruptive innovation, distributed ledger, double helix, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, everywhere but in the productivity statistics, family office, fiat currency, financial innovation, George Akerlof, global supply chain, Hernando de Soto, hive mind, information asymmetry, Internet of things, inventory management, iterative process, Jean Tirole, Jeff Bezos, jimmy wales, John Markoff, joint-stock company, Joseph Schumpeter, Kickstarter, law of one price, longitudinal study, Lyft, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Marc Andreessen, Mark Zuckerberg, meta analysis, meta-analysis, Mitch Kapor, moral hazard, multi-sided market, Myron Scholes, natural language processing, Network effects, new economy, Norbert Wiener, Oculus Rift, PageRank, pattern recognition, peer-to-peer lending, performance metric, plutocrats, Plutocrats, precision agriculture, prediction markets, pre–internet, price stability, principal–agent problem, Ray Kurzweil, Renaissance Technologies, Richard Stallman, ride hailing / ride sharing, risk tolerance, Ronald Coase, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, slashdot, smart contracts, Snapchat, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Pinker, supply-chain management, TaskRabbit, Ted Nelson, The Market for Lemons, The Nature of the Firm, Thomas Davenport, Thomas L Friedman, too big to fail, transaction costs, transportation-network company, traveling salesman, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, ubercab, Watson beat the top human players on Jeopardy!, winner-take-all economy, yield management, zero day

Of course, this pattern of machines taking over tasks has been unfolding for many decades inside factories, where engineers can achieve high levels of what our MIT colleague David Autor calls “environmental control,” or “radically simplify[ing] the environment in which machines work to enable autonomous operation, as in the familiar example of a factory assembly line.” Environmental control is necessary when pieces of automation have primitive brains and no ability to sense their environments. As all the elements of DANCE improve together, however, pieces of automation can leave the tightly controlled environment of the factory and head out into the wide world. This is exactly what robots, drones, autonomous vehicles, and many other forms of digital machines are doing at present. They’ll do much more of it in the near future. What Humans Do in a World Full of Robots How will our minds and bodies work in tandem with these machines? There are two main ways. First, as the machines are able to do more work in the physical world, we’ll do less and less of it, and instead use our brains in the ways described in earlier chapters, and in the next one.

So, we can date the start of phase one of the second machine age to the middle of the 1990s. Phase two, which we believe we’re in now, has a start date that’s harder to pin down. It’s the time when science fiction technologies—the stuff of movies, books, and the controlled environments of elite research labs—started to appear in the real world. In 2010, Google unexpectedly announced that a fleet of completely autonomous cars had been driving on US roads without mishap. In 2011, IBM’s Watson supercomputer beat two human champions at the TV quiz show Jeopardy! By the third quarter of 2012, there were more than a billion users of smartphones, devices that combined the communication and sensor capabilities of countless sci-fi films. And of course, the three advances described at the start of this chapter happened in the past few years.

Their capabilities are impressive. As Urmson recounted at the 2015 TED conference, “Our vehicles were driving through Mountain View, and this is what we encountered. This is a woman in an electric wheelchair chasing a duck in circles on the road. Now it turns out, there is nowhere in the DMV handbook that tells you how to deal with that, but our vehicles were able to encounter that, slow down, and drive safely.” Autonomous cars that can drive safely in all circumstances and conditions are not here yet. But we think they’re coming quickly. The ability of machine language to overcome Polanyi’s Paradox is starting to be put to use in white-collar back-office work that has, to date, proved surprisingly resistant to complete automation. “Back office” is a catchall term for knowledge work that takes place out of sight of the customer, including purchasing, accounting, and IT.


pages: 293 words: 90,714

Copenhagenize: The Definitive Guide to Global Bicycle Urbanism by Mikael Colville-Andersen

active transport: walking or cycling, Airbnb, Albert Einstein, autonomous vehicles, business cycle, car-free, congestion charging, corporate social responsibility, Donald Trump, Edward Snowden, Enrique Peñalosa, functional fixedness, if you build it, they will come, Induced demand, intermodal, Jane Jacobs, Johann Wolfgang von Goethe, Kickstarter, Mahatma Gandhi, meta analysis, meta-analysis, neurotypical, out of africa, place-making, Ralph Waldo Emerson, self-driving car, sharing economy, smart cities, starchitect, transcontinental railway, urban planning, urban sprawl, Yogi Berra

There are, it must be said, examples of car companies greenwashing with bikes in the commercial, filming the car in the city from bikes, and even producing bikes that fit in the trunks of their cars. They’re worried and they don’t know exactly what to do. Two of the main Big Auto players, BMW and Ford, are trying to reinvent themselves as “mobility companies,” but largely the industry is still stuck in its ways. Add to this the concerted effort being made to hype electric vehicles and autonomous vehicles as the next big thing that will change the world. The former only eliminates one aspect of the problem—emissions. The latter brings new problems with it. I recall reading a quote on Twitter that “In Amsterdam, a Google self-driving car would park itself after a few minutes and start crying.” Both of them still occupy an arrogant amount of urban space. I spoke at the State of Design Festival in Melbourne a few years back.

Desperately trying to cement, in the public consciousness of its citizens, the rather outdated philosophy that cars rule supreme and everyone else is a mere pawn to be swept aside without regret. When look at similarities between all these organizations, one thing is shockingly clear. None of them will ever say that a drastic reduction of cars would save lives. It’s all talk and no serious action. They also have a tendency to support electric vehicles and autonomous vehicles, not at all aware of the irony that such vehicles still take up public space and will still contribute to death and injury. None of them have urban-planning experience, or if they do touch upon it, they never mention it. They spend most of their time vehemently protecting their status as “traffic safety authorities.” I know this from personal experience. I have been interviewed in various Nordic newspapers about the science of bicycle helmets and the negative effect that promotion and legislation have on cycling levels.

A few years ago I was watching Back to the Future with my son, who was nine at the time. The film ended and he asked me what year it was made in. I told him it was 1985. He laughed. “So Doc went 30 years into the future … that’s like … NOW! But there are no flying cars and goofy clothes …” Nope. He nailed it. A century of technological—and fashion—promises that failed to deliver. A saeculum horribilis from which we need to recover. Feel free to lump autonomous cars and the hype surrounding them into the same category. When I speak of the importance of going to back to the future, I mean to a place where we were rational and realistic. Back to a time—or times—where we did things that made sense. Graph by Professor Phil Goodwin showing traffic projections by Britain’s Department for Transport (in color) and the actual car traffic growth (black). Graph by Sightline Institute showing similar traffic projections by Washington State’s DoT and the actual car traffic growth.


pages: 375 words: 88,306

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

additive manufacturing, Airbnb, AltaVista, Amazon Mechanical Turk, autonomous vehicles, barriers to entry, basic income, bitcoin, blockchain, Burning Man, call centre, collaborative consumption, collaborative economy, collective bargaining, commoditize, corporate social responsibility, cryptocurrency, David Graeber, distributed ledger, employer provided health coverage, Erik Brynjolfsson, Ethereum, ethereum blockchain, Frank Levy and Richard Murnane: The New Division of Labor, future of work, George Akerlof, gig economy, housing crisis, Howard Rheingold, information asymmetry, Internet of things, inventory management, invisible hand, job automation, job-hopping, Kickstarter, knowledge worker, Kula ring, Lyft, Marc Andreessen, megacity, minimum wage unemployment, moral hazard, moral panic, Network effects, new economy, Oculus Rift, pattern recognition, peer-to-peer, peer-to-peer lending, peer-to-peer model, peer-to-peer rental, 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, Ross Ulbricht, 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, uber lyft, universal basic income, Zipcar

The results of a wide variety of independent study projects undertaken by my NYU undergraduates and MBA students have helped mold my early-stage research and thinking: the ones that stand out were by Humaira Faiz, Sydnee Grushack, Andrew Ng, and Jara Small (on inclusive growth in the sharing economy); Jonah Blumstein, Valeriya Greene and Eric Jacobson (on Airbnb and city regulations); Andrew Covell, Varun Jain, and June Khin (on the organization of sharing economy platforms); Phil Hayes (on surge pricing); Dmitrios Theocharis and Siri Zhan (on the on-demand workforce); Ann Dang, Louise Lai, and Daniella Tapia (on the global variation in regulation); Lauren Tai (on regulating autonomous vehicles); Karl Gourgue, Manasa Grandhi, and Joyce Fei (on decentralized models of research); Arra Malek, Ansh Patel, and Haley Zhou (on apparel rental models); Laura Kettell and Karina Alkhasyan (on peer-to-peer finance); and Keerthi Moudgal (on peer-to-peer retailing). Although I have been captivated by the sharing economy for many years now, the emergence of this book was catalyzed by a series of email messages that my editor at the MIT Press, Emily Taber, sent me in April 2015.

In aiming to strike a balance between practicality and prophesy, I have focused the book more heavily on the immediate future of crowd-based capitalism, rather than on the more distant future. The accommodation, transportation, and freelance labor sectors have been the earliest to see big changes induced by crowd-based capitalism, but commercial real estate, health care provision and energy production and distribution will soon follow. And the digitization of the physical will, over the coming decade, yield mass-market autonomous vehicles in the United States, Western Europe and parts of Asia, radically reshaping the automobile industry, shifting market power away from today’s leading manufacturers and towards a range of technology platforms—Uber, Lyft, Didi Kuaidi and Ola, as well as Apple, Google, and perhaps even Amazon. In parallel, the additive manufacturing revolution will change how artifacts are made, shifting more and more production into the crowd.

Indeed, taxi drivers (most of whom in larger cities do not own their cars or “medallions”) switch to Uber every day; we have already seen evidence of a drop of about 30% in the price of a New York City yellow cab medallion.30 And in July 2015, Evgeny Freidman, the largest owner of yellow cab medallions in New York, filed a petition to put many of his medallion-owning companies into bankruptcy.31 And the eventual impact of on-demand transportation will likely be on the automobile industry as a whole, accelerated by autonomous cars becoming a mass-market commercial reality over the next decade. A significant fraction of consumer spending on automobiles will shift to a growing variety of on-demand mobility services. Industrial organization economics teaches us that as product variety increases, people will consume more rather than less. This is partially the case because people who previously were not consuming are able to do so, or to do so more often (and in the case of accommodations, for longer periods of time in a wider variety of locations).


pages: 302 words: 95,965

How to Be the Startup Hero: A Guide and Textbook for Entrepreneurs and Aspiring Entrepreneurs by Tim Draper

3D printing, Airbnb, Apple's 1984 Super Bowl advert, augmented reality, autonomous vehicles, basic income, Berlin Wall, bitcoin, blockchain, Buckminster Fuller, business climate, carried interest, connected car, crowdsourcing, cryptocurrency, Deng Xiaoping, discounted cash flows, disintermediation, Donald Trump, Elon Musk, Ethereum, ethereum blockchain, family office, fiat currency, frictionless, frictionless market, high net worth, hiring and firing, Jeff Bezos, Kickstarter, low earth orbit, Lyft, Mahatma Gandhi, Mark Zuckerberg, Menlo Park, Metcalfe's law, Metcalfe’s law, Mikhail Gorbachev, Minecraft, Moneyball by Michael Lewis explains big data, Nelson Mandela, Network effects, peer-to-peer, Peter Thiel, pez dispenser, Ralph Waldo Emerson, risk tolerance, Robert Metcalfe, Ronald Reagan, Rosa Parks, Sand Hill Road, school choice, school vouchers, self-driving car, sharing economy, short selling, Silicon Valley, Skype, smart contracts, Snapchat, sovereign wealth fund, stealth mode startup, stem cell, Steve Jobs, Tesla Model S, Uber for X, uber lyft, universal basic income, women in the workforce, Y Combinator, zero-sum game

Every new innovation adds to the wealth of a society, and with all the innovation that comes from a world of people who are educated and up to date on new innovations, our world should grow substantially. The people of the world will have access to global information, global governance, global currency and global markets. They will be mobile and less tied to any single geographic region. And now for my wilder predictions: People will be traveling in autonomous vehicles and communicating through virtual (or augmented) reality. They will be living very long lives, while cures for cancer and the aging gene are discovered. Their health will be monitored by sensors designed into their clothes, which will be optically programmable to the owner’s tastes to match what is required for any occasion. Education will be a competitive, accountable industry, where teachers are regularly ranked with the best becoming enormous media celebrities and the worst no longer teaching.

AI, marketplaces and the blockchain promise to make life better for us as consumers, make our society wealthier as a whole, and free up working people to do more abstract and higher-level things with their lives. The jobs that computers can potentially do better are the monotonous jobs, like driving us or our things from one place to another, analyzing data patterns of customers, and administering regulations. These monotonous jobs will give way to jobs that are more abstract (and frankly more interesting) like monitoring autonomous vehicles, enhancing customer experiences, and improving banking, legal, accounting or even government service. While some may have difficulty adjusting to the new world, the jobs in the new world will be more interesting and more fulfilling. After all, before the industrial revolution, most people had to be out working on the farms, but with automation, a lot of those manual jobs were replaced with more interesting abstract jobs, and we adjusted.

It seems counterintuitive to say that a business should abstract itself away from today’s marketplace, but that is exactly what I am recommending. A more abstract business takes what is out there today and anticipates or even “guesses” what will happen in the future. Seeing Uber’s success, you might think of a delivery business, but there are many of them already out there, so you can abstract from there and think of a drone delivery business or an autonomous car delivery business. You might think that those abstract businesses would have a lower chance of success. They do! But I want you to think about success as a Startup Hero. A Startup Hero transforms the world, disrupts the status quo and looks for opportunities that others might think are crazy. The odds of success may be lower, but your expected value is greater if you are a Startup Hero. Using probability, let’s say you enter a startup business that already has fifty competitors.


pages: 347 words: 97,721

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

AI winter, Andy Kessler, artificial general intelligence, asset allocation, Automated Insights, autonomous vehicles, basic income, Baxter: Rethink Robotics, business intelligence, business process, call centre, carbon-based life, Clayton Christensen, clockwork universe, commoditize, conceptual framework, dark matter, David Brooks, deliberate practice, deskilling, digital map, disruptive innovation, Douglas Engelbart, Edward Lloyd's coffeehouse, Elon Musk, Erik Brynjolfsson, estate planning, fixed income, follow your passion, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, game design, general-purpose programming language, global pandemic, 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 Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joi Ito, Khan Academy, knowledge worker, labor-force participation, lifelogging, longitudinal study, 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, risk tolerance, Robert Shiller, Robert Shiller, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, six sigma, Skype, social intelligence, 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

The usual pattern, of course, is that once automation tackles relatively primitive tasks it moves up the ladder of complexity. We see no reason why this wouldn’t happen in surgery over the next couple of decades. Autonomous vehicles are another area of intelligent technology involving physical tasks—moving and getting things around. These vehicles employ a combination of GPS and digital maps, light radar (“lidar”), video cameras, and ultrasonic, radar, and odometry sensors to generate and analyze a massive amount of data about the vehicle’s position and surroundings. We probably don’t have to tell you too much about this area, because it gets more than its share of media attention. But it’s a good bet that autonomous cars and trucks will be commonplace on our streets within the next decade. If they’re not, it will probably be because of slow regulatory change processes, rather than technical limitations.

The second: “A robot must obey orders given to it by human beings, except where such orders would conflict with the first law.” 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.”


pages: 448 words: 117,325

Click Here to Kill Everybody: Security and Survival in a Hyper-Connected World by Bruce Schneier

23andMe, 3D printing, autonomous vehicles, barriers to entry, bitcoin, blockchain, Brian Krebs, business process, cloud computing, cognitive bias, computer vision, connected car, corporate governance, crowdsourcing, cryptocurrency, cuban missile crisis, Daniel Kahneman / Amos Tversky, David Heinemeier Hansson, Donald Trump, drone strike, Edward Snowden, Elon Musk, fault tolerance, Firefox, Flash crash, George Akerlof, industrial robot, information asymmetry, Internet of things, invention of radio, job automation, job satisfaction, John Markoff, Kevin Kelly, license plate recognition, loose coupling, market design, medical malpractice, Minecraft, MITM: man-in-the-middle, move fast and break things, move fast and break things, national security letter, Network effects, pattern recognition, profit maximization, Ralph Nader, RAND corporation, ransomware, Rodney Brooks, Ross Ulbricht, security theater, self-driving car, Shoshana Zuboff, Silicon Valley, smart cities, smart transportation, Snapchat, Stanislav Petrov, Stephen Hawking, Stuxnet, The Market for Lemons, too big to fail, Uber for X, Unsafe at Any Speed, uranium enrichment, Valery Gerasimov, web application, WikiLeaks, zero day

We prefer the more accurate machine-learning diagnostic system over the human technician, even though it can’t explain itself. For this reason, machine-learning systems are becoming more pervasive in many areas of society. For the same reasons, we’re allowing algorithms to become more autonomous. Autonomy is the ability of systems to act independently, without human supervision or control. Autonomous systems will soon be everywhere. A 2014 book, Autonomous Technologies, has chapters on autonomous vehicles in farming, autonomous landscaping applications, and autonomous environmental monitors. Cars now have autonomous features such as staying within lane markers, following a fixed distance behind another car, and braking without human intervention to avert a collision. Agents—software programs that do things on your behalf, like buying a stock if the price drops below a certain point—are already common.

Everyone wants to be central, essential, and in control of your world. And companies will give away services for free to get that access. Just as Google and Facebook give away services in exchange for the ability to spy on their users, companies will do the same thing with the IoT. Companies will offer free IoT stuff in exchange for the data they receive from monitoring the people using it. Companies owning fleets of autonomous cars might offer free rides in exchange for the ability to show ads to the passengers, mine their contacts, or route them past or make an intermediate stop at particular stores and restaurants. Battles for control of customers and users are going to heat up in the coming years. And while the monopolistic positions of companies like Amazon, Google, Facebook, and Comcast allow them to exert significant control over their users, smaller, less obviously tech-based companies—like John Deere—are attempting to do the same.

This is exactly the same issue as the copyright problem. Digital rights management was the technical solution that failed, and the DMCA was the law that came after. It has only been effective at preventing hobbyists from making copies of digital music and movies. It hasn’t prevented professionals from doing the same thing, and it hasn’t prevented the spread of copyrighted works with the DRM protections removed. The fear of hacked autonomous-car software or printed killer viruses will be much greater than the fear of illegally copied songs. The industries that will be affected are much more powerful than the entertainment industry. Both government and the private sector will look at the entertainment industry’s experience with DRM and correctly conclude that the problem is that computers are, by nature, extensible. They will look at the DMCA and conclude that the law wasn’t sufficiently onerous and restrictive.


pages: 419 words: 109,241

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

3D printing, agricultural Revolution, AI winter, Airbnb, Albert Einstein, algorithmic trading, artificial general intelligence, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, blue-collar work, British Empire, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, computer age, computer vision, computerized trading, creative destruction, David Graeber, David Ricardo: comparative advantage, demographic transition, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, drone strike, Edward Glaeser, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, financial innovation, future of work, gig economy, Gini coefficient, Google Glasses, Gödel, Escher, Bach, income inequality, income per capita, industrial robot, interchangeable parts, invisible hand, Isaac Newton, Jacques de Vaucanson, James Hargreaves, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joi Ito, Joseph Schumpeter, Kenneth Arrow, Khan Academy, Kickstarter, low skilled workers, lump of labour, Marc Andreessen, Mark Zuckerberg, means of production, Metcalfe’s law, natural language processing, Network effects, Occupy movement, offshore financial centre, Paul Samuelson, Peter Thiel, pink-collar, precariat, purchasing power parity, Ray Kurzweil, ride hailing / ride sharing, road to serfdom, Robert Gordon, Sam Altman, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Snapchat, social intelligence, software is eating the world, sovereign wealth fund, spinning jenny, Stephen Hawking, Steve Jobs, strong AI, telemarketer, The Future of Employment, The Rise and Fall of American Growth, the scientific method, The Wealth of Nations by Adam Smith, Thorstein Veblen, Travis Kalanick, Turing test, Tyler Cowen: Great Stagnation, universal basic income, upwardly mobile, Watson beat the top human players on Jeopardy!, We are the 99%, wealth creators, working poor, working-age population, Y Combinator

Not So Elementary, My Dear Watson,” Jefferies Franchise Note, 12 July 2017. See https://javatar.bluematrix.com/pdf/fO5xcWjc.   9.  Avery Hartmans, “These 18 Incredible Products Didn’t Exist 10 Years Ago,” Business Insider UK, 16 July 2017. 10.  Andre Esteva, Brett Kuprel, Roberto A. Novoa, et al., “Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks,” Nature 542 (2017): 115–18. 11.  See Jeff Reinke, “From Old Steel Mill to Autonomous Vehicle Test Track,” Thomas, 19 October 2017; Michael J. Coren, “Tesla Has 780 Million Miles of Driving Data, and Adds Another Million Every 10 Hours,” Quartz, 28 May 2016; and Alexis C. Madrigal, “Inside Waymo’s Secret World for Training Self-Driving Cars,” Atlantic, 23 August 2017. 12.  David McCandless, “Codebases: Millions of Lines of Code,” 24 September 2015, https://informationisbeautiful.net/visualizations/million-lines-of-code/ (accessed 25 April 2018). 13.  

See artificial narrow intelligence antitrust legislation apathy apotheosis Apple Archilochus Arendt, Hannah Ariely, Dan aristocracy Aristotle Arrow, Kenneth artificial general intelligence (AGI) artificial intelligence (AI) automata and bottom-up vs. top-down economics and fallacy of first wave of general history of priority shift and second wave of artificial narrow intelligence (ANI) artificial neural networks artisan class assembly lines AT&T Atari video games Atkinson, Anthony ATMs (automatic teller machines) automata automation, number of jobs at risk of automation anxiety automation risk autonomous vehicles Autor, David bandwagon effect bank tellers basic income conditional overview of universal Becker, Gary Bell, Daniel Berlin, Isaiah Beveridge, William Beveridge Report bigger-pie effect Big State capital-sharing and conditional basic income and income-sharing and labor-supporting meaning creation and overview of taxation and welfare state vs. Big Tech economic case against oversight of overview of political case against reasons for size of birds Blair, Tony bluffing boredom Bostrom, Nick botany bottom-up creation Brexit bricklaying robot Butler, Rab capital inequality between labor and sharing of taxing of two types of capital income, inequality in caregiving Carlsen, Magnus car manufacturing industry Carney, Mark Caruana, Fabiano car washes casinos category mistakes Catholic Church CBI.

We have seen examples of this at work already: AlphaGo, the first version of Google’s go-playing system, learned in part from an archive of thirty million past moves by the best human players; Stanford’s system for detecting skin cancer used almost 130,000 images of lesions, more than a human doctor could expect to review in their lifetime.10 Sometimes, though, the necessary data is not readily available and has to be gathered or generated in costly ways. Consider what it takes to train and evaluate a car-driving system, for example. To do this, Uber built an entire mock town on the site of an old steel mill in Pennsylvania, complete with plastic pedestrians that occasionally throw themselves into traffic, and gathers data as their cars drive around it. Tesla, meanwhile, collects data from its non-autonomous cars as they are driven by their owners, with about a million miles’ worth of data reportedly flowing in every hour. Google has adopted yet another approach to the problem, creating entire virtual worlds to gather data from cars driving around in these simulations.11 Then there is the matter of the software. Beneath all of these new technologies is the code that makes them work. Google’s assorted Internet services, for instance, require two billion lines of code: if these were to be printed out on paper and stacked up, the tower would be about 2.2 miles high.12 Writing good code requires talented—and expensive—software engineers.


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Soonish: Ten Emerging Technologies That'll Improve And/or Ruin Everything by Kelly Weinersmith, Zach Weinersmith

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 23andMe, 3D printing, Airbnb, Alvin Roth, augmented reality, autonomous vehicles, connected car, double helix, Elon Musk, en.wikipedia.org, Google Glasses, hydraulic fracturing, industrial robot, information asymmetry, Kickstarter, low earth orbit, market design, megastructure, microbiome, moral hazard, multiplanetary species, orbital mechanics / astrodynamics, personalized medicine, placebo effect, Project Plowshare, QR code, Schrödinger's Cat, self-driving car, Skype, stem cell, Tunguska event

Oxford and New York: Oxford University Press, 2009. Boeke, J. D., Church, G., Hessel, A., Kelley, N. J., Arkin, A., Cai, Y., Carlson, R., Chakravarti, A., Cornish, V. W., Holt, L., et al. The Genome Project-Write. Science 353, no. 6295 (2016):126–27. Bolonkin, Alexander. Non-Rocket Space Launch and Flight. Amsterdam and Oxford: Elsevier Science, 2006. Bonnefon, J.-F., Shariff, A., and Rahwan, I. “The Social Dilemma of Autonomous Vehicles.” Science 352, no. 6293 (2016): 1573–76. Bornholt, J., Lopez, R., Carmean, D. M., Ceze, L., Seelig, G., and Strauss, K. “A DNA-Based Archival Storage System.” Proceedings of the Twenty-First International Conference on Architectural Support (2016):637–49. Bostrom, Nick, and Cirkovic, Milan M. Global Catastrophic Risks. Oxford and New York: Oxford University Press, 2011. Botella, C., Bretón-López, J., Quero, S., Baños, R., and García-Palacios, A.

LiDAR can generate accurate 3D models of the environment, which is exactly what you’d want for augmented reality. Instead of comparing a universe of flawed 2D image files, you get the outline of local buildings and compare to a single 3D file. Sounds great! The problem is that it’s historically been superexpensive. Like, only used by huge government agencies expensive. But over time the cost has come down. In fact, one of the reasons autonomous cars are starting to come to market is that you can get a decent LiDAR system on your van for only a few thousand bucks. The downside is that the lightest ones still weigh around 10 to 20 pounds. Still, the technology for visual AR is coming along nicely. “But,” you interject, “what about my other senses? I WANT IT TO SOUND LIKE BIRDSONG WHEN I TURN THIS PAGE!” Well, first of all, stop yelling.

., 47, 313n arsenic, 211 art, 183 artemisinin, 198–200 artificial intelligence, 136, 139–40 artificial organs, see bioprinting artspeak, 138 Artsutanov, Yuri, 35 Asian elephants, 223 Asians, 196n asteroid mining, 52–69, 320n benefits of, 68–69 environmental degradation in, 66–67 finances of, 54–56 law and order in, 65–66 problems facing, 58–65 rights to, 63–64 safety and, 67 asteroid-moving technology, 67 asteroids: escape velocity of, 55 landing on, 62–63 net capture of, 63 rubble pile, 62 types of, 53–54 atmosphere, density of, 25, 29 atomic bombs, 79, 96, 98 atomic gardening, 191–92 ATP, 286 augmented reality (AR), 8n accuracy required for, 171 audio in, 174 benefits of, 183–86 concerns about, 180–83 hacking of, 183 hardware for, 168 location detection for, 171–74 markers in, 169–71 motion sickness in, 168 possible uses for, 164–66, 168, 177–79, 183–86 reference images in, 172–73 registration in, 166–68, 172–73 smell in, 174–75 vs. virtual reality, 165 where are we now in, 175–79 Augmented Reality Lab, 173, 177–78 Auschwitz, 183 Australia, 219 automotive industry, 136, 137 autonomous cars, 174 baby teeth, 99 bacteria, 203–5, 206, 210, 218 in bioprinted organs, 273 environmental monitoring by, 211–12 immune system of, 212–14 synthetic, 220–21 Bad Astronomy (blog), 36 Barbados, 47 Bartlett School of Graduate Studies, 141 Baseline Study, 254 bases, 192–93 Bauby, Jean-Dominique, 316 B cell, 242 behavior patterns, 231 Belize, 315, 316 Belleau Wood, Battle of, 178 Berger, Theodore, 308 beryllium, 92 Billinghurst, Mark, 176 biochar, 211, 239 bio-ink, 263–66 components of, 266–67 biomarkers, 230–31, 247 bioprinting, 144, 206, 257–81 benefits of, 274–75 concerns about, 272–74 state of the art in, 268–92 sugar sintering method in, 270 two techniques for, 263–66 Biostatistics Research and Consulting Center, 235 bioterrorism, 217 birds, 225 Blenner, Mark, 160 blind people, 310 bloodletting, 229 blood type, 195–96 blood vessels, 262, 268 bioprinting of, 269–71 Bloomberg View, 154 Boeing, 179 Bolonkin, Alexander, 62n bomb threats, 130 Booth, Serena, 129–30 Botella, Cristina, 179 Bovine Elite, LLC, 197n brain: drugs for modifying, 308 electric signals in, 285–86 invasive reading of, 294–95 metabolic signals in, 286–87 noninvasive electromagnetic reading of, 287–90 noninvasive metabolic reading of, 290–93 optimal conditions for learning in, 304–5 reading of, 285–99 superinvasive reading of, 295–99 upgrading of, 299–305 writing to the, 306–8 brain-computer interfaces, 282–317 benefits of, 311–14 brain reading and, 285–99 concerns about, 308–10 games for, 312 brain-to-brain connection, 312–13 brain tumors, 242–43 Brassica oleracea, 190 breast cancer, 240 breast exams, 238 breeding, 191 brewer’s yeast, 199 Brexit, 22n bricklaying, 139–42, 154 Brown University, 28 Brunner, Daniel, 91 Brussels, 50 Bucket of Stuff, 116–20, 124–25, 126, 128 Bull, Gerald, 45–50 Bull’s Eye: The Life and Times of Supergun Inventor Gerald Bull (Adams), 49 Bureau of Labor Statistics, 153, 155 Burj Khalifa, 25n Business Insider, 175 Butcher, Jonathan, 269 calcium, 99 California, University of: at Berkeley, 199, 212 at Davis, 234n, 328 at Santa Barbara, 176 at Santa Cruz, 222 Canada, 45–47, 48, 58, 321n Canadian Space Society, 53 cancer, 3, 206, 231, 234 continuing mutation of, 241 diagnosis of, 238–41 monitoring of, 243–44 treatment of, 241–43 cane toads, 219 Canterbury, University of, 176 capillaries, 262, 271 Caplan, Bryan, 56, 154 caraway seeds, 334–35 carbon, 52, 94, 211 carbon dioxide, 208–9, 210 carbon fiber, 143 carbon nanotube, 35–36 cardiac hypertrophy, 246–47 cars, 15, 24n cartilage, 271–72 Case for Space Solar Power, The (Mankins), 320 Case Western Reserve University, 151n Cas (protein), 213 cat bricks, 111 CD19 (molecule), 242 Cell and Organ Printing (Ringeisen), 259 cells, 192–93, 208, 260 bioprinting and, 264–66 mutant, 238 cellulose, 210 Center for Smell and Taste, 334 Centers for Disease Control, 217n Ceres, 60 Chagan, Lake, 100 Charpentier, Emmanuelle, 212 ChemBot, 124 chemical loop, 205 chemotherapy, 241, 247 Chicken McNuggets, 193n children, 110–11 children’s birthday parties, 178 China, 146, 219, 258 Chinese sweet wormwood plants, 198–99 chirality, 332–33 Church, George, 203, 214, 220, 223n, 252, 332, 335 CIA (Central Intelligence Agency), 48, 50 cilia, 187–88 Clemson University, 160 climate change, 41, 94 clothing, 154 cloud cover, 41 CNSA (China National Space Administration), 65 coal, 73 “Cobotics,” 141n cochlear implants, 306–7, 310 cognitive abilities, 304–5 cold fusion, 5 Cold War, 38 Collins, Francis, 214 Colorado, University of, 176 Comcast, 262 Comic-Con, 78n communications satellites, 34 Complete Anatomy Lab, 185 computerized manufacturing, 137 computers, 2, 101, 139 brain as, 283–84 prosthetics and, 322–23 quantum, 328–30 see also brain-computer interfaces concrete, 145, 155 Congress, U.S., 18, 64, 250, 274 construction, robotic, see robotic construction construction industry, 153–55 Construction Robotics, 141 construction workers, robots as, 139–44 contact lens, 176 Contour Crafting, 145, 146, 149, 156, 158 copper, 325 copper wire, 4, 5 Cornell University, 150, 162 cosmetic surgery, 185, 303 Cosmos 954, 58 Coulomb barrier, 77 cows, 210 CPS, 171 Craig, Alan, 182–83, 184 CRISPR-Cas9, 207, 212–14, 219, 236–37 Crohn’s disease, 247 ctDNA (circulating tumor DNA), 240, 244 C-type (carbonaceous) asteroids, 53 cyborg ear, 271–72 cystic fibrosis, 236–37, 248 D’Andrea, Raffaello, 152 Danforth, Christopher, 247 Daniels, Karen, 63 DAQRI, 179 DARPA (Defense Advanced Research Projects Agency), 124 Darth Vader (char.), 324 data encryption, 329 Dawn mission (NASA), 60 deaf people, 310 deep brain stimulation, 299–302, 304, 309 Deep Space Industries, 53 de-extinction, 221–25 Defense Department, U.S., 47 Delp, Michael, 59n Demaine, Erik, 102, 107–8, 118, 122, 128 dementia, 307 Dempsey, Gaia, 179 depression, 245, 247, 250, 301, 302 depth perception, via smell, 187 Derleth, Jason, 25–27, 35–36, 40 designer babies, 219 deuterium, 73–74, 77, 83 deuterium gas, 81–82 Deutsch, David, 330 diabetes, 245 diminished reality, 181–82 dinosaurs, 225 disease, 198–203, 217 Disney, Walt, 97 Diving Bell and the Butterfly, The (Bauby), 316 d-limonene, 210 DNA, 191, 192–98, 201–2, 204, 205, 213–14, 217, 221, 222, 234, 236, 239, 332 of mammoths, 222–23 as memory storage, 220 Doctor Who (TV show), 82 dogs, 187 Domburg, Jeroen, 161 Dong, Suyang, 177 Doudna, Jennifer, 212 Dowling, Jonathan, 330n drones, 152–53 drugs, 269 drug trials, 254–55, 268–69 Duff, David, 116 ears, 186 Earth, 16, 25, 31, 32, 33, 34, 37, 38, 39, 41, 42, 43, 49, 52, 53, 55, 56, 57, 59, 60, 67, 68, 69, 159, 169, 319 earwax, 196n East Germany, 135 ECoG (electrocorticography), 294–95, 298, 302 École Polytechnique Fédérale de Lausanne (EPFL), 112 ecology, 219 Edison, Thomas, 134, 146 education, 183–84 Edwards, Bradley, 31 EEG (electroencephalogram), 287–90, 291, 292, 294, 298, 299, 310 efficiency, 125–26 eGenesis, 207 EGFRvIII, 243 Egyptians, ancient, 6 Eiben, Gusz, 120n Eiffel Tower, 150, 171 Eisen, Jonathan, 234n electric shock therapy, 299 electromagnetic railgun, 24–25 electrons, 5 Elvis, Martin, 65–67, 68, 320n embryonic stem cells, 273 “emergency guide robot,” 130–32 Empire State Building, 172 environment: biosynthetic monitoring of, 210–12 fusion power and, 94 programmable matter in, 128 robotic construction and, 155–56 space flight damage to, 39–40 synthetic organisms and, 218–19 environmental movement, 97–98 EPFL Laboratory for Timber Construction, 143–44 epilepsy, 295, 302 escape velocity, 55 Escherichia coli, 198 ethanol, 286 Ethnobotany Study Book, 176 European Space Agency (ESA), 22, 27, 65 European Union, 22n Everett, Daniel, 140n evolution, 196 extinction, 221–25 eyes, 186 Faber, Daniel, 53, 68, 69 Fabricated: The New World of 3D Printing (Lipson and Kurman), 159 Fabric of Reality, The (Deutsch), 330 Facebook, 6n, 111, 180, 254 face-tracking software, 180 Falcon 9 rocket, 8n, 19 Faraday, Michael, 4, 6 fiducial marker, 169–70 fingertips, pruney, 126 Fisher, Caitlin, 173, 177–78 fission, 79n FitBit, 252n flexible electrode arrays, 298 Florida, University of, 300 Center for Smell and Taste at, 334 Florida State University, 59n flu, 247 flu vaccines, 217 flux pinning, 326–27 flying cars, 2 fMRI (functional magenetic resonance imaging), 290–91 fMRS (functional magnetic resonance spectroscopy), 292 fNIRS (functional near-infrared spectroscopy), 291 food, printed, 159–63 Food and Drug Administration (FDA), 254, 315, 316 foods, 190–91 Ford Motor Company, 97 Forgacs, Gabor, 268–69, 272 forked tongue, 187 “4D printing,” 103–5 France, 93n Frankenfood, 221 free fall, 42–43 “freezing of gait,” 301 Frostruder, 162 fuel cells, 208–9 fuels, 20, 208–10, 221 furniture, 127 Fusion: The Energy of the Universe (McCracken), 77 fusion bombs, 79 fusion power, 73–100 benefits of, 93–94 blast approach to, 84–85 breakeven point in, 88 concerns about, 91–93 confining and heating approach to, 85 research funding for, 92–93 where we are now with, 86–90 fusion reactors, 314 Fusor.net, 80 fusors, tabletop, 80–84 Gaia (robot), 129–30 Gatenholm, Dr., 269 gene drive, 201–3 gene expression, 239 General Fusion, 89 genes, 195–96, 197, 204, 215 gene sequencing, 2 Genetic Access Control (app), 251–52 genetic disorders, 3, 219, 235–37 Genetic Information Non-discrimination Act (2008), 250–51 genetic mutations, 40, 231 George Mason University, 56 Georgia Institute of Technology, 130 geostationary orbit, 32, 34, 43 Germany, Nazi, 135 Gilpin, Kyle, 118 GitHub, 251 Global Catastrophic Risks (book of essays), 125n glucose, 286 GMOs (genetically modified organisms), 221 Go-Between, The (Hartley), 331 gold, 52, 92 “Golden Promise” barley, 192 Google, 111, 180, 197n, 232, 254, 290 Google Glass, 175–76, 179, 186 Google Scholar, 247 gophers, 96–97 GPS, 171, 173 Gramazio, Fabio, 152 granite, 144 Grant, Dale, 46 gravity, 15–16, 42, 43, 52, 56, 78 “Gray Goo Scenario,” 125 gray wolves, 224 Graz University of Technology, 177 Great Britain, 22 Great Depression, 45 Greeks, ancient, 6 Greenpeace, 94n Gunduz, Aysegul, 300, 301 guns, 3D printed, 125 hacking, of brain implants, 309 hands, 323–24, 332 haptic pen, 175 Haque Design + Research (Umbrellium), 111 hard hats, 179 Hartley, L.


pages: 345 words: 75,660

Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, Avi Goldfarb

"Robert Solow", Ada Lovelace, AI winter, Air France Flight 447, Airbus A320, artificial general intelligence, autonomous vehicles, basic income, Bayesian statistics, Black Swan, blockchain, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, collateralized debt obligation, computer age, creative destruction, Daniel Kahneman / Amos Tversky, data acquisition, data is the new oil, deskilling, disruptive innovation, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, Google Glasses, high net worth, ImageNet competition, income inequality, information retrieval, inventory management, invisible hand, job automation, John Markoff, Joseph Schumpeter, Kevin Kelly, Lyft, Minecraft, Mitch Kapor, Moneyball by Michael Lewis explains big data, Nate Silver, new economy, On the Economy of Machinery and Manufactures, pattern recognition, performance metric, profit maximization, QWERTY keyboard, race to the bottom, randomized controlled trial, Ray Kurzweil, ride hailing / ride sharing, Second Machine Age, self-driving car, shareholder value, Silicon Valley, statistical model, Stephen Hawking, Steve Jobs, Steven Levy, strong AI, The Future of Employment, The Signal and the Noise by Nate Silver, Tim Cook: Apple, Turing test, Uber and Lyft, uber lyft, US Airways Flight 1549, Vernor Vinge, Watson beat the top human players on Jeopardy!, William Langewiesche, Y Combinator, zero-sum game

., mining); (2) the returns to speed of action in response to prediction are high (e.g., driverless cars); and (3) the returns to reduced waiting time for predictions are high (e.g., space exploration). An important distinction between autonomous vehicles operating on a city street versus those in a mine site is that the former generates significant externalities while the latter does not. Autonomous vehicles operating on a city street may cause an accident that incurs costs borne by individuals external to the decision maker. In contrast, accidents caused by autonomous vehicles operating on a mine site only incur costs affecting assets or people associated with the mine. Governments regulate activities that generate externalities. Thus, regulation is a potential barrier to full automation for applications that generate significant externalities.

Kathryn Howe, of Integrate.ai, calls the ability to see a problem and reframe it as a prediction problem “AI Insight,” and, today, engineers all over the world are acquiring it. For example, we are transforming transportation into a prediction problem. Autonomous vehicles have existed in controlled environments for over two decades. They were limited, however, to places with detailed floor plans such as factories and warehouses. The floor plans meant engineers could design their robots to maneuver with basic “if-then” logical intelligence: if a person walks in front of the vehicle, then stop. If the shelf is empty, then move to the next one. However, no one could use those vehicles on a regular city street. Too many things could happen—too many “ifs” to possibly code. Autonomous vehicles could not function outside a highly predictable, controlled environment—until engineers reframed navigation as a prediction problem.

This transforms decision making by expanding options. KEY POINTS * * * Enhanced prediction enables decision makers, whether human or machine, to handle more “ifs” and more “thens.” That leads to better outcomes. For example, in the case of navigation, illustrated in this chapter with the mail robot, prediction machines liberate autonomous vehicles from their previous limitation of operating only in controlled environments. These settings are characterized by their limited number of “ifs” (or states). Prediction machines allow autonomous vehicles to operate in uncontrolled environments, like on a city street, because rather than having to code all the potential “ifs” in advance, the machine can instead learn to predict what a human controller would do in any particular situation. Similarly, the example of airport lounges illustrates how enhanced prediction facilitates more “thens” (e.g., “then leave at time X or Y or Z,” depending on the prediction of how long it will take to get to the airport at a particular time on a particular day), rather than always leaving early “just in case” and then spending extra time waiting in the airport lounge.


Demystifying Smart Cities by Anders Lisdorf

3D printing, artificial general intelligence, autonomous vehicles, bitcoin, business intelligence, business process, chief data officer, clean water, cloud computing, computer vision, continuous integration, crowdsourcing, data is the new oil, digital twin, distributed ledger, don't be evil, Elon Musk, en.wikipedia.org, facts on the ground, Google Glasses, income inequality, Infrastructure as a Service, Internet of things, Masdar, microservices, Minecraft, platform as a service, ransomware, RFID, ride hailing / ride sharing, risk tolerance, self-driving car, smart cities, smart meter, software as a service, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Stuxnet, Thomas Bayes, Turing test, urban sprawl, zero-sum game

Being human we are so used to constantly evaluate tradeoffs, with unclear and frequently changing goals that we don’t even think about it. This is not just hard but downright impossible for any current artificial intelligence. Autonomous vehicles and ethics Let us look at this through the lens of an existing AI problem. Today many cities have begun allowing companies to test autonomous vehicles (AV) on their streets. On virtually every parameter, they are performing well and well above their human counterparts if the vendors are to be trusted. There is the occasional accident that spurs quite a lot of media attention. Given the low scale AV testing is currently carried out, this will be amplified significantly when it is rolled out. While the autonomous vehicles are very good at following rules and identifying the proper ones in a given situation, what happens in situations where the rules might be conflicting and they even have to make a tradeoff decision with ethical impact?

While the autonomous vehicles are very good at following rules and identifying the proper ones in a given situation, what happens in situations where the rules might be conflicting and they even have to make a tradeoff decision with ethical impact? Here is a thought experiment to illustrate the issue. An autonomous vehicle is driving on a sunny spring afternoon through the streets of New York. It is a good day, and it is able to keep a good pace. On its right is a sidewalk with a lot of pedestrians; on its left is a traffic lane going the opposite direction. Now suddenly a child runs out into the road in front of the AV, and it is impossible for it to brake in time. The autonomous vehicle needs to make a choice. It has three options:1)It runs over the child and kills it while not hurting the people inside the AV or the pedestrians on the sidewalk. 2)It makes an evasive maneuver to the right hitting pedestrians, thereby killing or injuring one or more people while not hurting the people inside the AV. 3)It makes an evasive maneuver to the left hitting cars going the other direction, thereby killing the people in the AV and the people in the other car but sparing the child and the pedestrians.

What if there were two children in the autonomous vehicle itself? Does the age factor in to the decision? Is it better to kill old people than younger? The AI would then have to scan people and try to identify their age before it makes a decision, which is technically perfectly feasible. What about medical conditions? Would it not be better to hit a terminal cancer patient than a healthy young mother? The AI would have to try to extract medical information maybe look up medical records based on facial recognition that identified the social security number of the person. This is also perfectly feasible even in real time with today’s technology. As should be clear, this line of reasoning is one that humans prefer not to go into, but that would be necessary since the AI in the autonomous vehicle needs someone to tell it what to do or at least give it a set of values to make it possible to arrive at a decision.


pages: 304 words: 80,143

The Autonomous Revolution: Reclaiming the Future We’ve Sold to Machines by William Davidow, Michael Malone

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, agricultural Revolution, Airbnb, American Society of Civil Engineers: Report Card, Automated Insights, autonomous vehicles, basic income, bitcoin, blockchain, blue-collar work, Bob Noyce, business process, call centre, cashless society, citizen journalism, Clayton Christensen, collaborative consumption, collaborative economy, collective bargaining, creative destruction, crowdsourcing, cryptocurrency, disintermediation, disruptive innovation, distributed ledger, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, Francis Fukuyama: the end of history, Geoffrey West, Santa Fe Institute, gig economy, Gini coefficient, Hyperloop, income inequality, industrial robot, Internet of things, invention of agriculture, invention of movable type, invention of the printing press, invisible hand, Jane Jacobs, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, license plate recognition, Lyft, Mark Zuckerberg, mass immigration, Network effects, new economy, peer-to-peer lending, QWERTY keyboard, ransomware, Richard Florida, Robert Gordon, Ronald Reagan, Second Machine Age, self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Simon Kuznets, Snapchat, speech recognition, Stuxnet, TaskRabbit, The Death and Life of Great American Cities, The Rise and Fall of American Growth, the scientific method, trade route, Turing test, Uber and Lyft, uber lyft, universal basic income, uranium enrichment, urban planning, zero day, zero-sum game, Zipcar

That’s the good news. The bad news is that this would cause about 1.5 million jobs to disappear in automotive related industries—manufacturing, service, insurance, and so forth. The effects of autonomous vehicles will also be felt in wider swaths of the economy. Level 5 Autonomous Trucks—aTrucks—will move goods faster, more efficiently, and more safely than trucks driven by people. There are 3.5 million professional truck drivers in our country, and about 8.7 million people employed in the trucking business.26 Many of them will be displaced. If just one-quarter of them are, that’s more than 2 million jobs.27 No doubt autonomous vehicles will also drive down the costs of delivery services. The business model for groceries, retail stores, and many commodity products will consist of large automated warehouses that deliver products ordered over the Internet to the customer’s home or to a convenient location for pick-up within a few hours.

Allen, 143–144 Brattain, Walter, 55 Britain, Industrial Revolution in, 29–30 Brown, Tina, 63 business models: customers becoming products in new, 120–123 financial industry transformation of, 74–83 freemium, 70, 73, 121–123, 129–130, 169 media industry shift in, 9–10, 72–73 non-monetizable productivity relation to shifting, 60, 65 sharing economy, 83–87 substitutional equivalence and smaller markets in new, 87–88, 103 Butler, Nicholas, 2–3 Campbell, W. Keith, 146 Capek, Karel, 45 cars. See automobile industry; autonomous vehicles cash, 41–42, 82 casinos. See gambling addiction Chandler, Alfred, 33 change: harbingers of radical, 1–4 Hegel on late understanding of, xiii Heraclitus on constant of, ix–x, xiv. See also phase change; social phase change; timeline/rates of change chess-playing computers, 45, 46 China, 10, 76, 115, 132, 186 ChoicePoint, 118–119, 130 Christensen, Clayton, 87 Christianity, 27, 162–163, 166 Chua, Amy, 163, 167 church authority, 28, 152, 162 CIA, 119, 172 cities/urban environments: Agricultural Revolution’s role in origin of, 24, 25–26, 35, 151–152, 183–184 autonomous vehicles’ role in population of, 108–109 Industrial Revolution’s role in growth of, 30, 160 Transportation Revolution’s impact on, 31–32 citizens: democracy dependent on unity of, 163, 166, 168 polarization of, 95, 115–116, 158, 161, 165, 167–168, 189–190, 194, 195 social networking impacts on unity of, 166–170 unity role in government function, 161–166, 168 value system unity and commitment of, 193–195 ZEV, 12, 48–49 Clark, Luke, 140 climate change, 21–22 Collison, Patrick and John, 78 commercial entities: behavior manipulation by, 13, 117, 121, 123 displacement business for, 71, 72–73, 99 Industrial Revolution’s rise in power of, 17, 34 virtual, 3–4, 7, 34, 50, 64–65, 89–92 commercial trends, key, 4, 71, 73 communications revolution, 27–28 computer industry: artificial intelligence history in, 45–47 integrated circuitry history of and impact on, 55–56, 58 and microprocessor history, 55–56, 172 monetizable productivity with early, 55–56 non-monetizable productivity in, 18, 58 semiconductor history of and impacts on, 54–55, 56–57 timeline and adoption rate for, 22–23, 55–56 virus/malware history in, 39–40, 172–173 Computer Revolution, 29, 39–40, 99, 111, 182 constitutional rights, 113, 114, 127, 159–160, 162, 168–170 consumers.

We were right in our prediction—it was indeed a paradigm shift—but wrong in our calendar. We expected that virtual corporations would take a decade or more to become ubiquitous. But a hidden force was about to burst on the scene that would propel this revolution faster than we envisioned: the World Wide Web. The genesis of this book was similar. Having been present at the birth of social networking, massive multiplayer games, autonomous vehicles, modern artificial intelligence, and all of the other defining new technologies of the twenty-first century, we have watched with growing dismay, even horror, at how many of these developments have morphed into increasingly malevolent threats to human privacy and liberty. Living in Silicon Valley, we watched firsthand, with growing trepidation, the effects of the modern networked, digital—virtual—world on its most passionate users.


pages: 280 words: 74,559

Fully Automated Luxury Communism by Aaron Bastani

"Robert Solow", autonomous vehicles, banking crisis, basic income, Berlin Wall, Bernie Sanders, Bretton Woods, capital controls, cashless society, central bank independence, collapse of Lehman Brothers, computer age, computer vision, David Ricardo: comparative advantage, decarbonisation, dematerialisation, Donald Trump, double helix, Elon Musk, energy transition, Erik Brynjolfsson, financial independence, Francis Fukuyama: the end of history, future of work, G4S, housing crisis, income inequality, industrial robot, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Isaac Newton, James Watt: steam engine, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Kuiper Belt, land reform, liberal capitalism, low earth orbit, low skilled workers, M-Pesa, market fundamentalism, means of production, mobile money, more computing power than Apollo, new economy, off grid, pattern recognition, Peter H. Diamandis: Planetary Resources, post scarcity, post-work, price mechanism, price stability, private space industry, Productivity paradox, profit motive, race to the bottom, RFID, rising living standards, Second Machine Age, self-driving car, sensor fusion, shareholder value, Silicon Valley, Simon Kuznets, Slavoj Žižek, stem cell, Stewart Brand, technoutopianism, the built environment, the scientific method, The Wealth of Nations by Adam Smith, Thomas Malthus, transatlantic slave trade, Travis Kalanick, universal basic income, V2 rocket, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, working-age population

As a result these vehicles can navigate streets and motorways by relying on precise GPS data, huge amounts of information regarding maps, and a continuous stream of real-time updates on other cars, potential obstacles, pedestrians and all the variables human drivers have to consider. All of this is achieved with a myriad of sensors, lasers and cameras processing information as 1s and 0s. Even in isolation the arrival of autonomous vehicles likely spells the disappearance of whole professions. In 2014, driving accounted for around 4 million jobs in the US alone, and according to a report by Goldman Sachs the country could see job losses at a rate of 300,000 a year as autonomous vehicles become an integrated feature of modern society. From the perspective of business that would be entirely understandable: logistics vehicles running twenty-four hours a day, seven days a week, offer massive savings. And while there is a temptation to say machines can’t be liable for accidents, with over 1.3 million annual road deaths worldwide, and 40,000 in the US alone, it won’t be long before the technology is sufficiently advanced that such an argument could be reversed.

Digitisation is more than simply a process that applies to things like words, pictures, film and music – that these are now digital objects rather than physical ones is important, but not to be overstated. More vital is how digitisation has allowed progressively greater amounts of cognition and memory to be performed in 0s and 1s, with the price–performance ratio of anything that does so falling every year for decades. It is this which allows contemporary camera technology to land rockets and, increasingly, drive autonomous vehicles; it is what will provide robots with fine motor coordination and dexterity equivalent to that found in humans; it will permit the built environment to know more about us, in certain respects, than we know about ourselves. It will even allow us to edit DNA – the building blocks of life – to remove hereditary disease and sequence genomes at such low cost, and with such regularity, that we will cure ourselves of cancer before it reaches Stage 1.

But while robots whose movements authentically resemble those of humans aren’t quite here yet, another category of machine – drawing on the same gains in digitisation and the dividend of exponential progress – is on the verge of transforming whole industries. It is the leading edge of a transformation which will mean not only the loss of countless jobs, but entire professions. And just like the acrobatics of Atlas, nobody saw it coming – until it was right in front of them. Autonomous Vehicles In 2002 the American defence agency DARPA announced a ‘Grand Challenge’ for driverless cars scheduled to take place in the Mojave Desert in spring 2004. The proposed route was two hundred and forty kilometres long and the prize, for whichever car finished first, was set at $1 million. While some of the most brilliant minds in America applied themselves to the task, not one of the fifteen teams present at the start line was able to complete the course.


pages: 515 words: 126,820

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

Airbnb, altcoin, asset-backed security, autonomous vehicles, barriers to entry, bitcoin, blockchain, Blythe Masters, Bretton Woods, business process, buy and hold, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, clean water, cloud computing, cognitive dissonance, commoditize, corporate governance, corporate social responsibility, creative destruction, Credit Default Swap, crowdsourcing, cryptocurrency, disintermediation, disruptive innovation, distributed ledger, Donald Trump, double entry bookkeeping, Edward Snowden, Elon Musk, Erik Brynjolfsson, Ethereum, 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, information asymmetry, intangible asset, interest rate swap, Internet of things, Jeff Bezos, jimmy wales, Kickstarter, knowledge worker, Kodak vs Instagram, Lean Startup, litecoin, Lyft, M-Pesa, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, means of production, microcredit, mobile money, money market fund, Network effects, new economy, Oculus Rift, off grid, pattern recognition, peer-to-peer, peer-to-peer lending, peer-to-peer model, performance metric, Peter Thiel, planetary scale, Ponzi scheme, prediction markets, price mechanism, Productivity paradox, QR code, 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 intelligence, social software, standardized shipping container, 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, uber lyft, unbanked and underbanked, underbanked, unorthodox policies, wealth creators, X Prize, Y2K, Zipcar

In addition, cities will use the sensors to help manage the transportation infrastructure, including asset management of infrastructure and fleets, monitoring rail line and pavement conditions, generating maintenance plans and budgets, and dispatching repair crews when necessary. What’s truly powerful, the systems work together—intelligent vehicles operating on an intelligent infrastructure. While there will still be business for drivers of shared vehicles, autonomous vehicles will be able to operate safely on city streets with their built-in navigation and safety systems, often interacting with the intelligent infrastructure to find and pay for an accelerated lane, or parking, or to search for and find a preferred route. The ready availability, affordability, and reliability of the autonomous vehicles will significantly reduce the number of private vehicles that, like the commercial real estate example above, are often just parked waiting and unused. And it won’t just be technology or car companies that will make this happen.

It has become clear that concentrated powers in business and government have bent the original democratic architecture of the Internet to their will. Huge institutions now control and own this new means of production and social interaction—its underlying infrastructure; massive and growing treasure troves of data; the algorithms that increasingly govern business and daily life; the world of apps; and extraordinary emerging capabilities, machine learning, and autonomous vehicles. From Silicon Valley and Wall Street to Shanghai and Seoul, this new aristocracy uses its insider advantage to exploit the most extraordinary technology ever devised to empower people as economic actors, to build spectacular fortunes and strengthen its power and influence over economies and societies. Many of the dark side concerns raised by early digital pioneers have pretty much materialized.17 We have growth in gross domestic product but not commensurate job growth in most developed countries.

All you need is a decentralized value transfer protocol to allow them to safely and securely transact with one another. These platforms instill subsidiary rights in all our assets. You need to decide the extent to which you want to assign others usage and access rights—even the right to exclude others from using your assets—and what to charge for those rights. This can work for physical assets too. For example, we’ve heard a lot about autonomous vehicles. We can build an open transportation network on the blockchain where owners each have a private encrypted key (number) that lets them reserve a car. Using the public key infrastructure and existing blockchain technologies like EtherLock and Airlock, they can unlock and use the car for a certain amount of time, as specified by the rules of the smart contract—all the while paying the vehicle (or its owners) in real time for the time and energy that they use—as metered on a blockchain.


pages: 305 words: 79,303

The Four: How Amazon, Apple, Facebook, and Google Divided and Conquered the World by Scott Galloway

activist fund / activist shareholder / activist investor, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Amazon Web Services, Apple II, autonomous vehicles, barriers to entry, Ben Horowitz, Bernie Sanders, big-box store, Bob Noyce, Brewster Kahle, business intelligence, California gold rush, cloud computing, commoditize, cuban missile crisis, David Brooks, disintermediation, don't be evil, Donald Trump, Elon Musk, follow your passion, future of journalism, future of work, global supply chain, Google Earth, Google Glasses, Google X / Alphabet X, Internet Archive, invisible hand, Jeff Bezos, Jony Ive, Khan Academy, longitudinal study, Lyft, Mark Zuckerberg, meta analysis, meta-analysis, Network effects, new economy, obamacare, Oculus Rift, offshore financial centre, passive income, Peter Thiel, profit motive, race to the bottom, RAND corporation, ride hailing / ride sharing, risk tolerance, Robert Mercer, Robert Shiller, Robert Shiller, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, shareholder value, Silicon Valley, Snapchat, software is eating the world, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Stewart Brand, supercomputer in your pocket, Tesla Model S, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, undersea cable, Whole Earth Catalog, winner-take-all economy, working poor, young professional

Amazon is going underwater with the world’s largest oxygen tank, forcing other retailers to follow it, match its prices, and deal with changed customer delivery expectations. The difference is other retailers have just the air in their lungs and are drowning. Amazon will surface and have the ocean of retail largely to itself. Making Type 2 investments also desensitizes Amazon’s shareholders to failure. All of the Four share this—look at Apple and Google with their not-so-secret autonomous vehicle projects, and Facebook with its regular introduction of new features to further monetize its users, which it then pulls back when the experiments don’t pan out. Remember Lighthouse? As Bezos also wrote in that first annual letter: “Failure and invention are inseparable twins. To invent you have to experiment, and if you know in advance that it’s going to work, it’s not an experiment.”53 Red, White, and Blue The Four are all disciplined about getting out in front of their skis, taking big, bold, smart bets, and tolerating failure.

In fact, it wasn’t until 2009 that Google’s CEO at the time, Eric Schmidt, saw the conflict of interest collisions ahead and resigned (or was asked to leave) from Apple’s board of directors. Since then, the four giants have moved inexorably into each other’s turf. At least two or three of them now compete in each other’s markets, whether it’s advertising, music, books, movies, social networks, cell phones—or lately, autonomous vehicles. But Apple stands alone as a luxury brand. That difference presents an immense advantage, providing fatter margins and a competitive edge. Luxury insulates the Apple brand, and hoists it above the price wars raging below. For now, I see modest competition for Apple from the other horsemen. Amazon sells cut-rate tablets. Facebook is no sexier than a phone book. And Google’s one venture into wearable computing, Google Glass, was a prophylactic, guaranteeing that the wearer would never have the chance to conceive a child, as nobody would get near them.

Leave aside the likelihood that once the company becomes old news, Congress and the Justice Department might just decide the search engine is a public utility and regulate the firm as such. Google is a long way from that fate—but notice that it too is basically a one-trick (and one trick only) pony. There is search (YouTube is a search engine) and there is . . . well, Android—but that’s an industry smartphone standard, devised by Schmidt to counter the iPhone, and its biggest players are other companies. All of the other stuff—autonomous vehicles, drones—is just chaff, designed to keep customers and, even more so, employees pumped up. To date their contribution is less than Microsoft’s fading Internet Explorer. There are other parallels between Google and Microsoft. Microsoft at its peak was notorious for having the most insufferable asshole employees in American business. They were arrogant, smug, and totally convinced—in a classic high-tech industry mistake—that what was also luck, timing, and success was, in fact, genius.


pages: 417 words: 109,367

The End of Doom: Environmental Renewal in the Twenty-First Century by Ronald Bailey

3D printing, additive manufacturing, agricultural Revolution, Albert Einstein, Asilomar, autonomous vehicles, business cycle, Cass Sunstein, Climatic Research Unit, Commodity Super-Cycle, conceptual framework, corporate governance, creative destruction, credit crunch, David Attenborough, decarbonisation, dematerialisation, demographic transition, disruptive innovation, diversified portfolio, double helix, energy security, failed state, financial independence, Gary Taubes, hydraulic fracturing, income inequality, Induced demand, Intergovernmental Panel on Climate Change (IPCC), invisible hand, knowledge economy, meta analysis, meta-analysis, Naomi Klein, oil shale / tar sands, oil shock, pattern recognition, peak oil, Peter Calthorpe, phenotype, planetary scale, price stability, profit motive, purchasing power parity, race to the bottom, RAND corporation, rent-seeking, Stewart Brand, Tesla Model S, trade liberalization, University of East Anglia, uranium enrichment, women in the workforce, yield curve

Researchers at the University of Texas, devising a realistic simulation of vehicle use in cities that took into account issues like congestion and rush-hour usage, found that each shared autonomous vehicle could replace eleven conventional vehicles. Notionally then, it would take only about 800 million vehicles to supply all the transportation services for 9 billion people. That figure is 200 million vehicles fewer than the current world fleet of 1 billion automobiles. In the Texas simulations, riders waited an average of 18 seconds for a driverless vehicle to show up, and each vehicle served 31 to 41 travelers per day. Less than half of 1 percent of travelers waited more than five minutes for a vehicle. In addition, shared autonomous vehicles would also cut an individual’s average cost of travel by as much as 75 percent in comparison to conventional driver-owned vehicles.

significant gains in energy productivity: Alliance Commission on National Energy Efficiency Policy, “History of Energy Efficiency.” Alliance to Save Energy, January 2013, 4. www.ase.org/sites/ase.org/files/resources/Media%20browser/ee_commission_history_report_2-1-13.pdf. a realistic simulation: Daniel J. Fagnant and Kara M. Kockelman, “The Travel and Environmental Implications of Shared Autonomous Vehicles, Using Agent-Based Model Scenarios.” Transportation Research Part C: Emerging Technologies 40 (March 2014): 1–13. www.sciencedirect.com/science/article/pii/S0968090X13002581. shared autonomous vehicles: Lawrence Burns, William Jordan, and Bonnie Scarborough, “Transforming Personal Mobility.” The Earth Institute, Columbia University, New York, 2013. resource consumption trends: Iddo Wernick and Jesse Ausubel, “Making Nature Useless? Global Resource Trends, Innovation, and Implications for Conservation.”

In addition, shared autonomous vehicles would also cut an individual’s average cost of travel by as much as 75 percent in comparison to conventional driver-owned vehicles. This could actually lead to the contraction of the world’s vehicle fleet as more people forgo the costs and hassles of ownership. In addition, a shift to fleets of autonomous vehicles makes the clean electrification of transportation much more feasible, since such automobiles could drive themselves off for recharging and cleaning during periods of low demand. Such vehicles would also be much smaller and packed more tightly on roads, since they can travel safely at higher speeds than human-driven automobiles. Such a switch would imply the construction of far less material-heavy transportation infrastructure. And fewer vehicles means that much of the 20 percent of urban land devoted to parking can be transformed into housing and businesses. Smil worries that energy production and consumption technologies are so capital intensive that humanity will be locked into dependence on increasingly scarce and expensive fossil fuels for decades to come.


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Applied Artificial Intelligence: A Handbook for Business Leaders by Mariya Yao, Adelyn Zhou, Marlene Jia

Airbnb, Amazon Web Services, artificial general intelligence, autonomous vehicles, business intelligence, business process, call centre, chief data officer, computer vision, conceptual framework, en.wikipedia.org, future of work, industrial robot, Internet of things, iterative process, Jeff Bezos, job automation, Marc Andreessen, natural language processing, new economy, pattern recognition, performance metric, price discrimination, randomized controlled trial, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, skunkworks, software is eating the world, source of truth, speech recognition, statistical model, strong AI, technological singularity

Research finds that while 45 percent of tasks are automatable, only five percent of overall jobs have been supplanted by automation.(50) AI systems largely handle individual tasks, not whole jobs. High costs, legal regulations, and social resistance to AI all hinder the progress of technology adoption. With the rise of autonomous vehicles, many believe that the jobs of America’s 1.7 million truck drivers are in imminent danger. The reality is that trucking jobs will likely require many years to replace. Michael Chui, a McKinsey partner, told The New York Times that the replacement and retrofitting of America’s truck fleet with autonomous navigation will require a trillion-dollar investment that few, if any, companies will immediately undertake.(51) Even if financing can be secured, autonomous vehicle technology is not yet approved for industrial or for individual use. If humans can outsource repetitive and mundane tasks to AI, then they can devote more attention to tasks requiring strategic skills such as judgment, communication, and creative thinking.

In sales, for example, machine learning approaches to lead scoring can perform better than rule-based or statistical methods. Once the machine has produced a prediction on the quality of a lead, the salesperson then applies human judgment to decide how to follow up. More complex systems, such as self-driving cars and industrial robotics, handle everything from gathering the initial data to executing the action resulting from its analysis. For example, an autonomous vehicle must turn video and sensor feeds into accurate predictions of the surrounding world and adjust its driving accordingly. Systems That Create We humans like to think we’re the only beings capable of creativity, but computers have been used for generative design and art for decades. Recent breakthroughs in neural network models have inspired a resurgence of computational creativity, with computers now capable of producing original writing, imagery, music, industrial designs, and even AI software!

Through wearables, standard computing devices, and the burgeoning Internet of Things (IoT), AI will inevitably permeate every corner of our existence. This means that our physical security, digital security, and even political security will be at risk of attack. While we spend much of our productive hours tethered to digital devices and roaming cyberspace, we still inhabit physical bodies and live in a material world. Nefarious AI can infect autonomous vehicles, connected appliances, and other devices to inflict bodily harm and property damage. Digital attacks may come as a coordinated and adversarial disruption of corporate data with the goal of compromising, devaluing, or altogether destroying an organization’s data architecture. Finally, the use of technology—including AI, predictive analytics, automation, and social media bots—can have far-ranging social impact.


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The Perfect Weapon: War, Sabotage, and Fear in the Cyber Age by David E. Sanger

active measures, autonomous vehicles, Bernie Sanders, bitcoin, British Empire, call centre, Cass Sunstein, Chelsea Manning, computer age, cryptocurrency, cuban missile crisis, Donald Trump, drone strike, Edward Snowden, Google Chrome, Google Earth, Jacob Appelbaum, John Markoff, Mark Zuckerberg, MITM: man-in-the-middle, mutually assured destruction, RAND corporation, ransomware, Sand Hill Road, Silicon Valley, Silicon Valley ideology, Skype, South China Sea, Steve Jobs, Steven Levy, Stuxnet, Tim Cook: Apple, too big to fail, undersea cable, uranium enrichment, Valery Gerasimov, WikiLeaks, zero day

A government that still gave lip service to communism had figured out venture capitalism—and concluded it was the shortest path to get the technologies the country needed. The numbers that Brown and Singh gathered, all from public sources, told the story. China participated in more than 10 percent of all venture deals in 2015, the report found, focusing on early-stage innovations critical to both commercial and military uses: artificial intelligence, robotics, autonomous vehicles, virtual reality, financial technology, and gene-editing. When they broke down who was investing in US-based venture-backed companies between 2015 and 2017, American investors ranked first, with $59 billion in investment. Europe was second, with $36 billion. And China was right behind, with $24 billion. Some of the biggest direct investments came from Baidu and Tencent, but there were also a surprising number from venture-capital firms with Western-sounding names—West Summit Capital and Westlake Ventures—that were wholly Chinese-owned.

Schneier’s point is that even as we build far greater defenses, our vulnerabilities are expanding dramatically. With huge investments, the top tier of the financial industry and the electric utilities have done the best job of safeguarding their networks—meaning that a North Korean hacker aiming at those industries would likely have more luck targeting smaller banks and rural power companies. But as we put autonomous cars on the road, connect Alexas to our lights and our thermostats, put ill-protected Internet-connected video cameras on our houses, and conduct our financial lives over our cell phones, our vulnerabilities expand exponentially. During the Cold War, we learned how to live, uneasily, with the knowledge that the Soviet Union and China had nuclear weapons pointed at us. There were no perfect defenses.


pages: 397 words: 110,222

Habeas Data: Privacy vs. The Rise of Surveillance Tech by Cyrus Farivar

Apple's 1984 Super Bowl advert, autonomous vehicles, call centre, citizen journalism, cloud computing, computer age, connected car, do-ocracy, Donald Trump, Edward Snowden, en.wikipedia.org, failed state, Ferguson, Missouri, Frank Gehry, Golden Gate Park, John Markoff, license plate recognition, Lyft, national security letter, Occupy movement, optical character recognition, Port of Oakland, RAND corporation, Ronald Reagan, sharing economy, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, Steven Levy, The Hackers Conference, Tim Cook: Apple, transaction costs, uber lyft, WikiLeaks, Zimmermann PGP

Law enforcement can wantonly drive police cars and indiscriminately scan license plates, as it’s not considered a search. Same goes for allowing a “TiVo-in-the-sky” to capture days’ worth of human activity down below. Without a legislative body or a judge to step in, it seems inevitable that these actions will continue to expand through pervasive monitoring, advanced facial recognition, DNA, biochemical analysis, constant location capture via autonomous vehicles, and more. Today, so long as the search remains “reasonable” and doesn’t conflict with an “expectation” that “society is prepared to recognize as reasonable,” then law enforcement behavior is permitted. Or, to put it in e-mail spam terms, Fourth Amendment law is basically a blacklist: police actions are generally permitted unless they run into conditions that tell them to stop, such as conducting a physical search of “persons, houses, papers, and effects,” which requires a specific warrant.

They’re probably Muslim. Can a car be found outside Beer Revolution a great number of times? May be a craft beer enthusiast—although possibly with a drinking problem.” As I continued to report on LPRs, I realized the same questions I had about this technology applied to so much more: telephone metadata, cell-site simulators (aka stingrays), body-worn cameras, drones, facial-recognition technology, autonomous cars, artificial intelligence, and more. There was a torrent of technology that was becoming more ubiquitous and cheaper by the day, with little standing in its way. Legislators have generally seemed unable or unwilling to halt the ever-advancing technological mission creep. Courts seemed to always lag behind—by the time a technology was finally raised at an appellate court or at the US Supreme Court, it was far out of date.


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

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, basic income, Benevolent Dictator For Life (BDFL), 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, Douglas Engelbart, Edward Snowden, Elon Musk, en.wikipedia.org, Eratosthenes, Ethereum, 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, Intergovernmental Panel on Climate Change (IPCC), intermodal, Internet of things, invisible hand, Jacob Appelbaum, Jaron Lanier, Joan Didion, John Markoff, Joi Ito, Jony Ive, Julian Assange, Khan Academy, liberal capitalism, lifelogging, linked data, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, Masdar, McMansion, means of production, megacity, megastructure, Menlo Park, Minecraft, MITM: man-in-the-middle, Monroe Doctrine, Network effects, new economy, offshore financial centre, oil shale / tar sands, packet switching, PageRank, pattern recognition, peak oil, peer-to-peer, performance metric, personalized medicine, Peter Eisenman, Peter Thiel, phenotype, Philip Mirowski, Pierre-Simon Laplace, place-making, planetary scale, RAND corporation, recommendation engine, reserve currency, RFID, Robert Bork, 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, undersea cable, universal basic income, urban planning, Vernor Vinge, Washington Consensus, web application, Westphalian system, WikiLeaks, working poor, Y Combinator

At the very least, as we are shuttled here and there in the vast multitudes of such machines, how human Users are physically positioned and what we spend our time doing will certainly not be the same as it is now.60 As discussed in the Interfaces chapter, as the “car” becomes a Cloud platform, it becomes available to an Apps economy, and to the extent that the Google Car is just a very large Android device with a very large, next generation Google Glass display, there is much for designers to work with. At the same time, such a system would bring potential problems of the same order of magnitude as those it alleviates. The software and sovereignty questions don't abide easy answers. First, the legal identity of this composite User is not immediately clear. Several states have already passed legislation indicating that autonomous vehicles are legal to operate on their roads, thereby establishing the baseline that such machines are at least not criminal. But considering the quantity, complexity, and sensitivity of the data generated by such technologies, all working in concert, as well as the expertise and infrastructure necessary to conduct the rhythms of the swarm safely and effectively, it's not likely that any Department of Motor Vehicles is a likely candidate to govern a network of pilotless vehicles.

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.

Her absence and lack of interaction is not an absence of information at all; it is information of absence. To the consternation of suspicious persons, the “mobile phone” with a CCD (charge-coupled device) absorbing light and a microphone absorbing sound waves is also a sensor, and for it the principle of information by absence of interaction holds true. One sensor makes use of the information haul of another, such as an autonomous vehicle that can navigate terrain based on LiDAR mapping (a portmanteau of “laser” and “radar”), motion detection sensors, and street maps (among other sensors). Ultimately, as a User experience design problem, the sense of a device's relative autonomy and intelligence will be a key criterion in everyday HRI (human-robotics interaction) but is a separate issue from the actual autonomy or dependence of that device.


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In the Plex: How Google Thinks, Works, and Shapes Our Lives by Steven Levy

23andMe, AltaVista, Anne Wojcicki, Apple's 1984 Super Bowl advert, autonomous vehicles, book scanning, Brewster Kahle, Burning Man, business process, clean water, cloud computing, crowdsourcing, Dean Kamen, discounted cash flows, don't be evil, Donald Knuth, Douglas Engelbart, Douglas Engelbart, El Camino Real, fault tolerance, Firefox, Gerard Salton, Gerard Salton, Google bus, Google Chrome, Google Earth, Googley, HyperCard, hypertext link, IBM and the Holocaust, informal economy, information retrieval, Internet Archive, Jeff Bezos, John Markoff, Kevin Kelly, Kickstarter, Mark Zuckerberg, Menlo Park, one-China policy, optical character recognition, PageRank, Paul Buchheit, Potemkin village, prediction markets, recommendation engine, risk tolerance, Rubik’s Cube, Sand Hill Road, Saturday Night Live, search inside the book, second-price auction, selection bias, Silicon Valley, skunkworks, Skype, slashdot, social graph, social software, social web, spectrum auction, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Levy, Ted Nelson, telemarketer, trade route, traveling salesman, turn-by-turn navigation, undersea cable, Vannevar Bush, web application, WikiLeaks, Y Combinator

Since its earliest days, Brin and Page have been consistent in framing Google as an artificial intelligence company—one that gathers massive amounts of data and processes that information with learning algorithms to create a machinelike intelligence that augments the collective brain of humanity. Google’s autonomous cars are information-collectors, scanning their environment with lasers and sensors, and augmenting their knowledge with Street View data. (Unlike human drivers, they always know what’s around the corner.) “This is all information,” says Thrun. “And it will make our physical world more accessible.” What will Google’s explorations in artificial intelligence eventually yield? Will we routinely cruise in autonomous cars powered by Google—undoubtedly capable of pointing out sightseeing highlights and culinary opportunities as they whisk us to destinations? Will the brain “implant” that Larry Page referred to in 2004 become a Google product at some point?

(In late 2010, introducing the Google Instant search product—once referred to internally as “psychic search”—Sergey Brin had repeated the sentiment: “We want Google to be the third half of your brain.”) Google, after all, was founded on the premise that the best path to success is doing what the conventional wisdom says you cannot do. In an era of unprecedented technology leaps, that has turned out to be an excellent premise. “It’s quite amazing how the horizon of impossibility is drifting these days,” says Thrun. The revelation of the autonomous vehicle program at the end of 2010 had all the earmarks of a Larry Page project—scary ambition, groundbreaking AI, massive processing of information in real time, and rigidly enforced stealth. (Only when a reporter learned of the project did Google agree to talk about it.) The glimpse it provided of Page’s priorities turned out to be more significant than expected when an apparently predestined change in Google’s leaders occurred sooner than observers had expected.


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Seriously Curious: The Facts and Figures That Turn Our World Upside Down by Tom Standage

agricultural Revolution, augmented reality, autonomous vehicles, blood diamonds, corporate governance, Deng Xiaoping, Donald Trump, Elon Musk, failed state, financial independence, gender pay gap, gig economy, Gini coefficient, high net worth, income inequality, index fund, industrial robot, Internet of things, invisible hand, job-hopping, Julian Assange, life extension, Lyft, M-Pesa, Mahatma Gandhi, manufacturing employment, mega-rich, megacity, Minecraft, mobile money, natural language processing, Nelson Mandela, plutocrats, Plutocrats, price mechanism, purchasing power parity, ransomware, reshoring, ride hailing / ride sharing, Ronald Coase, self-driving car, Silicon Valley, Snapchat, South China Sea, speech recognition, stem cell, supply-chain management, transaction costs, Uber and Lyft, uber lyft, undersea cable, US Airways Flight 1549, WikiLeaks

As cars did in the 20th century, AVs will redefine retailing and reshape cities, as well as providing a convenient new form of mobility. As with cars, which lead to road deaths, pollution and congestion, there are likely to be unanticipated (and unpleasant) consequences for society from autonomous vehicles, such as a loss of privacy and the potential to use them as a means of social control. Removing the horse from horse-drawn carriages was an apparently simple change that had far-reaching effects. Similarly, there is much more to autonomous vehicles than simply removing the need for a driver – and much of their impact will be a consequence of the fact that they will mostly be shared, not owned. How ride-hailing apps reduce drink-driving Gun violence in America gets plenty of attention, but cars kill more people.

By one estimate, the M-Pesa mobile-money system alone lifted about 2% of Kenyan households out of poverty between 2008 and 2014. Technology cannot solve all of Africa’s problems, but it can help with some of them. Why self-driving cars will mostly be shared, not owned When will you be able to buy a driverless car that will work anywhere? This commonly asked question contains three assumptions: that autonomous vehicles (AVs) will resemble cars; that people will buy them; and that they will be capable of working on all roads in all conditions. All three of those assumptions may be wrong. Although today’s experimental vehicles are modified versions of ordinary cars, with steering wheels that eerily turn by themselves, future AVs will have no steering wheel or pedals and will come in all sorts of shapes and sizes; pods capable of carrying six or eight people may prove to be the most efficient design.

For more explainers and charts from The Economist, visit economist.com Index A Africa child marriage 84 democracy 40 gay and lesbian rights 73, 74 Guinea 32 mobile phones 175–6 see also individual countries agriculture 121–2 Aguiar, Mark 169 air pollution 143–4 air travel and drones 187–8 flight delays 38–9 Akitu (festival) 233 alcohol beer consumption 105–6 consumption in Britain 48, 101–2 craft breweries 97–8 drink-driving 179–80 wine glasses 101–2 Alexa (voice assistant) 225 Algeria food subsidies 31 gay and lesbian rights 73 All I Want for Christmas Is You (Carey) 243 alphabet 217–18 Alternative for Germany (AfD) 223, 224 Alzheimer’s disease 140 Amazon (company) 225 America see United States and 227–8 Angola 73, 74 animals blood transfusions 139–40 dog meat 91–2 gene drives 153–4 size and velocity 163–4 and water pollution 149–50 wolves 161–2 Arctic 147–8 Argentina gay and lesbian rights 73 lemons 95–6 lithium 17–18 Ariel, Barak 191 Arizona 85 arms trade 19–20 Asia belt and road initiative 117–18 high-net-worth individuals 53 wheat consumption 109–10 see also individual countries Assange, Julian 81–3 asteroids 185–6 augmented reality (AR) 181–2 August 239–40 Australia avocados 89 forests 145 inheritance tax 119 lithium 17, 18 shark attacks 201–2 autonomous vehicles (AVs) 177–8 Autor, David 79 avocados 89–90 B Babylonians 233 Baltimore 99 Bangladesh 156 bank notes 133–4 Bateman, Tim 48 beer consumption 105–6 craft breweries 97–8 Beijing air pollution 143–4 dogs 92 belt and road initiative 117–18 betting 209–10 Bier, Ethan 153 Bils, Mark 169 birds and aircraft 187 guinea fowl 32–3 birth rates Europe 81–3 United States 79–80 black money 133–4 Black Power 34, 35 Blade Runner 208 blood transfusions 139–40 board games 199–200 body cameras 191–2 Boko Haram 5, 15–16 Bolivia 17–18 Bollettieri, Nick 197 bookmakers 209–10 Borra, Cristina 75 Bosnia 221–2 brain computers 167–8 Brazil beer consumption 105, 106 Christmas music 243, 244 end-of-life care 141–2 gay and lesbian rights 73 murder rate 45, 46 shark attacks 202 breweries 97–8 Brexit, and car colours 49–50 brides bride price 5 diamonds 13–14 Britain alcohol consumption 101–2 car colours 49–50 Christmas music 244 cigarette sales 23–4 craft breweries 98 crime 47–8 Easter 238 gay population 70–72 housing material 8 inheritance tax 119 Irish immigration 235 life expectancy 125 manufacturing jobs 131 national identity 223–4 new-year resolutions 234 police body cameras 191 sexual harassment 67, 68, 69 sperm donation 61 see also Scotland Brookings Institution 21 Browning, Martin 75 bubonic plague 157–8 Bush, George W. 119 C cables, undersea 193–4 California and Argentine lemons 95, 96 avocados 90 cameras 191–2 Canada diamonds 13 drones 188 lithium 17 national identity 223–4 capitalism, and birth rates 81–2 Carey, Mariah 243 Carnegie Endowment for International Peace 21 cars colours 49–50 self-driving 177–8 Caruana, Fabiano 206 Charles, Kerwin 169 cheetahs 163, 164 chess 205–6 Chetty, Raj 113 Chicago 100 children birth rates 79–80, 81–3 child marriage 84–5 in China 56–7 crime 47–8 and gender pay gap 115–16, 135–6 obesity 93–4 Chile gay and lesbian rights 73 lithium 17–18 China air pollution 143–5 arms sales 19–20 avocados 89 beer consumption 105 belt and road initiative 117–18 childhood obesity 93 construction 7 dog meat 91–2 dragon children 56–7 flight delays 38–9 foreign waste 159–60 lithium 17 rice consumption 109–10 Choi, Roy 99 Christian, Cornelius 26 Christianity Easter 237–8 new year 233–4 Christmas 246–7 music 243–5 cigarettes affordability 151–2 black market 23–4 cities, murder rates 44–6 Citizen Kane 207 citrus wars 95–6 civil wars 5 Clarke, Arthur C. 183 Coase, Ronald 127, 128 cocaine 44 cochlear implants 167 Cohen, Jake 203 Colen, Liesbeth 106 colleges, US 113–14 Colombia 45 colours, cars 49–50 commodities 123–4 companies 127–8 computers augmented reality 181–2 brain computers 167–8 emojis 215–16 and languages 225–6 spam e-mail 189–90 Connecticut 85 Connors, Jimmy 197 contracts 127–8 Costa Rica 89 couples career and family perception gap 77–8 housework 75–6 see also marriage cows 149–50 craft breweries 97–8 crime and avocados 89–90 and dog meat 91–2 murder rates 44–6 young Britons 47–8 CRISPR-Cas9 153 Croatia 222 Croato-Serbian 221–2 D Daily-Diamond, Christopher 9–10 Davis, Mark 216 De Beers 13–14 death 141–2 death taxes 119–20 democracy 40–41 Deng Xiaoping 117 Denmark career and family perception gap 78 gender pay gap 135–6 sex reassignment 65 Denver 99 Devon 72 diamonds 13–14, 124 digitally remastering 207–8 Discovery Channel 163–4 diseases 157–8 dog meat 91–2 Dorn, David 79 Dr Strangelove 207 dragon children 56–7 drink see alcohol drink-driving 179–80 driverless cars 177–8 drones and aircraft 187–8 and sharks 201 drugs cocaine trafficking 44 young Britons 48 D’Souza, Kiran 187 E e-mail 189–90 earnings, gender pay gap 115–16, 135–6 Easter 237–8 economy and birth rates 79–80, 81–2 and car colours 49–50 and witch-hunting 25–6 education and American rich 113–14 dragon children 56–7 Egal, Muhammad Haji Ibrahim 40–41 Egypt gay and lesbian rights 73 marriage 5 new-year resolutions 233 El Paso 100 El Salvador 44, 45 emojis 215–16 employment gender pay gap 115–16, 135–6 and gender perception gap 77–8 job tenure 129–30 in manufacturing 131–2 video games and unemployment 169–70 English language letter names 217–18 Papua New Guinea 219 environment air pollution 143–4 Arctic sea ice 147–8 and food packaging 103–4 waste 159–60 water pollution 149–50 Equatorial Guinea 32 Eritrea 40 Ethiopia 40 Europe craft breweries 97–8 summer holidays 239–40 see also individual countries Everson, Michael 216 exorcism 36–7 F Facebook augmented reality 182 undersea cables 193 FANUC 171, 172 Federer, Roger 197 feminism, and birth rates 81–2 fertility rates see birth rates festivals Christmas 246–7 Christmas music 243–5 new-year 233–4 Feuillet, Catherine 108 films 207–8 firms 127–8 5G 173–4 flight delays 38–9 Florida and Argentine lemons 95 child marriage 85 Foley, William 220 food avocados and crime 89–90 dog meat 91–2 lemons 95–6 wheat consumption 109–10 wheat genome 107–8 food packaging 103–4 food trucks 99–100 football clubs 211–12 football transfers 203–4 forests 145–6, 162 Fountains of Paradise, The (Clarke) 183 fracking 79–80 France career and family perception gap 78 Christmas music 244 exorcism 36–7 gender-inclusive language 229–30 job tenure 130 sex reassignment 66 sexual harassment 68–9 witch-hunting 26, 27 wolves 161–2 G gambling 209–10 games, and unemployment 169–70 Gandhi, Mahatma 155 gang members 34–5 Gantz, Valentino 153 gas 124 gay population 70–72 gay rights, attitudes to 73–4 gender sex reassignment 65–6 see also men; women gender equality and birth rates 81–2 in language 229–30 gender pay gap 115–16, 135–6 gene drives 153–4 Genghis Khan 42 genome, wheat 107–8 ger districts 42–3 Germany beer consumption 105 job tenure 130 national identity 223–4 sexual harassment 68, 69 vocational training 132 witch-hunting 26, 27 Ghana 73 gig economy 128, 130 glasses, wine glasses 101–2 Goddard, Ceri 72 Google 193 Graduate, The 207 Greece forests 145 national identity 223–4 sex reassignment 65 smoking ban 152 Gregg, Christine 9–10 grunting 197–8 Guatemala 45 Guinea 32 guinea fowl 32–3 guinea pig 32 Guinea-Bissau 32 Guo Peng 91–2 Guyana 32 H Haiti 5 Hale, Sarah Josepha 242 Hanson, Gordon 79 Hawaii ’Oumuamua 185 porn consumption 63–4 health child obesity 93–4 life expectancy 125–6 plague 157–8 and sanitation 155 high-net-worth individuals (HNWIs) 53 Hiri Motu 219 holidays Easter 237–8 St Patrick’s Day 235–6 summer holidays 239–40 Thanksgiving 241–2 HoloLens 181–2 homicide 44–6 homosexuality attitudes to 73–4 UK 70–72 Honduras 44, 45 Hong Kong 56 housework 75–6, 77–8 Hudson, Valerie 5 Hungary 223–4 Hurst, Erik 169 I ice 147–8 Ikolo, Prince Anthony 199 India bank notes 133–4 inheritance tax 119 languages 219 rice consumption 109 sand mafia 7 sanitation problems 155–6 Indonesia polygamy and civil war 5 rice consumption 109–10 inheritance taxes 119–20 interest rates 51–2 interpunct 229–30 Ireland aitch 218 forests 145 St Patrick’s Day 235–6 same-sex marriage 73 sex reassignment 65 Italy birth rate 82 end of life care 141–2 forests 145 job tenure 130 life expectancy 126 J Jacob, Nitya 156 Jamaica 45 Japan 141–2 Jighere, Wellington 199 job tenure 129–30 jobs see employment Johnson, Bryan 168 junk mail 189 K Kazakhstan 6 Kearney, Melissa 79–80 Kennedy, John F. 12 Kenya democracy 40 mobile-money systems 176 Kiribati 7 Kleven, Henrik 135–6 knots 9–10 Kohler, Timothy 121 Kyrgyzstan 6 L laces 9–10 Lagos 199 Landais, Camille 135–6 languages and computers 225–6 gender-inclusive 229–30 letter names 217–18 and national identity 223–4 Papua New Guinea 219–20 Serbo-Croatian 221–2 Unicode 215 World Bank writing style 227–8 Latimer, Hugh 246 Leeson, Peter 26 leisure board games in Nigeria 199–200 chess 205–6 gambling 209–10 video games and unemployment 169–70 see also festivals; holidays lemons 95–6 letter names 217–18 Libya 31 life expectancy 125–6 Lincoln, Abraham 242 lithium 17–18 London 71, 72 longevity 125–6 Lozère 161–2 Lucas, George 208 M McEnroe, John 197 McGregor, Andrew 204 machine learning 225–6 Macri, Mauricio 95, 96 Macron, Emmanuel 143 Madagascar 158 Madison, James 242 MagicLeap 182 Maine 216 Malaysia 56 Maldives 7 Mali 31 Malta 65 Manchester United 211–12 manufacturing jobs 131–2 robots 171–2 summer holidays 239 Maori 34–5 marriage child marriage 84–5 polygamy 5–6 same-sex relationships 73–4 see also couples Marteau, Theresa 101–2 Marx, Karl 123 Maryland 85 Massachusetts child marriage 85 Christmas 246 Matfess, Hilary 5, 15 meat dog meat 91–2 packaging 103–4 mega-rich 53 men career and family 77–8 housework 75–6 job tenure 129–30 life expectancy 125 polygamy 5–6 sexual harassment by 67–9 video games and unemployment 169 Mexico avocados 89, 90 gay and lesbian rights 73 murder rate 44, 45 microbreweries 97–8 Microsoft HoloLens 181–2 undersea cables 193 migration, and birth rates 81–3 mining diamonds 13–14 sand 7–8 mobile phones Africa 175–6 5G 173–4 Mocan, Naci 56–7 Mongolia 42–3 Mongrel Mob 34 Monopoly (board game) 199, 200 Monty Python and the Holy Grail 25 Moore, Clement Clarke 247 Moretti, Franco 228 Morocco 7 Moscato, Philippe 36 movies 207–8 Mozambique 73 murder rates 44–6 music, Christmas 243–5 Musk, Elon 168 Myanmar 118 N Nadal, Rafael 197 national identity 223–4 natural gas 124 Netherlands gender 66 national identity 223–4 neurostimulators 167 New Jersey 85 New Mexico 157–8 New York (state), child marriage 85 New York City drink-driving 179–80 food trucks 99–100 New Zealand avocados 89 gang members 34–5 gene drives 154 water pollution 149–50 new-year resolutions 233–4 Neymar 203, 204 Nigeria board games 199–200 Boko Haram 5, 15–16 population 54–5 Nissenbaum, Stephen 247 Northern Ireland 218 Norway Christmas music 243 inheritance tax 119 life expectancy 125, 126 sex reassignment 65 Nucci, Alessandra 36 O obesity 93–4 oceans see seas Odimegwu, Festus 54 O’Reilly, Oliver 9–10 Ortiz de Retez, Yñigo 32 Oster, Emily 25–6 ostriches 163, 164 ’Oumuamua 185–6 P packaging 103–4 Pakistan 5 Palombi, Francis 161 Papua New Guinea languages 219–20 name 32 Paris Saint-Germain (PSG) 203 Passover 237 pasta 31 pay, gender pay gap 115–16, 135–6 Peck, Jessica Lynn 179–80 Pennsylvania 85 Peru 90 Pestre, Dominique 228 Pew Research Centre 22 Phelps, Michael 163–4 Philippe, Édouard 230 phishing 189 Phoenix, Arizona 177 Pilgrims 241 plague 157–8 Plastic China 159 police, body cameras 191–2 pollution air pollution 143–4 water pollution 149–50 polygamy 5–6 pornography and Britain’s gay population 70–72 and Hawaii missile alert 63–4 Portugal 145 Puerto Rico 45 punctuation marks 229–30 Q Qatar 19 R ransomware 190 Ravenscroft, George 101 Real Madrid 211 religious observance and birth rates 81–2 and Christmas music 244 remastering 207–8 Reynolds, Andrew 70 Rhodes, Cecil 13 rice 109–10 rich high-net-worth individuals 53 US 113–14 ride-hailing apps and drink-driving 179–80 see also Uber RIWI 73–4 robotaxis 177–8 robots 171–2 Rogers, Dan 240 Romania birth rate 81 life expectancy 125 Romans 233 Romer, Paul 227–8 Ross, Hana 23 Royal United Services Institute 21 Russ, Jacob 26 Russia arms sales 20 beer consumption 105, 106 fertility rate 81 Rwanda 40 S Sahara 31 St Louis 205–6 St Patrick’s Day 235–6 salt, in seas 11–12 same-sex relationships 73–4 San Antonio 100 sand 7–8 sanitation 155–6 Saudi Arabia 19 Scotland, witch-hunting 25–6, 27 Scott, Keith Lamont 191 Scrabble (board game) 199 seas Arctic sea ice 147–8 salty 11–12 undersea cables 193–4 secularism, and birth rates 81–2 Seles, Monica 197 self-driving cars 177–8 Serbia 222 Serbo-Croatian 221–2 Sevilla, Almudena 75 sex reassignment 65–6 sexual harassment 67–9, 230 Sharapova, Maria 197 sharks deterring attacks 201–2 racing humans 163–4 shipping 148 shoelaces 9–10 Silk Road 117–18 Singapore dragon children 56 land reclamation 7, 8 rice consumption 110 single people, housework 75–6 Sinquefeld, Rex 205 smart glasses 181–2 Smith, Adam 127 smoking black market for cigarettes 23–4 efforts to curb 151–2 smuggling 31 Sogaard, Jakob 135–6 Somalia 40 Somaliland 40–41 South Africa childhood obesity 93 diamonds 13 gay and lesbian rights 73 murder rate 45, 46 South Korea arms sales 20 rice consumption 110 South Sudan failed state 40 polygamy 5 space elevators 183–4 spaghetti 31 Spain forests 145 gay and lesbian rights 73 job tenure 130 spam e-mail 189–90 sperm banks 61–2 sport football clubs 211–12 football transfers 203–4 grunting in tennis 197–8 Sri Lanka 118 Star Wars 208 sterilisation 65–6 Strasbourg 26 submarine cables 193–4 Sudan 40 suicide-bombers 15–16 summer holidays 239–40 Sutton Trust 22 Sweden Christmas music 243, 244 gay and lesbian rights 73 homophobia 70 inheritance tax 119 overpayment of taxes 51–2 sex reassignment 65 sexual harassment 67–8 Swinnen, Johan 106 Switzerland sex reassignment 65 witch-hunting 26, 27 T Taiwan dog meat 91 dragon children 56 Tamil Tigers 15 Tanzania 40 taxes death taxes 119–20 Sweden 51–2 taxis robotaxis 177–8 see also ride-hailing apps tennis players, grunting 197–8 terrorism 15–16 Texas 85 Thailand 110 Thanksgiving 241–2 think-tanks 21–2 Tianjin 143–4 toilets 155–6 Tok Pisin 219, 220 transgender people 65–6 Trump, Donald 223 Argentine lemons 95, 96 estate tax 119 and gender pay gap 115 and manufacturing jobs 131, 132 Tsiolkovsky, Konstantin 183 Turkey 151 turkeys 33 Turkmenistan 6 U Uber 128 and drink-driving 179–80 Uganda 40 Ulaanbaatar 42–3 Uljarevic, Daliborka 221 undersea cables 193–4 unemployment 169–70 Unicode 215–16 United Arab Emirates and Somaliland 41 weapons purchases 19 United Kingdom see Britain United States and Argentine lemons 95–6 arms sales 19 beer consumption 105 chess 205–6 child marriage 84–5 Christmas 246–7 Christmas music 243, 244 drink-driving 179–80 drones 187–8 end of life care 141–2 estate tax 119 fertility rates 79–80 food trucks 99–100 forests 145 gay and lesbian rights 73 getting rich 113–14 Hawaiian porn consumption 63–4 job tenure 129–30 letter names 218 lithium 17 manufacturing jobs 131–2 murder rate 45, 46 national identity 223–4 new-year resolutions 234 plague 157–8 police body cameras 191–2 polygamy 6 robotaxis 177 robots 171–2 St Patrick’s Day 235–6 sexual harassment 67, 68 sperm banks 61–2 Thanksgiving 241–2 video games and unemployment 169–70 wealth inequality 121 unmanned aerial vehicles (UAVs) see drones V video games 169–70 Vietnam weapons purchases 19 wheat consumption 110 Virginia 85 virtual reality (VR) 181, 182 Visit from St Nicholas, A (Moore) 247 W Wang Yi 117 Warner, Jason 15 wars 5 Washington, George 242 Washington DC, food trucks 99 waste 159–60 water pollution 149–50 wealth getting rich in America 113–14 high-net-worth individuals 53 inequality 120, 121–2 weather, and Christmas music 243–5 Weinstein, Harvey 67, 69 Weryk, Rob 185 wheat consumption 109–10 genome 107–8 Wilson, Riley 79–80 wine glasses 101–2 Winslow, Edward 241 wireless technology 173–4 witch-hunting 25–7 wolves 161–2 women birth rates 79–80, 81–3 bride price 5 career and family 77–8 child marriage 84–5 housework 75–6 job tenure 129–30 life expectancy 125 pay gap 115–16 sexual harassment of 67–9 suicide-bombers 15–16 World Bank 227–8 World Health Organisation (WHO) and smoking 151–2 transsexualism 65 X Xi Jinping 117–18 Y young people crime 47–8 job tenure 129–30 video games and unemployment 169–70 Yu, Han 56–7 Yulin 91 yurts 42–3 Z Zubelli, Rita 239


pages: 196 words: 54,339

Team Human by Douglas Rushkoff

1960s counterculture, autonomous vehicles, basic income, Berlin Wall, big-box store, bitcoin, blockchain, Burning Man, carbon footprint, clean water, clockwork universe, cloud computing, collective bargaining, corporate personhood, disintermediation, Donald Trump, drone strike, European colonialism, Filter Bubble, full employment, future of work, game design, gig economy, Google bus, Gödel, Escher, Bach, Internet of things, invention of the printing press, invention of writing, invisible hand, iterative process, Kevin Kelly, knowledge economy, life extension, lifelogging, Mark Zuckerberg, Marshall McLuhan, means of production, new economy, patient HM, pattern recognition, peer-to-peer, Peter Thiel, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Ronald Reagan, Ronald Reagan: Tear down this wall, shareholder value, sharing economy, Silicon Valley, social intelligence, sovereign wealth fund, Steve Jobs, Steven Pinker, Stewart Brand, technoutopianism, theory of mind, trade route, Travis Kalanick, Turing test, universal basic income, Vannevar Bush, winner-take-all economy, zero-sum game

In a digital media environment there is no resistance, only opposition. 42. It’s hard for human beings to oppose the dominance of digital technology when we are becoming so highly digital ourselves. Whether by fetish or mere habit, we begin acting in ways that accommodate or imitate our machines, remaking our world and, eventually, ourselves in their image. For instance, the manufacturers of autonomous vehicles are encouraging cities to make their streets and signals more compatible with the navigation and sensor systems of the robotic cars, changing our environment to accommodate the needs of the robots with which we will be sharing the streets, sidewalks, and, presumably, air space. This isn’t so bad in itself, but if history is any guide, remaking the physical world to accommodate a new technology—such as the automobile—favors the companies selling the technologies more than the people living alongside them.

The initial monopoly can then expand to other industries, like retail, movies, or cloud services. Such businesses end up destroying the marketplaces on which they initially depend. When the big box store does this, it simply closes one location and starts the process again in another. When a digital business does this, it pivots or expands from its original market to the next—say, from books to toys to all of retail, or from ride-sharing to restaurant delivery to autonomous vehicles—increasing the value of its real product, the stock shares, along the way. The problem with this model, from a shareholder perspective, is that it eventually stops working. Even goosed by digital platforms, corporate returns on assets have been steadily declining for over seventy-five years. Corporations are still great at sucking all of the money out of a system, but they’re awful at deploying those assets once they have them.

So computer scientists feed the algorithms reams and reams of data, and let them recognize patterns and draw conclusions themselves. They get this data by monitoring human workers doing their jobs. The ride-hailing app on cab drivers’ phones also serves as a recording device, detailing the way they handle various road situations. The algorithms then parse data culled from thousands of drivers to write their own autonomous vehicle programs. Online task systems pay people pennies per task to do things that computers can’t yet do, such as translate certain phrases, label the storefronts in photos, or identify abusive social media posts. The companies paying for the millions of human microtasks may not actually need any of the answers themselves. The answers are being fed directly into machine learning routines. The humans’ only real job is to make themselves obsolete. 54.


pages: 320 words: 90,526

Squeezed: Why Our Families Can't Afford America by Alissa Quart

Affordable Care Act / Obamacare, Airbnb, Automated Insights, autonomous vehicles, barriers to entry, basic income, Bernie Sanders, business intelligence, Donald Trump, Downton Abbey, East Village, Elon Musk, full employment, future of work, gig economy, glass ceiling, haute couture, income inequality, Jaron Lanier, job automation, late capitalism, Lyft, minimum wage unemployment, moral panic, new economy, nuclear winter, obamacare, Ponzi scheme, post-work, precariat, price mechanism, rent control, ride hailing / ride sharing, school choice, sharing economy, Silicon Valley, Skype, Snapchat, surplus humans, TaskRabbit, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, union organizing, universal basic income, upwardly mobile, wages for housework, women in the workforce, working poor

Some activists are so concerned about automation in trucking and about driverless vehicles that they’re already starting to organize. The not-for-profit organization New York Communities for Change (NYCC), for instance, has been agitating against automation in trucking and driving and launched a campaign targeting the U.S. Department of Transportation, which has billions of dollars set aside to subsidize the development and spread of autonomous vehicles. “Many truckers are very fearful,” said Zachary Lerner, the group’s senior director of labor organizing, who has been organizing drivers against the autonomous vehicles. “Trucking is not the best job, but it pays the most in lots of rural communities. They worry: Are they going to support their families? And what will happen to all of the small towns built off the trucking economy?” Driverless Ubers may indeed threaten the gig economy freelancers we met earlier—the schoolteachers who drive for rideshare services in order to pay their bills.

Driverless Ubers may indeed threaten the gig economy freelancers we met earlier—the schoolteachers who drive for rideshare services in order to pay their bills. (Ironies compound: as the writer Douglas Rushkoff has noted, today’s drivers are themselves now part of the research and development for what will most likely be the driverless future, building up a company with their labor in preparation for a time when the company will do away with them.) “Our demand is to freeze all the subsidies for the research on autonomous vehicles until there is a plan for workers who are going to lose their jobs,” Lerner said. As part of this effort, NYCC regularly puts together conference calls between dozens of taxi, Uber, and Lyft drivers. They discuss how they’ve all gotten massive loans to buy cars for Uber and how they are still going to be paying off these loans when the robots come for their jobs—the robot vehicles Uber has promised within the decade.

I wondered: what if the critics of robots, like the labor organizers I spoke with who are trying to get truckers to resist driverless trucks, could explain their position to robot-lovers and techno-positivists? Or what if we simply decelerated our robot interlopers and established a “slow tech” movement to match our “slow food” and “slow fashion” trends? Or at the very least, what if we started to rethink who owns, say, autonomous trucks? The effect of robotization would be profoundly different if truckers themselves owned their own autonomous vehicles rather than a corporation controlling them all. Robots are always spoken of as emblems of the future, but they seem to me part of the past as well, akin to the Luddites’ weaving machines. What if the Luddites had been members of co-ops, with a stake in the automated looms that replaced them? IF I HAVE GIVEN SHORT SHRIFT TO THE ROBOT ENTHUSIASTS, IT’S due to my personal predilection for people over efficiencies.


pages: 484 words: 104,873

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

"Robert Solow", 3D printing, additive manufacturing, Affordable Care Act / Obamacare, AI winter, algorithmic trading, Amazon Mechanical Turk, artificial general intelligence, assortative mating, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Bernie Madoff, Bill Joy: nanobots, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chris Urmson, Clayton Christensen, clean water, cloud computing, collateralized debt obligation, commoditize, computer age, creative destruction, debt deflation, deskilling, disruptive innovation, diversified portfolio, Erik Brynjolfsson, factory automation, financial innovation, Flash crash, Fractional reserve banking, Freestyle chess, full employment, Goldman Sachs: Vampire Squid, Gunnar Myrdal, High speed trading, income inequality, indoor plumbing, industrial robot, informal economy, iterative process, Jaron Lanier, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kenneth Arrow, Khan Academy, knowledge worker, labor-force participation, liquidity trap, low skilled workers, low-wage service sector, Lyft, manufacturing employment, Marc Andreessen, McJob, moral hazard, Narrative Science, Network effects, new economy, Nicholas Carr, Norbert Wiener, obamacare, optical character recognition, passive income, Paul Samuelson, performance metric, Peter Thiel, plutocrats, Plutocrats, post scarcity, precision agriculture, price mechanism, Ray Kurzweil, rent control, rent-seeking, reshoring, RFID, Richard Feynman, Rodney Brooks, Sam Peltzman, secular stagnation, self-driving car, Silicon Valley, Silicon Valley startup, single-payer health, software is eating the world, sovereign wealth fund, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Steven Pinker, strong AI, Stuxnet, technological singularity, telepresence, telepresence robot, The Bell Curve by Richard Herrnstein and Charles Murray, The Coming Technological Singularity, The Future of Employment, Thomas L Friedman, too big to fail, Tyler Cowen: Great Stagnation, uber lyft, union organizing, Vernor Vinge, very high income, Watson beat the top human players on Jeopardy!, women in the workforce

See also deep learning The Atlantic (magazine), 71, 237, 254, 273 AT&T, 135, 159, 166 Audi, 184 Australian agriculture, x–xi, 24–25 Australian Centre for Field Robotics (ACFR), 24–25 AutoDesk, 234 automated invention machines, 110 automated trading algorithms, 56, 113–115 automation alien invasion parable, 194–196, 240 anti-automation view, 253–257 cars and (see autonomous cars) effect on Chinese manufacturing, 3, 10–11, 225–226 effect on prices, 215–216 health care jobs and, 172–173 information technology and, 52 job-market polarization and, 50–51 low-wage jobs and, 26–27 offshoring as precursor to, 115, 118–119 predictions of effect of, 30–34 reshoring and, 10 retail sector and, 16–20 risk of, 256 service sector and, 12–20 solutions to rise of, 273–278 (see also basic income guarantee) as threat to workers with varying education and skill levels, xiv–xv, 59 of total US employment, 223 Triple Revolution report, 30–31 white-collar, 85–86, 105–106, 126–128 See also robotics; robots automotive industry, 3, 76, 193–194 autonomous cars, xiii, 94, 176, 181–191 as shared resource, 186–190 Autor, David, 50 Average Is Over (Cowen), 123, 126n aviation, 66–67, 179, 256 AVT, Inc., 18 Ayres, Ian, 125 Babbage, Charles, 79 Baker, Stephen, 96n, 102n Barra, Hugo, 121 Barrat, James, 231, 238–239 basic income guarantee, 31n, 257–261 approaches to, 261–262 downsides and risks of, 268–271 economic argument for, 264–267 economic risk taking and, 267–268 incentives and, 261–264 paying for, 271–273 Baxter (robot), 5–6, 7, 10 BD Focal Point GS Imaging System, 153 Beaudry, Paul, 127 Beijing Genomics Institute, 236n Bell Labs, 159 Berg, Andrew G., 214–215 Bernanke, Ben, 37 big data, xv, 25n, 86–96 collection of, 86–87 correlation vs. cause and, 88–89, 102 deep learning and, 92–93 health care and, 159–160 knowledge-based jobs and, 93–96 machine learning and, 89–92 The Big Switch (Carr), 72 Bilger, Burkhard, 186 “BinCam,” 125n “Bitter Pill” (Brill), 160 Blinder, Alan, 117–118, 119 Blockbuster, 16, 19 Bloomberg, 113–114 Bluestone, Barry, 220 Borders, 16 Boston Consulting Group, 9 Boston Globe (newspaper), 149 Boston Red Sox, 83 Boston University, 141 Bowley, Arthur, 38 Bowley’s Law, 38–39, 41 box-moving robot, 1–2, 5–6 brain, reverse engineering of human, 237 breast cancer screening, 152 Brill, Steven, 160, 163 Brin, Sergey, 186, 188, 189, 236 Brint, Steven, 251 Brooks, Rodney, 5 Brown, Jerry, 134 Brynjolfsson, Erik, 60, 122, 254 Bureau of Labor Statistics, 13, 16, 38n, 158, 222–223, 281 Bush, George W., 116 business interest lobbying, economic policy and, 57–58 “Busy child scenario,” (Barrat) 238–239 Calico, 236 California Institute of Technology, 133 Canada, 41, 58, 167n, 251 “Can Nanotechnology Create Utopia?”

., 150n risk, Peltzman effect and, 267–268 RoboBusiness conference/tradeshow, 7 Robot & Frank (film), 155 robotics, 6–8 cloud, 20–23 See also automation; robots robotic walkers, 157 robots in agriculture, 23–26 box-moving, 1–2, 5–6 consumer, 197n educational, 7 elder-care, 155–158 hospital and pharmacy, 153–155 industrial, 1–5, 10–11 personal, 7 telepresence, 119–120, 157 Rolling Stone (magazine), 56 Romney, Mitt, 272 Roosevelt, Franklin, 279 Rosenthal, Elisabeth, 160, 163 Rosenwald, Michael, 107 ROS (Robot Operating System), 6, 7 Russell, Stuart, 229 Rutter, Brad, 101 Sachs, Jeffrey, 60 Saez, Emmanuel, 46 safety, autonomous cars and, 184–185, 187 Salesforce.com, 134 Samsung Electronics, 70n Samuelson, Paul, x Sand, Benjamin M., 127 San Jose State University, 134 Sankai, Yoshiyuki, 156–157 Santelli, Rick, 170 savings, China’s high rate of, 224–225 SBTC. See skill biased technological change (SBTC) Schlosser, Eric, 210 Schmidt, Michael, 108, 109 Schwarzenegger, Arnold, 22 S-curves, 66–67, 68, 69, 70–71, 250 secular stagnation, 274n self-driving cars, See autonomous cars Selingo, Jeffrey J., 140, 141 Semiconductor Industry Association, 80 service sector, 12–20 The Shallows (Carr), 254 Shang-Jin Wei, 225 Silvercar, 20 Simonyi, Charles, 71 single-payer health care system, 165–167, 169 The Singularity, 233–238, 248 The Singularity Is Near (Kurzweil), 234 Singularity University, 234 Siu, Henry E., 49, 50 skill biased technological change (SBTC), 48 skills, acquisition of by computers, xv–xvi Skipper, John, 201 “Skynet,” 22 Slate (magazine), 153 Smalley, Richard, 244–245 Smith, Adam, 73 Smith, Noah, 219–220, 273 Smith, Will, 111 social media response program, 93–94 social safety net, 278.

YouTube, Instagram, and WhatsApp are, of course, all examples drawn directly from the information technology sector, where we’ve come to expect tiny workforces and huge valuations and revenues. To illustrate how a similar phenomenon is likely to unfold on a much broader front, let’s look in a bit more depth at two specific technologies that have the potential to loom large in the future: 3D printing and autonomous cars. Both are poised to have a significant impact within the next decade or so, and could eventually unleash a dramatic transformation in both the job market and the overall economy. 3D Printing Three-dimensional printing, also known as additive manufacturing, employs a computer-controlled print head that fabricates solid objects by repeatedly depositing thin layers of material. This layer-by-layer construction method enables 3D printers to easily create objects with curves and hollows that might be difficult, or even impossible, to produce using traditional manufacturing techniques.


pages: 286 words: 79,305

99%: Mass Impoverishment and How We Can End It by Mark Thomas

"Robert Solow", 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, additive manufacturing, Albert Einstein, anti-communist, autonomous vehicles, bank run, banks create money, bitcoin, business cycle, call centre, central bank independence, complexity theory, conceptual framework, creative destruction, credit crunch, declining real wages, distributed ledger, Donald Trump, Erik Brynjolfsson, eurozone crisis, fiat currency, Filter Bubble, full employment, future of work, Gini coefficient, gravity well, income inequality, inflation targeting, Internet of things, invisible hand, Jeff Bezos, jimmy wales, job automation, Kickstarter, labour market flexibility, laissez-faire capitalism, light touch regulation, Mark Zuckerberg, market clearing, market fundamentalism, Martin Wolf, money: store of value / unit of account / medium of exchange, Nelson Mandela, North Sea oil, Occupy movement, offshore financial centre, Own Your Own Home, Peter Thiel, Piper Alpha, plutocrats, Plutocrats, profit maximization, quantitative easing, rent-seeking, Ronald Reagan, Second Machine Age, self-driving car, Silicon Valley, smart cities, Steve Jobs, The Great Moderation, The Wealth of Nations by Adam Smith, wealth creators, working-age population

The security of your Internet banking could be made or broken by quantum computing. Other problems which may benefit from this kind of high-power computing include mapping of entire proteins in the way that genes can be mapped today – or even entire genomes – and, of course, the development of full AI. Narrow AI for applications such as autonomous vehicles Proof of concept studies on self-driving cars have been underway for almost a decade and large-scale trials are now in progress. Google, for example, has more than twenty autonomous vehicles in the US, and NuTonomy has trials of taxis underway in Singapore. Many commentators believe that the first commercially available self-driving cars will hit the market before 2020.9 The heads of automakers General Motors and Nissan have both confirmed that they expect driverless cars on the roads by 2020.

In combination, these new technologies give us the power to create an improvement in human lives over the next thirty-five years at least comparable to that during the Golden Age of Capitalism. A huge range of high-value products and services, and indeed new business models, will become feasible. The total cost to the world – including environmental cost – will fall dramatically. And, economically, the size of the pie could expand enormously. Autonomous vehicles alone will have a huge impact on society: people who are not mobile because they cannot drive or cannot afford a car will become mobile; fewer people will feel the need to own a car since one could arrive at their doorstep when they need it; roads lined with cars that spend most of their lives immobile will be a thing of the past; the total number of cars needed will be far fewer and congestion may even reduce; and people whose jobs today involve driving buses, taxis, lorries and trucks will no longer be employed in those areas.


pages: 491 words: 77,650

Humans as a Service: The Promise and Perils of Work in the Gig Economy by Jeremias Prassl

3D printing, Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, Andrei Shleifer, autonomous vehicles, barriers to entry, call centre, cashless society, Clayton Christensen, collaborative consumption, collaborative economy, collective bargaining, creative destruction, crowdsourcing, disruptive innovation, Donald Trump, Erik Brynjolfsson, full employment, future of work, George Akerlof, gig economy, global supply chain, hiring and firing, income inequality, information asymmetry, invisible hand, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kickstarter, low skilled workers, Lyft, Mahatma Gandhi, Mark Zuckerberg, market friction, means of production, moral hazard, Network effects, new economy, obamacare, pattern recognition, platform as a service, Productivity paradox, race to the bottom, regulatory arbitrage, remote working, ride hailing / ride sharing, Robert Gordon, Ronald Coase, Rosa Parks, Second Machine Age, secular stagnation, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley ideology, Simon Singh, software as a service, Steve Jobs, TaskRabbit, The Future of Employment, The Market for Lemons, The Nature of the Firm, The Rise and Fall of American Growth, transaction costs, transportation-network company, Travis Kalanick, two tier labour market, two-sided market, Uber and Lyft, Uber for X, uber lyft, union organizing, working-age population

Entrepreneurs’ reliance on cheap out- workers was said to ‘perpetuate the use of imperfect and inferior machin- ery . . . and thus prevent the adoption of improved and more economical models of production’.74 Two immediate counter-arguments come to mind. First, what about Uber’s well-publicized efforts to develop self-driving cars? And, in any event, might a delay in innovative automation not be to the benefit of workers whose jobs would otherwise be threatened by a rise of the robots? As regards the first of these arguments, Uber’s emphasis on autonomous vehicles has increasingly been questioned by experts from both technological and eco- nomic perspectives: the company’s efforts seem to lag significantly behind competitors’ technological advances.75 In any event, why would Uber replace its current asset-light model, under which drivers bear the full cost of pro- viding cars, petrol, and their time, with a massive investment in an expensive fleet of self-driving cars?

There isn’t a page in this book which hasn’t been discussed with and challenged and improved by her, or a day of its writing which went by without stimulating discussion and crucial input. I am incredibly lucky to have found a soulmate and co-conspirator. This book is dedicated to Abi with my deepest love and admiration. J.F.B.B.P. Magdalen College, Oxford Hilary Term MMXVIII * * * * * * Index Aasmäe, Mailin 183 automata 1 Adams, Abi 111, 178 automation 89, 135, 136 ‘additional income’ 81–2 limits of 137–9 airbnb 143 robots 136–7 Airtasker 114 autonomous vehicles 89, 137 Akerlof, George 158 autonomy 53–5 (see also Albin, Einat 175, 176 self-determination) algorithms 2, 5, 7, 8, 12, 13, 84 algorithmic control and 55–8 control mechanisms 55–8 sanctions and 61–3 limitations 138, 139 wages and 58–61 rating algorithms 54, 55, 87–8 Autor, David 138–9, 185, 186 discrimination 113 Avent, Ryan 89, 171 Amazon ‘artificial artificial intelligence’ 6, 139 Badger, Emily 182 CEO 1–2, 3, 6 Balaram, Brhmie 38, 149, 150, 153, ‘humans as a service’ 3 155, 158, 180 MTurk 2, 3, 4, 11, 12, 24–5, 76, 139, Balkin, Jack 170 161–2, 163 bargaining power 9, 48, 65, 66, 82, 107, algorithmic control mechanisms 56 110, 111, 113, 116 (see also business model 100, 101, 103, 104 collective bargaining) commission deductions 63 Barry, Erin 166 digital work intermediation 14, 15 Benjamin, Robert 73 matching 19 Bertram, Jo 115 payment in gift vouchers 105 Berwick, Barbara Ann 99 quality control 120 Bevin, Ernest 86 TurkOpticon 114, 162, 163, 179 Bezos, Jeff 1–2, 3, 6, 72 wage rates 59, 60, 61 Bhuiyan, Johana 162 termination of agreements 63 Biewald, Lukas 4 ‘web services’ 1–2 bilateral relationships 100 Andersen, Hans Christian 71, 166 BlaBlaCar 43 Apple 35 BlancRide 43 apps 5 Blasio, Bill de 36 arbitration clauses 67, 165 Bonaparte, Napoleon 1 Arlidge, John 163 Booth, Robert 182 ‘artificial artificial intelligence’ 6, 139 Boswell, Josh 182 Ashley, Mike 40 Bradshaw, Tim 151 associated costs 60 Brazil, Noli 133, 184 asymmetric information 32, 54, 87, 131 Bruckner, Caroline 126–7, 183 Australia 109, 110–11, 114, 121, 176, 177 Brynjolfsson, Erik 137, 138, 185 * * * 192 Index Burger King 60 consumer satisfaction 25 business models 12–13, 44, 100, 101, 102 contracts of employment 94 structural imbalances 130–2 bilateral relationships 100 Busque, Leah 46, 51 contractual agreements 8 Butler, Sarah 155 contractual prohibitions 66–7 Bythell, Duncan 89, 166, 167, 168, control mechanisms 54, 55–7 169, 172 ‘cost of switching’ 165 Craigslist 20 Cala, Ryan 123, 131, 182, 184 Croft, Jane 173, 182, 186 Callaway, Andrew 58, 161 ‘crowd-based capitalism’ 40, 73 capitalism 2, 3, 40, 73 CrowdFlower 4, 58 Carr, Paul Bradley 39, 154 wage rates 59 Carson, Biz 173 crowdsourcing 7, 11 Case, Steve 73, 166 classification and differentiation 13 cash burn 22–3 crowdwork 2, 54 cashless payment 5 classification and differentiation 13 ‘casual earners’ 29 Crump, W.

L. 176 Chen, Keith 122 Davies, Paul 174 Cherry 38 Davies, Rob 151 Cherry, Miriam 97, 99, 132, 173, 174, 184 Day, Iris 177 chess robots 1, 6 Deakin, Simon 36, 112, 130, 131, 152, 172, China 12, 38, 153 174, 177, 178, 184, 185 Chowdhry, Amit 181 deductions from pay 15, 19, 60, 63, 67 Christenson, Clayton M. 39 Deep Blue 1 ‘churn’/worker turnover 68 Deliveroo 2, 11, 12, 13, 115 Clark, Shelby 46 collective action by drivers 113 classificatory schemes 13, 28–9, 147 contractual prohibitions 66–7 misclassification 95, 96–100 employment litigation 99 Clement, Barrie 162 internal guidelines 43–4 Clover, Charles 153 safety and liability 122–3 Coase, Ronald 19, 94, 101, 172 wage rates 65 Coase’s theory 19, 20 delivery apps 2 Codagnone, Cristiano 150 demand fluctuations 78 Cohen, Molly 36, 37, 152, 157 Denmark 36 ‘collaborative consumption’ 42 deregulation 37, 40 (see also regulation) collective action 113–15 Dholakia, Utpal 150 collective bargaining rights 48, 65, 82 Didi 2, 12, 38 commission deductions 15, 19, 60, 63, 67 differential wage rates 109–11 commodification of work 76, 77, 110 digital disruption 49, 50 competition 88 ‘digital feudalism’ 83 consumer demand 17–18 digital innovation see innovation consumer protection 10, 112, 121, 128–9 digital market manipulation 123 safety and liability 122–3, 128–9 digital payment systems 5 * * * Index 193 digital work intermediation 5, 11, 13–16 borderline cases 100 disability discrimination 62, 121 identifying the employer 100 discriminatory practices 62, 94, 113, easy cases 102–3 121, 180 functional concept of the disputes 66 employer 101–2, 104 disruptive innovation 39–40, 49, 50, 95 genuine entrepreneurs 103 dockyards 78, 79–80 harder cases 103–4 ‘doublespeak’ 31–50, 71, 95, 97–8, 133 multiple employers 103 Doug H 160, 163 platforms as employers 102–3 down-time 60, 65, 76, 77 ‘independent worker’ 48 Downs, Julie 180 misclassification 95, 96–100 Drake, Barbara 168 ‘personal scope question’ 93 drink driving 133, 184–5 employment taxes 125–7 Dzieza, Josh 163 Engels, Friedrich 81, 168 ‘entrepreneur-coordinator’ 101 economic crises 145 entrepreneurship 6, 8, 21, 32, 42, 43, economic drivers 7, 18–24 45–6, 50, 52 (see also micro- Edwards, Jim 146 entrepreneurs) efficiency 7 autonomy 53–5 Elejalde-Ruiz, Alexia 175 algorithmic control and 55–8 ‘elite worker’ status 61, 67 sanctions and 61–3 ‘emperor’s new clothes’ 71 wages and 58–61 empirical studies 28–9 freedom 8, 14, 27, 29, 47, 49, 51, 52, employer responsibility 104 53, 55, 65–8, 69, 85, 96, 108, 110, employment contracts 94 112, 113 bilateral relationships 100 on-demand trap and 68–70 employment law 4, 9, 10, 38, 84 risk and 86 (see also regulation) genuine entrepreneurs 102, 103 continuing importance 139–40 misclassification 96–7, 98, 101 control/protection trade-off 93–4, 95 ‘personal scope question’ 93 European Union 107, 111, 112, 178 self-determination 63–5 flexibility and environmental impacts 21, 26 innovation and 90 Estlund, Cynthia 137, 185 measuring working time 105–7 Estonia 127 mutuality of obligation 174 Estrada, David 41 new proposals 46–9 euphemisms 44–5 rebalancing the scales 107–8 European Union law 107, 111, 112, 178 collective action 113–15 exploitation 26–7 portable ratings 111–13 Ezrachi, Ariel 150 surge pricing 108–11 ‘risk function’ 131, 132 Facebook 35, 57 workers’ rights 105 FairCrowdWork 114, 179 rights vs flexibility 115–17 Farrell, Sean 164 employment litigation FedEx 97 FedEx 97, 173 feedback 5, 15–16 France 99 Feeney, Matthew 35, 151 Uber 45, 48, 54–5, 98, 99, 106, 115 Field, Frank 26 UK 45, 48, 98–9, 106, 115 financial losses 22–3 US 54–5, 97, 98, 99 ‘financially strapped’ 29 employment status 21, 45, 47 Finkin, Matthew 74, 84, 166, 169 * * * 194 Index Fiverr 12, 13, 24, 78 historical precedents and CEO 17 problems 72, 73–85 Fleischer, Victor 20, 147 rebranding work 4–6, 32 flexibility 8, 10, 12, 107, 108 labour as a technology 5–6 vs rights 115–17 market entrants 88 food-delivery apps 12 matching 13, 14, 18–20 Foodora 2, 12 monopoly power 23–4, 28 Foucault, Michel 55, 159 network effects 23–4 founding myths 34–5 overview 2–3 Fox, Justin 182 perils 6, 26–8, 31 fragmented labour markets 83, 84, 86, platform paradox 5 90, 113 platforms as a service 7–8 France 78 consumer protection 10 employment litigation 99 potential 6, 7, 12, 24–6, 31 Labour Code 114, 176, 179 regulation 9–10 (see also regulation) regulatory battles 36 real cost of on-demand services 119, tax liability 126 121–2 (see also structural ‘free agents’ 28–9 imbalances) Freedland, Mark 174, 175 regulation see regulation Freedman, Judith 111, 178 regulatory arbitrage 20–2 freedom 8, 14, 27, 29, 47, 49, 51, 52, 53, size of the phenomenon 16–17, 145–6 55, 65–8, 69, 85, 96, 108, 110, work on demand 11–29 112, 113 gigwork 13 on-demand trap and 68–70 Giliker, Paula 183 risk and 86 global economic crises 145 Frey, Carl 136, 185 Goodley, Simon 173 Fried, Ina 183 GPS 5, 57 Greenhouse, Steven 66, 164 Gardner-Selby, W. 185 Griswold, Alison 164, 181 gender parity 144 (see also Grossman, Nick 46 discriminatory practices) Gumtree 20 Germany Gurley, Bill 161 regulatory battles 36 Guyoncourt, Sally 178 workers’ rights 114 gift vouchers 105 Hacker, Jacob 86, 170 gig economy Hall, Jonathan 60, 162, 165 business models 12–13, 44, 100 Hammond, Philip 126, 182 cash burn 22–3 Hancock, Matthew 46, 166 clash of narratives 8 Handy 18 classification 13, 28–9 Hardy, Tess 176 critics 2, 3, 8 Harman, Greg 163 digital work intermediation 5, 11, Harris, Seth 48, 49, 105, 157, 175 13–16 Hatton, Erin 82, 169 economic drivers 7, 18–24 Heap, Lisa 177 empirical studies 28–9 Helpling 2 employment law and see employment Hemel, Daniel 147, 170 law Hesketh, Scott 181 enthusiasts 3, 4, 8 hiring practices: historical gigwork vs crowdwork 13 perspective 78, 79 growth 17–18 historical perspective 72, 73–85 ‘humans as a service’ 3–6 Hitch 38 * * * Index 195 Hitlin, Paul 162 Internet Holtgrewe, Ursula 169 collective action 113 HomeJoy 132 Third Wave 73 Hook, Leslie 153 Irani, Lilly 6, 114, 142, 162, 179 Horan, Hubert 22, 148 Isaac, Mike 170, 171 Horowith, Sara 144 Issa, Darrell 41 hostile takeovers 111–12 Howe, Jeff 7, 11, 142 jargon 42–5 Huet, Ellen 153 Jensen, Vernon 167, 168, 170 Human Intelligence Tasks (HITs) 60, 93 Jobs, Steve 35 ‘humans as a service’ 3–6 joint and several liability 104 historical precedents and problems 72, Justia Trademarks 143 73–85 rebranding work 4–6, 32, 40–50 Kalanick, Travis 43, 86 Hunter, Rachel 106, 176 Kalman, Frank 16, 144 Huws, Ursula 27, 141, 150 Kaminska, Izabella 22–3, 44, 90, 148, 156, 169, 171, 172 ‘idle’ time 60, 65, 76, 77 Kaplow, Louis 184 illegal practices 57 Kasparov, Garry 1 immigrant workers 77 Katz, Lawrence 16 incentive structures 67–8 Katz, Vanessa 116, 179 independent contractors 21 Kaufman, Micha 17, 145, 149 Independent Workers Union of Great Kempelen, Wolfgang von 1 Britain (IWGB) 113, 179 Kennedy, John F. 135, 185 industrialization 75 Kenya 36 industry narratives 32–3, 49–50 Kessler, Sarah 151 information asymmetries 32, 54, 87, 131 Keynes, John Maynard 135, 185 innovation 3, 6, 8, 9, 10, 31, 32, 42, 45–6, King, Tom, Lord King of Bridgwater 71 110 cheap labour and 89 Kirk, David 133, 184 disruptive innovation 39–40, 49, 95 Kitchell, Susan 166 historical precedents and problems 72, Klemperer, Paul 165 73–85 Krueger, Alan 16, 48, 49, 60, 105, 106, incentives 86–90 157, 162, 165, 175 myths 72, 83 Krugman, Paul 170 obstacles to 88–90 Kucera, David 186 paradox 72, 87 problematic aspects 85–90 labour law see employment law productivity and 87 Lagarde, Christine 86, 170 shifting risk 85–6 Leimeister, Jan Marco 13 workers’ interests and 89–90 Leonard, Andrew 33, 151 innovation law perspective 36 Lewis, Mervyn 168 ‘Innovation Paradox’ 9 Liepman, Lindsay 184 insecure work 9, 10, 12, 27, 42, 107 Lloyd-Jones, Roger 168 historical perspective 80, 81 loan facilities 68 insurance 123 lobbying groups 32, 47, 48 intermediaries 83 (see also digital work Lobel, Orly 11, 37–8 intermediation) low-paid work 9, 26–7, 40–2, historical perspective 79–80 59, 61 International Labour Organization low-skilled work 76, 77, 82 (ILO) 4, 83, 97, 169, 173 automation and 138 * * * 196 Index Lukes, Steven 159 Murgia, Madhumita 182 Lyft 2, 12, 13, 38, 41, 42, 76 mutuality of obligation 174 algorithmic control mechanisms 56 network effects 23–4 regulatory battles 35 Newcomer, Eric 148, 165 Uber’s competitive strategies 88 Newton, Casey 164 Nowag, Julian 183 McAfee, Andrew 137, 138, 185 Machiavelli, Niccolo 93, 172 O’Connor, Sarah 43, 155 machine learning 136, 137 ODesk 60 McCurry, Justin 186 O’Donovan, Caroline 144, 164, 181 Malone, Tom 73 Oei, Shu-Yi 124, 125, 132, 147, 182, 184 Mamertino, Mariano 161, 163 Ola 2, 12 market entrants 88 on-demand trap 68–70 market manipulation 123 on-demand work 11– 29 Markowitz, Harry 184 real cost of on-demand services 119, Marsh, Grace 182 121–2 (see also structural Marshall, Aarian 186 imbalances) Martens, Bertin 150 Orwell, George 31, 151 Marvit, Moshe 142 Osborne, Hilary 164 Marx, Patricia 119–20, 180 Osborne, Michael 136, 185 matching 13, 14, 18–20 outsourcing Maugham, Jolyon 182 agencies 40 Mayhew, Henry 77, 78, 79, 167 ‘web services’ 2 Mechanical Turk 1, 2, 6 outwork industry 74–5, 76–7, 79, 80, 89 mental harm 57–8 Owen, Jonathan 178 Meyer, Jared 149 ‘micro-entrepreneurs’ 8, 21, 46, 49, Padget, Marty 186 52–3, 63 Pannick, David, Lord Pannick 110 ‘micro-wages’ 27 Pasquale, Frank 8, 40, 154 middlemen 80 Peck, Jessica Lynn 26 minimum wage levels 3, 9, 21, 26, 27, 59, peer-to-peer collaboration 42, 43 94, 104, 105 Peers.org 32–3 minimum working hour guarantees 108 performance standard probations 61 misidentification 95, 96–100 personal data 112, 178 mobile payment mechanisms 5 ‘personal scope question’ 93 monopoly power 23–4, 28 Pissarides, Christopher 19, 147 Morris, David Z. 171 platform paradox 5 Morris, Gillian 174 platform responsibility 122–3, 128 MTurk 2, 3, 4, 11, 12, 24–5, 76, 139, platforms as a service 7–8 161–2, 163 consumer protection 10 algorithmic control mechanisms 56 regulation 9–10 (see also regulation) business model 100, 101, 103, 104 Plouffe, David 154 commission deductions 63 Poe, Edgar Allen 1 digital work intermediation 14, 15 Polanyi’s paradox 138–9 matching 19 political activism 114 payment in gift vouchers 105 portable ratings 111–13 quality control 120 Porter, Eduardo 171 TurkOpticon 114 ‘postindustrial corporations’ 20 wage rates 59, 60, 61 Postmates 57, 63, 121 * * * Index 197 Poyntz, Juliet Stuart 168 structural imbalances 130, 131 Prassl, Jeremias 174, 175, 176, 177, robots 136–7 178, 183 Mechanical Turk 1, 6 precarious work 9, 10, 12, 27, 42, 107 Rodgers, Joan 177 historical perspective 80, 81 Rodriguez, Joe Fitzgerald 181 price quotes 121–2 Rönnmar, Mia 175 surge pricing 58, 108–11, 122 Roosevelt, Franklin D. 133, 185 Primack, Dan 148 Rosenblat, Alex 54, 56, 65, 123, 131, 159, productivity 87 160, 163, 164, 182, 184 public discourse 69 Rosenblat, Joel 165 public health implications 27 Rubery, Jill 84, 169 punishment 57 (see also sanctions) Ryall, Jenny 181 quality control 5, 80, 120 safe harbours 47, 49 safety and liability 122–3, 128–9 rating mechanisms 5, 15–16, 53–4 sanctions 61–3 (see also punishment) algorithms 54, 55, 87–8 Sandbu, Martin 87, 170 discrimination 62, 113 Scheiber, Noam 164 portable ratings 111–13 Schmiechen, James 167, 168, 169 sanctions and 61–3 Schumpeter, Joseph 133 rebranding work 4–6, 32, 40–50 self-dealing 123 regulation 9–10 (see also employment law) self-determination 36–7, 47, 63–5 industry narratives 32–3, 49–50 (see also autonomy) new proposals 31, 46–9, 50 self-driving cars 89, 137 opponents 31, 33–4 sexual assaults 121, 180–1 Disruptive Davids 34–7 sexual discrimination 62, 144, 180 disruptive innovation theory ‘sham self-employment’ 97 39–40, 49 sharing economy 7, 20, 51 New Goliaths 37–40 critics 32–3 regulatory battles 35–7, 47–9 disruptive innovation 39, 49 safe harbours 47, 49 enthusiasts 61 self-regulation 36–7, 47 Sharing Economy UK 33, 37 shaping 32–3, 45–9 sharing platforms 116 regulatory arbitrage 20 –2, 147 Shavell, Steven 184 regulatory experimentation 36 Shleifer, Andrei 111, 178 Reich, Robert 108, 176 Shontell, Alyson 161 Relay Rides 46 Silberman, Six 61, 114, 162, 163, 179 ‘reluctants’ 29 Silver, James 156, 158 reputation algorithms 54 Singer, Natasha 43, 155, 156 ride-sharing/ridesharing 2, 21, 38, 41 Slee, Tom 32, 53, 142, 151, 155, 158, 159 (see also taxi apps) Smith, Adam 73 algorithmic control mechanisms 55–6 Smith, Jennifer 170 business model 102–3 Smith, Yves 148 discriminatory practices 62, 121 social media 114 maltreatment of passengers 121 social partners 10, 94 ride-sharing laws 47 social security contributions 21, 125–7 Ries, Brian 181 social security provision 3, 48, 131 Ring, Diane 124, 125, 132, 147, 182, 184 sociological critique 27–8 Risak, Martin 102, 175 specialization 75 risk shift 85–6 Spera 51, 158 * * * 198 Index Sports Direct 40–1 taxi regulation 21, 36, 37, 38, 114 Standage, Tom 141 vetting procedures 121 standardized tasks 76 tech:NYC 33 Stark, Luke 54, 56, 65, 159, 160, 163, 164 technological exceptionalism 6, 128 start-up loans 68 technological innovation see innovation Stefano, Valerio De 84, 169 technology 5–6, 27 Stigler, George 32, 151 unemployment and 135, 137, 140 Stone, Katherine 67, 165 terminology 42–5 structural imbalances time pressure 57 business model 130–2 Titova, Jurate 183 digital market manipulation 123 TNC, see transportation network levelling the playing field 127–32 company platform responsibility 122–3, 128 Tolentino, Jia 166 real cost of on-demand services 119, Tomassetti, Julia 20, 147, 156, 171 121–2 Tomlinson, Daniel 163 safety and liability 128–9 trade unions 65, 113, 114, 178, 179 sustainability 132–3 transaction cost 19 tax obligations 123–4, 129, 131, 132 transport network company (TNC) employment taxes and social regulation 47–8 security contributions 125–7 Truck arrangements 105 VAT 124–5, 129 Tsotsis, Alex 151 Stucke, Maurice 150 TurkOpticon 114, 162, 163, 179 Sullivan, Mike 180 Summers, Lawrence 111, 131, 178, 184 Uber 2, 11, 12, 43 Sundararajan, Arun 36, 37, 41, 73, 74, 75, algorithmic control mechanisms 56, 151, 152, 157, 166, 167 57, 58 Supiot, Alain 130–1, 177, 184 arbitration 165 surge pricing 58, 108–11, 122 autonomous vehicles and 89 survey responses 120 ‘churn’/worker turnover 68 Swalwell, Eric 41, 154 commission deductions 63 competitive strategies 88 takeovers 111–12 consumer demand 18 ‘task economies’ 76, 77, 79 control mechanisms 54 Task Rabbit 2, 12, 13, 46, 143–4, 163 creation of new job business model 100, 101, 160 opportunities 77–8 company law 56 digital work intermediation 14, 15 contractual prohibitions 66 disruptive innovation 39 digital work intermediation 14, 15–16 driver income projections 51 financial losses 22 Driver-Partner Stories 25, 149 founding myth 34–5 driver-rating system 158, 160 regulatory arbitrage 20 employment litigation terms of service 44, 53, 122, 158, 181 France 99 wage rates 64 UK 45, 48, 98, 106, 115 working conditions 57 US 54–5, 99 Taylor, Frederick 52–3, 72, 158 financial losses 22, 23 tax laws 84 ‘Greyball’ 88, 170 tax obligations 123–4, 129, 131, 132 ‘Hell’ 88, 170 employment taxes and social security loss-making tactics and market share 64 contributions 125–7 monopoly power 23 VAT 124–5, 129 positive externality claims 132–3 taxi apps 12, 20 regulatory arbitrage 20 * * * Index 199 regulatory battles 35, 36 Vaidhyanathan, Siva 40, 154 resistance to unionization 65, 178 value creation 18–19, 20 risk shift 86 van de Casteele, Mounia 182 safety and liability 122–3, 180–1 VAT 124–5, 129 sale of Chinese operation 38 Verhage, Julie 147 surge pricing 58, 122 vicarious liability 128 tax liability 125, 126, 127 unexpected benefits 26 wage rates 58–61, 64, 65 wage rates 58, 59, 60–1, 64, 65, 127 Wakabayashi, Daisuke 171 working conditions 113, 178 Warne, Dan 115 UberLUX 14 Warner, Mark 16 UberX 14, 51, 60 Warren, Elizabeth 127, 183 UK Webb, Beatrice and Sidney 80, 168 collective action 113 Weil, David 83, 169 employment litigation 45, 48, 98–9, 106 welfare state 130, 131 tax liability 124–5, 126 Wilkinson, Frank 84, 130, 131, 169, unemployment 135, 137, 140, 145 172, 184, 185 Union Square Ventures 46 Wong, Julia Carrie 170 unionization 10, 65, 113, 114, 178, 179 work on demand 11–29 ‘unpooling’ 147 worker classification 28–9, 147 Unterschutz, Joanna 178 misclassification 95, 96–100 Upwork 12, 76, 144 workers’ rights 105 algorithmic control mechanisms 56 vs flexibility 115–17 business model 100, 160 working conditions 57, 68–9 commission deductions 63, 67 historical perspective 77, 81 US Uber 113, 178 discriminatory practices 121 working time 105–7 employment litigation 54–5, 97, 98, 99 Wosskow, Debbie 157 regulatory battles 36, 47 Wujczyk, Marcin 178 tax liabilities 126–7 taxi regulation 36, 114 Yates, Joanne 73 transport network company (TNC) YouTube 58 regulation 47–8 user ratings 5, 15–16, 53–4, 55 Zaleski, Olivia 165 portable ratings 111–13 zero-hours contracts 40, 41, 107 sanctions and 61–3 Zuckerberg, Mark 35 * * * Document Outline Cover Humans as a Service: The Promise and Perils of Work in the Gig Economy Copyright Dedication Contents Introduction Welcome to the Gig Economy Humans as a Service Rebranding Work The Platform Paradox Labour as a Technology Making the Gig Economy Work Platforms as a Service Exploring the Gig Economy Charting Solutions A Broader Perspective 1.


There Is No Planet B: A Handbook for the Make or Break Years by Mike Berners-Lee

air freight, autonomous vehicles, call centre, carbon footprint, cloud computing, dematerialisation, Elon Musk, energy security, energy transition, food miles, Gini coefficient, global supply chain, global village, Hans Rosling, income inequality, Intergovernmental Panel on Climate Change (IPCC), land reform, neoliberal agenda, off grid, performance metric, profit motive, shareholder value, Silicon Valley, smart cities, Stephen Hawking, The Spirit Level, The Wealth of Nations by Adam Smith, trickle-down economics, urban planning

Taking carbon, particles and nitrogen dioxide all into account, there is a clear hierarchy with diesels at the bottom, petrol in the mediocre middle, and electric cars at the top by a long way. Whatever type you chose, it’s good if you get a small one, drive it less and share it more. Could autonomous cars be a disaster? Or brilliant? It all depends on how much we use them. Driverless cars undoubtedly stand to be more efficient because they can slip stream each other and optimise every 110 4 TRAVEL AND TRANSPORT manoeuvre. The first difficulty, as we’ve seen, is that efficiency leads to yet more trouble unless the total global carbon use is capped. The issue is particularly extreme with autonomous cars because they also stand to be far less stressful and safer. We could sleep on the way to work or sleep all night while it drives us hundreds of miles to a meeting. We could send our kids huge distances to school every day, and if they forgot their packed lunch we could just send the car out again to deliver it.

Just because we invented it, it doesn’t mean we are forced to adopt it, even though as we will see later, it may be hard to resist the pressure. Is the experience of being alive in a driverless car going to be better than the experience of being behind the wheel, or not in a car at all? My instinct is that once the novelty has worn off, it will be almost as inherently dull as frequent flying. The question of autonomous cars takes us back to two questions. Can we cap the carbon? And, more widely, can enough be enough? If the answers are yes, then autonomous cars can help us towards sustainable living in the Anthropocene. If not, they will only make things worse. How can we fly in the low carbon world? An A380 carrying 550 passengers from New York to Hong Kong burns through 192 tonnes of fuel. That is about 36% of the weight on take-off. Without using fossil fuel, the challenge is to carry enough energy on board.

Can enough energy ever be enough? Energy solution summary Energy: What can I do? 4 TRAVEL AND TRANSPORT How much do we travel today? How much travel will we want in the future? How many travel miles can we get from a square meter of land? How can we sort out urban transport? Will shared transport make life better or worse? Should I buy an electric car? How urgently should I ditch my diesel? Could autonomous cars be a disaster? Or brilliant? How can we fly in the low carbon world? Should I fly? Do virtual meetings save energy and carbon? How bad are boats? And can they be electrified? E-bikes or pedals? When might we emigrate to another planet? ix 73 74 75 75 77 78 79 81 82 84 85 87 89 91 93 94 95 97 99 99 100 101 104 105 106 107 109 110 112 113 114 116 117 x CONTENTS 5 GROWTH, MONEY AND METRICS Which kinds of growth can be healthy in the Anthropocene?


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