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, algorithmic bias, augmented reality, autonomous vehicles, blockchain, call centre, carbon footprint, clean tech, computer vision, conceptual framework, congestion pricing, connected car, crowdsourcing, cyber-physical system, DARPA: Urban Challenge, data acquisition, deep learning, demand response, digital map, disruptive innovation, driverless car, Elon Musk, fault tolerance, fear of failure, global supply chain, industrial cluster, intermodal, Internet of things, Jeff Bezos, John Zimmer (Lyft cofounder), 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, trolley problem, uber lyft, upwardly mobile, urban planning, Zipcar

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.

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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.

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Google is involved in the development of fleets of autonomous vehicles that are mainly designed for use in urban traffic (see Figure 6.3). It will be possible to reserve these robo-cars via smartphone and to be picked up by them at certain places in city centres. It is not yet clear who will operate these fleets, but they will certainly compete with public transport, which is why railroad and bus companies are interested in such fleets of self-driving cars. Will this make Google into an automobile manufacturer? Probably not; it is more likely that Google will become a supplier of software for controlling autonomous vehicles, which can be offered to all automobile manufacturers.

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, Boeing 747, butterfly effect, call centre, car-free, Cesare Marchetti: Marchetti’s constant, Chris Urmson, commoditize, computer vision, congestion charging, connected car, DARPA: Urban Challenge, data science, deep learning, DeepMind, deskilling, disruptive innovation, Donald Shoup, driverless car, edge city, Elon Musk, en.wikipedia.org, fake news, Ford Model T, future of work, General Motors Futurama, hype cycle, invention of the wheel, Just-in-time delivery, Lewis Mumford, 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, technological determinism, technoutopianism, TED Talk, the built environment, Thorstein Veblen, traffic fines, transit-oriented development, Travis Kalanick, trolley problem, Uber and Lyft, Uber for X, uber lyft, Unsafe at Any Speed, urban planning, urban sprawl, warehouse robotics, 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.

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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

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The starting point was to require special driver’s license endorsements for anyone operating an autonomous or semi-autonomous car requiring, that the driver take lessons and tests on the capabilities and limits of the autonomous vehicle, as well as how to take over control in an emergency. Other recommendations include state limitations on where and in what types of conditions autonomous cars can operate on public roads, requirements that the cars operate for a certain number of miles on private roads before use on public roads, and a system to record and report any failures or accidents of autonomous cars. The document concluded with a realisation that further research and updates would be required; “As innovation in this area continues and the maturity of self-driving technology increases, we will reconsider our present position on this issue”.

Robot, Take the Wheel: The Road to Autonomous Cars and the Lost Art of Driving by Jason Torchinsky

autonomous vehicles, barriers to entry, call centre, commoditize, computer vision, connected car, DARPA: Urban Challenge, data science, driverless car, Elon Musk, en.wikipedia.org, interchangeable parts, job automation, Philippa Foot, ransomware, self-driving car, sensor fusion, side project, Tesla Model S, trolley problem, urban sprawl

Also, in the early stages, it may prove helpful for robotic vehicles to make their presence very obvious. With this in mind, something like a federally mandated external robotic/autonomous vehicle warning lamp could prove effective. It may sound a bit like I’m paranoid about a massive robo-car uprising and I want to be able to see potential uprisers, but that’s not it at all. I think being able to visually see where the autonomous cars are will help them be accepted into mainstream traffic, and provide tools to study their interactions with other autonomous cars, meat-driven cars, pedestrians, and all the other chaos of life on the roads. I also think nervous human drivers might feel more comfortable if there’s an easy way to identify which cars are machine-driven, so, if they choose, they can safely avoid those cars.

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., Dynamic Machine Vision, http://www.dyna-vision.de/. 22 “DARPA Announces Third Grand Challenge,” May 1, 2006, https://www.grandchallenge.org/grandchallenge/docs/PR_UC_Announce_Update_12_06.pdf. 23 Tartan Racing, http://www.tartanracing.org/. Chapter 3 How Do They Work, Anyway? If we’re going to talk and think about autonomous cars, self-­driving cars, robo-cars, drive-o-droids or whatever the hell we want to call these things, we should get a sense of exactly what they do and how they do it. Those of us who drive may want to think back to a time before driving was all ingrained muscle memory, something we now do almost automatically, except when we’re trying, like idiots, to drive while texting someone something super important about this hilarious drunken fight we think we saw behind a supermarket.

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⁴⁸ Here Asimov accurately describes the very simple operation of an autonomous car (punch in a destination, let it go), but also acknowledges that such cars will likely be much more expensive than conventional cars, and suggests that ownership of private autonomous cars will be limited to the rich, with less wealthy folk using the “omnibus-automatics,” where you “call a company and have one stop at your door in a matter of minutes and take you where you wanted to go.” That sounds a hell of a lot like Uber, which, of course, is conducting a lot of research into autonomous vehicles. The emphasis on safety as the primary justification for autonomous car development is something we see a lot of today.

Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard

"Susan Fowler" uber, 1960s counterculture, A Declaration of the Independence of Cyberspace, Ada Lovelace, AI winter, Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, autonomous vehicles, availability heuristic, barriers to entry, Bernie Sanders, Big Tech, bitcoin, Buckminster Fuller, Charles Babbage, Chris Urmson, Clayton Christensen, cloud computing, cognitive bias, complexity theory, computer vision, Computing Machinery and Intelligence, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data science, deep learning, Dennis Ritchie, digital map, disruptive innovation, Donald Trump, Douglas Engelbart, driverless car, easy for humans, difficult for computers, Electric Kool-Aid Acid Test, Elon Musk, fake news, Firefox, gamification, gig economy, global supply chain, Google Glasses, Google X / Alphabet X, Greyball, Hacker Ethic, independent contractor, Jaron Lanier, Jeff Bezos, Jeremy Corbyn, John Perry Barlow, John von Neumann, Joi Ito, Joseph-Marie Jacquard, life extension, Lyft, machine translation, Mark Zuckerberg, mass incarceration, Minecraft, minimum viable product, Mother of all demos, move fast and break things, Nate Silver, natural language processing, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, One Laptop per Child (OLPC), opioid epidemic / opioid crisis, PageRank, Paradox of Choice, 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, Silicon Valley billionaire, speech recognition, statistical model, Steve Jobs, Steven Levy, Stewart Brand, TechCrunch disrupt, Tesla Model S, the High Line, The Signal and the Noise by Nate Silver, theory of mind, traumatic brain injury, Travis Kalanick, trolley problem, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, We are as Gods, Whole Earth Catalog, women in the workforce, work culture , yottabyte

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.

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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.

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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.

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

Air France Flight 447, air gap, Apollo 11, Apollo 13, Apollo Guidance Computer, autonomous vehicles, Beryl Markham, Boeing 747, Captain Sullenberger Hudson, Charles Lindbergh, Chris Urmson, digital map, disruptive innovation, driverless car, drone strike, Easter island, en.wikipedia.org, Erik Brynjolfsson, fudge factor, Gene Kranz, human-factors engineering, index card, John Markoff, low earth orbit, Mars Rover, Neil Armstrong, 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.

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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.

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It Depends On Whom You Work For,” IEEE Spectrum Cars That Think, February 24, 2015, http://spectrum.ieee.org/cars-that-think/transportation/human-factors/how-much-training-do-you-need-to-be-a-robocar-test-driver-it-depends-on-whom-you-work-for. He put a video camera on the dashboard of his car: John Leonard, “Conversations on Autonomy,” presentation, MIT, March 13, 2014. John Markoff, “Police, Pedestrians and the Social Ballet of Merging: The Real Challenges for Self-Driving Cars,” Bits Blog, http://bits.blogs.nytimes.com/2014/05/29/police-bicyclists-and-pedestrians-the-real-challenges-for-self-driving-cars/, accessed July 10, 2014. 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.

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

1960s counterculture, Airbus A320, airport security, augmented reality, autonomous vehicles, behavioural economics, Bernie Sanders, Big Tech, Boeing 737 MAX, British Empire, Burning Man, business logic, call centre, classic study, collective bargaining, confounding variable, congestion pricing, crony capitalism, data science, David Sedaris, deskilling, digital map, don't be evil, Donald Trump, driverless car, Elon Musk, emotional labour, en.wikipedia.org, Fellow of the Royal Society, Ford Model T, gamification, gentrification, gig economy, Google Earth, Great Leap Forward, Herbert Marcuse, hive mind, Ian Bogost, income inequality, informal economy, Internet of things, Jane Jacobs, labour mobility, Lyft, mirror neurons, Network effects, New Journalism, New Urbanism, Nicholas Carr, planned obsolescence, Ponzi scheme, precautionary principle, 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, surveillance capitalism, tacit knowledge, tech worker, technoutopianism, the built environment, The Death and Life of Great American Cities, the High Line, time dilation, too big to fail, traffic fines, Travis Kalanick, trolley problem, 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.

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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.

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At each stage, remaining pockets of human judgment and discretion appear as bugs that need to be solved. Put more neutrally, human intelligence and machine intelligence have a hard time sharing control. This becomes evident in the problems posed by partially autonomous cars, and is evident also in the problems posed when fully autonomous cars have to share the road with human drivers. Driverless cars are programmed to follow traffic rules to the letter and err on the side of caution, making them an awkward fit with other cars piloted by humans. The New York Times reports that one Google car “couldn’t get through a four-way stop because its sensors kept waiting for other (human) drivers to stop completely and let it go.

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

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

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.

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In general, car companies and transportation officials take a longer view, estimating that driverless cars will be a mainstay on public roads sometime after the year 2025. According to the automotive market research firm IHS, the first sales of autonomous vehicles will begin around the year 2025.14 IHS analysts estimate that by the year 2035, roughly 10 percent of new cars sold will be autonomous, a total number of 11.8 million cars each year. After 2050, IHS predicts that almost all new vehicles sold will be autonomous. Car companies have preferred a staged, gradual approach to autonomous driving, another factor that makes it impossible to pin down an exact date for the adoption of driverless cars.

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. … Anticipation of green phases of stoplights could navigate vehicles through an urban area on a “green wave” with the appropriate engine performance and minimized fuel consumption.14 Perhaps someday, autonomous vehicles will communicate with one another over some kind of network. Even if this day arrives, however, it’s unlikely V2X-equipped cars of the future will use the same costly infrastructure of short-wave radio technology that figures in current USDOT strategy. Once the majority of cars on the road are fully self-driving, by then, it’s highly likely that connectedness will be achieved for a lower price. Fully autonomous cars will be able to communicate with one another and with roadside traffic-management servers using cheaper forms of networks such as the existing cellphone infrastructure.

Ghost Road: Beyond the Driverless Car by Anthony M. Townsend

A Pattern Language, active measures, AI winter, algorithmic trading, Alvin Toffler, Amazon Robotics, asset-backed security, augmented reality, autonomous vehicles, backpropagation, big-box store, bike sharing, Blitzscaling, Boston Dynamics, business process, Captain Sullenberger Hudson, car-free, carbon footprint, carbon tax, circular economy, company town, computer vision, conceptual framework, congestion charging, congestion pricing, connected car, creative destruction, crew resource management, crowdsourcing, DARPA: Urban Challenge, data is the new oil, Dean Kamen, deep learning, deepfake, deindustrialization, delayed gratification, deliberate practice, dematerialisation, deskilling, Didi Chuxing, drive until you qualify, driverless car, drop ship, Edward Glaeser, Elaine Herzberg, Elon Musk, en.wikipedia.org, extreme commuting, financial engineering, financial innovation, Flash crash, food desert, Ford Model T, fulfillment center, Future Shock, General Motors Futurama, gig economy, Google bus, Greyball, haute couture, helicopter parent, independent contractor, inventory management, invisible hand, Jane Jacobs, Jeff Bezos, Jevons paradox, jitney, job automation, John Markoff, John von Neumann, Joseph Schumpeter, Kickstarter, Kiva Systems, Lewis Mumford, loss aversion, Lyft, Masayoshi Son, megacity, microapartment, minimum viable product, mortgage debt, New Urbanism, Nick Bostrom, North Sea oil, Ocado, openstreetmap, pattern recognition, Peter Calthorpe, random walk, Ray Kurzweil, Ray Oldenburg, rent-seeking, ride hailing / ride sharing, Rodney Brooks, self-driving car, sharing economy, Shoshana Zuboff, Sidewalk Labs, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, SoftBank, software as a service, sovereign wealth fund, Stephen Hawking, Steve Jobs, surveillance capitalism, technological singularity, TED Talk, Tesla Model S, The Coming Technological Singularity, The Death and Life of Great American Cities, The future is already here, The Future of Employment, The Great Good Place, too big to fail, traffic fines, transit-oriented development, Travis Kalanick, Uber and Lyft, uber lyft, urban planning, urban sprawl, US Airways Flight 1549, Vernor Vinge, vertical integration, Vision Fund, warehouse automation, warehouse robotics

., User Perspectives on Autonomous Driving: A Use-Case-Driven Study in Germany (Berlin, Germany: DLR Institute of Transport Research, 2016), 13; Chris Tennant et al., Executive Summary, Autonomous Vehicles—Negotiating a Place on the Road (London, UK: London School of Economics, 2016), 1–10. 31“Why do all of these interior designs”: Alanis King, “Autonomous Cars Aren’t Even Here Yet and I’m Already Bored with Them,” Jalopnik, September 11, 2017, https://jalopnik.com/autonomous-cars-arent-even-here-yet-and-im-already-bore-1803756153. 31Audi has recruited Disney: Reese Counts, “We Try Audi and Disney’s New In-Car Entertainment System on the Track,” Autoblog, January 9, 2019, https://www.autoblog.com/2019/01/09/audi-disney-holoride-car-vr-entertainment/. 31Kia built a concept car: Laura Bliss, “The ‘Driverless Experience’ Looks Awfully Distracting,” CityLab, January 11, 2019, https://www.citylab.com/transportation/2019/01/self-driving-car-technology-consumer-electronics-show/580027/. 31bigger . . . than the entire auto industry today: Lanctot, Accelerating the Future, 5. 31serve up precision-targeted media: Joann Muller, “One Big Thing: What Your Car Will Know about You,” Axios, May 10, 2019, https://www.axios.com/newsletters/axios-autonomous-vehicles-7b382e7a-e9f1-466b-9c7b-33e4aadc03f4.html; sensors. . . uniquely identify your heartbeat: “Goode Intelligence Forecasts That Biometrics Market for the Connected Car Will Be Just under $1bn by 2023,” Goode Intelligence, November 13, 2017, https://www.goodeintelligence.com/wp-content/uploads/2017/11/Goode-Intelligence-Biometrics-for-the-Connected-Car_Nov17_-news_release-13112017.pdf. 32GM already tracks: Jamie LaReau, “GM Tracked Radio Listening Habits for 3 Months: Here’s Why,” Detroit Free Press, October 1, 2018, https://www.freep.com/story/money/cars/general-motors/2018/10/01/gm-radio-listening-habits-advertising/1424294002/. 32“We know how long they’ve lived”: Phoebe Wall Howard, “Data Could Be What Ford Sells Next as It Looks for New Revenue,” Detroit Free Press, November 13, 2018, https://www.freep.com/story/money/cars/2018/11/13/ford-motor-credit-data-new-revenue/1967077002/. 33odds of a crash instantly double: National Highway Traffic Safety Administration, Overview of the National Highway Traffic Safety Administration’s Driver Distraction Program, DOT HS 811 299, April 2010, https://www.nhtsa. gov/sites/nhtsa.dot.gov/files/811299.pdf. 33road crashes in the US declined: Wikipedia, s.v.

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Even as this self-driving paradise takes shape, it becomes a killing field. In a gruesome twist, a cocaine-tainted batch of gasoline turns the robocars against their human masters. “There seemed to be a personal devil in each of them, directing their actions,” Keller writes. “Cars, without control, coursed the public highways, chasing pedestrians, killing little children, smashing fences. . . . Fifty million machines were on a wild riot of uncontrolled destruction.” The renegade robocars even discover how to gas themselves up at service stations, perpetuating the reign of terror indefinitely. For all its B-movie sensationalism, the lessons of “The Living Machine” remain fresh and relevant today.

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Then I Tried One,” Opinion, New York Times, October 22, 2017, https://www.nytimes.com/2017/10/22/opinion/driverless-cars-test-drive.html. 960 million people were killed: Death: A Self-Portrait, 2012, Richard Harris Collection, London, UK: Wellcome Collection, exhibition. 9time wasted in traffic: “INRIX Global Traffic Scorecard,” INRIX, accessed February 15, 2018, http://inrix.com/scorecard. 1025 million people have disabilities that limit travel: Stephen Brumbaugh, Travel Patterns of Americans with Disabilities (Washington, DC: Bureau of Transportation Statistics, 2018), https://www.bts.gov/sites/bts.dot.gov/files/docs/explore-topics-and-geography/topics/passenger-travel/222466/travel-patterns-american-adults-disabilities-9-6-2018_1.pdf. 10By 2030 . . . tens of millions: BlackRock Investment Group, Future of the Vehicle: Winners and Losers: From Cars and Cameras to Chips (BlackRock Investment Institute, 2017), 8. 10two billion human-driven cars and trucks: Daniel Sperling and Deborah Gordon, Two Billion Cars: Driving toward Sustainability (New York: Oxford University Press, 2009). 10“The future is already here”: Marianne Trench, Cyberpunk (New York: Intercon Production, 1990), YouTube video. 11a mix of both worlds: Bern Grush and John Niles, The End of Driving: Transportation Systems and Public Policy Planning for Autonomous Vehicles (Cambridge, MA: Elsevier, 2018). 11Half will end up in China: “Autonomous Vehicle Sales to Surpass 33 Million Annually in 2040, Enabling New Autonomous Mobility in More Than 26 Percent of New Car Sales, IHS Markit Says,” IHS Markit, January 2, 2018, https://technology.ihs.com/599099/autonomous-vehicle-sales-to-surpass-33-million-annually-in-2040-enabling-new-autonomous-mobility-in-more-than-26-percent-of-new-car-sales-ihs-markit-says. 11$2 trillion global auto-manufacturing industry: Roger Lanctot, Accelerating the Future: The Economic Impact of the Emerging Passenger Economy (Strategy Analytics, June 2017), https://newsroom. intel. com/newsroom/wp-content/uploads/sites/11/2017/05/passenger-economy.pdf; roughly the size of the entire EU economy: “The Economy,” European Union (website), accessed April 11, 2019, https://europa.eu/european-union/about-eu/figures/economy_en. 11capture a $1.7 trillion annual share by 2030: Author’s calculation based on Peter Campbell, “Waymo Forecast to Capture 60% of Driverless Market,” Financial Times, May 10, 2018, https://www.ft.com/content/3355f5b0-539d-11e8-b24e-cad6aa67e23e. 11of many shapes and sizes will have replaced them: Scott Corwin et al., “The Future of Mobility: What’s Next?”

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

23andMe, 3D printing, Airbnb, AlphaGo, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, benefit corporation, Bernie Sanders, bitcoin, blockchain, clean water, correlation does not imply causation, CRISPR, deep learning, DeepMind, distributed ledger, Donald Trump, double helix, driverless car, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, gigafactory, Google bus, Hyperloop, income inequality, information security, Internet of things, job automation, Kevin Kelly, Khan Academy, Kickstarter, Law of Accelerating Returns, license plate recognition, life extension, longitudinal study, Lyft, M-Pesa, Mary Meeker, Menlo Park, microbiome, military-industrial complex, mobile money, new economy, off-the-grid, One Laptop per Child (OLPC), personalized medicine, phenotype, precision agriculture, radical life extension, RAND corporation, Ray Kurzweil, recommendation engine, Ronald Reagan, Second Machine Age, self-driving car, seminal paper, Silicon Valley, Skype, smart grid, stem cell, Stephen Hawking, Steve Wozniak, Stuxnet, supercomputer in your pocket, synthetic biology, Tesla Model S, The future is already here, 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

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.

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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?

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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.

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, bike sharing, carbon tax, Chris Urmson, collaborative consumption, commoditize, congestion pricing, crowdsourcing, DARPA: Urban Challenge, dematerialisation, driverless car, Dutch auction, Elon Musk, en.wikipedia.org, Ford Model T, Google Hangouts, high-speed rail, Induced demand, intermodal, invention of the printing press, jitney, John Markoff, labor-force participation, Lewis Mumford, lifelogging, Lyft, means of production, megacity, Menlo Park, Network effects, Occam's razor, oil shock, place-making, pneumatic tube, post-work, printed gun, Ray Kurzweil, rent-seeking, ride hailing / ride sharing, Robert Gordon, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, tacit knowledge, techno-determinism, technological singularity, Tesla Model S, the built environment, The future is already here, 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

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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In short: speed decentralizes.248 And, some places, including suburbs, exurbs, and rural areas will likely see increased decentralization. But in cities, there is increasing evidence to suggest the reverse. These phenomena will likely be context dependent. Autonomous vehicles will likely be faster, particularly on freeways, especially after widespread deployment once either human drivers are banned or a network of separate lanes are designated for autonomous cars. Coupling with just the faster speed, the fully autonomous vehicle lowers the cognitive burden on the former driver/now passenger. Modes with lower cognitive burden tend to have longer trip durations. Time matters. What you does with that time (the quality of the experience) also matters.

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Thus, capacity at bottlenecks should increase, both in throughput per lane and the number of lanes per unit road width. These cars still need to go somewhere, so auto-mobility still requires some capacity on city streets as well as freeways, but ubiquitous adoption of autonomous vehicles would save space on parking and lane width. Cars without people. Autonomous cars can drive without people at all. They can be used for pickup and delivery, in addition to the dead-heading from drop-off to parking, or from drop-off of one passenger to pick-up of another, or for recharging or refueling. All of this can increase total travel on the road.

Robot Rules: Regulating Artificial Intelligence by Jacob Turner

"World Economic Forum" Davos, Ada Lovelace, Affordable Care Act / Obamacare, AI winter, algorithmic bias, algorithmic trading, AlphaGo, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, autonomous vehicles, backpropagation, Basel III, bitcoin, Black Monday: stock market crash in 1987, blockchain, brain emulation, Brexit referendum, Cambridge Analytica, Charles Babbage, Clapham omnibus, cognitive dissonance, Computing Machinery and Intelligence, corporate governance, corporate social responsibility, correlation does not imply causation, crowdsourcing, data science, deep learning, DeepMind, Demis Hassabis, distributed ledger, don't be evil, Donald Trump, driverless car, easy for humans, difficult for computers, effective altruism, Elon Musk, financial exclusion, financial innovation, friendly fire, future of work, hallucination problem, hive mind, Internet of things, iterative process, job automation, John Markoff, John von Neumann, Loebner Prize, machine readable, machine translation, medical malpractice, Nate Silver, natural language processing, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, nudge unit, obamacare, off grid, OpenAI, paperclip maximiser, pattern recognition, Peace of Westphalia, Philippa Foot, race to the bottom, Ray Kurzweil, Recombinant DNA, 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, trolley problem, 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?​

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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.

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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”.

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, Computing Machinery and Intelligence, corporate governance, crowdsourcing, driverless car, drop ship, Easter island, en.wikipedia.org, Erik Brynjolfsson, estate planning, Fairchild Semiconductor, 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, Kiva Systems, Larry Ellison, Loebner Prize, Mark Zuckerberg, mortgage debt, natural language processing, Nick Bostrom, Own Your Own Home, pattern recognition, Satoshi Nakamoto, school choice, Schrödinger's Cat, Second Machine Age, self-driving car, sentiment analysis, short squeeze, Silicon Valley, Silicon Valley startup, Skype, software as a service, The Chicago School, The Future of Employment, Turing test, Vitalik Buterin, Watson beat the top human players on Jeopardy!, winner-take-all economy, women in the workforce, working poor, Works Progress Administration

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.

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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.

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., 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.

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

23andMe, 3D printing, Air France Flight 447, Airbnb, airport security, algorithmic bias, algorithmic management, augmented reality, autonomous vehicles, backpropagation, Brixton riot, Cambridge Analytica, chief data officer, computer vision, crowdsourcing, DARPA: Urban Challenge, data science, deep learning, DeepMind, Douglas Hofstadter, driverless car, Elon Musk, fake news, Firefox, Geoffrey Hinton, Google Chrome, Gödel, Escher, Bach, Ignaz Semmelweis: hand washing, John Markoff, Mark Zuckerberg, meta-analysis, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, 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, sparse data, speech recognition, Stanislav Petrov, statistical model, Stephen Hawking, Steven Levy, systematic bias, TED Talk, Tesla Model S, The Wisdom of Crowds, Thomas Bayes, trolley problem, Watson beat the top human players on Jeopardy!, web of trust, William Langewiesche, you are the product

Because there’s another layer of difficulty to contend with when trying to build that sci-fi fantasy of a go-anywhere, do-anything, steering-wheel-free driverless car, and it’s one that goes well beyond the technical challenge. A fully autonomous car will also have to deal with the tricky problem of people. Jack Stilgoe, a sociologist from University College London and an expert in the social impact of technology, explains: ‘People are mischievous. They’re active agents, not just passive parts of the scenery.’38 Imagine, for a moment, a world where truly, perfectly autonomous vehicles exist. The number one rule in their on-board algorithms will be to avoid collisions wherever possible. And that changes the dynamics of the road.

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‘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.

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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.

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, carbon tax, Chris Urmson, Clayton Christensen, clean tech, Colonization of Mars, connected car, crony capitalism, Deng Xiaoping, Didi Chuxing, disinformation, disruptive innovation, Donald Trump, driverless car, Elon Musk, Fairchild Semiconductor, Ford Model T, gigafactory, Google Glasses, Hyperloop, information security, Internet of things, Jeff Bezos, John Markoff, low earth orbit, Lyft, Marc Andreessen, margin call, Mark Zuckerberg, Max Levchin, 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, Solyndra, South China Sea, special economic zone, stealth mode startup, Steve Jobs, tech worker, TechCrunch disrupt, TED Talk, Tesla Model S, Tim Cook: Apple, Tony Fadell, Uber and Lyft, uber lyft, universal basic income, urban planning, urban sprawl, Zenefits, Zipcar

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. But to the extent that the spread of autonomous vehicles depends on electric vehicles, so, too, must they depend on the expansion of electric vehicle infrastructure, especially the proliferation of charging stations. And who wouldn’t want a car that could refuel itself?

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Level 5 was the highest, at which a car would have no controls for human drivers whatsoever. 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.

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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.

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

Ada Lovelace, AI winter, Alignment Problem, AlphaGo, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, artificial general intelligence, autonomous vehicles, backpropagation, Bernie Sanders, Big Tech, Boston Dynamics, Cambridge Analytica, Charles Babbage, Claude Shannon: information theory, cognitive dissonance, computer age, computer vision, Computing Machinery and Intelligence, dark matter, deep learning, DeepMind, Demis Hassabis, Douglas Hofstadter, driverless car, Elon Musk, en.wikipedia.org, folksonomy, Geoffrey Hinton, 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, machine translation, Mark Zuckerberg, natural language processing, Nick Bostrom, Norbert Wiener, ought to be enough for anybody, paperclip maximiser, 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, tacit knowledge, tail risk, TED Talk, the long tail, theory of mind, There's no reason for any individual to have a computer in his home - Ken Olsen, trolley problem, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, world market for maybe five computers

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.

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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.

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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.

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

3D printing, Adam Curtis, Anthropocene, augmented reality, autonomous vehicles, behavioural economics, Berlin Wall, Boeing 747, Buckminster Fuller, capitalist realism, Cass Sunstein, computer age, corporate governance, David Attenborough, en.wikipedia.org, Fall of the Berlin Wall, game design, General Motors Futurama, global village, Google X / Alphabet X, haute couture, Herman Kahn, intentional community, life extension, machine readable, Mark Zuckerberg, mouse model, New Urbanism, Peter Eisenman, RAND corporation, Richard Thaler, Ronald Reagan, self-driving car, Silicon Valley, social software, synthetic biology, systems thinking, technoutopianism, Wall-E

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.

New Laws of Robotics: Defending Human Expertise in the Age of AI by Frank Pasquale

affirmative action, Affordable Care Act / Obamacare, Airbnb, algorithmic bias, Amazon Mechanical Turk, Anthropocene, augmented reality, Automated Insights, autonomous vehicles, basic income, battle of ideas, Bernie Sanders, Big Tech, Bill Joy: nanobots, bitcoin, blockchain, Brexit referendum, call centre, Cambridge Analytica, carbon tax, citizen journalism, Clayton Christensen, collective bargaining, commoditize, computer vision, conceptual framework, contact tracing, coronavirus, corporate social responsibility, correlation does not imply causation, COVID-19, critical race theory, cryptocurrency, data is the new oil, data science, decarbonisation, deep learning, deepfake, deskilling, digital divide, digital twin, disinformation, disruptive innovation, don't be evil, Donald Trump, Douglas Engelbart, driverless car, effective altruism, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, fake news, Filter Bubble, finite state, Flash crash, future of work, gamification, general purpose technology, Google Chrome, Google Glasses, Great Leap Forward, green new deal, guns versus butter model, Hans Moravec, high net worth, hiring and firing, holacracy, Ian Bogost, independent contractor, informal economy, information asymmetry, information retrieval, interchangeable parts, invisible hand, James Bridle, Jaron Lanier, job automation, John Markoff, Joi Ito, Khan Academy, knowledge economy, late capitalism, lockdown, machine readable, Marc Andreessen, Mark Zuckerberg, means of production, medical malpractice, megaproject, meta-analysis, military-industrial complex, Modern Monetary Theory, Money creation, move fast and break things, mutually assured destruction, natural language processing, new economy, Nicholas Carr, Nick Bostrom, Norbert Wiener, nuclear winter, obamacare, One Laptop per Child (OLPC), open immigration, OpenAI, opioid epidemic / opioid crisis, paperclip maximiser, paradox of thrift, pattern recognition, payday loans, personalized medicine, Peter Singer: altruism, Philip Mirowski, pink-collar, plutocrats, post-truth, pre–internet, profit motive, public intellectual, QR code, quantitative easing, race to the bottom, RAND corporation, Ray Kurzweil, recommendation engine, regulatory arbitrage, Robert Shiller, Rodney Brooks, Ronald Reagan, self-driving car, sentiment analysis, Shoshana Zuboff, Silicon Valley, Singularitarianism, smart cities, smart contracts, software is eating the world, South China Sea, Steve Bannon, Strategic Defense Initiative, surveillance capitalism, Susan Wojcicki, tacit knowledge, TaskRabbit, technological solutionism, technoutopianism, TED Talk, telepresence, telerobotics, The Future of Employment, The Turner Diaries, Therac-25, Thorstein Veblen, too big to fail, Turing test, universal basic income, unorthodox policies, wage slave, Watson beat the top human players on Jeopardy!, working poor, workplace surveillance , Works Progress Administration, zero day

She also concludes that China is in the middle of the two paradigms, with ongoing political and cultural debate on the fundamental premises of human-robot interaction. As these distinctive approaches evolve, the economics profession should not presume to put a finger on the scale in favor of substitutive automation. It can just as readily prescribe human-led care. THE POLITICAL ECONOMY OF ROBO-CARE Larger trends in workforce participation and demand also matter. Robotic caregiving makes far more sense in a society where the adult children of the elderly are under constant pressure to work more; an overburdened “sandwich generation” has to sacrifice somewhere. If, by contrast, productivity gains were better distributed, demand for robots in elder care would likely diminish.

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Legislators are already grappling with the question of whether to require such vehicles to revert control to a person upon request or to give that control to police when they demand it.63 I used the ambiguous term “person” in the last sentence because we still do not have a good term for occupants of a semi-autonomous vehicle. Both law and norms will shape that new identity over time.64 None of these decisions should be made solely—or even predominantly—by the programmers and corporations developing algorithms for self-driving cars. They involve governance and participation by a much wider range of experts, ranging from urban-studies scholars to regulators to police and attorneys.

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But to deploy such powerful technology to ticket speeders, ferret out benefits fraud, or catch petty thieves is like using a sledgehammer to kill a fly.28 Stark’s proposal is particularly insightful because it extends a widely agreed logic for limiting the power of machines: restricting violence. No one should be able to buy or equip an autonomous vehicle with machine guns on its hood without some kind of license; there’s simply a common sense that tanks are for war, not personal armies. Many uses of facial recognition technology portend structural violence: systematic efforts to typecast individuals, to keep them monitored in their place, or to ransack databases for ways to manipulate them.

Cities Under Siege: The New Military Urbanism by Stephen Graham

"hyperreality Baudrillard"~20 OR "Baudrillard hyperreality", addicted to oil, airport security, Alan Greenspan, Anthropocene, anti-communist, autonomous vehicles, Berlin Wall, call centre, carbon footprint, clean tech, clean water, congestion charging, creative destruction, credit crunch, DARPA: Urban Challenge, defense in depth, deindustrialization, digital map, disinformation, Dr. Strangelove, driverless car, edge city, energy security, European colonialism, export processing zone, failed state, Food sovereignty, gentrification, Gini coefficient, global supply chain, Global Witness, Google Earth, illegal immigration, income inequality, knowledge economy, late capitalism, Lewis Mumford, loose coupling, machine readable, market fundamentalism, mass incarceration, McMansion, megacity, military-industrial complex, moral panic, mutually assured destruction, Naomi Klein, New Urbanism, offshore financial centre, one-state solution, pattern recognition, peak oil, planetary scale, post-Fordism, private military company, Project for a New American Century, RAND corporation, RFID, Richard Florida, Scramble for Africa, Seymour Hersh, Silicon Valley, SimCity, 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

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.

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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.

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For example, in an attempt to stimulate further development of robotic ground vehicles for use in both the US military and on the streets of US cities, the Pentagon’s high-tech R&D arm, the Defense Advanced Research Projects Agency (DARPA), has initiated a series of high-profile rob otic-vehicle competitions. 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.

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

3D printing, additive manufacturing, adjacent possible, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Apollo 11, augmented reality, autonomous vehicles, Boston Dynamics, Charles Lindbergh, cloud computing, company town, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deal flow, deep learning, dematerialisation, deskilling, disruptive innovation, driverless car, Elon Musk, en.wikipedia.org, Exxon Valdez, fail fast, Fairchild Semiconductor, fear of failure, Firefox, Galaxy Zoo, Geoffrey Hinton, Google Glasses, Google Hangouts, gravity well, hype cycle, ImageNet competition, industrial robot, information security, 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, microbiome, minimum viable product, move fast and break things, Narrative Science, Netflix Prize, Network effects, Oculus Rift, OpenAI, 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, Scaled Composites, self-driving car, sentiment analysis, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart grid, SpaceShipOne, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, Stuart Kauffman, superconnector, Susan Wojcicki, synthetic biology, technoutopianism, TED Talk, telepresence, telepresence robot, Turing test, urban renewal, Virgin Galactic, Wayback Machine, web application, X Prize, Y Combinator, zero-sum game

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?

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As Arduino hacker Charalampos Doukas says, as sensor prices crash downward, “The only limit is your imagination.” 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.

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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.

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

"World Economic Forum" Davos, Alan Greenspan, 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, driverless car, Erik Brynjolfsson, everywhere but in the productivity statistics, Fall of the Berlin Wall, Ford Model T, 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, microcredit, minimum wage unemployment, Mont Pelerin Society, Nathan Meyer Rothschild: antibiotics, Occupy movement, offshore financial centre, Paul Samuelson, Peter Thiel, post-industrial society, precariat, public intellectual, radical decentralization, RAND corporation, randomized controlled trial, Ray Kurzweil, Ronald Reagan, Rutger Bregman, Second Machine Age, Silicon Valley, Simon Kuznets, Skype, stem cell, Steven Pinker, TED Talk, telemarketer, The future is already here, The Future of Employment, The Spirit Level, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thomas Malthus, Thorstein Veblen, Tyler Cowen, 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.

Rationality: What It Is, Why It Seems Scarce, Why It Matters by Steven Pinker

affirmative action, Albert Einstein, autonomous vehicles, availability heuristic, Ayatollah Khomeini, backpropagation, basic income, behavioural economics, belling the cat, Black Lives Matter, butterfly effect, carbon tax, Cass Sunstein, choice architecture, classic study, clean water, Comet Ping Pong, coronavirus, correlation coefficient, correlation does not imply causation, COVID-19, critical race theory, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, data science, David Attenborough, deep learning, defund the police, delayed gratification, disinformation, Donald Trump, Dr. Strangelove, Easter island, effective altruism, en.wikipedia.org, Erdős number, Estimating the Reproducibility of Psychological Science, fake news, feminist movement, framing effect, George Akerlof, George Floyd, germ theory of disease, high batting average, if you see hoof prints, think horses—not zebras, index card, Jeff Bezos, job automation, John Nash: game theory, John von Neumann, libertarian paternalism, Linda problem, longitudinal study, loss aversion, Mahatma Gandhi, meta-analysis, microaggression, Monty Hall problem, Nash equilibrium, New Journalism, Paul Erdős, Paul Samuelson, Peter Singer: altruism, Pierre-Simon Laplace, placebo effect, post-truth, power law, QAnon, QWERTY keyboard, Ralph Waldo Emerson, randomized controlled trial, replication crisis, Richard Thaler, scientific worldview, selection bias, social discount rate, social distancing, Social Justice Warrior, Stanford marshmallow experiment, Steve Bannon, Steven Pinker, sunk-cost fallacy, TED Talk, the scientific method, Thomas Bayes, Tragedy of the Commons, trolley problem, twin studies, universal basic income, Upton Sinclair, urban planning, Walter Mischel, yellow journalism, zero-sum game

The human visual system is one of the wonders of the world. It is a precision instrument that can detect a single photon, recognize thousands of shapes, and negotiate rocky trails and high-speed autobahns. It outperforms our best artificial vision systems, which is why at the time of this writing autonomous vehicles have not been loosed on city streets despite decades of R&D. The vision modules of the robocars are apt to mistake a tractor trailer for a billboard, or a traffic sign plastered with stickers for a refrigerator filled with food.60 The shape and shading illusions are not bugs but features. The goal of the visual system is to provide the rest of the brain with an accurate description of the 3-D shapes and material composition of the objects in front of us.61 This is a hard problem because the information coming into the brain from the retina doesn’t reflect reality directly.

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

air traffic controllers' union, Alan Greenspan, assortative mating, autonomous vehicles, Bear Stearns, blue-collar work, Bonfire of the Vanities, Branko Milanovic, business cycle, call centre, carbon tax, collective bargaining, compensation consultant, 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, Ford Model T, Frank Levy and Richard Murnane: The New Division of Labor, Gini coefficient, government statistician, Gunnar Myrdal, income inequality, independent contractor, industrial robot, invisible hand, It's morning again in America, 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, proprietary trading, purchasing power parity, refrigerator car, rent control, Richard Feynman, Ronald Reagan, shareholder value, Silicon Valley, Simon Kuznets, Stephen Hawking, Steve Jobs, subprime mortgage crisis, The Spirit Level, too big to fail, trickle-down economics, Tyler Cowen, 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

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.

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

Abraham Maslow, agricultural Revolution, air freight, Anton Chekhov, artificial general intelligence, autonomous vehicles, barriers to entry, big-box store, cashless society, clean water, complexity theory, deindustrialization, digital divide, eurozone crisis, failed state, financial innovation, Ford Model T, Garrett Hardin, gentleman farmer, Global Witness, government statistician, illegal immigration, income inequality, informal economy, it's over 9,000, 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, new economy, off grid, oil shale / tar sands, pension reform, profit motive, randomized controlled trial, rolling blackouts, school choice, school vouchers, Scramble for Africa, side project, Silicon Valley, Simon Kuznets, Skype, spinning jenny, subscription business, The Chicago School, the payments system, trade route, Tragedy of the Commons, Travis Kalanick, uranium enrichment, urban planning, wealth creators, white picket fence, working-age population, Y Combinator, young professional

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.

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, driverless car, Elon Musk, gigafactory, high net worth, independent contractor, RAND corporation, ride hailing / ride sharing, self-driving car, Silicon Valley, smart cities, technological determinism, 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).

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But politicians need to be properly informed about the risks and the potential. One potential negative is if legislators and regulators feel they have to create a framework that is favourable to autonomous cars, in the same way as happened in the early days of the motor car. There is a contradiction at the heart of the happy talk from Silicon Valley about autonomous vehicles. The introduction of autonomous cars is often presented as a liberating force, providing people with greater mobility and wider access to all kinds of services. In fact, it is obvious from all the above that any move towards universal autonomy would require radical intervention from government.

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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.

Ludicrous: The Unvarnished Story of Tesla Motors by Edward Niedermeyer

autonomous vehicles, barriers to entry, Bear Stearns, bitcoin, business climate, call centre, carbon footprint, Clayton Christensen, clean tech, Colonization of Mars, computer vision, crowdsourcing, disruptive innovation, Donald Trump, driverless car, Elon Musk, en.wikipedia.org, facts on the ground, fake it until you make it, family office, financial engineering, Ford Model T, gigafactory, global supply chain, Google Earth, housing crisis, hype cycle, Hyperloop, junk bonds, Kaizen: continuous improvement, Kanban, Kickstarter, Lyft, Marc Andreessen, Menlo Park, minimum viable product, new economy, off grid, off-the-grid, OpenAI, Paul Graham, peak oil, performance metric, Ponzi scheme, ride hailing / ride sharing, risk tolerance, Sand Hill Road, self-driving car, short selling, short squeeze, side project, Silicon Valley, Silicon Valley startup, Skype, smart cities, Solyndra, stealth mode startup, Steve Jobs, Steve Jurvetson, tail risk, technoutopianism, Tesla Model S, too big to fail, Toyota Production System, Uber and Lyft, uber lyft, union organizing, vertical integration, WeWork, work culture , Zipcar

CNBC, May 19, 2017. https://www.cnbc.com/2017/05/19/tesla-stock-valuation-driverless-taxi.html 175Gartner released its latest “hype cycle” analysis: Mike Ramsey. “Autonomous Vehicles Fall Into the Trough of Disillusionment . . . But That’s Good.” Forbes, August 14, 2018. https://www.forbes.com/sites/enroute/2018/08/14/autonomous-vehicles-fall-into-the-trough-of-disillusionment-but-thats-good/#3b5a3c6e7b5a 176John Krafcik publicly admitted that autonomous vehicles might never work in all locations: Sam Abuelsamid. “Transition to Autonomous Cars Will Take Longer Than You Think, Waymo CEO Tells Governors.” Forbes, July 20, 2018. https://www.forbes.com/sites/samabuelsamid/2018/07/20/waymo-ceo-tells-governors-av-time-will-be-longer-than-you-think/#277b432cd7da 178reports of heavy discounts during the third quarter of 2016: Liane Yvkoff.

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“[It is] something that I think does not reflect well upon the media. It really doesn’t. Because, and really you need to think carefully about this, because if, in writing some article that’s negative, you effectively dissuade people from using an autonomous vehicle, you’re killing people.” Musk was correct in the sense that the media’s Autopilot coverage had contributed to a death. By encouraging the hands-free use of Autopilot and heralding it as the first (sort of) autonomous car when it launched, the media had helped create the overconfidence in Autopilot’s abilities that ultimately killed Joshua Brown. If anything, the wave of negative coverage that followed Brown’s death was a long-overdue examination of the risks inherent to Tesla’s Autopilot strategy, which should have been given a more thorough airing at launch.

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Increasingly, the entire goal of a Level 5 vehicle, capable of full autonomy anywhere, was being dismissed in favor of Level 4 vehicles, which operate fully autonomously but only in a limited “geofenced” area. By restricting the operational design domain of an autonomous car to a specific thoroughly mapped area and optimizing the system for the conditions found there, you could reduce the possible scope of edge cases to a manageable level. This retreat from the hype of 2016 isolated Tesla in an awkward contrast. Companies developing autonomous vehicles that had vastly more sophisticated sensor and compute systems than Tesla’s were saying that Level 5 was a fantasy . . . while Tesla continued to collect payments for a Level 5 system.

A Brief History of Motion: From the Wheel, to the Car, to What Comes Next by Tom Standage

accelerated depreciation, active transport: walking or cycling, autonomous vehicles, back-to-the-city movement, bike sharing, car-free, carbon footprint, Cesare Marchetti: Marchetti’s constant, Chris Urmson, City Beautiful movement, Clapham omnibus, congestion charging, coronavirus, COVID-19, deep learning, Didi Chuxing, Donald Shoup, driverless car, Elaine Herzberg, Elon Musk, flex fuel, Ford Model T, Ford paid five dollars a day, garden city movement, General Motors Futurama, Ida Tarbell, Induced demand, interchangeable parts, invention of the wheel, James Watt: steam engine, Jane Jacobs, jitney, Joan Didion, John Zimmer (Lyft cofounder), Lewis Mumford, lockdown, Lyft, Marshall McLuhan, minimum wage unemployment, oil shock, Own Your Own Home, peak oil, prompt engineering, Ralph Nader, Richard Florida, ride hailing / ride sharing, Rosa Parks, safety bicycle, self-driving car, social distancing, Steve Jobs, streetcar suburb, tech bro, The Death and Life of Great American Cities, trade route, Travis Kalanick, Uber and Lyft, uber lyft, unbiased observer, Unsafe at Any Speed, Upton Sinclair, urban planning, urban sprawl, Victor Gruen, W. E. B. Du Bois, walkable city, white flight, wikimedia commons, Yom Kippur War, Zipcar

“Drivers could bowl along with their hands off the wheel, free to enjoy the scenery or hug their girls.” With self-driving cars there would be no traffic and no accidents—claims that are once again being made by proponents of autonomous vehicles today. Are they right? We may soon find out because driverless cars are no longer the figment of a designer’s imagination. Fittingly, just as the modern automotive era began with the Paris–Rouen race of 1894, recent efforts to build autonomous cars also began with an unusual competition. Known as the Grand Challenge, it was organized by DARPA, America’s main military-research agency, and took place in March 2004 in the Mojave Desert.

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But many engineers working on self-driving cars believe that semiautonomous systems are inherently dangerous because of the risk that drivers will come to trust them too much and fail to supervise them. Opponents of semiautonomy, including John Krafcik, the chief executive of Waymo, believe that it is safer to focus on building fully autonomous vehicles where passengers are never required to intervene, as his firm is doing. But such vehicles need to be able to handle absolutely any situation without human intervention, and that is a tall order. By 2020 Waymo’s fleet of autonomous cars had driven more than 20 million miles on public roads in twenty-five cities, and billions more miles in simulation. The company says its vehicles drove 1.45 million miles in California in 2019, with a disengagement rate of 0.076 per 1,000 miles (equivalent to one disengagement every thirteen thousand miles).

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Building one that can handle that final 10 percent has turned out to be far more difficult than expected. HOW AUTONOMOUS CARS WORK Building a fully autonomous car can be broken down into three separate problems: perception (figuring out what is going on in the world), prediction (determining what will happen next), and driving policy (steering left or right, accelerating or braking). The last part, driving policy, is considered relatively simple. Perception and prediction pose the biggest challenges. Autonomous cars perceive the world using a combination of sensors including cameras, radar, and lidar—a radar-like technique that uses invisible pulses of light, rather than radio waves, to create a high-resolution 3D map of the surroundings.

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

A Declaration of the Independence of Cyberspace, AI winter, airport security, Andy Rubin, Apollo 11, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, basic income, Baxter: Rethink Robotics, Bill Atkinson, Bill Duvall, bioinformatics, Boston Dynamics, Brewster Kahle, Burning Man, call centre, cellular automata, Charles Babbage, Chris Urmson, Claude Shannon: information theory, Clayton Christensen, clean water, cloud computing, cognitive load, collective bargaining, computer age, Computer Lib, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data acquisition, Dean Kamen, deep learning, DeepMind, deskilling, Do you want to sell sugared water for the rest of your life?, don't be evil, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dr. Strangelove, driverless car, dual-use technology, Dynabook, Edward Snowden, Elon Musk, Erik Brynjolfsson, Evgeny Morozov, factory automation, Fairchild Semiconductor, Fillmore Auditorium, San Francisco, From Mathematics to the Technologies of Life and Death, future of work, Galaxy Zoo, General Magic , Geoffrey Hinton, Google Glasses, Google X / Alphabet X, Grace Hopper, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, Hans Moravec, haute couture, Herbert Marcuse, hive mind, hype cycle, hypertext link, indoor plumbing, industrial robot, information retrieval, Internet Archive, Internet of things, invention of the wheel, Ivan Sutherland, Jacques de Vaucanson, Jaron Lanier, Jeff Bezos, Jeff Hawkins, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, John von Neumann, Kaizen: continuous improvement, Kevin Kelly, Kiva Systems, knowledge worker, Kodak vs Instagram, labor-force participation, loose coupling, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, military-industrial complex, Mitch Kapor, Mother of all demos, natural language processing, Neil Armstrong, new economy, Norbert Wiener, PageRank, PalmPilot, pattern recognition, Philippa Foot, pre–internet, RAND corporation, Ray Kurzweil, reality distortion field, Recombinant DNA, Richard Stallman, Robert Gordon, Robert Solow, Rodney Brooks, Sand Hill Road, Second Machine Age, self-driving car, semantic web, Seymour Hersh, 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, Strategic Defense Initiative, strong AI, superintelligent machines, tech worker, technological singularity, Ted Nelson, TED Talk, telemarketer, telepresence, telepresence robot, Tenerife airport disaster, The Coming Technological Singularity, the medium is the message, Thorstein Veblen, Tony Fadell, trolley problem, Turing test, Vannevar Bush, Vernor Vinge, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, We are as Gods, 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.

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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.

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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.

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, AlphaGo, Amazon Mechanical Turk, Amazon Robotics, augmented reality, autonomous vehicles, barriers to entry, Big Tech, biodiversity loss, bitcoin, blockchain, blood diamond, Boston Dynamics, Burning Man, call centre, cashless society, Charles Babbage, Charles Lindbergh, Clayton Christensen, clean water, cloud computing, Colonization of Mars, computer vision, creative destruction, CRISPR, crowdsourcing, cryptocurrency, data science, Dean Kamen, deep learning, deepfake, DeepMind, delayed gratification, dematerialisation, digital twin, disruptive innovation, Donald Shoup, driverless car, Easter island, Edward Glaeser, Edward Lloyd's coffeehouse, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Ethereum, ethereum blockchain, experimental economics, fake news, food miles, Ford Model T, fulfillment center, game design, Geoffrey West, Santa Fe Institute, gig economy, gigafactory, Google X / Alphabet X, gravity well, hive mind, housing crisis, Hyperloop, impact investing, indoor plumbing, industrial robot, informal economy, initial coin offering, intentional community, 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, Kiva Systems, late fees, Law of Accelerating Returns, life extension, lifelogging, loss aversion, Lyft, M-Pesa, Mary Lou Jepsen, Masayoshi Son, mass immigration, megacity, meta-analysis, microbiome, microdosing, mobile money, multiplanetary species, Narrative Science, natural language processing, Neal Stephenson, Neil Armstrong, Network effects, new economy, New Urbanism, Nick Bostrom, Oculus Rift, One Laptop per Child (OLPC), out of africa, packet switching, peer-to-peer lending, Peter H. Diamandis: Planetary Resources, Peter Thiel, planned obsolescence, QR code, RAND corporation, Ray Kurzweil, RFID, Richard Feynman, Richard Florida, ride hailing / ride sharing, risk tolerance, robo advisor, Satoshi Nakamoto, Second Machine Age, self-driving car, Sidewalk Labs, Silicon Valley, Skype, smart cities, smart contracts, smart grid, Snapchat, SoftBank, sovereign wealth fund, special economic zone, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Jurvetson, Steven Pinker, Stewart Brand, supercomputer in your pocket, supply-chain management, tech billionaire, technoutopianism, TED Talk, Tesla Model S, Tim Cook: Apple, transaction costs, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, urban planning, Vision Fund, VTOL, warehouse robotics, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, X Prize

The big deal here is both safety and a nearly unassailable market advantage. All that data puts Waymo far ahead of the competition. 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.

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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.

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But what happens over the next decade, when autonomous vehicles take to the road and change every aspect of that calculation? Right now, human error sits at the center of auto insurance. People—distractible, emotional, and sometimes irrational—are responsible for 90 percent of the 1.2 million traffic fatalities a year. Yet, without humans in the driver seat, 90 percent of that danger gets removed. For an insurance industry built on assessing risk, this alone is a huge change. Now take it a step further. Today, we insure the stuff we own. But autonomous cars shift us from car-as-property to car-as-service, removing the need for consumer-facing auto insurance altogether.

Succeeding With AI: How to Make AI Work for Your Business by Veljko Krunic

AI winter, Albert Einstein, algorithmic trading, AlphaGo, Amazon Web Services, anti-fragile, anti-pattern, artificial general intelligence, autonomous vehicles, Bayesian statistics, bioinformatics, Black Swan, Boeing 737 MAX, business process, cloud computing, commoditize, computer vision, correlation coefficient, data is the new oil, data science, deep learning, DeepMind, en.wikipedia.org, fail fast, Gini coefficient, high net worth, information retrieval, Internet of things, iterative process, job automation, Lean Startup, license plate recognition, minimum viable product, natural language processing, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, six sigma, smart cities, speech recognition, statistical model, strong AI, tail risk, The Design of Experiments, the scientific method, web application, zero-sum game

Those considerations are that AI could cause physical harm and that collecting data for training and improving AI needs to be done differently than when you have a nonphysical system. This section elaborates on those considerations. For example, AI is applied in physical systems to autonomous vehicles [38] and the Internet of Things (IoT) [46]. Companies ranging from Google to Tesla and Uber have autonomous driving projects. Autonomous cars aren’t possible without AI, and a fully autonomous vehicle requires capabilities superior to what AI can provide today. AI is also used to analyze data harvested from IoT devices (an example being smart thermostats such as Nest [36]). Let’s take a look at some of the related concerns. 8.2.1 First, do no harm When you’re controlling heavy metal objects moving with substantial speed, it’s imperative to keep people safe—humans are vulnerable in collisions with metal objects.

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For example, in the case of the AI-powered autonomous vehicle [38], you’d ship the vehicle itself. Once the vehicle is delivered, an additional capability could be added to it as a software upgrade. But you’re stuck with the sensors and effectors (engine, brakes, steering mechanisms, horn, signal lights, headlights, and so forth) that are shipped with the car. Once you distribute physical systems to your customers/users, it’s often impossible (or expensive) to add the capacity to perform new actions that you didn’t envision at design time. Whatever autonomous cars we have in the future, it’s a safe bet that some of their capabilities will be fixed at the time the car is manufactured and will be difficult to change later. 2.5.3 Using AI to automate part of the business process One of the uses of AI that’s getting increasing attention in both industry and the popular press is the use of AI to perform actions that previously required humans.

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Even then, there are significant limitations on how much the algorithm can change. While the previous approach might be acceptable if you’re building an AI for research purposes (such as an AI that learns to play old computer games better than a human [137]), such cavalier treatment of human safety doesn’t work when applied to autonomous vehicles. You can’t let an autonomous car learn to drive by letting it drive in the physical world and drive itself right off a cliff—or worse! Local vs. global models One taxonomy of AI methods divides models between those that are global versus those that are local. In the global models, there’s a limited capacity to instruct the algorithm to change the results for only a single input without affecting the results for other inputs (more on global models in section 8.5).

Right of Way: Race, Class, and the Silent Epidemic of Pedestrian Deaths in America by Angie Schmitt

active transport: walking or cycling, autonomous vehicles, car-free, congestion pricing, COVID-19, crossover SUV, desegregation, Donald Trump, Elaine Herzberg, gentrification, global pandemic, high-speed rail, invention of air conditioning, Lyft, megacity, move fast and break things, off-the-grid, Ralph Nader, Richard Florida, Ronald Reagan, self-driving car, Silicon Valley, Skype, subprime mortgage crisis, super pumped, Uber and Lyft, uber lyft, Unsafe at Any Speed, urban planning, urban sprawl, white flight, wikimedia commons

Simon Romero, “Wielding Rocks and Knives, Arizonans Attack Self-Driving Cars,” New York Times, December 31, 2018, https://www.nytimes.com/2018/12/31/us/waymo-self-driving-cars-arizona-attacks.html. 8. Governors Highway Safety Association, “Autonomous Vehicles,” accessed February 29, 2020, https://www.ghsa.org/state-laws/issues/autonomous%20vehicles. 9. Kyle Wiggers, “Waymo’s Autonomous Cars Have Driven 20 Million Miles on Public Roads,” VentureBeat, January 6, 2020, https://venturebeat.com/2020/01/06/waymos-autonomous-cars-have-driven-20-million-miles-on-public-roads/. 10. Amri Efrati, “The Uber Whistleblower’s Email,” The Information, December 10, 2018, https://www.theinformation.com/articles/the-uber-whistleblowers-email. 11.

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Governors like Ducey, for their part, have been relatively enthusiastic about inviting the companies to test on public streets. Autonomous car testing offers government officials an aura of tech and business friendliness—and perhaps some jobs—usually without requiring any upfront public investment. Twelve US states currently allow companies to test or market vehicles that operate without a driver.8 Google’s self-driving car operation, Waymo, claimed to have driven twenty million miles on public roads as of January 2020.9 That company already offers driverless taxi service to a small group of screened riders, who sign nondisclosure agreements, in Arizona. Uber and other autonomous vehicle companies like Arizona in part because environments like Mill Avenue—where Herzberg was struck—are so hostile to pedestrians.

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This kind of technology could, for example, prevent red-light-running crashes by sending a signal to the vehicle and instructing it to brake after the traffic light turns red. The NHTSA has estimated that vehicle-to-vehicle communication alone could save 1,366 lives annually and prevent 615,000 injuries.44 V2V and V2I safety systems have a big potential benefit over autonomous vehicles as well: they can be installed retroactively in used cars. A safety improvement based on autonomous vehicles, by contrast, would require waiting perhaps fifteen years for the US vehicle fleet to entirely turn over. For a time a few years ago, it looked as if this kind of safety tech—V2V, V2I—was inevitable. In late 2016, the Obama administration proposed adding DSRC to all new vehicles.

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

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, access to a mobile phone, additive manufacturing, Airbnb, Alan Greenspan, 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, Boston Dynamics, British Empire, business cycle, business intelligence, business process, call centre, carbon tax, Charles Lindbergh, Chuck Templeton: OpenTable:, clean water, combinatorial explosion, computer age, computer vision, congestion charging, congestion pricing, corporate governance, cotton gin, creative destruction, crowdsourcing, data science, David Ricardo: comparative advantage, digital map, driverless car, employer provided health coverage, en.wikipedia.org, Erik Brynjolfsson, factory automation, Fairchild Semiconductor, 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, general purpose technology, global village, GPS: selective availability, Hans Moravec, 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, Jevons paradox, 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, Kiva Systems, 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, One Laptop per Child (OLPC), pattern recognition, Paul Samuelson, payday loans, post-work, power law, price stability, Productivity paradox, profit maximization, Ralph Nader, Ray Kurzweil, recommendation engine, Report Card for America’s Infrastructure, Robert Gordon, Robert Solow, Rodney Brooks, Ronald Reagan, search costs, 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 Cathedral and the Bazaar, the long tail, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, Vernor Vinge, warehouse robotics, Watson beat the top human players on Jeopardy!, winner-take-all economy, Y2K

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.

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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.

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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.

Rule of the Robots: How Artificial Intelligence Will Transform Everything by Martin Ford

AI winter, Airbnb, algorithmic bias, algorithmic trading, Alignment Problem, AlphaGo, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, Automated Insights, autonomous vehicles, backpropagation, basic income, Big Tech, big-box store, call centre, carbon footprint, Chris Urmson, Claude Shannon: information theory, clean water, cloud computing, commoditize, computer age, computer vision, Computing Machinery and Intelligence, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, data is the new oil, data science, deep learning, deepfake, DeepMind, Demis Hassabis, deskilling, disruptive innovation, Donald Trump, Elon Musk, factory automation, fake news, fulfillment center, full employment, future of work, general purpose technology, Geoffrey Hinton, George Floyd, gig economy, Gini coefficient, global pandemic, Googley, GPT-3, high-speed rail, hype cycle, ImageNet competition, income inequality, independent contractor, industrial robot, informal economy, information retrieval, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jeff Bezos, job automation, John Markoff, Kiva Systems, knowledge worker, labor-force participation, Law of Accelerating Returns, license plate recognition, low interest rates, low-wage service sector, Lyft, machine readable, machine translation, Mark Zuckerberg, Mitch Kapor, natural language processing, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, Ocado, OpenAI, opioid epidemic / opioid crisis, passive income, pattern recognition, Peter Thiel, Phillips curve, post scarcity, public intellectual, Ray Kurzweil, recommendation engine, remote working, RFID, ride hailing / ride sharing, Robert Gordon, Rodney Brooks, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, Silicon Valley startup, social distancing, SoftBank, South of Market, San Francisco, special economic zone, speech recognition, stealth mode startup, Stephen Hawking, superintelligent machines, TED Talk, The Future of Employment, The Rise and Fall of American Growth, the scientific method, Turing machine, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, universal basic income, very high income, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y Combinator

Major manufacturers including Toyota and Nissan likewise promised self-driving vehicles by 2020.56 All those predictions have now been rolled back. Urmson remains confident and said in 2019 that he expects at least “hundreds” of fully autonomous vehicles to be deployed on public roads within five years,57 and that there may be 10,000 or more such cars operating within ten years.58 My own view is that even those predictions could well turn out to be optimistic. I’d say there’s a real danger that truly autonomous cars are going to remain five years in the future for many years to come. The reality is that the routine operation of autonomous cars on both highways and in more urban environments—in other words, situations where things work more or less as expected—has largely been solved.

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A connection achieved through eye contact, a wave of a hand, pausing midstride to wait for a driver’s acknowledgement and numerous other nuanced behaviors make up a kind of unspoken language that is somehow understood by nearly everyone who shares the road. I think it is quite possible that it may turn out that negotiating these types of interactions is simply beyond the capabilities of today’s deep learning systems. In other words, truly autonomous cars may require technology much further along the path toward general machine intelligence, and that could be a long wait. Many analysts believe that, given the difficulties faced by autonomous cars in urban settings, the first truly practical driverless vehicles to appear on our roads will be long-haul trucks. Driving on highways, after all, is a problem that has largely already been solved by systems like Tesla’s autopilot.

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To address these issues and eventually achieve the kind of productivity increases that will finally rein in healthcare’s cost disease, I think we will have little choice except to rely far more heavily on medical machine intelligence. SELF-DRIVING CARS AND TRUCKS: A LONGER THAN EXPECTED WAIT Elon Musk’s promise of a million robotic taxis operating on roads by the end of 2020 is only the most recent example of overexuberance in the autonomous vehicle industry. Perhaps because of the centrality of the automobile to our way of life, especially in the United States, no application of artificial intelligence has been subject to as much hype and hyperbolic enthusiasm as the self-driving car. Since the industry’s emergence following the Defense Advanced Research Projects Agency (DARPA) grand challenges in 2004 and 2005, the technology has achieved astonishing progress while at the same time regularly falling short of overinflated expectations.

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, bike sharing, car-free, City Beautiful movement, collaborative consumption, congestion charging, congestion pricing, crowdsourcing, desegregation, Donald Shoup, driverless car, Enrique Peñalosa, Ford Model T, Ford paid five dollars a day, Frederick Winslow Taylor, high-speed rail, if you build it, they will come, Induced demand, intermodal, invention of the wheel, lake wobegon effect, Lewis Mumford, Loma Prieta earthquake, longitudinal study, Lyft, Masdar, megacity, meta-analysis, moral hazard, Nate Silver, oil shock, parking minimums, Productivity paradox, Ralph Nader, rent control, ride hailing / ride sharing, Rosa Parks, scientific management, self-driving car, skinny streets, smart cities, smart grid, smart transportation, TED Talk, 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

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.

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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.

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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.)

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, Apollo 11, Apollo Guidance Computer, Apple II, artificial general intelligence, asset allocation, augmented reality, autonomous vehicles, barriers to entry, bitcoin, Bletchley Park, blockchain, Boston Dynamics, business intelligence, business process, call centre, chief data officer, Chris Urmson, Clayton Christensen, clean water, Computing Machinery and Intelligence, congestion charging, CRISPR, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, deskilling, different worldview, disruptive innovation, distributed generation, distributed ledger, double helix, drone strike, electricity market, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, fiat currency, financial exclusion, Flash crash, Flynn Effect, Ford Model T, future of work, gamification, Geoffrey Hinton, gig economy, gigafactory, Google Glasses, Google X / Alphabet X, Hans Lippershey, high-speed rail, 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, Kim Stanley Robinson, Kiva Systems, 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, Neal Stephenson, Neil Armstrong, Network effects, new economy, Nick Bostrom, obamacare, Occupy movement, Oculus Rift, off grid, off-the-grid, packet switching, pattern recognition, peer-to-peer, Ray Kurzweil, retail therapy, RFID, ride hailing / ride sharing, Robert Metcalfe, Salesforce, 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, Snow Crash, social graph, software as a service, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, synthetic biology, systems thinking, TaskRabbit, technological singularity, TED Talk, telemarketer, telepresence, telepresence robot, Tesla Model S, The future is already here, The Future of Employment, Tim Cook: Apple, trade route, Travis Kalanick, TSMC, Turing complete, Turing test, Twitter Arab Spring, uber lyft, undersea cable, urban sprawl, V2 rocket, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, white picket fence, WikiLeaks, yottabyte

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!

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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.

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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.

AI 2041 by Kai-Fu Lee, Chen Qiufan

3D printing, Abraham Maslow, active measures, airport security, Albert Einstein, AlphaGo, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, augmented reality, autonomous vehicles, basic income, bitcoin, blockchain, blue-collar work, Cambridge Analytica, carbon footprint, Charles Babbage, computer vision, contact tracing, coronavirus, corporate governance, corporate social responsibility, COVID-19, CRISPR, cryptocurrency, DALL-E, data science, deep learning, deepfake, DeepMind, delayed gratification, dematerialisation, digital map, digital rights, digital twin, Elon Musk, fake news, fault tolerance, future of work, Future Shock, game design, general purpose technology, global pandemic, Google Glasses, Google X / Alphabet X, GPT-3, happiness index / gross national happiness, hedonic treadmill, hiring and firing, Hyperloop, information security, Internet of things, iterative process, job automation, language acquisition, low earth orbit, Lyft, Maslow's hierarchy, mass immigration, mirror neurons, money: store of value / unit of account / medium of exchange, mutually assured destruction, natural language processing, Neil Armstrong, Nelson Mandela, OpenAI, optical character recognition, pattern recognition, plutocrats, post scarcity, profit motive, QR code, quantitative easing, Richard Feynman, ride hailing / ride sharing, robotic process automation, Satoshi Nakamoto, self-driving car, seminal paper, Silicon Valley, smart cities, smart contracts, smart transportation, Snapchat, social distancing, speech recognition, Stephen Hawking, synthetic biology, telemarketer, Tesla Model S, The future is already here, trolley problem, Turing test, uber lyft, universal basic income, warehouse automation, warehouse robotics, zero-sum game

Yang Juan pulled out her phone and passed it to Chamal. “Read this. How do you feel about your new title, hmm?” The headline said, “The Holy Driver: Sri Lankan Boy Saves 11 Lives Piloting Autonomous Vehicle.” The picture below was of Chamal’s silhouette, wearing his iconic helmet. ANALYSIS AUTONOMOUS VEHICLES, FULL AUTONOMY AND SMART CITIES, ETHICAL AND SOCIAL ISSUES From Knight Rider to Minority Report, science fiction has presented the arrival of autonomous vehicles (AVs) as a foregone conclusion. But AVs are actually one of the Holy Grails of artificial intelligence. Driving is a complex task with many subtasks and inputs, as well as the potential for uncertain environments and unlikely events.

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—JIMI HENDRIX, QUOTED IN JIMI HENDRIX: A BROTHER’S STORY, BY LEON HENDRIX NOTE FROM KAI-FU: Set in Sri Lanka, “The Holy Driver” imagines a society two decades from now that is in the midst of transitioning from human drivers to autonomous driving by AI. In the story, a talented young gamer is recruited for a mysterious project—one that reveals the capacity for both humans and AI to make mistakes, but very different ones. In my commentary, I will describe how autonomous vehicles work, and how and when fully autonomous vehicles will emerge. THE WRISTWATCH BUZZED, its face blinking with an urgent red flash. It was time for Chamal to race. A regular at the VR Café, he had developed a full pre-race routine. His expression solemn, Chamal would dress himself in the skintight haptic suit, carefully comb his hair, and then place the conch-shaped helmet on his head.

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Other questions include: How should we balance the livelihoods of millions of truck drivers against the millions of hours saved by autonomous vehicles? Is it acceptable to have interim AI that makes mistakes that human drivers would not make if, in five years, the total number of fatalities can be halved because the AI will improve after learning from billions of miles of experience? And the most fundamental question: Should we ever let a machine make decisions that may harm human lives? If the answer is no, it would put an end to autonomous vehicles. Since lives are at stake, every company must proceed with caution. There are two distinct approaches to take, each with different merits.

Radical Technologies: The Design of Everyday Life by Adam Greenfield

3D printing, Airbnb, algorithmic bias, algorithmic management, AlphaGo, augmented reality, autonomous vehicles, bank run, barriers to entry, basic income, bitcoin, Black Lives Matter, blockchain, Boston Dynamics, business intelligence, business process, Californian Ideology, call centre, cellular automata, centralized clearinghouse, centre right, Chuck Templeton: OpenTable:, circular economy, cloud computing, Cody Wilson, collective bargaining, combinatorial explosion, Computer Numeric Control, computer vision, Conway's Game of Life, CRISPR, cryptocurrency, David Graeber, deep learning, DeepMind, dematerialisation, digital map, disruptive innovation, distributed ledger, driverless car, drone strike, Elon Musk, Ethereum, ethereum blockchain, facts on the ground, fiat currency, fulfillment center, gentrification, global supply chain, global village, Goodhart's law, Google Glasses, Herman Kahn, Ian Bogost, IBM and the Holocaust, industrial robot, informal economy, information retrieval, Internet of things, Jacob Silverman, James Watt: steam engine, Jane Jacobs, Jeff Bezos, Jeff Hawkins, job automation, jobs below the API, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, John von Neumann, joint-stock company, Kevin Kelly, Kickstarter, Kiva Systems, late capitalism, Leo Hollis, 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, Nick Bostrom, Occupy movement, Oculus Rift, off-the-grid, PalmPilot, Pareto efficiency, pattern recognition, Pearl River Delta, performance metric, Peter Eisenman, Peter Thiel, planetary scale, Ponzi scheme, post scarcity, post-work, printed gun, proprietary trading, RAND corporation, recommendation engine, RFID, rolodex, Rutger Bregman, Satoshi Nakamoto, self-driving car, sentiment analysis, shareholder value, sharing economy, Shenzhen special economic zone , Sidewalk Labs, 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, Tony Fadell, transaction costs, Uber for X, undersea cable, universal basic income, urban planning, urban sprawl, vertical integration, Vitalik Buterin, warehouse robotics, When a measure becomes a target, Whole Earth Review, WikiLeaks, women in the workforce

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?

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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.

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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.

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

"Susan Fowler" uber, "World Economic Forum" Davos, Airbnb, always be closing, Amazon Web Services, asset light, autonomous vehicles, Ayatollah Khomeini, Benchmark Capital, business process, Chuck Templeton: OpenTable:, cognitive dissonance, corporate governance, DARPA: Urban Challenge, Didi Chuxing, Donald Trump, driverless car, Elon Musk, Erlich Bachman, gig economy, Golden Gate Park, Google X / Alphabet X, hustle culture, independent contractor, information retrieval, Jeff Bezos, John Zimmer (Lyft cofounder), Lyft, Marc Andreessen, Mark Zuckerberg, megacity, Menlo Park, multilevel marketing, new economy, pattern recognition, price mechanism, public intellectual, reality distortion field, ride hailing / ride sharing, Salesforce, San Francisco homelessness, Sand Hill Road, self-driving car, side hustle, Silicon Valley, Silicon Valley billionaire, Silicon Valley startup, Skype, Snapchat, South of Market, San Francisco, sovereign wealth fund, statistical model, Steve Jobs, super pumped, TaskRabbit, tech worker, Tony Hsieh, transportation-network company, Travis Kalanick, turn-by-turn navigation, Uber and Lyft, Uber for X, uber lyft, ubercab, young professional

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.

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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.

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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.

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

"World Economic Forum" Davos, AI winter, Airbnb, Albert Einstein, algorithmic bias, algorithmic trading, Alignment Problem, AlphaGo, artificial general intelligence, autonomous vehicles, barriers to entry, basic income, bike sharing, business cycle, Cambridge Analytica, cloud computing, commoditize, computer vision, corporate social responsibility, cotton gin, creative destruction, crony capitalism, data science, deep learning, DeepMind, Demis Hassabis, Deng Xiaoping, deskilling, Didi Chuxing, Donald Trump, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, fake news, full employment, future of work, general purpose technology, Geoffrey Hinton, gig economy, Google Chrome, Hans Moravec, happiness index / gross national happiness, high-speed rail, if you build it, they will come, ImageNet competition, impact investing, 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, machine translation, mandatory minimum, Mark Zuckerberg, Menlo Park, minimum viable product, natural language processing, Neil Armstrong, new economy, Nick Bostrom, OpenAI, 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, SoftBank, Solyndra, special economic zone, speech recognition, Stephen Hawking, Steve Jobs, strong AI, TED Talk, The Future of Employment, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, urban planning, vertical integration, Vision Fund, warehouse robotics, Y Combinator

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?

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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. We’ve already seen early signs of this happening, with unions representing truck drivers successfully lobbying Congress in 2017 to exclude trucks from legislation aimed at speeding up autonomous-vehicle deployment.

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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.

The Fourth Industrial Revolution by Klaus Schwab

"World Economic Forum" Davos, 3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Anthropocene, augmented reality, autonomous vehicles, barriers to entry, Baxter: Rethink Robotics, bitcoin, blockchain, Buckminster Fuller, call centre, circular economy, clean water, collaborative consumption, commoditize, conceptual framework, continuous integration, CRISPR, cross-border payments, crowdsourcing, digital divide, digital twin, disintermediation, disruptive innovation, distributed ledger, driverless car, Edward Snowden, Elon Musk, epigenetics, Erik Brynjolfsson, future of work, global value chain, Google Glasses, hype cycle, 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, Marc Benioff, mass immigration, megacity, meta-analysis, more computing power than Apollo, mutually assured destruction, Narrative Science, Network effects, Nicholas Carr, nuclear taboo, OpenAI, 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, social contagion, software as a service, Stephen Hawking, Steve Jobs, Steven Levy, Stuxnet, supercomputer in your pocket, synthetic biology, 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!, Wayback Machine, WikiLeaks, winner-take-all economy, women in the workforce, working-age population, Y Combinator, Zipcar

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.

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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.

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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.

I, Warbot: The Dawn of Artificially Intelligent Conflict by Kenneth Payne

Abraham Maslow, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, AlphaGo, anti-communist, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, Asperger Syndrome, augmented reality, Automated Insights, autonomous vehicles, backpropagation, Black Lives Matter, Bletchley Park, Boston Dynamics, classic study, combinatorial explosion, computer age, computer vision, Computing Machinery and Intelligence, coronavirus, COVID-19, CRISPR, cuban missile crisis, data science, deep learning, deepfake, DeepMind, delayed gratification, Demis Hassabis, disinformation, driverless car, drone strike, dual-use technology, Elon Musk, functional programming, Geoffrey Hinton, Google X / Alphabet X, Internet of things, job automation, John Nash: game theory, John von Neumann, Kickstarter, language acquisition, loss aversion, machine translation, military-industrial complex, move 37, mutually assured destruction, Nash equilibrium, natural language processing, Nick Bostrom, Norbert Wiener, nuclear taboo, nuclear winter, OpenAI, paperclip maximiser, pattern recognition, RAND corporation, ransomware, risk tolerance, Ronald Reagan, self-driving car, semantic web, side project, Silicon Valley, South China Sea, speech recognition, Stanislav Petrov, stem cell, Stephen Hawking, Steve Jobs, strong AI, Stuxnet, technological determinism, TED Talk, theory of mind, TikTok, Turing machine, Turing test, uranium enrichment, urban sprawl, V2 rocket, Von Neumann architecture, Wall-E, zero-sum game

DARPA was back in the hunt for autonomous vehicles. Its ‘Grand Challenge’ required vehicles to navigate 142 miles of off-road course, entirely without human input, within ten hours. There was a $1million prize, and fifteen competitors vying for it on the starting line. But no winners. The furthest anyone got that first year was 7.5 miles. Undeterred, DARPA relaunched the competition, and scheduled a new race eighteen months later. This time there was a winner: Stanford’s ‘Stanley’, a modified VW Tuareg, led home a field of five finishers, jump-starting the modern race for commercially viable autonomous cars.2 It was a powerful demonstration of the American approach to AI—once again harnessing universities and hobbyists to the defence complex.

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Ground combat is particularly hard for AI not just because of awkward terrain, but because it involves other humans. I’ll save a fuller discussion of the human dimension until the next chapter, when we consider the connection between autonomous cars and strategy, but for now it’s worth flagging. The major problem with autonomy for car manufacturers isn’t navigating the physical landscape but knowing how the humans in it will interact with your machine. In autonomous cars that’s an ethical issue. It is too in combat—a warbot must understand who is a combatant and who is on which side. But it’s also essential for fighting power. The machine needs to gauge how much effort is needed to win any engagement; for now it lacks that contextual understanding.

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Another warbot commissioned in the 1970s that came to maturity in this period was the Tomahawk cruise missile, launched from the sea and able to hug terrain, before arriving precisely on target over a thousand miles distant. Still another was the land-based Patriot missile interceptor, which like the Tomahawk entered public consciousness in the 1990/1 Gulf War. All suggested highly refined autonomy—but not much intelligence. But DARPA’s lumbering effort at an autonomous vehicle pointed to the stark limits of AI in the 1980s. It was rather closer to Shakey than KITT, its fictional counterpart in the Knight Rider television series. Precision-guided missiles like Aegis and Tomahawk were polished versions of the proximity fused artillery shell—a very limited sort of intelligence.

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

Affordable Care Act / Obamacare, An Inconvenient Truth, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, banking crisis, Bear Stearns, car-free, carbon footprint, clean tech, creative destruction, decarbonisation, deindustrialization, demand response, Deng Xiaoping, Donald Trump, driverless car, electricity market, Elon Musk, en.wikipedia.org, energy security, factory automation, Fairchild Semiconductor, Ford Model T, foreign exchange controls, gigafactory, global value chain, high-speed rail, hydrogen economy, index card, Intergovernmental Panel on Climate Change (IPCC), joint-stock company, Joseph Schumpeter, Kanban, Kickstarter, manufacturing employment, market design, megacity, Nixon shock, obamacare, off-the-grid, oil shock, planned obsolescence, Ralph Nader, RFID, rolodex, Ronald Reagan, Rubik’s Cube, self-driving car, shareholder value, Shenzhen special economic zone , short squeeze, Silicon Valley, Silicon Valley startup, skunkworks, smart cities, Solyndra, sovereign wealth fund, special economic zone, Steve Jobs, Tesla Model S, too big to fail, Unsafe at Any Speed, zero-sum game, Zipcar

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.

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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. And that is why one might say that automation is not simply the next frontier, but the finishing line in the Great Race. For in some profound sense, automated cars cease to be cars. They are, instead, robots.

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Inevitably people will worry about the safety of these robots. But it seems quite likely that autonomous vehicles will in fact be fundamentally safer. Unlike humans, they won’t get distracted by their kids fighting, or vacation plans, or a pretty face on the corner, or job stress. That’s because the car’s full-time job will be to get you to where you are going and to keep you safe. Insurance companies have already expressed interest in lowering their rates for autonomous vehicles. In fact, autonomous vehicles may end up being the most significant safety innovation since seat belts. How many uneasy teenagers have found themselves in a vehicle with an unsettlingly intoxicated classmate or friend?

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, algorithmic bias, AlphaGo, Andrew Wiles, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, basic income, behavioural economics, Bletchley Park, blockchain, Boston Dynamics, brain emulation, Cass Sunstein, Charles Babbage, Claude Shannon: information theory, complexity theory, computer vision, Computing Machinery and Intelligence, connected car, CRISPR, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deep learning, deepfake, DeepMind, delayed gratification, Demis Hassabis, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ernest Rutherford, fake news, Flash crash, full employment, future of work, Garrett Hardin, Geoffrey Hinton, Gerolamo Cardano, Goodhart's law, Hans Moravec, 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, luminiferous ether, machine readable, machine translation, Mark Zuckerberg, multi-armed bandit, Nash equilibrium, Nick Bostrom, Norbert Wiener, NP-complete, OpenAI, openstreetmap, P = NP, paperclip maximiser, Pareto efficiency, Paul Samuelson, Pierre-Simon Laplace, positional goods, probability theory / Blaise Pascal / Pierre de Fermat, profit maximization, RAND corporation, random walk, Ray Kurzweil, Recombinant DNA, recommendation engine, RFID, Richard Thaler, ride hailing / ride sharing, Robert Shiller, robotic process automation, 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, surveillance capitalism, Thales of Miletus, The Future of Employment, The Theory of the Leisure Class by Thorstein Veblen, Thomas Bayes, Thorstein Veblen, Tragedy of the Commons, transport as a service, trolley problem, Turing machine, Turing test, universal basic income, uranium enrichment, vertical integration, Von Neumann architecture, Wall-E, warehouse robotics, 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.

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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.

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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.

System Error by Rob Reich

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 2021 United States Capitol attack, A Declaration of the Independence of Cyberspace, Aaron Swartz, AI winter, Airbnb, airport security, Alan Greenspan, Albert Einstein, algorithmic bias, AlphaGo, AltaVista, artificial general intelligence, Automated Insights, autonomous vehicles, basic income, Ben Horowitz, Berlin Wall, Bernie Madoff, Big Tech, bitcoin, Blitzscaling, Cambridge Analytica, Cass Sunstein, clean water, cloud computing, computer vision, contact tracing, contact tracing app, coronavirus, corporate governance, COVID-19, creative destruction, CRISPR, crowdsourcing, data is the new oil, data science, decentralized internet, deep learning, deepfake, DeepMind, deplatforming, digital rights, disinformation, disruptive innovation, Donald Knuth, Donald Trump, driverless car, dual-use technology, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, Fairchild Semiconductor, fake news, Fall of the Berlin Wall, Filter Bubble, financial engineering, financial innovation, fulfillment center, future of work, gentrification, Geoffrey Hinton, George Floyd, gig economy, Goodhart's law, GPT-3, Hacker News, hockey-stick growth, income inequality, independent contractor, informal economy, information security, Jaron Lanier, Jeff Bezos, Jim Simons, jimmy wales, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, Lean Startup, linear programming, Lyft, Marc Andreessen, Mark Zuckerberg, meta-analysis, minimum wage unemployment, Monkeys Reject Unequal Pay, move fast and break things, Myron Scholes, Network effects, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, NP-complete, Oculus Rift, OpenAI, Panopticon Jeremy Bentham, Parler "social media", pattern recognition, personalized medicine, Peter Thiel, Philippa Foot, premature optimization, profit motive, quantitative hedge fund, race to the bottom, randomized controlled trial, recommendation engine, Renaissance Technologies, Richard Thaler, ride hailing / ride sharing, Ronald Reagan, Sam Altman, Sand Hill Road, scientific management, self-driving car, shareholder value, Sheryl Sandberg, Shoshana Zuboff, side project, Silicon Valley, Snapchat, social distancing, Social Responsibility of Business Is to Increase Its Profits, software is eating the world, spectrum auction, speech recognition, stem cell, Steve Jobs, Steven Levy, strong AI, superintelligent machines, surveillance capitalism, Susan Wojcicki, tech billionaire, tech worker, techlash, technoutopianism, Telecommunications Act of 1996, telemarketer, The Future of Employment, TikTok, Tim Cook: Apple, traveling salesman, Triangle Shirtwaist Factory, trolley problem, Turing test, two-sided market, Uber and Lyft, uber lyft, ultimatum game, union organizing, universal basic income, washing machines reduced drudgery, Watson beat the top human players on Jeopardy!, When a measure becomes a target, winner-take-all economy, Y Combinator, you are the product

In the context of autonomous cars, the problem asks whether a vehicle should be programmed to endanger or sacrifice the life of its sole passenger by running off the road in order to avoid potentially hitting five pedestrians crossing the road. As a society, we might prefer that the vehicle pick the option that saves the most lives. Accordingly, most participants in a 2016 survey preferred that such vehicles be programmed to minimize casualties on the road generally. But the same survey showed that when participants were asked to be a passenger—that is, potential purchaser—of an autonomous vehicle, “they would themselves prefer to ride in AVs that protect their passengers at all costs.”

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Reflecting on the race, Thrun crowed, “It’s a no-brainer that 50 to 60 years from now, cars will drive themselves.” His estimate was far too pessimistic. In 2020, at least thirty different countries were testing autonomous vehicles on their roads. California alone licensed more than fifty companies and more than five hundred autonomous vehicles, logging more than 2 million miles. One of the primary motivations for the commercial development of autonomous vehicles is their potential for improving road safety. The World Health Organization estimates that there were 1.25 million road traffic deaths globally in 2013. In 2017, 37,133 people were killed in motor vehicle crashes in the United States alone, and more than 90 percent of those crashes involved human error.

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The upshot is the common conviction that technological progress is rolling over us with the wheels of inevitability. Ordinary people can’t undo technological discoveries and innovations, and they can’t shape the products we buy or the effects of technology across society. What can a truck driver whose job may be replaced by autonomous vehicles do? What can a parent do about the apps a child is transfixed by, short of taking the phone out of the child’s hands? What can an employee do about the deployment of facial recognition at the office? What can a citizen do about the spread of disinformation on the platforms that deliver us news and information?

Architects of Intelligence by Martin Ford

3D printing, agricultural Revolution, AI winter, algorithmic bias, Alignment Problem, AlphaGo, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, Big Tech, bitcoin, Boeing 747, Boston Dynamics, business intelligence, business process, call centre, Cambridge Analytica, cloud computing, cognitive bias, Colonization of Mars, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, CRISPR, crowdsourcing, DARPA: Urban Challenge, data science, deep learning, DeepMind, Demis Hassabis, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, driverless car, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, fake news, Fellow of the Royal Society, Flash crash, future of work, general purpose technology, Geoffrey Hinton, gig economy, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, Hans Rosling, hype cycle, ImageNet competition, income inequality, industrial research laboratory, industrial robot, information retrieval, job automation, John von Neumann, Large Hadron Collider, Law of Accelerating Returns, life extension, Loebner Prize, machine translation, Mark Zuckerberg, Mars Rover, means of production, Mitch Kapor, Mustafa Suleyman, natural language processing, new economy, Nick Bostrom, OpenAI, opioid epidemic / opioid crisis, optical character recognition, paperclip maximiser, pattern recognition, phenotype, Productivity paradox, radical life extension, Ray Kurzweil, recommendation engine, Robert Gordon, Rodney Brooks, Sam Altman, self-driving car, seminal paper, sensor fusion, sentiment analysis, Silicon Valley, smart cities, social intelligence, sparse data, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, synthetic biology, systems thinking, Ted Kaczynski, TED Talk, 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, workplace surveillance , zero-sum game, Zipcar

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?

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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.

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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.

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

3D printing, autonomous vehicles, Berlin Wall, biodiversity loss, blockchain, Boris Johnson, carbon credits, carbon footprint, carbon tax, clean water, congestion charging, coronavirus, COVID-19, CRISPR, decarbonisation, deindustrialization, demand response, Deng Xiaoping, Donald Trump, electricity market, Extinction Rebellion, fixed income, food miles, Ford Model T, Francis Fukuyama: the end of history, general purpose technology, Great Leap Forward, green new deal, Greta Thunberg, Haber-Bosch Process, high-speed rail, hydrogen economy, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jevons paradox, lockdown, market design, means of production, microplastics / micro fibres, North Sea oil, ocean acidification, off grid, off-the-grid, oil shale / tar sands, oil shock, peak oil, planetary scale, precautionary principle, price mechanism, quantitative easing, remote working, reshoring, rewilding, Ronald Reagan, smart meter, South China Sea, sovereign wealth fund, statistical model, systems thinking, Thomas Malthus

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. 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.

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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.

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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.

Carmageddon: How Cars Make Life Worse and What to Do About It by Daniel Knowles

active transport: walking or cycling, autonomous vehicles, Bandra-Worli Sea Link, bank run, big-box store, bike sharing, Boeing 747, Boris Johnson, business cycle, car-free, carbon footprint, congestion charging, congestion pricing, coronavirus, COVID-19, Crossrail, decarbonisation, deindustrialization, Detroit bankruptcy, Donald Shoup, Donald Trump, driverless car, Elaine Herzberg, Elon Musk, first-past-the-post, Ford Model T, Frank Gehry, garden city movement, General Motors Futurama, gentrification, ghettoisation, high-speed rail, housing crisis, Hyperloop, Induced demand, James Watt: steam engine, Jane Jacobs, Jeremy Corbyn, Jevons paradox, Lewis Mumford, lockdown, Lyft, megacity, megastructure, New Urbanism, Northern Rock, parking minimums, Piers Corbyn, Richard Florida, ride hailing / ride sharing, safety bicycle, self-driving car, Silicon Valley, Southern State Parkway, Steve Jobs, TED Talk, Tesla Model S, The Death and Life of Great American Cities, the High Line, Traffic in Towns by Colin Buchanan, Uber and Lyft, uber lyft, upwardly mobile, urban planning, urban renewal, walkable city, white flight, white picket fence, Yom Kippur War, young professional

In that Wall Street Journal interview, in which he described automated cars as one of the most transformational technologies human civilization is likely to invent, he went on to argue that they will also force America to come up with “something to deal with extreme traffic,” he said. “As autonomous vehicles come to the fore, and it’s easier to drive without going through the pain of having to drive yourself . . . there will be more cars on the road and the traffic will get much worse.” He was quite right. And not even alone among car executives in realizing it. Bill Ford, the executive chairman of the Ford Motor Company, of all people, has also warned that autonomous vehicles could create “global gridlock” by 2050. “Our infrastructure cannot support such a large volume of vehicles without creating massive congestion,” he wrote in a newspaper opinion piece in 2014.

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Already, this is used as an argument for why investment in new public transport, such as trains, is redundant. In Britain Matt Ridley, a Conservative member of the House of Lords who was chairman of Northern Rock, the only British bank to suffer a bank run in more than a century, is among those who reckon that HS2, Britain’s new high-speed railway, will quickly be made redundant by autonomous vehicles. So too do the Taxpayers’ Alliance, a group that opposes any government spending on anything. In the immediate term, the problem with this is that despite decades of development, autonomous driving technology has scarcely improved enough to navigate normal suburban streets at 30 miles per hour.

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Sam Schwartz imagines businessmen taking meetings in Manhattan sending their cars to drive around in circles rather than find a parking space. As the technology gets cheaper, more and more people will buy them, and use them for ever more outlandish things. It will be chaos. Jevons paradox will win again. And yet, inevitably perhaps, given he is a car executive, Musk does not think that this means we should not all buy autonomous vehicles. Rather, the solution, apparently, is “some combination of tunnels and double-deckering freeways.” He did admit that “flying cars” are not the answer, because people do “not want the skies to be swarming with helicopters.” On that he is right, despite the vast sums of money being poured into the development of “flying cars” using the sort of quadcopter technology now used for smaller drones.

21 Lessons for the 21st Century by Yuval Noah Harari

"World Economic Forum" Davos, 1960s counterculture, accounting loophole / creative accounting, affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, algorithmic trading, augmented reality, autonomous vehicles, Ayatollah Khomeini, basic income, behavioural economics, Bernie Sanders, bitcoin, blockchain, Boris Johnson, Brexit referendum, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, carbon tax, carbon-based life, Charlie Hebdo massacre, cognitive dissonance, computer age, computer vision, cryptocurrency, cuban missile crisis, decarbonisation, DeepMind, deglobalization, disinformation, Donald Trump, Dr. Strangelove, failed state, fake news, 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, Mohammed Bouazizi, mutually assured destruction, Naomi Klein, obamacare, pattern recognition, post-truth, post-work, purchasing power parity, race to the bottom, RAND corporation, restrictive zoning, Ronald Reagan, Rosa Parks, Scramble for Africa, self-driving car, Silicon Valley, Silicon Valley startup, TED Talk, transatlantic slave trade, trolley problem, Tyler Cowen, 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; 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.

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., 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?’, 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.

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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. 20 Tim Adams, ‘Job Hunting Is a Matter of Big Data, Not How You Perform at an Interview’, Guardian, 10 May 2014. 21 For an extremely insightful discussion, see Cathy O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (New York: Crown, 2016).

Road to Nowhere: What Silicon Valley Gets Wrong About the Future of Transportation by Paris Marx

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, A Declaration of the Independence of Cyberspace, Airbnb, An Inconvenient Truth, autonomous vehicles, back-to-the-land, Berlin Wall, Bernie Sanders, bike sharing, Californian Ideology, car-free, carbon credits, carbon footprint, cashless society, clean tech, cloud computing, colonial exploitation, computer vision, congestion pricing, corporate governance, correlation does not imply causation, COVID-19, DARPA: Urban Challenge, David Graeber, deep learning, degrowth, deindustrialization, deskilling, Didi Chuxing, digital map, digital rights, Donald Shoup, Donald Trump, Douglas Engelbart, Douglas Engelbart, driverless car, Elaine Herzberg, Elon Musk, energy transition, Evgeny Morozov, Extinction Rebellion, extractivism, Fairchild Semiconductor, Ford Model T, frictionless, future of work, General Motors Futurama, gentrification, George Gilder, gig economy, gigafactory, global pandemic, global supply chain, Google Glasses, Google X / Alphabet X, green new deal, Greyball, high-speed rail, Hyperloop, independent contractor, Induced demand, intermodal, Jane Jacobs, Jeff Bezos, jitney, John Perry Barlow, Kevin Kelly, knowledge worker, late capitalism, Leo Hollis, lockdown, low interest rates, Lyft, Marc Benioff, market fundamentalism, minimum viable product, Mother of all demos, move fast and break things, Murray Bookchin, new economy, oil shock, packet switching, Pacto Ecosocial del Sur, Peter Thiel, pre–internet, price mechanism, private spaceflight, quantitative easing, QWERTY keyboard, Ralph Nader, Richard Florida, ride hailing / ride sharing, Ronald Reagan, safety bicycle, Salesforce, School Strike for Climate, self-driving car, Sidewalk Labs, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Silicon Valley startup, smart cities, social distancing, Southern State Parkway, Steve Jobs, Stewart Brand, Stop de Kindermoord, streetcar suburb, tech billionaire, tech worker, techlash, technological determinism, technological solutionism, technoutopianism, the built environment, The Death and Life of Great American Cities, TikTok, transit-oriented development, transportation-network company, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, Unsafe at Any Speed, urban planning, urban renewal, VTOL, walkable city, We are as Gods, We wanted flying cars, instead we got 140 characters, WeWork, Whole Earth Catalog, Whole Earth Review, work culture , Yom Kippur War, young professional

Duhigg’s report appeared in the New Yorker in 2018, at a time when the shine was already coming off the dream of autonomous vehicles and the hype machine was in free fall. But had that information been in the public domain years earlier when Brin was building the hype in the first place, the perception of autonomous vehicles and their capabilities could have been very different. Instead, we were treated to years of news cycles that helped create the myth that ubiquitous self-driving vehicles would solve all our transport problems, to such a degree that libertarian groups funded by the billionaire Koch brothers used the promise of autonomous vehicles to attack plans to expand public transit systems in the United States.

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In the same way that automobiles required a social reconstruction in addition to a physical reconstruction, so too will autonomous vehicles—and some people involved with them have already admitted it. As the date when autonomous vehicles were supposed to arrive came and went, and the challenges facing the technology became apparent both to those in the industry who were trying to make progress with their driving systems and to the public who began to see a growing number of stories about autonomous vehicles crashing in troubling ways, some experts started to discuss how more than just a smart AI would be necessary to bring their fantasies to life.

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Safety isn’t just about the quality of the AI technology.”15 What Ng described was a far cry from what people like Brin or Musk were saying autonomous vehicles would do. Instead of detecting anything around them, Ng’s response suggested that social norms and pedestrian behavior would once again have to be altered to make way for autonomous vehicles. The police will be necessary to ensure that people follow the new rules, reflecting how enforcing regulations on automobiles vastly expanded the powers of the police in the twentieth century. In the aftermath, there were autonomous vehicle advocates who rejected Ng’s comments, but his admission is a more realistic assessment of what will be required to make self-driving cars “work” in any meaningful way than the much more common utopian visions.

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

Albert Einstein, Alvin Toffler, autonomous vehicles, Beeching cuts, blue-collar work, Boris Johnson, British Empire, business intelligence, business process, business process outsourcing, California high-speed rail, 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, driverless car, Elon Musk, extreme commuting, Ford Model T, General Motors Futurama, global pandemic, Google bus, Great Leap Forward, Henri Poincaré, high-speed rail, Hyperloop, Jeff Bezos, lateral thinking, Lewis Mumford, low skilled workers, Marchetti’s constant, planned obsolescence, postnationalism / post nation state, Ralph Waldo Emerson, remote working, safety bicycle, self-driving car, Silicon Valley, social distancing, SpaceShipOne, stakhanovite, Steve Jobs, Suez crisis 1956, telepresence, Tesla Model S, Traffic in Towns by Colin Buchanan, urban planning, éminence grise

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.’

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Their business model relied on people loving driving themselves, if nothing else for the freedom it offered, and they were happy to let the idea of driverless vehicles sleep. However, it was revived by the American military after the 2003 Iraq War. Why send a soldier as well as a vehicle through a minefield? The Defense Advanced Research Projects Agency (DARPA), part of the US Department of Defense – hoping to inspire the creation of autonomous vehicles that might have martial applications – staged the DARPA Grand Challenges of 2004 and 2005, and the Urban Challenge of 2007, which offered million-dollar prizes and grants to teams who could create effective driverless cars. Various universities built entries, and while none met the challenge in 2004, four succeeded in 2005 and there were as many winners in the urban event.

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The World Health Organization estimates that 1.3 million people die in traffic accidents every year, and a further 50 million are maimed or crippled. Most accidents are caused by human error. 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.’

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

"World Economic Forum" Davos, 3D printing, additive manufacturing, agricultural Revolution, AI winter, Airbnb, AlphaGo, Alvin Toffler, Amazon Robotics, Andy Rubin, artificial general intelligence, augmented reality, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Berlin Wall, Bernie Sanders, bitcoin, blockchain, Boston Dynamics, bread and circuses, call centre, Chris Urmson, congestion charging, credit crunch, David Ricardo: comparative advantage, deep learning, DeepMind, Demis Hassabis, digital divide, Douglas Engelbart, Dr. Strangelove, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Fairchild Semiconductor, Flynn Effect, full employment, future of work, Future Shock, gender pay gap, Geoffrey Hinton, gig economy, Google Glasses, Google X / Alphabet X, Hans Moravec, Herman Kahn, hype cycle, 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, Kiva Systems, knowledge worker, lifelogging, lump of labour, Lyft, machine translation, Marc Andreessen, Mark Zuckerberg, Martin Wolf, McJob, means of production, Milgram experiment, Narrative Science, natural language processing, Neil Armstrong, new economy, Nick Bostrom, Occupy movement, Oculus Rift, OpenAI, 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, SoftBank, software is eating the world, speech recognition, Stephen Hawking, Steve Jobs, TaskRabbit, technological singularity, TED Talk, The future is already here, The Future of Employment, Thomas Malthus, transaction costs, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, universal basic income, Vernor Vinge, warehouse automation, warehouse robotics, working-age population, Y Combinator, young professional

It argued instead for the etymologically purer “motor-car”.[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.

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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.

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

"RICO laws" OR "Racketeer Influenced and Corrupt Organizations", 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, carbon tax, circular economy, clean tech, commodity super cycle, company town, coronavirus, COVID-19, decarbonisation, deep learning, Deng Xiaoping, Didi Chuxing, disruptive innovation, distributed generation, Donald Trump, driverless car, Edward Snowden, Elon Musk, energy security, energy transition, failed state, Ford Model T, geopolitical risk, gig economy, global pandemic, global supply chain, green new deal, Greta Thunberg, hydraulic fracturing, Indoor air pollution, Intergovernmental Panel on Climate Change (IPCC), inventory management, James Watt: steam engine, John Zimmer (Lyft cofounder), Kickstarter, LNG terminal, Lyft, Malacca Straits, Malcom McLean invented shipping containers, Masayoshi Son, Masdar, mass incarceration, megacity, megaproject, middle-income trap, 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, power law, price mechanism, purchasing power parity, RAND corporation, rent-seeking, ride hailing / ride sharing, rolling blackouts, Ronald Reagan, Russian election interference, self-driving car, Silicon Valley, smart cities, social distancing, South China Sea, sovereign wealth fund, Suez crisis 1956, super pumped, supply-chain management, TED Talk, trade route, Travis Kalanick, Twitter Arab Spring, Uber and Lyft, uber lyft, ubercab, UNCLOS, UNCLOS, uranium enrichment, vertical integration, women in the workforce

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.

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He had gone on to pioneer what became known as “probabilistic robotics,” which combines statistics to deal with the uncertainty that robots face in real-world settings. Thrun’s goal now was to create a vehicle with “the brainpower to make all the decisions along the way.” His interest in autonomous vehicles arose, he said, because he was “curious about human intelligence.” He also once explained that his “passion for cars that drive themselves” arose from traffic jams. “I feel I’ve lost a year or two just in traffic,” he explained. Thrun’s quest for an autonomous vehicle that would make driving safer had a personal dimension; his best friend had been killed in a car crash at age eighteen.3 Carnegie Mellon fielded two burly military-style vehicles.

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Many things were thrown overboard, including, at GM, the joint research program with Carnegie Mellon. 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.

Give People Money by Annie Lowrey

Abraham Maslow, affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, Airbnb, airport security, autonomous vehicles, barriers to entry, basic income, Bernie Sanders, bitcoin, Black Lives Matter, carbon tax, clean water, collective bargaining, computer age, crowdsourcing, cryptocurrency, deindustrialization, desegregation, Donald Trump, driverless car, Edward Glaeser, Elon Musk, ending welfare as we know it, everywhere but in the productivity statistics, full employment, gender pay gap, gentrification, 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, Modern Monetary Theory, mortgage tax deduction, multilevel marketing, new economy, obamacare, opioid epidemic / opioid crisis, Overton Window, Peter Thiel, post scarcity, post-work, Potemkin village, precariat, public intellectual, randomized controlled trial, ride hailing / ride sharing, Robert Bork, Robert Solow, Ronald Reagan, Rutger Bregman, Sam Altman, self-driving car, Silicon Valley, single-payer health, Steve Jobs, TaskRabbit, tech billionaire, The future is already here, 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, warehouse robotics, 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

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.

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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.

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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).

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, cognitive load, computerized trading, David Brooks, deep learning, deliberate practice, deskilling, digital map, Douglas Engelbart, driverless car, drone strike, Elon Musk, Erik Brynjolfsson, Evgeny Morozov, Flash crash, Frank Gehry, Frank Levy and Richard Murnane: The New Division of Labor, Frederick Winslow Taylor, future of work, gamification, global supply chain, Google Glasses, Google Hangouts, High speed trading, human-factors engineering, indoor plumbing, industrial robot, Internet of things, Ivan Sutherland, 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, low interest rates, Lyft, machine readable, 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, profit motive, Ralph Waldo Emerson, RAND corporation, randomized controlled trial, Ray Kurzweil, recommendation engine, robot derives from the Czech word robota Czech, meaning slave, scientific management, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley ideology, software is eating the world, Stephen Hawking, Steve Jobs, systems thinking, tacit knowledge, TaskRabbit, technological determinism, technological solutionism, technoutopianism, TED Talk, The Wealth of Nations by Adam Smith, turn-by-turn navigation, Tyler Cowen, US Airways Flight 1549, Watson beat the top human players on Jeopardy!, William Langewiesche

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.

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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.

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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, Abraham Maslow, AI winter, air gap, anthropic principle, artificial general intelligence, Asilomar, augmented reality, Automated Insights, autonomous vehicles, availability heuristic, backpropagation, blue-collar work, Boston Dynamics, brain emulation, call centre, cognitive bias, combinatorial explosion, computer vision, Computing Machinery and Intelligence, create, read, update, delete, cuban missile crisis, David Attenborough, DeepMind, disinformation, driverless car, 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, Hans Moravec, industrial robot, Isaac Newton, job automation, John von Neumann, Law of Accelerating Returns, license plate recognition, Mahatma Gandhi, mandelbrot fractal, natural language processing, Nick Bostrom, Parkinson's law, patent troll, patient HM, pattern recognition, phenotype, ransomware, Ray Kurzweil, Recombinant DNA, 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

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.

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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.

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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.

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, Alignment Problem, AlphaGo, artificial general intelligence, Asilomar, autonomous vehicles, basic income, Benoit Mandelbrot, Bill Joy: nanobots, Bletchley Park, Buckminster Fuller, cellular automata, Claude Shannon: information theory, Computing Machinery and Intelligence, CRISPR, Daniel Kahneman / Amos Tversky, Danny Hillis, data science, David Graeber, deep learning, DeepMind, Demis Hassabis, easy for humans, difficult for computers, Elon Musk, Eratosthenes, Ernest Rutherford, fake news, finite state, friendly AI, future of work, Geoffrey Hinton, Geoffrey West, Santa Fe Institute, gig economy, Hans Moravec, heat death of the universe, hype cycle, income inequality, industrial robot, information retrieval, invention of writing, it is difficult to get a man to understand something, when his salary depends on his not understanding it, James Watt: steam engine, Jeff Hawkins, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, Kickstarter, Laplace demon, Large Hadron Collider, Loebner Prize, machine translation, market fundamentalism, Marshall McLuhan, Menlo Park, military-industrial complex, mirror neurons, Nick Bostrom, Norbert Wiener, OpenAI, optical character recognition, paperclip maximiser, pattern recognition, personalized medicine, Picturephone, profit maximization, profit motive, public intellectual, quantum cryptography, RAND corporation, random walk, Ray Kurzweil, Recombinant DNA, 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, synthetic biology, systems thinking, technological determinism, technological singularity, technoutopianism, TED Talk, telemarketer, telerobotics, The future is already here, the long tail, the scientific method, theory of mind, trolley problem, Turing machine, Turing test, universal basic income, Upton Sinclair, Von Neumann architecture, Whole Earth Catalog, Y2K, you are the product, zero-sum game

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.

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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.

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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.

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

Abraham Maslow, 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, driverless car, 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, general purpose technology, 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, machine translation, minimum wage unemployment, patent troll, pattern recognition, Paul Samuelson, Ray Kurzweil, rising living standards, Robert Gordon, Robert Solow, self-driving car, shareholder value, Skype, the long tail, too big to fail, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, wealth creators, winner-take-all economy, zero-sum game

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.

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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).

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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.

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, Bletchley Park, British Empire, Broken windows theory, call centre, Cass Sunstein, Chris Urmson, cloud computing, collateralized debt obligation, Computing Machinery and Intelligence, crowdsourcing, deindustrialization, Donald Trump, Erdős number, experimental subject, Ferguson, Missouri, Filter Bubble, financial engineering, Frank Gehry, game design, global supply chain, Googley, Guggenheim Bilbao, Helicobacter pylori, high net worth, Inbox Zero, income inequality, industrial cluster, Internet of things, Jane Jacobs, Jeff Bezos, Loebner Prize, Louis Pasteur, machine readable, 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, Susan Wojcicki, tacit knowledge, TED Talk, telemarketer, the built environment, The Death and Life of Great American Cities, the strength of weak ties, Turing test, Tyler Cowen, urban decay, warehouse robotics, William Langewiesche

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.

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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. 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.

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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. • • • Every application of Wiener’s insight about large and small errors involves a trade-off. The GPS routinely saves me the minor hassle of planning before a trip, but at the cost of occasionally sending me scuttling apologetically into a rural church just ahead of the bridal procession.

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

"World Economic Forum" Davos, active measures, Air France Flight 447, air gap, algorithmic trading, AlphaGo, Apollo 13, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Black Monday: stock market crash in 1987, brain emulation, Brian Krebs, cognitive bias, computer vision, cuban missile crisis, dark matter, DARPA: Urban Challenge, data science, deep learning, DeepMind, DevOps, Dr. Strangelove, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, fail fast, fault tolerance, Flash crash, Freestyle chess, friendly fire, Herman Kahn, IFF: identification friend or foe, ImageNet competition, information security, Internet of things, Jeff Hawkins, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Korean Air Lines Flight 007, Loebner Prize, loose coupling, Mark Zuckerberg, military-industrial complex, moral hazard, move 37, mutually assured destruction, Nate Silver, Nick Bostrom, PalmPilot, paperclip maximiser, 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, Strategic Defense Initiative, Stuxnet, superintelligent machines, Tesla Model S, The Signal and the Noise by Nate Silver, theory of mind, Turing test, Tyler Cowen, universal basic income, Valery Gerasimov, Wall-E, warehouse robotics, William Langewiesche, Y2K, zero day

UNLEASHING MAYHEM: THE CYBER GRAND CHALLENGE DARPA tackles only the most difficult research problems, “DARPA hard” problems that others might deem impossible. DARPA does this every day, but when a technical problem is truly daunting even for DARPA, the organization pulls out its big guns in a Grand Challenge. 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.

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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.

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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. DARPA has since used the Grand Challenge approach as a way to tackle other truly daunting problems, harnessing the power of competition to generate the best ideas and launch a technology forward.

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

3D printing, AI winter, algorithmic management, algorithmic trading, AlphaGo, Amazon Mechanical Turk, Amazon Robotics, augmented reality, autonomous vehicles, blockchain, business process, call centre, carbon footprint, circular economy, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, data science, deep learning, DeepMind, digital twin, disintermediation, Douglas Hofstadter, driverless car, en.wikipedia.org, Erik Brynjolfsson, fail fast, friendly AI, fulfillment center, future of work, Geoffrey Hinton, Hans Moravec, industrial robot, Internet of things, inventory management, iterative process, Jeff Bezos, job automation, job satisfaction, knowledge worker, Lyft, machine translation, Marc Benioff, natural language processing, Neal Stephenson, personalized medicine, precision agriculture, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, robotic process automation, Rodney Brooks, Salesforce, Second Machine Age, self-driving car, sensor fusion, sentiment analysis, Shoshana Zuboff, Silicon Valley, Snow Crash, software as a service, speech recognition, tacit knowledge, telepresence, telepresence robot, text mining, the scientific method, uber lyft, warehouse automation, warehouse robotics

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.

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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.

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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.

After Steve: How Apple Became a Trillion-Dollar Company and Lost Its Soul by Tripp Mickle

"World Economic Forum" Davos, Airbnb, airport security, Apple II, Apple's 1984 Super Bowl advert, augmented reality, autonomous vehicles, banking crisis, Boeing 747, British Empire, business intelligence, Carl Icahn, Clayton Christensen, commoditize, coronavirus, corporate raider, COVID-19, desegregation, digital map, disruptive innovation, Donald Trump, Downton Abbey, driverless car, Edward Snowden, Elon Musk, Frank Gehry, General Magic , global pandemic, global supply chain, haute couture, imposter syndrome, index fund, Internet Archive, inventory management, invisible hand, John Markoff, Jony Ive, Kickstarter, Larry Ellison, lateral thinking, Mark Zuckerberg, market design, megacity, Murano, Venice glass, Ralph Waldo Emerson, self-driving car, Sheryl Sandberg, Silicon Valley, skeuomorphism, Stephen Fry, Steve Jobs, Steve Wozniak, Steven Levy, stock buybacks, Superbowl ad, supply-chain management, thinkpad, Tim Cook: Apple, Tony Fadell, Travis Kalanick, turn-by-turn navigation, Wayback Machine, WikiLeaks, Y2K

Software development for the fully driverless car he had imagined was lagging behind because of a lack of data and the complexity of building an autonomous system from the ground up. The hardware effort was making progress but trailing the company’s ambitious timeline. There was no way a fully autonomous car would be ready by the self-imposed deadline of 2019. Ive erupted. It was clear to everyone involved that the project was suffering under the weight of its ambitions. Ive’s vision for a fully autonomous vehicle had contributed to the build-out of a massive team of programmers and sensor experts, while hardware chief Dan Riccio’s focus on creating an electric vehicle had led to the development of a massive team of battery and automotive experts.

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The project had three leaders who appeared more focused on building out their corporate fiefdom than on moving a unified project forward. The challenges were reminiscent of the corporate infighting that plagued the Apple Watch. Lavish spending compounded the woes. The project costs had ballooned to a staggering $1 billion a year. Project Titan leaders had hired autonomous-vehicle researchers at $10 million each and invested in the development of lasers to aim into passengers’ eyes in a bid to reduce the motion sickness caused by a car’s abrupt movements. Apple’s R-and-D expenses mushroomed, nearly doubling to $8.1 billion by the end of 2015. It was loose change for a company with $200 billion in cash, but the engineers viewed the warehouse as the latest example of a Silicon Valley giant spending big with nothing to show for it.

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The heavyset engineer with close-cropped hair had a background in semiconductors and had risen to the top of Apple by delivering breakthroughs on products such as the MacBook Air. In Apple’s hierarchical structure, he commanded respect. Mansfield addressed the group bluntly, making clear what most people in the room already knew: the project was a mess. Though he acknowledged that he didn’t fully appreciate the technical challenges of autonomous vehicles, he planned to use a Thor’s hammer approach to get the work back on track. He announced that there would be layoffs—some two hundred of the staff would be let go—as he streamlined the operation and shifted its focus. It was clear to him that Apple had no business plowing ahead with the construction of a car until it determined how to structure the underlying software that would enable it to navigate roads without a driver.

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

"World Economic Forum" Davos, Adam Neumann (WeWork), Airbnb, augmented reality, autonomous vehicles, Benchmark Capital, Big Tech, bike sharing, blockchain, call centre, cashless society, Chuck Templeton: OpenTable:, clean tech, cloud computing, computer vision, connected car, corporate governance, cryptocurrency, data is the new oil, data science, deep learning, Deng Xiaoping, Didi Chuxing, digital map, disruptive innovation, Donald Trump, El Camino Real, electricity market, Elon Musk, fake news, family office, fear of failure, fulfillment center, glass ceiling, global supply chain, Great Leap Forward, income inequality, industrial robot, information security, Internet of things, invention of movable type, Jeff Bezos, Kickstarter, knowledge worker, Lyft, Mark Zuckerberg, Mary Meeker, 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, SoftBank, software as a service, South China Sea, sovereign wealth fund, speech recognition, stealth mode startup, Steve Jobs, stock buybacks, supply-chain management, tech billionaire, TechCrunch disrupt, TikTok, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, urban planning, Vision Fund, warehouse automation, WeWork, 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.

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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.

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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.

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

Albert Einstein, Anton Chekhov, autonomous vehicles, Barry Marshall: ulcers, behavioural economics, call centre, carbon credits, Cass Sunstein, colonial rule, Donald Shoup, driverless car, Edward Glaeser, Everything should be made as simple as possible, fail fast, food miles, gamification, Gary Taubes, Helicobacter pylori, income inequality, information security, 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, sunk-cost fallacy, Tony Hsieh, transatlantic slave trade, Wayback Machine, éminence grise

(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!”

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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.

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—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.

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, gamification, 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, Jeff Hawkins, jimmy wales, job automation, John Markoff, Kevin Kelly, Khan Academy, Kickstarter, Kiva Systems, 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, quantum entanglement, race to the bottom, Ray Kurzweil, recommendation engine, RFID, Rodney Brooks, selection bias, self-driving car, seminal paper, slashdot, smart cities, software as a service, software is eating the world, speech recognition, Steven Pinker, strong AI, synthetic biology, technological singularity, TED Talk, Turing test, Vernor Vinge, warehouse automation, warehouse robotics, 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.

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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.

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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.

Robot Futures by Illah Reza Nourbakhsh

3D printing, autonomous vehicles, Burning Man, business logic, commoditize, computer vision, digital divide, 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

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.

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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.

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, Alan Greenspan, Apollo 11, autonomous vehicles, basic income, Bernie Madoff, Bernie Sanders, Black Lives Matter, Boeing 747, Branko Milanovic, Brexit referendum, business cycle, Capital in the Twenty-First Century by Thomas Piketty, carbon tax, clean tech, clean water, coronavirus, Crossrail, David Brooks, David Ricardo: comparative advantage, deindustrialization, Dissolution of the Soviet Union, Donald Trump, driverless car, Elon Musk, Exxon Valdez, factory automation, failed state, fake news, full employment, Gini coefficient, Google Earth, Home mortgage interest deduction, hydraulic fracturing, Hyperloop, illegal immigration, impulse control, income inequality, independent contractor, 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, Modern Monetary Theory, obamacare, oil shale / tar sands, Paul Samuelson, peak oil, plant based meat, 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, Tyler Cowen, uber lyft, universal basic income, War on Poverty, Washington Consensus, We are all Keynesians now, WikiLeaks, working poor, Works Progress Administration

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.

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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.

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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.

The Future Is Analog: How to Create a More Human World by David Sax

Alvin Toffler, augmented reality, autonomous vehicles, Bernie Sanders, big-box store, bike sharing, Black Lives Matter, blockchain, bread and circuses, Buckminster Fuller, Cal Newport, call centre, clean water, cognitive load, commoditize, contact tracing, contact tracing app, COVID-19, crowdsourcing, cryptocurrency, data science, David Brooks, deep learning, digital capitalism, Donald Trump, driverless car, Elon Musk, fiat currency, Francis Fukuyama: the end of history, future of work, gentrification, George Floyd, indoor plumbing, informal economy, Jane Jacobs, Jaron Lanier, Jeff Bezos, Kickstarter, knowledge worker, lockdown, Lyft, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Minecraft, New Urbanism, nuclear winter, opioid epidemic / opioid crisis, Peter Thiel, RAND corporation, Ray Kurzweil, remote working, retail therapy, RFID, Richard Florida, ride hailing / ride sharing, Saturday Night Live, Shoshana Zuboff, side hustle, Sidewalk Labs, Silicon Valley, Silicon Valley startup, Skype, smart cities, social distancing, sovereign wealth fund, Steve Jobs, Superbowl ad, supply-chain management, surveillance capitalism, tech worker, technological singularity, technoutopianism, TED Talk, The Death and Life of Great American Cities, TikTok, Uber and Lyft, uber lyft, unemployed young men, urban planning, walkable city, Y2K, zero-sum game

In the century since, smart city designs have become more digitally driven but no less idealistic. Each promises to solve the pesky troubles of a city, from traffic and pollution to economic opportunity and citizen safety, by unleashing the latest in digital technology—computers and smart phones, cameras and sensors, flying robots and autonomous vehicles—and feeding reams of data into a central brain of computers that will use statistics and machine learning to solve the intractable problems all cities face. Smart cities would be cleaner, safer, more democratic, more equal, and brimming with the sexy digital innovation that would drive economic growth, jobs, and investment.

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“A combination of technologies, thoughtfully applied and integrated, can fundamentally alter nearly every dimension of quality of life in an urban environment,” Doctoroff said in an article he wrote for the consulting firm McKinsey’s website after the partnership was announced. “We’re convinced that by implementing a set of technologies—autonomous vehicles, modular building construction, or new infrastructure systems—we can, for example, reduce cost of living by 15 percent. With new mobility services and radical mixed-use development that brings homes near work, we can give people back an hour in their day.” At the launch event, which included representatives from every level of government, including Canadian prime minister Justin Trudeau, Sidewalk Toronto laid out its promise to build a new kind of mixed-use urban community, applying digital technology to create “people-centered neighborhoods.”

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On the one hand you have an issue like housing affordability, which requires years of study, politically risky debate, and complex policy interventions, from zoning alterations to tax breaks and subsidized apartment construction, with no guarantee of success and the certainty of pissing someone off. On the other hand you say, “Oh, don’t worry, I’ll give you a gadget!” Saxe joked, “Well, people say, ‘Thank God! I’ll take the gadget, because I don’t have to do things that are hard… If we can do the same thing and insert a sexy new technology, then great! Why do you think autonomous cars are so attractive? You don’t have to stop driving. You don’t have to change roads. It doesn’t cost governments a cent. We can still drive, and the government says, ‘Look, all our problems are better!’” The fundamental problem with pegging the future of cities to digital projects like Sidewalk Toronto is confusing invention and innovation.

Arriving Today: From Factory to Front Door -- Why Everything Has Changed About How and What We Buy by Christopher Mims

air freight, Airbnb, Amazon Robotics, Amazon Web Services, Apollo 11, augmented reality, autonomous vehicles, big-box store, blue-collar work, Boeing 747, book scanning, business logic, business process, call centre, cloud computing, company town, coronavirus, cotton gin, COVID-19, creative destruction, data science, Dava Sobel, deep learning, dematerialisation, deskilling, digital twin, Donald Trump, easy for humans, difficult for computers, electronic logging device, Elon Musk, Frederick Winslow Taylor, fulfillment center, gentrification, gig economy, global pandemic, global supply chain, guest worker program, Hans Moravec, heat death of the universe, hive mind, Hyperloop, immigration reform, income inequality, independent contractor, industrial robot, interchangeable parts, intermodal, inventory management, Jacquard loom, Jeff Bezos, Jessica Bruder, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, Kaizen: continuous improvement, Kanban, Kiva Systems, level 1 cache, Lewis Mumford, lockdown, lone genius, Lyft, machine readable, Malacca Straits, Mark Zuckerberg, market bubble, minimum wage unemployment, Nomadland, Ocado, operation paperclip, Panamax, Pearl River Delta, planetary scale, pneumatic tube, polynesian navigation, post-Panamax, random stow, ride hailing / ride sharing, robot derives from the Czech word robota Czech, meaning slave, Rodney Brooks, rubber-tired gantry crane, scientific management, self-driving car, sensor fusion, Shenzhen special economic zone , Shoshana Zuboff, Silicon Valley, six sigma, skunkworks, social distancing, South China Sea, special economic zone, spinning jenny, standardized shipping container, Steve Jobs, supply-chain management, surveillance capitalism, TED Talk, the scientific method, Tim Cook: Apple, Toyota Production System, traveling salesman, Turing test, two-sided market, Uber and Lyft, Uber for X, uber lyft, Upton Sinclair, vertical integration, warehouse automation, warehouse robotics, workplace surveillance

Getting through this light and the one just past it, which has funny timing and gates the entrance of vehicles onto the overpass proper, requires that the truck keep rolling even when it might normally stop. This situation is a good illustration of the frequently imperfect and entirely handcrafted world that autonomous vehicles must navigate. We all know that one intersection in our neighborhood where a bush obscures our view of traffic crossing our path, or the one where there should be a light, or that other one where people tend to take the curve too quickly and accidents happen all the time. To make it safe for autonomous vehicles to operate on our strange and inconsistent roads, we have to teach them not just how to drive under optimal conditions but how to handle the peculiarities of our built environment, just as we might teach a teenager just learning to drive.

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(Please do: This is a long book, and it’s my hope that some of its sections will be worth the price of entry for those of you with narrower interests than the entire saga it chronicles.) You will learn what technology your cell phone has in common with spacecraft, cruise missiles, and the navigation systems of Polynesian explorers. You’ll also find a (hopefully) accessible explanation of the “thinking” process of the AI (artificial intelligence) that drives an autonomous vehicle. You’ll learn why automated warehouses are like microchips that process stuff instead of bits, and how the two were designed with the same principles in mind. You will be introduced to “Bezosism,” that braiding together of management practices, AI, workplace surveillance, robots, and hard automation that is the engine of Amazon’s success, and possibly the future of all low-skilled labor.

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Then, with only a minimum of human intervention, it drove to us, never faster than four miles an hour. But to accomplish this sequence of events tens of thousands of times a week, in dozens of cities all over the world, in every kind of weather, and to make delivery via robot into a going concern—a real business that can eventually make money—is the real trick. While fully autonomous vehicles remain mostly a science project, as well as a potentially risky ongoing experiment on America’s roads, and drone delivery awaits both a workable business model and final approval by the FAA, Starship is the one company on the planet that, as of this writing, anyway, is doing autonomous delivery at scale.

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, asset light, autonomous vehicles, big data - Walmart - Pop Tarts, business intelligence, call centre, Clayton Christensen, cloud computing, commoditize, crowdsourcing, data science, disintermediation, diversification, Douglas Engelbart, Douglas Engelbart, future of work, Google Glasses, Google X / Alphabet X, independent contractor, Infrastructure as a Service, intangible asset, Internet of things, invention of writing, inventory management, iterative process, Jeff Bezos, job satisfaction, John Zimmer (Lyft cofounder), Kevin Kelly, Kickstarter, Larry Ellison, late fees, Lyft, Mark Zuckerberg, Mary Meeker, Oculus Rift, pirate software, ride hailing / ride sharing, Salesforce, self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, six sigma, software as a service, software patent, Steve Jobs, subscription business, systems thinking, TaskRabbit, Travis Kalanick, uber lyft, Wall-E, women in the workforce, Zipcar

—Richard Branson, The Virgin Group EVEN A HUNDRED-YEAR-OLD ORGANIZATION CAN INNOVATE ITS BUSINESS MODEL. 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.

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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?

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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.

Super Pumped: The Battle for Uber by Mike Isaac

"Susan Fowler" uber, "World Economic Forum" Davos, 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, Benchmark Capital, Big Tech, Burning Man, call centre, Cambridge Analytica, Chris Urmson, Chuck Templeton: OpenTable:, citizen journalism, Clayton Christensen, cloud computing, corporate governance, creative destruction, data science, Didi Chuxing, don't be evil, Donald Trump, driverless car, Elon Musk, end-to-end encryption, fake news, family office, gig economy, Google Glasses, Google X / Alphabet X, Greyball, Hacker News, high net worth, hockey-stick growth, hustle culture, impact investing, information security, Jeff Bezos, John Markoff, John Zimmer (Lyft cofounder), Kevin Roose, Kickstarter, Larry Ellison, lolcat, Lyft, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Masayoshi Son, mass immigration, Menlo Park, Mitch Kapor, money market fund, moral hazard, move fast and break things, Network effects, new economy, off grid, peer-to-peer, pets.com, Richard Florida, ride hailing / ride sharing, Salesforce, Sand Hill Road, self-driving car, selling pickaxes during a gold rush, shareholder value, Shenzhen special economic zone , Sheryl Sandberg, side hustle, side project, Silicon Valley, Silicon Valley startup, skunkworks, Snapchat, SoftBank, software as a service, software is eating the world, South China Sea, South of Market, San Francisco, sovereign wealth fund, special economic zone, Steve Bannon, Steve Jobs, stock buybacks, super pumped, TaskRabbit, tech bro, tech worker, the payments system, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, ubercab, union organizing, upwardly mobile, Vision Fund, WeWork, 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.

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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.

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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.

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 Robotics, Amazon Web Services, Atul Gawande, autonomous vehicles, basic income, Bernie Sanders, Big Tech, Black Swan, call centre, Cambridge Analytica, carbon tax, Carl Icahn, Chris Urmson, cloud computing, corporate raider, creative destruction, Danny Hillis, data science, deep learning, Donald Trump, Elon Musk, Erik Brynjolfsson, Fairchild Semiconductor, fake news, fulfillment center, future of work, gig economy, Glass-Steagall Act, Google Glasses, Google X / Alphabet X, income inequality, independent contractor, industrial robot, Internet of things, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Kevin Roose, Lyft, Marc Andreessen, Mark Zuckerberg, military-industrial complex, money market fund, natural language processing, no-fly zone, Ocado, pets.com, plutocrats, race to the bottom, ride hailing / ride sharing, Salesforce, Sand Hill Road, self-driving car, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Snapchat, speech recognition, Steve Jobs, Stewart Brand, supply-chain management, TED Talk, Tim Cook: Apple, too big to fail, Travis Kalanick, two-pizza team, Uber and Lyft, uber lyft, universal basic income, warehouse automation, warehouse robotics, wealth creators, web application, Whole Earth Catalog, work culture

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.

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Anderson ran Tesla’s autopilot program, Bagnell headed the autonomy and perception team at Uber, and Urmson was the former head of Google’s self-driving project, which has morphed into one of the leading self-driving car companies: Waymo. 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.

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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. That’s because carrying packages rather than humans greatly reduces the risk posed by self-driving vehicles. If an order of Dr. Bronner’s castile soap gets crushed in a fender bender, that’s unfortunate but not a tragedy. In an accident, the vans will be programmed to self-sacrifice themselves to avoid harm to pedestrians, bicyclists, or drivers of other vehicles.

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

"Susan Fowler" uber, "World Economic Forum" Davos, accounting loophole / creative accounting, Airbnb, Alan Greenspan, algorithmic bias, algorithmic management, AltaVista, Andy Rubin, autonomous vehicles, banking crisis, barriers to entry, behavioural economics, Bernie Madoff, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, book scanning, Brewster Kahle, Burning Man, call centre, Cambridge Analytica, cashless society, clean tech, cloud computing, cognitive dissonance, Colonization of Mars, computer age, corporate governance, creative destruction, Credit Default Swap, cryptocurrency, data is the new oil, data science, deal flow, death of newspapers, decentralized internet, Deng Xiaoping, digital divide, digital rights, disinformation, disintermediation, don't be evil, Donald Trump, drone strike, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Etonian, Evgeny Morozov, fake news, Filter Bubble, financial engineering, future of work, Future Shock, game design, gig economy, global supply chain, Gordon Gekko, Great Leap Forward, greed is good, income inequality, independent contractor, informal economy, information asymmetry, intangible asset, Internet Archive, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, junk bonds, Kenneth Rogoff, life extension, light touch regulation, low interest rates, Lyft, Mark Zuckerberg, Marshall McLuhan, Martin Wolf, Menlo Park, military-industrial complex, move fast and break things, Network effects, new economy, offshore financial centre, PageRank, patent troll, Paul Volcker talking about ATMs, 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, Sheryl Sandberg, Shoshana Zuboff, side hustle, Sidewalk Labs, Silicon Valley, Silicon Valley startup, smart cities, Snapchat, SoftBank, South China Sea, sovereign wealth fund, Steve Bannon, Steve Jobs, Steven Levy, stock buybacks, subscription business, supply-chain management, surveillance capitalism, TaskRabbit, tech billionaire, tech worker, TED Talk, Telecommunications Act of 1996, The Chicago School, the long tail, 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, warehouse robotics, WeWork, WikiLeaks, zero-sum game

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.

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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.

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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.

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, Bear Stearns, behavioural economics, Ben Horowitz, Bernie Sanders, call centre, corporate governance, cryptocurrency, data science, David Brooks, DeepMind, Donald Trump, Elon Musk, falling living standards, financial deregulation, financial engineering, 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, meritocracy, Narrative Science, new economy, passive income, performance metric, post-work, quantitative easing, reserve currency, Richard Florida, ride hailing / ride sharing, risk tolerance, robo advisor, Ronald Reagan, Rutger Bregman, Sam Altman, San Francisco homelessness, self-driving car, shareholder value, Silicon Valley, Simon Kuznets, single-payer health, Stephen Hawking, Steve Ballmer, supercomputer in your pocket, tech worker, technoutopianism, telemarketer, The future is already here, The Wealth of Nations by Adam Smith, traumatic brain injury, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, unemployed young men, universal basic income, urban renewal, warehouse robotics, white flight, winner-take-all economy, Y Combinator

“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.

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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.

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… 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.

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

3D printing, Airbnb, algorithmic trading, AlphaGo, Alvin Toffler, Amazon Web Services, anti-work, antiwork, artificial general intelligence, asset light, autonomous vehicles, basic income, behavioural economics, business cycle, cloud computing, collective bargaining, Computing Machinery and Intelligence, correlation does not imply causation, creative destruction, data is the new oil, data science, David Graeber, David Ricardo: comparative advantage, deep learning, DeepMind, deindustrialization, Demis Hassabis, deskilling, disintermediation, do what you love, Donald Trump, driverless car, Erik Brynjolfsson, fake news, feminist movement, Ford Model T, Frederick Winslow Taylor, future of work, Future Shock, general purpose technology, gig economy, global supply chain, income inequality, independent contractor, informal economy, Internet of things, Jarndyce and Jarndyce, Jarndyce and Jarndyce, job automation, job polarisation, 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, Nick Bostrom, off grid, pattern recognition, post-work, Ronald Coase, scientific management, Second Machine Age, self-driving car, sharing economy, SoftBank, Steve Jobs, strong AI, tacit knowledge, technological determinism, technoutopianism, TED Talk, 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

., 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.

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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.

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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.

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

Ada Lovelace, autonomous vehicles, Baxter: Rethink Robotics, behavioural economics, Black Swan, call centre, capital asset pricing model, commoditize, computer age, corporate governance, creative destruction, deskilling, driverless car, en.wikipedia.org, flying shuttle, Freestyle chess, future of work, Google Glasses, Grace Hopper, Hans Moravec, industrial cluster, industrial robot, interchangeable parts, job automation, knowledge worker, low skilled workers, Marc Andreessen, 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, Tyler Cowen

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.

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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.

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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.