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Driverless: Intelligent Cars and the Road Ahead by Hod Lipson, Melba Kurman
AI winter, Air France Flight 447, 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, digital map, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Google Earth, Google X / Alphabet X, high net worth, hive mind, ImageNet competition, income inequality, industrial robot, intermodal, Internet of things, job automation, Joseph Schumpeter, lone genius, Lyft, megacity, Network effects, New Urbanism, Oculus Rift, pattern recognition, performance metric, precision agriculture, RFID, ride hailing / ride sharing, Second Machine Age, self-driving car, Silicon Valley, smart cities, speech recognition, statistical model, Steve Jobs, technoutopianism, Tesla Model S, Travis Kalanick, Uber and Lyft, uber lyft, Unsafe at Any Speed
See also Mid-level controls Consumer acceptance, 11–13 Controls engineering Overview of, 47, 75–77 See also Low-level controls; Mid-level controls; High-level controls Convolutional neural networks (CNNs), 214–218 Corner cases, 4, 5, 89, 154 Creative destruction, 261–263 Crime, 273, 274 DARPA Challenges, 149, 150 DARPA Grand Challenge 2004 DARPA Grand Challenge 2005, 151, 152 DARPA Urban Challenge 2007, 156–158 Data CAN bus protocol, 193, 194 Data collection, 239, 240 Training data for deep learning, 218–220 See also Machine learning; Route-planning software; Traffic prediction software Deep learning History of, 197, 199–202, 219, 223–226 How deep learning works, 7, 8, 226–231 See also ImageNet competition; Neocognitron; Perceptron; SuperVision Demo 97, 134, 135 Digital cameras, 173–175 Disney Hall, Los Angeles, 36 Disney’s Magic Highway U.S.A.
Yoshimasa Goto and Anthony Tentz, “Mobile Robot Navigation: The CMU System,” IEEE Expert 1987. 10. DARPA award contract issued to Cornell University. 11. Chris Urmson et al., “Autonomous Driving in Urban Environments: Boss and the Urban Challenge,” Journal of Field Robotics 25 (9) (2008): 426–464. 12. “Autonomous Cars: Self-Driving the New Auto Industry Paradigm,” Morgan Stanley Blue Paper, November 6, 2013. 13. Google Official Blog, “The Latest Chapter for the Self-Driving Car: Mastering City Street Driving,” April 28, 2014, https://googleblog.blogspot.nl/2014/04/the-latest-chapter-for-self-driving-car.html 14. Google Annual Report, 2007. 15. Burkhard Bilger, “Has the Self-Driving Car at Last Arrived?”
The vehicle was unsafe for city streets since it took about ten seconds for the software to work through each navigation “task” on a clear stretch of road and up to twenty seconds or more in “cluttered” environments.9 Fast forward to another state-of-the-art autonomous vehicle in the year 2007 and the situation looked more promising. To equip their SUV for the 2007 DARPA Challenge, the Cornell team spent $195,850 to buy lidar and radar sensors, a GPS, and a camera, and $46,550 to purchase several desktop computers, laptops, and peripherals.10 Although it cost less to outfit an autonomous vehicle in 2007 than it did in 1980, computers and sensors were still too slow to support autonomous driving. In a postmortem analysis of the 2007 DARPA Challenge, the leader of the CMU team, Chris Urmson (who later helped lead Google’s self-driving car initiative), ruefully noted that “available off-the-shelf sensors are insufficient for urban driving.”11 Fast forward again to the present day and the situation looks much more promising.
Cities Under Siege: The New Military Urbanism by Stephen Graham
addicted to oil, airport security, anti-communist, autonomous vehicles, Berlin Wall, call centre, carbon footprint, clean water, congestion charging, creative destruction, credit crunch, DARPA: Urban Challenge, defense in depth, deindustrialization, digital map, disinformation, edge city, energy security, European colonialism, failed state, Food sovereignty, Gini coefficient, global supply chain, Google Earth, illegal immigration, income inequality, knowledge economy, late capitalism, loose coupling, market fundamentalism, mass incarceration, McMansion, megacity, moral panic, mutually assured destruction, Naomi Klein, New Urbanism, offshore financial centre, one-state solution, pattern recognition, peak oil, planetary scale, private military company, Project for a New American Century, RAND corporation, RFID, Richard Florida, Scramble for Africa, Seymour Hersh, Silicon Valley, smart transportation, surplus humans, The Bell Curve by Richard Herrnstein and Charles Murray, urban decay, urban planning, urban renewal, urban sprawl, Washington Consensus, white flight, white picket fence
After a close contest, with six finishers, the Tartan team, an alliance of General Motors and Pittsburgh’s Carnegie Mellon University, was declared the victor – gaining the $2 million first prize in part because their vehicle had not only finished the course but also complied with California traffic rules. 9.9 DARPA’s ‘Urban Challenge’ competition in November 2007. 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.
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.
Widespread campaigns, drawing on a long history of such activism, have targeted the militarized R&D that is carried on in US universities and so firmly underpins securocratic war, ubiquitous bordering, and the Long War.57 Two of the main centres for work on the robotization of weapons–the Robotics Institute and its commercial arm, the National Robotics Engineering Center (NREC) – are at Carnegie Mellon University in Pittsburgh, and both have been the target of a jamming campaign (Figure 10.13). (In Chapter 9 we already encountered NREC: its ‘robocar’ was the winner of DARPA’s 2007 Urban Challenge competition.) The Carnegie Mellon campaign, labelled ‘Barricade the War Machine’, is challenging the take-over of engineering sciences in the university and the local economy by military-robotics research in the service of the military-industrial-academic complex. It is also raising the key ethical question forced by the shift to fully autonomous weapons systems (see Chapter 5): ‘Who bears moral responsibility for outcomes that are caused by autonomous robotic systems?’
Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots by John Markoff
"Robert Solow", A Declaration of the Independence of Cyberspace, AI winter, airport security, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, basic income, Baxter: Rethink Robotics, Bill Atkinson, Bill Duvall, bioinformatics, Brewster Kahle, Burning Man, call centre, cellular automata, Chris Urmson, Claude Shannon: information theory, Clayton Christensen, clean water, cloud computing, collective bargaining, computer age, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data acquisition, Dean Kamen, deskilling, don't be evil, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dynabook, Edward Snowden, Elon Musk, Erik Brynjolfsson, factory automation, From Mathematics to the Technologies of Life and Death, future of work, Galaxy Zoo, Google Glasses, Google X / Alphabet X, Grace Hopper, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, haute couture, Herbert Marcuse, hive mind, hypertext link, indoor plumbing, industrial robot, information retrieval, Internet Archive, Internet of things, invention of the wheel, Jacques de Vaucanson, Jaron Lanier, Jeff Bezos, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, knowledge worker, Kodak vs Instagram, labor-force participation, loose coupling, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, Mitch Kapor, Mother of all demos, natural language processing, new economy, Norbert Wiener, PageRank, pattern recognition, pre–internet, RAND corporation, Ray Kurzweil, Richard Stallman, Robert Gordon, Rodney Brooks, Sand Hill Road, Second Machine Age, self-driving car, semantic web, 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, strong AI, superintelligent machines, technological singularity, Ted Nelson, telemarketer, telepresence, telepresence robot, Tenerife airport disaster, The Coming Technological Singularity, the medium is the message, Thorstein Veblen, Turing test, Vannevar Bush, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, William Shockley: the traitorous eight, zero-sum game
Thrun looked at Montemerlo and it was obvious that although on paper he was the quintessential pessimist, everything in his demeanor was saying yes. Sebastian Thrun (left) and Mike Montemerlo (right) in front of the Stanford University autonomous vehicle while it was being tested to take part in DARPA’s Urban Challenge in 2007. (Photo courtesy of the author) Soon afterward Thrun threw himself into the DARPA competition with passion. For the first time in his life he felt like he was focusing on something that was genuinely likely to have broad impact. Living in the Arizona desert for weeks on end, surviving on pizza, the team worked on the car until it was able to drive the backcountry roads flawlessly.
Viewing the event, in which the cars glided seemingly endlessly through a makeshift town previously used for training military troops in urban combat, it didn’t seem to be a race at all. It felt more like an afternoon of stop-and-go Sunday traffic in a science-fiction movie like Blade Runner. Indeed, by almost any standard it was an odd event. The DARPA Urban Challenge pitted teams of roboticists, artificial intelligence researchers, students, automotive engineers, and software hackers against each other in an effort to design and build robot vehicles capable of driving autonomously in an urban traffic setting. The event was the third in the series of contests that Tether organized.
He stayed for four years and then found his way back to SRI. DARPA knocked on Cheyer’s door with an offer to head up Tony Tether’s ambitious national CALO effort, which DARPA anticipated would draw on the efforts of AI researchers around the country. Usually DARPA would simultaneously fund many research labs and not integrate the results. The new DARPA program, however, called for SRI to marshal all the research into the development of CALO. Everyone would report to the SRI team and develop a single integrated system. Cheyer helped write the initial DARPA proposal, and when SRI received the award, he became engineering architect for the project.
Hello World: Being Human in the Age of Algorithms by Hannah Fry
23andMe, 3D printing, Air France Flight 447, Airbnb, airport security, algorithmic bias, augmented reality, autonomous vehicles, backpropagation, Brixton riot, chief data officer, computer vision, crowdsourcing, DARPA: Urban Challenge, Douglas Hofstadter, Elon Musk, Firefox, Google Chrome, Gödel, Escher, Bach, Ignaz Semmelweis: hand washing, John Markoff, Mark Zuckerberg, meta-analysis, pattern recognition, Peter Thiel, RAND corporation, ransomware, recommendation engine, ride hailing / ride sharing, selection bias, self-driving car, Shai Danziger, Silicon Valley, Silicon Valley startup, Snapchat, speech recognition, Stanislav Petrov, statistical model, Stephen Hawking, Steven Levy, Tesla Model S, The Wisdom of Crowds, Thomas Bayes, Watson beat the top human players on Jeopardy!, web of trust, William Langewiesche, you are the product
DARPA, Urban Challenge: Overview, http://archive.darpa.mil/grandchallenge/overview.html. 5. Sebastian Thrun, ‘Winning the DARPA Grand Challenge, 2 August 2006’, YouTube, 8 Oct. 2007, https://www.youtube.com/watch?v=j8zj5lBpFTY. 6. DARPA, Urban Challenge: Overview. 7. ‘DARPA Grand Challenge 2004 – road to . . .’ , YouTube, 22 Jan. 2014, https://www.youtube.com/watch?v=FaBJ5sPPmcI. 8. Alex Davies, ‘An oral history of the DARPA Grand Challenge, the grueling robot race that launched the self-driving car’, Wired, 8 March 2017, https://www.wired.com/story/darpa-grand-challenge-2004-oral-history/. 9. ‘Desert race too tough for robots’, BBC News, 15 March, 2004, http://news.bbc.co.uk/1/hi/technology/3512270.stm. 10.
Cars 1. DARPA, Grand Challenge 2004: Final Report (Arlington, VA: Defence Advanced Research Projects Agency, 30 July 2004), http://www.esd.whs.mil/Portals/54/Documents/FOID/Reading%20Room/DARPA/15-F-0059_GC_2004_FINAL_RPT_7-30-2004.pdf. 2. The Worldwide Guide to Movie Locations, 7 Sept. 2014, http://www.movie-locations.com/movies/k/Kill_Bill_Vol_2.html#.WkYiqrTQoQ8. 3. Mariella Moon, What you need to know about DARPA, the Pentagon’s mad science division, Engadget, 7 July 2014, https://www.engadget.com/2014/07/07/darpa-explainer/. 4. DARPA, Urban Challenge: Overview, http://archive.darpa.mil/grandchallenge/overview.html. 5.
‘Desert race too tough for robots’, BBC News, 15 March, 2004, http://news.bbc.co.uk/1/hi/technology/3512270.stm. 10. Davies, ‘An oral history of the DARPA Grand Challenge’. 11. Denise Chow, ‘DARPA and drone cars: how the US military spawned self-driving car revolution’, LiveScience, 21 March 2014, https://www.livescience.com/44272-darpa-self-driving-car-revolution.html. 12. Joseph Hooper, ‘From Darpa Grand Challenge 2004 DARPA’s debacle in the desert’, Popular Science, 4 June 2004, https://www.popsci.com/scitech/article/2004-06/darpa-grand-challenge-2004darpas-debacle-desert. 13. Davies, ‘An oral history of the DARPA Grand Challenge’. 14. DARPA, Report to Congress: DARPA Prize Authority. Fiscal Year 2005 Report in Accordance with 10 U.S.C. 2374a, March 2006, http://archive.darpa.mil/grandchallenge/docs/grand_challenge_2005_report_to_congress.pdf. 15.
Army of None: Autonomous Weapons and the Future of War by Paul Scharre
active measures, Air France Flight 447, algorithmic trading, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, brain emulation, Brian Krebs, cognitive bias, computer vision, cuban missile crisis, dark matter, DARPA: Urban Challenge, DevOps, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, fault tolerance, Flash crash, Freestyle chess, friendly fire, IFF: identification friend or foe, ImageNet competition, Internet of things, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Loebner Prize, loose coupling, Mark Zuckerberg, moral hazard, mutually assured destruction, Nate Silver, pattern recognition, Rodney Brooks, Rubik’s Cube, self-driving car, sensor fusion, South China Sea, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Ballmer, Steve Wozniak, Stuxnet, superintelligent machines, Tesla Model S, The Signal and the Noise by Nate Silver, theory of mind, Turing test, universal basic income, Valery Gerasimov, Wall-E, William Langewiesche, Y2K, zero day
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. From 2013 to 2015, DARPA held a Robotics Challenge to advance the field of humanoid robotics, running robots through a set of tasks simulating humanitarian relief and disaster response.
(Boston: Pearson, 2009). 68 “offensive autonomous weapons beyond meaningful human control”: “Autonomous Weapons: An Open Letter From AI & Robotics Researchers,” Future of Life Institute, https://futureoflife.org/open-letter-autonomous-weapons/. 69 “The challenge for the teams now”: DARPA, “FLA Program Takes Flight,” DARPA.mil, February 12, 2016, http://www.darpa.mil/news-events/2016-02-12. 69 “FLA technologies could be especially useful”: Ibid. 70 Lee explained: Daniel Lee, email to author, June 3, 2016. 70 “localization, mapping, obstacle detection”: Ibid. 70 “applications to search and rescue”: Vijay Kumar, email to author, June 3, 2016. 71 “foreshadow planned uses”: Stuart Russell, “Take a Stand on AI Weapons,” Nature.com, May 27, 2015, http://www.nature.com/news/robotics-ethics-of-artificial-intelligence-1.17611. 71 wasn’t “cleanly directed only at”: Stuart Russell, interview, June 23, 2016. 71 “You can make small, lethal quadcopters”: Ibid. 71 “if you were wanting to develop autonomous weapons”: Ibid. 71 “certainly think twice” about working on: Ibid. 72 “collaborative autonomy—the capability of groups”: DARPA, “Collaborate Operations in Denied Environments,” DARPA.com, http://www.darpa.mil/program/collaborative-operations-in-denied-environment. 72 “just as wolves hunt in coordinated packs”: DARPA, “Establishing the CODE for Unmanned Aircraft to Fly as Collaborative Teams,” DARPA.com, http://www.darpa.mil/news-events/2015-01-21. 72 “multiple CODE-enabled unmanned aircraft”: Ibid. 72 Graphics on DARPA’s website: DARPA, “Collaborate Operations in Denied Environments.” 72 “contested electromagnetic environments”: Ibid. 73 methods of communicating stealthily: Sayler, “Talk Stealthy to Me.”
Alexander’s comments on automation come up in the question-and-answer period, starting at 1:14:00. 217 DARPA held a Robotics Challenge: DARPA, “DARPA Robotics Challenge (DRC),” accessed June 14, 2017, http://www.darpa.mil/program/darpa-robotics-challenge. DARPA, “Home | DRC Finals,” accessed June 14, 2017, http://archive.darpa.mil/roboticschallenge/. 217 “automatically check the world’s software”: David Brumley, “Why CGC Matters to Me,” ForAllSecure, July 26, 2016, https://forallsecure.com/blog/2016/07/26/why-cgc-matters-to-me/. 217 “fully autonomous system for finding and fixing”: David Brumley, “Mayhem Wins DARPA CGC,” ForAllSecure, August 6, 2016, https://forallsecure.com/blog/2016/08/06/mayhem-wins-darpa-cgc/. 217 vulnerability is analogous to a weak lock: David Brumley, interview, November 24, 2016. 218 “There’s grades of security”: Ibid. 218 “an autonomous system that’s taking all of those things”: Ibid. 218 “Our goal was to come up with a skeleton key”: Ibid. 219 “true autonomy in the cyber domain”: Michael Walker, interview, December 5, 2016. 219 comparable to a “competent” computer security professional: David Brumley, interview, November 24, 2016. 219 DEF CON hacking conference: Daniel Tkacik, “CMU Team Wins Fourth ‘World Series of Hacking’ Competition,” CMU.edu, July 31, 2017. 219 Brumley’s team from Carnegie Mellon: Ibid. 219 Mirai: Brian Krebs, “Who Makes the IoT Things Under Attack?”
Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard
1960s counterculture, A Declaration of the Independence of Cyberspace, Ada Lovelace, AI winter, Airbnb, algorithmic bias, Amazon Web Services, autonomous vehicles, availability heuristic, barriers to entry, Bernie Sanders, bitcoin, Buckminster Fuller, Chris Urmson, Clayton Christensen, cloud computing, cognitive bias, complexity theory, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, digital map, disruptive innovation, Donald Trump, Douglas Engelbart, easy for humans, difficult for computers, Electric Kool-Aid Acid Test, Elon Musk, Firefox, gig economy, global supply chain, Google Glasses, Google X / Alphabet X, Hacker Ethic, independent contractor, Jaron Lanier, Jeff Bezos, John von Neumann, Joi Ito, Joseph-Marie Jacquard, life extension, Lyft, Mark Zuckerberg, mass incarceration, Minecraft, minimum viable product, Mother of all demos, move fast and break things, move fast and break things, Nate Silver, natural language processing, PageRank, payday loans, paypal mafia, performance metric, Peter Thiel, price discrimination, Ray Kurzweil, ride hailing / ride sharing, Ross Ulbricht, Saturday Night Live, school choice, self-driving car, Silicon Valley, speech recognition, statistical model, Steve Jobs, Steven Levy, Stewart Brand, Tesla Model S, the High Line, The Signal and the Noise by Nate Silver, theory of mind, Travis Kalanick, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, women in the workforce
In the competition, the 2007 Grand Challenge, Little Ben would need to drive itself through an empty “city” made out of a decommissioned military base. No remote controls, no preprogrammed paths through the city: just eighty-nine autonomous vehicles trying to drive down streets, around corners, through intersections, and around each other. The sponsor, the Defense Advanced Research Projects Agency (DARPA), promised a $2 million prize to the fastest finisher, plus $1 million and $500,000 prizes to the runners-up. Robot-car technology was already assisting everyday drivers in 2007. By then, Lexus had released a car that could parallel-park itself under specific conditions.
It’s not great for operating a two-ton killing machine on streets that are teeming with gloriously unpredictable masses of people. Since the 2007 Grand Challenge, DARPA has moved on from autonomous vehicles. Their current funding priorities don’t include self-driving cars. “Life is by definition unpredictable. It is impossible for programmers to anticipate every problematic or surprising situation that might arise, which means existing ML systems remain susceptible to failures as they encounter the irregularities and unpredictability of real-world circumstances,” said DARPA’s Hava Siegelmann, program manager for the Lifelong Learning Machines Program, in 2017.
I wrote the story and assumed that the tech would fizzle out or be absorbed into another project, fading into tech obscurity like RealPlayer video or Macromedia Director or Jaz drives. After I filed my story, I forgot about the Penn robot car. Meanwhile, Little Ben still had a race to win. On the morning of the DARPA Grand Challenge, November 3, 2007, the vehicles lined up at the starting gate. Their goal was to traverse the streets of George Air Force Base, a decommissioned military base in Nevada. There were roads and signs and escort vehicles. It was a motley crew of jerry-rigged vehicles lined up at the starting line. The task was to navigate sixty miles through the base, obeying street signs and avoiding other cars.
The Road to Conscious Machines by Michael Wooldridge
Ada Lovelace, AI winter, algorithmic bias, Andrew Wiles, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, basic income, British Empire, call centre, combinatorial explosion, computer vision, DARPA: Urban Challenge, don't be evil, Donald Trump, Elon Musk, Eratosthenes, factory automation, future of work, gig economy, Google Glasses, intangible asset, James Watt: steam engine, job automation, John von Neumann, Loebner Prize, Minecraft, Nash equilibrium, Norbert Wiener, NP-complete, P = NP, pattern recognition, RAND corporation, Ray Kurzweil, Rodney Brooks, self-driving car, Silicon Valley, Stephen Hawking, Steven Pinker, strong AI, technological singularity, telemarketer, Tesla Model S, The Coming Technological Singularity, The Future of Employment, the scientific method, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, Turing machine, Turing test, universal basic income, Von Neumann architecture
Universal Turing Machine A general type of Turing machine, which provided the template for the modern computer. While a Turing machine encodes just one specific recipe/algorithm, a Universal Turing Machine can be given any recipe/algorithm. (unsound) reasoning In logic, where we derive conclusions that are not warranted by the premises. See also (sound) reasoning. Urban Challenge A 2007 follow-on to the DARPA Grand Challenge, in which autonomous vehicles were required to autonomously traverse a built-up urban environment. utilitarianism The idea that we should choose to act so as to maximize the benefit for society. In a Trolley Problem, a utilitarian would choose to kill one person in order to save five lives.
The 2005 Grand Challenge was followed by a series of other challenges, of which probably the most important was the 2007 Urban Challenge. While the 2005 competition tested vehicles on rural roads, the 2007 challenge aimed to test them in built-up urban environments. Driverless cars were required to complete a course, while obeying Californian road traffic laws, and coping with everyday situations like parking, intersections and traffic jams. Thirty-six teams made it to the national qualifying event, and of these 11 were selected for the final, held on a disused former airport in southern California on 3 November 2007. Six teams successfully completed the challenge, with the winner, from Carnegie Mellon University, averaging approximately 14 m.p.h. throughout the four-hour challenge.
A A* 77 À la recherche du temps perdu (Proust) 205–8 accountability 257 Advanced Research Projects Agency (ARPA) 87–8 adversarial machine learning 190 AF (Artificial Flight) parable 127–9, 243 agent-based AI 136–49 agent-based interfaces 147, 149 ‘Agents That Reduce Work and Information Overload’ (Maes) 147–8 AGI (Artificial General Intelligence) 41 AI – difficulty of 24–8 – ethical 246–62, 284, 285 – future of 7–8 – General 42, 53, 116, 119–20 – Golden Age of 47–88 – history of 5–7 – meaning of 2–4 – narrow 42 – origin of name 51–2 – strong 36–8, 41, 309–14 – symbolic 42–3, 44 – varieties of 36–8 – weak 36–8 AI winter 87–8 AI-complete problems 84 ‘Alchemy and AI’ (Dreyfus) 85 AlexNet 187 algorithmic bias 287–9, 292–3 alienation 274–7 allocative harm 287–8 AlphaFold 214 AlphaGo 196–9 AlphaGo Zero 199 AlphaZero 199–200 Alvey programme 100 Amazon 275–6 Apple Watch 218 Argo AI 232 arithmetic 24–6 Arkin, Ron 284 ARPA (Advanced Research Projects Agency) 87–8 Artificial Flight (AF) parable 127–9, 243 Artificial General Intelligence (AGI) 41 artificial intelligence see AI artificial languages 56 Asilomar principles 254–6 Asimov, Isaac 244–6 Atari 2600 games console 192–6, 327–8 augmented reality 296–7 automated diagnosis 220–1 automated translation 204–8 automation 265, 267–72 autonomous drones 282–4 Autonomous Vehicle Disengagement Reports 231 autonomous vehicles see driverless cars autonomous weapons 281–7 autonomy levels 227–8 Autopilot 228–9 B backprop/backpropagation 182–3 backward chaining 94 Bayes nets 158 Bayes’ Theorem 155–8, 365–7 Bayesian networks 158 behavioural AI 132–7 beliefs 108–10 bias 172 black holes 213–14 Blade Runner 38 Blocks World 57–63, 126–7 blood diseases 94–8 board games 26, 75–6 Boole, George 107 brains 43, 306, 330–1 see also electronic brains branching factors 73 Breakout (video game) 193–5 Brooks, Rodney 125–9, 132, 134, 243 bugs 258 C Campaign to Stop Killer Robots 286 CaptionBot 201–4 Cardiogram 215 cars 27–8, 155, 223–35 certainty factors 97 ceteris paribus preferences 262 chain reactions 242–3 chatbots 36 checkers 75–7 chess 163–4, 199 Chinese room 311–14 choice under uncertainty 152–3 combinatorial explosion 74, 80–1 common values and norms 260 common-sense reasoning 121–3 see also reasoning COMPAS 280 complexity barrier 77–85 comprehension 38–41 computational complexity 77–85 computational effort 129 computers – decision making 23–4 – early developments 20 – as electronic brains 20–4 – intelligence 21–2 – programming 21–2 – reliability 23 – speed of 23 – tasks for 24–8 – unsolved problems 28 ‘Computing Machinery and Intelligence’ (Turing) 32 confirmation bias 295 conscious machines 327–30 consciousness 305–10, 314–17, 331–4 consensus reality 296–8 consequentialist theories 249 contradictions 122–3 conventional warfare 286 credit assignment problem 173, 196 Criado Perez, Caroline 291–2 crime 277–81 Cruise Automation 232 curse of dimensionality 172 cutlery 261 Cybernetics (Wiener) 29 Cyc 114–21, 208 D DARPA (Defense Advanced Research Projects Agency) 87–8, 225–6 Dartmouth summer school 1955 50–2 decidable problems 78–9 decision problems 15–19 deduction 106 deep learning 168, 184–90, 208 DeepBlue 163–4 DeepFakes 297–8 DeepMind 167–8, 190–200, 220–1, 327–8 Defense Advanced Research Projects Agency (DARPA) 87–8, 225–6 dementia 219 DENDRAL 98 Dennett, Daniel 319–25 depth-first search 74–5 design stance 320–1 desktop computers 145 diagnosis 220–1 disengagements 231 diversity 290–3 ‘divide and conquer’ assumption 53–6, 128 Do-Much-More 35–6 dot-com bubble 148–9 Dreyfus, Hubert 85–6, 311 driverless cars 27–8, 155, 223–35 drones 282–4 Dunbar, Robin 317–19 Dunbar’s number 318 E ECAI (European Conference on AI) 209–10 electronic brains 20–4 see also computers ELIZA 32–4, 36, 63 employment 264–77 ENIAC 20 Entscheidungsproblem 15–19 epiphenomenalism 316 error correction procedures 180 ethical AI 246–62, 284, 285 European Conference on AI (ECAI) 209–10 evolutionary development 331–3 evolutionary theory 316 exclusive OR (XOR) 180 expected utility 153 expert systems 89–94, 123 see also Cyc; DENDRAL; MYCIN; R1/XCON eye scans 220–1 F Facebook 237 facial recognition 27 fake AI 298–301 fake news 293–8 fake pictures of people 214 Fantasia 261 feature extraction 171–2 feedback 172–3 Ferranti Mark 1 20 Fifth Generation Computer Systems Project 113–14 first-order logic 107 Ford 232 forward chaining 94 Frey, Carl 268–70 ‘The Future of Employment’ (Frey & Osborne) 268–70 G game theory 161–2 game-playing 26 Gangs Matrix 280 gender stereotypes 292–3 General AI 41, 53, 116, 119–20 General Motors 232 Genghis robot 134–6 gig economy 275 globalization 267 Go 73–4, 196–9 Golden Age of AI 47–88 Google 167, 231, 256–7 Google Glass 296–7 Google Translate 205–8, 292–3 GPUs (Graphics Processing Units) 187–8 gradient descent 183 Grand Challenges 2004/5 225–6 graphical user interfaces (GUI) 144–5 Graphics Processing Units (GPUs) 187–8 GUI (graphical user interfaces) 144–5 H hard problem of consciousness 314–17 hard problems 84, 86–7 Harm Assessment Risk Tool (HART) 277–80 Hawking, Stephen 238 healthcare 215–23 Herschel, John 304–6 Herzberg, Elaine 230 heuristic search 75–7, 164 heuristics 91 higher-order intentional reasoning 323–4, 328 high-level programming languages 144 Hilbert, David 15–16 Hinton, Geoff 185–6, 221 HOMER 141–3, 146 homunculus problem 315 human brain 43, 306, 330–1 human intuition 311 human judgement 222 human rights 277–81 human-level intelligence 28–36, 241–3 ‘humans are special’ argument 310–11 I image classification 186–7 image-captioning 200–4 ImageNet 186–7 Imitation Game 30 In Search of Lost Time (Proust) 205–8 incentives 261 indistinguishability 30–1, 37, 38 Industrial Revolutions 265–7 inference engines 92–4 insurance 219–20 intelligence 21–2, 127–8, 200 – human-level 28–36, 241–3 ‘Intelligence Without Representation’ (Brooks) 129 Intelligent Knowledge-Based Systems 100 intentional reasoning 323–4, 328 intentional stance 321–7 intentional systems 321–2 internal mental phenomena 306–7 Internet chatbots 36 intuition 311 inverse reinforcement learning 262 Invisible Women (Criado Perez) 291–2 J Japan 113–14 judgement 222 K Kasparov, Garry 163 knowledge bases 92–4 knowledge elicitation problem 123 knowledge graph 120–1 Knowledge Navigator 146–7 knowledge representation 91, 104, 129–30, 208 knowledge-based AI 89–123, 208 Kurzweil, Ray 239–40 L Lee Sedol 197–8 leisure 272 Lenat, Doug 114–21 lethal autonomous weapons 281–7 Lighthill Report 87–8 LISP 49, 99 Loebner Prize Competition 34–6 logic 104–7, 121–2 logic programming 111–14 logic-based AI 107–11, 130–2 M Mac computers 144–6 McCarthy, John 49–52, 107–8, 326–7 machine learning (ML) 27, 54–5, 168–74, 209–10, 287–9 machines with mental states 326–7 Macintosh computers 144–6 magnetic resonance imaging (MRI) 306 male-orientation 290–3 Manchester Baby computer 20, 24–6, 143–4 Manhattan Project 51 Marx, Karl 274–6 maximizing expected utility 154 Mercedes 231 Mickey Mouse 261 microprocessors 267–8, 271–2 military drones 282–4 mind modelling 42 mind-body problem 314–17 see also consciousness minimax search 76 mining industry 234 Minsky, Marvin 34, 52, 180 ML (machine learning) 27, 54–5, 168–74, 209–10, 287–9 Montezuma’s Revenge (video game) 195–6 Moore’s law 240 Moorfields Eye Hospital 220–1 moral agency 257–8 Moral Machines 251–3 MRI (magnetic resonance imaging) 306 multi-agent systems 160–2 multi-layer perceptrons 177, 180, 182 Musk, Elon 238 MYCIN 94–8, 217 N Nagel, Thomas 307–10 narrow AI 42 Nash, John Forbes Jr 50–1, 161 Nash equilibrium 161–2 natural languages 56 negative feedback 173 neural nets/neural networks 44, 168, 173–90, 369–72 neurons 174 Newell, Alan 52–3 norms 260 NP-complete problems 81–5, 164–5 nuclear energy 242–3 nuclear fusion 305 O ontological engineering 117 Osborne, Michael 268–70 P P vs NP problem 83 paperclips 261 Papert, Seymour 180 Parallel Distributed Processing (PDP) 182–4 Pepper 299 perception 54 perceptron models 174–81, 183 Perceptrons (Minsky & Papert) 180–1, 210 personal healthcare management 217–20 perverse instantiation 260–1 Phaedrus 315 physical stance 319–20 Plato 315 police 277–80 Pratt, Vaughan 117–19 preference relations 151 preferences 150–2, 154 privacy 219 problem solving and planning 55–6, 66–77, 128 programming 21–2 programming languages 144 PROLOG 112–14, 363–4 PROMETHEUS 224–5 protein folding 214 Proust, Marcel 205–8 Q qualia 306–7 QuickSort 26 R R1/XCON 98–9 radiology 215, 221 railway networks 259 RAND Corporation 51 rational decision making 150–5 reasoning 55–6, 121–3, 128–30, 137, 315–16, 323–4, 328 regulation of AI 243 reinforcement learning 172–3, 193, 195, 262 representation harm 288 responsibility 257–8 rewards 172–3, 196 robots – as autonomous weapons 284–5 – Baye’s theorem 157 – beliefs 108–10 – fake 299–300 – indistinguishability 38 – intentional stance 326–7 – SHAKEY 63–6 – Sophia 299–300 – Three Laws of Robotics 244–6 – trivial tasks 61 – vacuum cleaning 132–6 Rosenblatt, Frank 174–81 rules 91–2, 104, 359–62 Russia 261 Rutherford, Ernest (1st Baron Rutherford of Nelson) 242 S Sally-Anne tests 328–9, 330 Samuel, Arthur 75–7 SAT solvers 164–5 Saudi Arabia 299–300 scripts 100–2 search 26, 68–77, 164, 199 search trees 70–1 Searle, John 311–14 self-awareness 41, 305 see also consciousness semantic nets 102 sensors 54 SHAKEY the robot 63–6 SHRDLU 56–63 Simon, Herb 52–3, 86 the Singularity 239–43 The Singularity is Near (Kurzweil) 239 Siri 149, 298 Smith, Matt 201–4 smoking 173 social brain 317–19 see also brains social media 293–6 social reasoning 323, 324–5 social welfare 249 software agents 143–9 software bugs 258 Sophia 299–300 sorting 26 spoken word translation 27 STANLEY 226 STRIPS 65 strong AI 36–8, 41, 309–14 subsumption architecture 132–6 subsumption hierarchy 134 sun 304 supervised learning 169 syllogisms 105, 106 symbolic AI 42–3, 44, 181 synapses 174 Szilard, Leo 242 T tablet computers 146 team-building problem 78–81, 83 Terminator narrative of AI 237–9 Tesla 228–9 text recognition 169–71 Theory of Mind (ToM) 330 Three Laws of Robotics 244–6 TIMIT 292 ToM (Theory of Mind) 330 ToMnet 330 TouringMachines 139–41 Towers of Hanoi 67–72 training data 169–72, 288–9, 292 translation 204–8 transparency 258 travelling salesman problem 82–3 Trolley Problem 246–53 Trump, Donald 294 Turing, Alan 14–15, 17–19, 20, 24–6, 77–8 Turing Machines 18–19, 21 Turing test 29–38 U Uber 168, 230 uncertainty 97–8, 155–8 undecidable problems 19, 78 understanding 201–4, 312–14 unemployment 264–77 unintended consequences 263 universal basic income 272–3 Universal Turing Machines 18, 19 Upanishads 315 Urban Challenge 2007 226–7 utilitarianism 249 utilities 151–4 utopians 271 V vacuum cleaning robots 132–6 values and norms 260 video games 192–6, 327–8 virtue ethics 250 Von Neumann and Morgenstern model 150–5 Von Neumann architecture 20 W warfare 285–6 WARPLAN 113 Waymo 231, 232–3 weak AI 36–8 weapons 281–7 wearable technology 217–20 web search 148–9 Weizenbaum, Joseph 32–4 Winograd schemas 39–40 working memory 92 X XOR (exclusive OR) 180 Z Z3 computer 19–20 PELICAN BOOKS Economics: The User’s Guide Ha-Joon Chang Human Evolution Robin Dunbar Revolutionary Russia: 1891–1991 Orlando Figes The Domesticated Brain Bruce Hood Greek and Roman Political Ideas Melissa Lane Classical Literature Richard Jenkyns Who Governs Britain?
Ghost Road: Beyond the Driverless Car by Anthony M. Townsend
A Pattern Language, active measures, AI winter, algorithmic trading, asset-backed security, augmented reality, autonomous vehicles, backpropagation, big-box store, bike sharing scheme, business process, Captain Sullenberger Hudson, car-free, carbon footprint, computer vision, conceptual framework, congestion charging, connected car, creative destruction, crew resource management, crowdsourcing, DARPA: Urban Challenge, data is the new oil, Dean Kamen, deindustrialization, delayed gratification, deliberate practice, dematerialisation, deskilling, drive until you qualify, Edward Glaeser, Elon Musk, en.wikipedia.org, extreme commuting, financial innovation, Flash crash, gig economy, Google bus, haute couture, helicopter parent, independent contractor, inventory management, invisible hand, Jane Jacobs, Jeff Bezos, jitney, job automation, John Markoff, John von Neumann, Joseph Schumpeter, Kickstarter, loss aversion, Lyft, megacity, minimum viable product, mortgage debt, New Urbanism, North Sea oil, 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, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, software as a service, sovereign wealth fund, Stephen Hawking, Steve Jobs, surveillance capitalism, technological singularity, 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
Autonomists make a big fuss about how far computer vision has come in such a short time. But considering that few AVs have ventured far from familiar territory, there’s good reason to fear this progress has been oversold. Waymo’s massive Arizona rollout is a mere 280 miles to the east of California’s George Air Force Base, the site of the 2007 DARPA Grand Challenge (often called the “DARPA Urban Challenge” for its simulated city terrain). In all that time, Google’s ballyhooed team has merely replicated success in one set of idealized conditions—scant rainfall, ample sunshine, and roads built to modern engineering standards—in a largely identical region. The journey from here to the urban wilds of Dhaka, or even Detroit, will be a long and uncertain one.
In the early 2000s, the Pentagon took a growing interest in this emerging technology. To focus the efforts of scattered research groups and catalyze stronger ties with the defense and auto industries, the Defense Advanced Research Projects Agency (DARPA)—the US military’s most independent research-funding arm—organized a series of open competitions in 2004, 2005, and 2007. These “Grand Challenges,” as they were called, offered millions of dollars in prize money and priceless prestige, and attracted dozens of teams from academia and industry. Putting their best hardware and software to the test, the competitors watched from afar as their AVs tried to traverse both open country and more suburban settings on an abandoned military base.
A History of Autonomous Vehicles,” CHM Blog, Computer History Museum, May 8, 2014, https://www.computerhistory.org/atchm/where-to-a-history-of-auton omous-vehicles/. 6retrofitted a Mercedes-Benz van with self-driving gadgets: Janosch Delcker, “The Man Who Invented the Self-Driving Car (in 1986),” Politico, July 19, 2018, https://www.politico.eu/article/delf-driving-car-born-1986-ernst-dickmanns-mercedes/. 7Stanford University’s winning vehicle: Sebastian Thrun et al., “Stanley: The Robot That Won the DARPA Grand Challenge,” Journal of Field Robotics 23, no. 9 (2006): 661–92, http://isl.ecst.csuchico.edu/DOCS/darpa2005/DARPA%20 2005%20Stanley.pdf. 7Silicon Valley moved forward: Lawrence D. Burns and Christopher Shulgan, Autonomy: The Quest to Build the Driverless Car—and How It Will Reshape Our World (New York: Ecco, 2018), 137–57. 7Larry Page’s lifelong interest in AVs: Burns and Shulgan, Autonomy, 3–11. 8$80 billion surged into self-driving vehicle technologies: Cameron F.
The Code: Silicon Valley and the Remaking of America by Margaret O'Mara
"side hustle", A Declaration of the Independence of Cyberspace, accounting loophole / creative accounting, affirmative action, Airbnb, AltaVista, Amazon Web Services, Apple II, Apple's 1984 Super Bowl advert, autonomous vehicles, back-to-the-land, barriers to entry, Ben Horowitz, Berlin Wall, Bob Noyce, Buckminster Fuller, Burning Man, business climate, Byte Shop, California gold rush, carried interest, clean water, cleantech, cloud computing, cognitive dissonance, commoditize, computer age, continuous integration, cuban missile crisis, Danny Hillis, DARPA: Urban Challenge, deindustrialization, different worldview, don't be evil, Donald Trump, Doomsday Clock, Douglas Engelbart, Dynabook, Edward Snowden, El Camino Real, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Frank Gehry, George Gilder, gig economy, Googley, Hacker Ethic, high net worth, hockey-stick growth, Hush-A-Phone, immigration reform, income inequality, informal economy, information retrieval, invention of movable type, invisible hand, Isaac Newton, Jeff Bezos, Joan Didion, job automation, job-hopping, John Markoff, Julian Assange, Kitchen Debate, knowledge economy, knowledge worker, Lyft, Marc Andreessen, Mark Zuckerberg, market bubble, mass immigration, means of production, mega-rich, Menlo Park, Mikhail Gorbachev, millennium bug, Mitch Kapor, Mother of all demos, move fast and break things, move fast and break things, mutually assured destruction, new economy, Norbert Wiener, old-boy network, pattern recognition, Paul Graham, Paul Terrell, paypal mafia, Peter Thiel, pets.com, pirate software, popular electronics, pre–internet, Ralph Nader, RAND corporation, Richard Florida, ride hailing / ride sharing, risk tolerance, Robert Metcalfe, Ronald Reagan, Sand Hill Road, Second Machine Age, self-driving car, shareholder value, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, skunkworks, Snapchat, social graph, software is eating the world, speech recognition, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, supercomputer in your pocket, technoutopianism, Ted Nelson, the market place, the new new thing, There's no reason for any individual to have a computer in his home - Ken Olsen, Thomas L Friedman, Tim Cook: Apple, transcontinental railway, Uber and Lyft, uber lyft, Unsafe at Any Speed, upwardly mobile, Vannevar Bush, War on Poverty, We wanted flying cars, instead we got 140 characters, Whole Earth Catalog, WikiLeaks, William Shockley: the traitorous eight, Y Combinator, Y2K
Morgenthaler Partners had been an investor in the company that made that voice recognition software, called Siri in homage to being developed at SRI. A DARPA grant had helped seed its early development. See SRI International, “Siri,” https://www.sri.com/work/time line-innovation/timeline.php?timeline=computing-digital#!&innovation=siri, archived at https://perma.cc/7SNR-V6MQ. 5. “For Apple Chief, Gadgets’ Glitter Outshines Scandal,” The New York Times, January 9, 2007, B1; Erica Sadun, “Macworld 2007 Keynote Liveblog,” Engadget, January 9, 2007, https://www.engadget.com/2007/01/09/macworld-2007-keynote-liveblog/, archived at https://perma.cc/4394-QYDG. 6. Martyn Williams, “In his own words: The best quotes of Steve Ballmer,” PC World, August 19, 2014. 7.
This also was a reminder of the Pentagon spending still lurking behind the Valley’s entrepreneurial audacity, for a DARPA “Grand Challenge” competition a decade earlier had revved up the race to bring driverless vehicles to market. As ever, the Valley’s next generation was helped along by the military’s willingness to make far-out bets. See Alex Davies, “Inside the Races that Jump-Started the Self-Driving Car,” Wired, November 10, 2017, https://www.wired.com/story/darpa-grand-urban-challenge-self-driving-car/, archived at https://perma.cc/EWN5-8XCD. 2. Tiernan Ray and Alex Eule, “John Doerr on Leadership, Education, Google, and AI,” Barron’s, May 5, 2018, https://www.barrons.com/articles/john-doerr-on-leadership-education-google-and-ai-1525478401, archived at https://perma.cc/S2W5-5GMY [inactive]; James Morra, “Groq to reveal potent artificial intelligence chip next year,” ee News: Europe, November 17, 2017, http://www.eenewseurope.com/news/groq-reveal-potent-artificial-intelligence-chip-next-year, archived at https://perma.cc/FQ3G-YAEK. 3.
ARPA had added on a “D” for defense and become DARPA, yet it remained the Lady Bountiful of computer science. In 1983, as Democrats rebuilt the road to economic opportunity and Ed Zschau and David Morgenthaler urged another capital gains tax cut, DARPA began a push for a new program: one to meet the national security challenges that the Japanese economic threat represented. And it was all about computers. September 1982—the same month that Jerry Brown’s innovation commission issued its fifty-point plan for the economic future—the people of DARPA released “A Defense Program in Supercomputation from Microelectronics to Artificial Intelligence for the 1990s.”
Wild Ride: Inside Uber's Quest for World Domination by Adam Lashinsky
"side hustle", Airbnb, always be closing, Amazon Web Services, autonomous vehicles, Ayatollah Khomeini, business process, Chuck Templeton: OpenTable:, cognitive dissonance, corporate governance, DARPA: Urban Challenge, Donald Trump, Elon Musk, gig economy, Golden Gate Park, Google X / Alphabet X, hustle culture, independent contractor, information retrieval, Jeff Bezos, Lyft, Marc Andreessen, Mark Zuckerberg, megacity, Menlo Park, new economy, pattern recognition, price mechanism, ride hailing / ride sharing, San Francisco homelessness, Sand Hill Road, self-driving car, Silicon Valley, Silicon Valley startup, Skype, Snapchat, South of Market, San Francisco, sovereign wealth fund, statistical model, Steve Jobs, super pumped, TaskRabbit, Tony Hsieh, transportation-network company, Travis Kalanick, turn-by-turn navigation, Uber and Lyft, Uber for X, uber lyft, ubercab, young professional
“They can leapfrog us and basically replace Uber with a self-driving version of Uber,” Holden said. Holden joined Uber in February 2014, and together with an engineer named Matt Sweeney, he spent his first six months canvassing the world for robotics talent. They studied the teams that had competed in another DARPA competition, the DARPA Urban Challenge, and then they set out to meet as many people as they could. Three academic centers emerged as fertile hunting grounds for the type of robotics talent that would lend itself to autonomous vehicles: MIT, Oxford, and Carnegie Mellon, the school where Sebastian Thrun had begun his research.
Thrun had been a robotics professor at Carnegie Mellon University, home to some of the most advanced research on machine learning and artificial intelligence, when a research arm of the Pentagon started a competition to develop driverless cars. The so-called DARPA Grand Challenge was a bid by the Defense Advanced Research Projects Agency to encourage academics to help the U.S. military field a fighting force that relied less on putting soldiers in harm’s way. Between the time of the first challenge and the third, in 2005, Thrun had abandoned CMU in Pittsburgh for the sunnier climes of Stanford University, in the heart of Silicon Valley. His research team there won the DARPA competition, beating out a team from CMU, which took second place. Google’s founders, all-but-dissertation former computer scientists themselves, were part of a Silicon Valley coterie of technologists who loved such competitions.
(Larry Page would join the XPRIZE Foundation’s board of trustees.) And they knew their technology history: it was DARPA’s predecessor, ARPA, which sponsored the research that created the Internet, the basis for their fortunes. So suffice it to say they paid keen attention to scientific breakthrough prizes that engaged the computer science departments from which they continued to recruit as well as the government agency that sponsored the competitions. Several years after the DARPA Grand Challenge, Larry Page and Sergey Brin decided they wanted to build a self-driving car. Never mind that it had little to do with Google’s information-quest mission.
Life as a Passenger: How Driverless Cars Will Change the World by David Kerrigan
3D printing, Airbnb, airport security, Albert Einstein, autonomous vehicles, big-box store, butterfly effect, call centre, car-free, Cesare Marchetti: Marchetti’s constant, Chris Urmson, commoditize, computer vision, congestion charging, connected car, DARPA: Urban Challenge, deskilling, disruptive innovation, edge city, Elon Musk, en.wikipedia.org, future of work, invention of the wheel, Just-in-time delivery, loss aversion, Lyft, Marchetti’s constant, Mars Rover, megacity, Menlo Park, Metcalfe’s law, Minecraft, Nash equilibrium, New Urbanism, QWERTY keyboard, Ralph Nader, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Rodney Brooks, Sam Peltzman, self-driving car, sensor fusion, Silicon Valley, Simon Kuznets, smart cities, Snapchat, Stanford marshmallow experiment, Steve Jobs, technoutopianism, the built environment, Thorstein Veblen, traffic fines, transit-oriented development, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, Unsafe at Any Speed, urban planning, urban sprawl, Yogi Berra, young professional, zero-sum game, Zipcar
The DARPA event 2004 was a failure for those who took part, but it was an unqualified success in that it spurred a period of intense development that has seen dramatic progress and the realization of capabilities that were considered impossible. In 2005, five driverless cars successfully navigated the route that had proven so baffling just a year earlier. And just two years later, in 2007, six teams finished the new DARPA Urban Challenge, with the participating Autonomous Vehicles (AV) required to obey traffic rules, deal with blocked routes and manoeuvre around fixed and moving obstacles, together providing realistic, everyday urban driving scenarios. Learning to Drive Driving is probably the most complex everyday thing that most of us do (unless you are a surgeon or rocket scientist driving to work).
But as of May 2017, there are trials of the technology by 30 different companies authorized in California alone. Driven by DARPA Although not a household name, the US Department of Defence agency, DARPA, has touched billions of lives. Its best-known project? The creation of ARPANET, the basis for the future Internet. So when DARPA turned its attention to autonomous cars in 2002, the tech community took notice. It announced a challenge - create an autonomous car that could navigate a 150-mile (240 km) route in the Mojave Desert region of the United States. On a March morning in 2004, 15 teams assembled take part in this first DARPA challenge for autonomous cars. All failed.
Yet just a few years later, Google announced it had made major progress. And as I said in the foreword, a Google-powered Lexus drove itself past me less than 10 years later on a public street in California. How has this come to pass? The first DARPA challenge in 2004 is now a footnote in transport history. Just over 10 years ago, a robotic car couldn’t make it more than a few miles on a dedicated course. Now, it can mix it with regular traffic in downtown streets. The DARPA event 2004 was a failure for those who took part, but it was an unqualified success in that it spurred a period of intense development that has seen dramatic progress and the realization of capabilities that were considered impossible.
The End of Traffic and the Future of Transport: Second Edition by David Levinson, Kevin Krizek
2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, American Society of Civil Engineers: Report Card, autonomous vehicles, barriers to entry, Bay Area Rapid Transit, big-box store, Chris Urmson, collaborative consumption, commoditize, crowdsourcing, DARPA: Urban Challenge, dematerialisation, Elon Musk, en.wikipedia.org, Google Hangouts, Induced demand, intermodal, invention of the printing press, jitney, John Markoff, labor-force participation, lifelogging, Lyft, means of production, megacity, Menlo Park, Network effects, Occam's razor, oil shock, place-making, post-work, Ray Kurzweil, rent-seeking, ride hailing / ride sharing, Robert Gordon, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, technological singularity, Tesla Model S, the built environment, 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
Carnegie Mellon University's robot vehicle made it the furthest, completing almost 5 percent of the route, but was ultimately not awarded the prize. A second run of the event saw five vehicles completing the course in October 2005 with Stanford University's team winning with a time of just under 7 hours.154 Within 18 months, vehicle automation technology rapidly improved. Two years later, in November 2007, DARPA established the Urban Challenge on a closed course at a decommissioned Air Force base outside of Los Angeles. The 60 mile (96 km) route resembled an urban obstacle course. In this race, Carnegie Mellon took first (4 hours) and Stanford was second (4.5 hours). In this event, unlike the previous Grand Challenges, cars had to have more sophisticated and intelligent sensors.
Even general purpose lanes can be designated and redesigned to AV-only traffic in order to increase total system throughput. We may get special AV lanes on highways as an interim step before all lanes on all highways are for AVs only, and before non-AV cars are prohibited. After six decades of technological dormancy, the automakers are responding to the DARPA Urban Challenge. Google and others (including Uber) are seriously investing in advances to remove the driver from the loop for vehicle control. For instance, Delphi, an auto parts manufacturer spun-off from General Motors, drove an automated Audi 3,500 miles (5,600 km) cross-country in March of 2015, with hands-off control 99 percent of the time.163 In fact, Delphi’s forerunner (GM Subsidiary) Delco sponsored a similar trip in 1995 by Carnegie Mellon scientists, where the computer navigated 98.7% of the time.164 Timeline Cumulatively, the driverless distances that have been 'driven' are rising every year.165 The time to perfection is far from clear, but one day, soon-ish, you will awake, give a voice command to a car, and never again touch a steering wheel, gears, accelerator, or brakes (which won't be available for your use anyway)— and so will everyone else.
Connnexion on 16th of December 2014, URL: http://en.forumviesmobiles.org/video/2012/12/11/3d-printing-towards-freightless-future-510 http://en.forumviesmobiles.org/video/2012/12/11/3d-printing-towards-freightless-future-510 152 Andy Greenberg (2014-05-14) How 3D Printed Guns Evolved into Serious Weapons in Just One Year. Wired http://www.wired.com/2014/05/3-D-printed-guns/ 153 DARPA stands for Defense Advanced Research Projects Agency; it is a unit of the Department of Defense, as driverless cars have obvious military application. 154 Carnegie Mellon teams took second and third place. The Gray Insurance Company from New Orleans and Oshkosh Trucks also completed the course. 155 Markoff, John (2010) Google Cars Drive Themselves, in Traffic.
Rush Hour: How 500 Million Commuters Survive the Daily Journey to Work by Iain Gately
Albert Einstein, autonomous vehicles, Beeching cuts, blue-collar work, Boris Johnson, British Empire, business intelligence, business process, business process outsourcing, call centre, car-free, Cesare Marchetti: Marchetti’s constant, Clapham omnibus, cognitive dissonance, congestion charging, connected car, corporate raider, DARPA: Urban Challenge, Dean Kamen, decarbonisation, Deng Xiaoping, Detroit bankruptcy, don't be evil, Elon Musk, extreme commuting, global pandemic, Google bus, Henri Poincaré, Hyperloop, Jeff Bezos, lateral thinking, low skilled workers, Marchetti’s constant, postnationalism / post nation state, Ralph Waldo Emerson, remote working, self-driving car, Silicon Valley, stakhanovite, Steve Jobs, telepresence, Tesla Model S, urban planning, éminence grise
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.
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. The grand challenges advanced autonomous technology by leaps and bounds, and also created a pool of engineering expertise, eager to take the concept further. The potential that the DARPA challengers had demonstrated revived commercial interest in driverless cars. Companies including Nissan, GM, Lexus, Google, Mercedes Benz, Ford, Skoda, Audi and Volvo are now researching or building vehicles with varying degrees of autonomy. Volvo, for instance, will offer a self-parking system on its new XC90. It’s eerie to watch a prototype in action.
In the same speech in which CFO Patrick Pichette dismissed telecommuting, he also stated that, in an ideal world, ‘nobody should be driving cars… Look at factorial math and probabilities of everything that could go wrong, times the number of cars out there… That’s why you have gridlock… It makes no sense to make people drive cars.’ Google’s ambitions for autonomous vehicles reach beyond safety: Its lead developer Sebastian Thrun, a veteran of the DARPA ’05 Grand Challenge, sums them up as: (1) We can reduce traffic accidents by 90%. (2) We can reduce wasted commute time and energy by 90%. (3) We can reduce the number of cars by 90%. Although if you began with objective (3) you’d probably also succeed with (2) and (1) by default, progress the other way isn’t so simple.
Physics of the Future: How Science Will Shape Human Destiny and Our Daily Lives by the Year 2100 by Michio Kaku
agricultural Revolution, AI winter, Albert Einstein, Asilomar, augmented reality, Bill Joy: nanobots, bioinformatics, blue-collar work, British Empire, Brownian motion, caloric restriction, caloric restriction, cloud computing, Colonization of Mars, DARPA: Urban Challenge, delayed gratification, double helix, Douglas Hofstadter, en.wikipedia.org, friendly AI, Gödel, Escher, Bach, hydrogen economy, I think there is a world market for maybe five computers, industrial robot, Intergovernmental Panel on Climate Change (IPCC), invention of movable type, invention of the telescope, Isaac Newton, John Markoff, John von Neumann, life extension, Louis Pasteur, Mahatma Gandhi, Mars Rover, mass immigration, megacity, Mitch Kapor, Murray Gell-Mann, new economy, oil shale / tar sands, optical character recognition, pattern recognition, planetary scale, postindustrial economy, Ray Kurzweil, refrigerator car, Richard Feynman, Rodney Brooks, Ronald Reagan, Search for Extraterrestrial Intelligence, Silicon Valley, Simon Singh, social intelligence, speech recognition, stem cell, Stephen Hawking, Steve Jobs, telepresence, The future is already here, The Wealth of Nations by Adam Smith, Thomas L Friedman, Thomas Malthus, trade route, Turing machine, uranium enrichment, Vernor Vinge, Wall-E, Walter Mischel, Whole Earth Review, X Prize
They had to drive on roads that included 100 sharp turns, three narrow tunnels, and paths with sheer drop-offs on either side. Some critics said that robotic cars might be able to travel in the desert but never in midtown traffic. So in 2007, DARPA sponsored an even more ambitious project, the Urban Challenge, in which robotic cars had to complete a grueling 60-mile course through mock-urban territory in less than six hours. The cars had to obey all traffic laws, avoid other robot cars along the course, and negotiate four-way intersections. Six teams successfully completed the Urban Challenge, with the top three claiming the $2 million, $1 million, and $500,000 prizes. The Pentagon’s goal is to make fully one-third of the U.S. ground forces autonomous by 2015.
This is similar to the hypothetical concept of smart dust being pursued by the Pentagon: billions of particles sent into the air, each one with tiny sensors to do reconnaissance. Each sensor is not very intelligent, but collectively they can relay back mountains of information. The Pentagon’s DARPA has funded this research for possible military applications, such as monitoring enemy positions on the battlefield. In 2007 and 2009, the Air Force released position papers detailing plans for the coming decades, outlining everything from advanced versions of the Predator (which today cost $4.5 million apiece) to swarms of tiny sensors smaller than a moth costing pennies.
Commuting to work won’t be such an agonizing chore because cars will drive themselves. Already, driverless cars, using GPS to locate their position within a few feet, can drive over hundreds of miles. The Pentagon’s Defense Advanced Research Projects Agency (DARPA) sponsored a contest, called the DARPA Grand Challenge, in which laboratories were invited to submit driverless cars for a race across the Mojave Desert to claim a $1 million prize. DARPA was continuing its long-standing tradition of financing risky but visionary technologies. (Some examples of Pentagon projects include the Internet, which was originally designed to connect scientists and officials during and after a nuclear war, and the GPS system, which was originally designed to guide ICBM missiles.
Architects of Intelligence by Martin Ford
3D printing, agricultural Revolution, AI winter, algorithmic bias, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, bitcoin, business intelligence, business process, call centre, cloud computing, cognitive bias, Colonization of Mars, computer vision, correlation does not imply causation, crowdsourcing, DARPA: Urban Challenge, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Fellow of the Royal Society, Flash crash, future of work, gig economy, Google X / Alphabet X, Gödel, Escher, Bach, Hans Rosling, ImageNet competition, income inequality, industrial robot, information retrieval, job automation, John von Neumann, Law of Accelerating Returns, life extension, Loebner Prize, Mark Zuckerberg, Mars Rover, means of production, Mitch Kapor, natural language processing, new economy, optical character recognition, pattern recognition, phenotype, Productivity paradox, Ray Kurzweil, recommendation engine, Robert Gordon, Rodney Brooks, Sam Altman, self-driving car, sensor fusion, sentiment analysis, Silicon Valley, smart cities, social intelligence, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, Ted Kaczynski, The Rise and Fall of American Growth, theory of mind, Thomas Bayes, Travis Kalanick, Turing test, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, women in the workforce, working-age population, zero-sum game, Zipcar
As an undergraduate at Carnegie Mellon in the late ‘90s, I did a class with Dean Pomerleau working on their autonomous car project that steered the vehicle based an input video image. The technology was great, but it wasn’t ready for its time. Then at Stanford, I was a peripheral part of the DARPA Urban Challenge in 2007. We flew down to Victorville, and it was the first time I saw so many self-driving cars in the same place. The whole Stanford team were all fascinated for the first five minutes, watching all these cars zip around without drivers, and the surprising thing was that after five minutes, we acclimatized to it, and we turned our backs to it.
At the time, like many of my friends and colleagues, such as Bobby Rao, who had also been in the Robotics Research Lab with me, I was interested in building systems that could compete in the DARPA driverless car challenge. This was because a lot of our algorithms were applicable to autonomous vehicles and driverless cars and back then, the DARPA challenge was one of the places where you could apply those algorithms. All of my friends were moving to Silicon Valley then. Bobby was at that time a post-doc at Berkeley working with Stuart Russell and others, and so I thought I should take this McKinsey offer in San Francisco. It was a way to be close to Silicon Valley and to be close to where some of the action, including the DARPA challenge, was taking place.
Before the Smalltalk system was finished, though, I realized that children’s stories were not just stories to be read and understood, but that they’re meant to inculcate a culture, and that Alan’s challenge to me was going to be really hard to meet. During that time, the first group of speech-understanding systems were also being developed through DARPA projects, and the people at SRI International who were working on one of them said to me, “If you’re willing to take the risk of working on children’s stories, why don’t you come work with us on a more objective kind of language, task-oriented dialogues, but using speech not text?” As a result, I got involved in the DARPA speech work, which was on systems that would assist people in getting tasks done, and that’s really when I started to do AI research. It was that work which led to my discovery of how dialogue among people, when they’re working on a task together, has a structure that depends on the task structure—and that a dialogue is much more than just question-answer pairs.
Rebooting India: Realizing a Billion Aspirations by Nandan Nilekani
Airbnb, Atul Gawande, autonomous vehicles, barriers to entry, bitcoin, call centre, cashless society, clean water, cloud computing, collaborative consumption, congestion charging, DARPA: Urban Challenge, dematerialisation, demographic dividend, Edward Snowden, en.wikipedia.org, energy security, financial exclusion, Google Hangouts, illegal immigration, informal economy, Khan Academy, Kickstarter, knowledge economy, land reform, law of one price, M-Pesa, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, mobile money, Mohammed Bouazizi, more computing power than Apollo, Negawatt, Network effects, new economy, offshore financial centre, price mechanism, price stability, rent-seeking, RFID, Ronald Coase, school choice, school vouchers, self-driving car, sharing economy, Silicon Valley, Skype, smart grid, smart meter, software is eating the world, source of truth, Steve Jobs, The future is already here, The Nature of the Firm, transaction costs, WikiLeaks
http://blog.knowyourclimate.org/2014/07/a-citizen-weather-network/ 14. Bhosale, Jayashree, and Sally, Madhvi. 4 June 2014. ‘Private forecaster Skymet declares monsoon arrival, IMD disagrees’. Economic Times. http://articles.economictimes.indiatimes.com/2014-06-04/news/50330087_1_skymet-monsoon-arrival-imd 15. DARPA Urban Challenge 2005. http://archive.darpa.mil/grandchallenge05/ 16. Fisher, Adam. 18 September 2013. ‘Inside Google’s Quest To Popularize Self-Driving Cars’. Popular Science. http://www.popsci.com/cars/article/2013-09/google-self-driving-car Winkler, Rolfe, and Macmillan, Douglas. 2 February, 2015. ‘Uber Chases Google in Self-Driving Cars With Carnegie Mellon Deal’.
While transport service aggregators may be making the headlines right now for their regulatory troubles, it is naive to assume that such issues will not arise in other areas of business. The government needs to be prepared. The public —private divide In her book The Entrepreneurial State, the economist Mariana Mazzucato offers an interesting perspective on the state funding of innovation.12 Agencies such as the US’s Defense Advanced Research Projects Agency (DARPA), the National Science Foundation, the National Institutes of Health, the Department of Energy and others have a history of funding basic research at universities and research labs, often at a nascent stage where the risks are high enough to discourage investment from venture capitalists. This approach has helped to support some of the biggest technological and scientific breakthroughs of our time—consider that the development of the internet, the human genome project, nuclear technology, antibiotics and GPS have all benefited from early government support.
Private weather forecasters like Skymet and the Bangalore-based Citizen Weather Network are entering a domain that was until recently the exclusive preserve of the Indian Meteorological Department (IMD).13 The holy grail of weather forecasting in India is predicting the onset of the annual monsoon season, and Skymet has already clashed with the IMD by releasing monsoon predictions and analyses that differ from the IMD’s interpretation.14 Much like GPS, a more recent instance of a military technology being opened to the public is that of autonomous vehicles. Through DARPA—coincidentally the agency that also birthed the internet—the US government has been funding such endeavours for over a decade.15 The underlying technology has now entered the commercial space; Google is testing self-driving cars using its Google Chauffeur platform, Uber has just announced an academic collaboration with Carnegie Mellon University to ‘develop driverless car and mapping technology’, and Apple is reportedly investigating technologies for building electric and self-driving cars.16 While we may not see a fleet of self-driving cars taking over our streets in the near future, it’s worthwhile to consider that various US state governments are already starting to pass laws that permit driverless cars to operate on state roads.17 Once again, government regulations need to anticipate innovation by keeping a close eye on emerging trends and assessing their potential impact and chances of widespread adoption.
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, cleantech, computer vision, conceptual framework, connected car, crowdsourcing, cyber-physical system, DARPA: Urban Challenge, data acquisition, demand response, digital map, disruptive innovation, Elon Musk, fault tolerance, fear of failure, global supply chain, industrial cluster, intermodal, Internet of things, Jeff Bezos, Lyft, manufacturing employment, market fundamentalism, Mars Rover, Masdar, megacity, Pearl River Delta, peer-to-peer rental, precision agriculture, QWERTY keyboard, RAND corporation, ride hailing / ride sharing, self-driving car, sensor fusion, sharing economy, Silicon Valley, smart cities, smart grid, smart meter, Steve Jobs, Tesla Model S, Tim Cook: Apple, uber lyft, upwardly mobile, urban planning, Zipcar
PROJECTS An invitation to tender by the Defense Advanced Research Project Agency (DARPA) of the US Defense Department brought signiﬁcant advances. This competition brought together universities, manufacturers, suppliers and start-up companies with the goal of building the autonomous vehicle of the future. This project was originally started with military applications in mind, but the focus shifted to civilian applications over a period of several years. The DARPA Challenge was at ﬁrst held on a desert course in 2004 and 2005, and then on an urban course in 2007. In the ﬁrst several years, none of the participating vehicles reached the destination; the ﬁrst winner was a Volkswagen Touareg modiﬁed by Stanford University in 2007.
So autonomous driving is not just a technical challenge, but as already implied, it is also an economic, social and cultural challenge. K e y T a ke a w a y s Mass motorisation in the 1920s caused many deaths, which already triggered the idea of replacing the driver. The idea of autonomous mobility inspired many authors to depict selfdriving cars in their novels and movies: Herbie for example. The DARPA Challenge of the US Defense Department ensured that the idea of autonomous driving found its way into the research and development departments of car manufacturers, automotive suppliers and technology companies. Autonomous driving is more than just a new technology that changes mobility. It is an economic, social and cultural challenge.
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.
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, Elon Musk, en.wikipedia.org, interchangeable parts, job automation, ransomware, self-driving car, sensor fusion, side project, Tesla Model S, urban sprawl
Since there was no winner in 2004, the Grand Challenge was held again in 2005. This time five vehicles successfully completed the course, with the winner being Stanford University’s Stanley, a converted Volkswagen Touareg. Stanley navigated the route in six hours and fifty-four minutes. In 2007, the Grand Challenge was back, this time with an “urban challenge” designed to replicate the challenging environment of city driving. This challenge required that the cars meet all traffic laws and interact with one another legally,²² following regulations and conventions such as moving in the proper order when four cars meet at a four-way stop sign.
They benefitted from much upgraded hardware from the van era, at first using transputers (a parallel processing architecture) and then later PowerPC 601 chips, which you geeks may remember as the architecture that Apple Macintoshes used after the chips from the Motorola 68000 family and before Intel chips. Dickmanns’s work was hugely influential, and laid the template for AVs to follow. 2004: The DARPA Grand Challenge If there was one final crucible that truly made autonomous vehicles a viable, achievable possibility, it had to be the DARPA Grand Challenge. The Grand Challenge was a project run by the US Department of Defense’s Defense Advanced Research Projects Agency, which was authorized by Congress to offer one million dollars of prize money to the first team that could build an autonomous vehicle capable of traversing a 150-mile-long route in the Mojave Desert that follows the path of Interstate 15.
., http://www-ie.meijo-u.ac.jp/~tsugawa/sub1.html. 20 Schaub, Alexander, Robust Perception from Optical Sensors for Reactive Behaviors in Autonomous Robotic Vehicles, Springer Verlag, Berlin, 2018, pp. 17–18. 21 Dickmanns, Ernst D., 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.
Superintelligence: Paths, Dangers, Strategies by Nick Bostrom
agricultural Revolution, AI winter, Albert Einstein, algorithmic trading, anthropic principle, anti-communist, artificial general intelligence, autonomous vehicles, backpropagation, barriers to entry, Bayesian statistics, bioinformatics, brain emulation, cloud computing, combinatorial explosion, computer vision, cosmological constant, dark matter, DARPA: Urban Challenge, data acquisition, delayed gratification, demographic transition, different worldview, Donald Knuth, Douglas Hofstadter, Drosophila, Elon Musk, en.wikipedia.org, endogenous growth, epigenetics, fear of failure, Flash crash, Flynn Effect, friendly AI, Gödel, Escher, Bach, income inequality, industrial robot, informal economy, information retrieval, interchangeable parts, iterative process, job automation, John Markoff, John von Neumann, knowledge worker, longitudinal study, Menlo Park, meta-analysis, mutually assured destruction, Nash equilibrium, Netflix Prize, new economy, Norbert Wiener, NP-complete, nuclear winter, optical character recognition, pattern recognition, performance metric, phenotype, prediction markets, price stability, principal–agent problem, race to the bottom, random walk, Ray Kurzweil, recommendation engine, reversible computing, social graph, speech recognition, Stanislav Petrov, statistical model, stem cell, Stephen Hawking, strong AI, superintelligent machines, supervolcano, technological singularity, technoutopianism, The Coming Technological Singularity, The Nature of the Firm, Thomas Kuhn: the structure of scientific revolutions, Tragedy of the Commons, transaction costs, Turing machine, Vernor Vinge, Watson beat the top human players on Jeopardy!, World Values Survey, zero-sum game
Available at http://web.archive.org/web/20090615040912/http://www.aeiveos.com/~bradbury/MatrioshkaBrains/MatrioshkaBrainsPaper.html. Brinton, Crane. 1965. The Anatomy of Revolution. Revised ed. New York: Vintage Books. Bryson, Arthur E., Jr., and Ho, Yu-Chi. 1969. Applied Optimal Control: Optimization, Estimation, and Control. Waltham, MA: Blaisdell. Buehler, Martin, Iagnemma, Karl, and Singh, Sanjiv, eds. 2009. The DARPA Urban Challenge: Autonomous Vehicles in City Traffic. Springer Tracts in Advanced Robotics 56. Berlin: Springer. Burch-Brown, J. 2014. “Clues for Consequentialists.” Utilitas 26 (1): 105–19. Burke, Colin. 2001. “Agnes Meyer Driscoll vs. the Enigma and the Bombe.” Unpublished manuscript. Retrieved February 22, 2013.
INDEX A Afghan Taliban 215 Agricultural Revolution 2, 80, 261 AI-complete problem 14, 47, 71, 93, 145, 186 AI-OUM, see optimality notions AI-RL, see optimality notions AI-VL, see optimality notions algorithmic soup 172 algorithmic trading 16–17 anthropics 27–28, 126, 134–135, 174, 222–225 definition 225 Arendt, Hannah 105 Armstrong, Stuart 280, 291, 294, 302 artificial agent 10, 88, 105–109, 172–176, 185–206; see also Bayesian agent artificial intelligence arms race 64, 88, 247 future of 19, 292 greater-than-human, see superintelligence history of 5–18 overprediction of 4 pioneers 4–5, 18 Asimov, Isaac 139 augmentation 142–143, 201–203 autism 57 automata theory 5 automatic circuit breaker 17 automation 17, 98, 117, 160–176 B backgammon 12 backpropagation algorithm 8 bargaining costs 182 Bayesian agent 9–11, 123, 130; see also artificial agent and optimality notions Bayesian networks 9 Berliner, Hans 12 biological cognition 22, 36–48, 50–51, 232 biological enhancement 36–48, 50–51, 142–143, 232; see also cognitive enhancement boxing 129–131, 143, 156–157 informational 130 physical 129–130 brain implant, see cyborg brain plasticity 48 brain–computer interfaces 44–48, 51, 83, 142–143; see also cyborg Brown, Louise 43 C C. elegans34–35, 266, 267 capability control 129–144, 156–157 capital 39, 48, 68, 84–88, 99, 113–114, 159–184, 251, 287, 288, 289 causal validity semantics 197 CEV, see coherent extrapolated volition Chalmers, David 24, 265, 283, 295, 302 character recognition 15 checkers 12 chess 11–22, 52, 93, 134, 263, 264 child machine 23, 29; see also seed AI CHINOOK 12 Christiano, Paul 198, 207 civilization baseline 63 cloning 42 cognitive enhancement 42–51, 67, 94, 111–112, 193, 204, 232–238, 244, 259 coherent extrapolated volition (CEV) 198, 211–227, 296, 298, 303 definition 211 collaboration (benefits of) 249 collective intelligence 48–51, 52–57, 67, 72, 142, 163, 203, 259, 271, 273, 279 collective superintelligence 39, 48–49, 52–59, 83, 93, 99, 285 definition 54 combinatorial explosion 6, 9, 10, 47, 155 Common Good Principle 254–259 common sense 14 computer vision 9 computing power 7–9, 24, 25–35, 47, 53–60, 68–77, 101, 134, 155, 198, 240–244, 251, 286, 288; see also computronium and hardware overhang computronium 101, 123–124, 140, 193, 219; see also computing power connectionism 8 consciousness 22, 106, 126, 139, 173–176, 216, 226, 271, 282, 288, 292, 303; see also mind crime control methods 127–144, 145–158, 202, 236–238, 286; see also capability control and motivation selection Copernicus, Nicolaus 14 cosmic endowment 101–104, 115, 134, 209, 214–217, 227, 250, 260, 283, 296 crosswords (solving) 12 cryptographic reward tokens 134, 276 cryptography 80 cyborg 44–48, 67, 270 D DARPA, see Defense Advanced Research Projects Agency DART (tool) 15 Dartmouth Summer Project 5 data mining 15–16, 232, 301 decision support systems 15, 98; see also tool-AI decision theory 10–11, 88, 185–186, 221–227, 280, 298; see also optimality notions decisive strategic advantage 78–89, 95, 104–112, 115–126, 129–138, 148–149, 156–159, 177, 190, 209–214, 225, 252 Deep Blue 12 Deep Fritz 22 Defense Advanced Research Projects Agency (DARPA) 15 design effort, see optimization power Dewey, Daniel 291 Differential Technological Development (Principle of) 230–237 Diffie–Hellman key exchange protocol 80 diminishing returns 37–38, 66, 88, 114, 273, 303 direct reach 58 direct specification 139–143 DNA synthesis 39, 98 Do What I Mean (DWIM) 220–221 domesticity 140–143, 146–156, 187, 191, 207, 222 Drexler, Eric 239, 270, 276, 278, 300 drones 15, 98 Dutch book 111 Dyson, Freeman 101, 278 E economic growth 3, 160–166, 179, 261, 274, 299 Einstein, Albert 56, 70, 85 ELIZA (program) 6 embryo selection 36–44, 67, 268 emulation modulation 207 Enigma code 87 environment of evolutionary adaptedness 164, 171 epistemology 222–224 equation solvers 15 eugenics 36–44, 268, 279 Eurisko 12 evolution 8–9, 23–27, 44, 154, 173–176, 187, 198, 207, 265, 266, 267, 273 evolutionary selection 187, 207, 290 evolvable hardware 154 exhaustive search 6 existential risk 4, 21, 55, 100–104, 115–126, 175, 183, 230–236, 239–254, 256–259, 286, 301–302 state risks 233–234 step risks 233 expert system 7 explicit representation 207 exponential growth, see growth external reference semantics 197 F face recognition 15 failure modes 117–120 Faraday cage 130 Fields Medal 255–256, 272 Fifth-Generation Computer Systems Project 7 fitness function 25; see also evolution Flash Crash (2010) 16–17 formal language 7, 145 FreeCell (game) 13 G game theory 87, 159 game-playing AI 12–14 General Problem Solver 6 genetic algorithms 7–13, 24–27, 237–240; see also evolution genetic selection 37–50, 61, 232–238; see also evolution genie AI 148–158, 285 definition 148 genotyping 37 germline interventions 37–44, 67, 273; see also embryo selection Ginsberg, Matt 12 Go (game) 13 goal-content 109–110, 146, 207, 222–227 Good Old-Fashioned Artificial Intelligence (GOFAI) 7–15, 23 Good, I.
These still depend mainly on remote control by human operators, but work is underway to extend their autonomous capabilities. Intelligent scheduling is a major area of success. The DART tool for automated logistics planning and scheduling was used in Operation Desert Storm in 1991 to such effect that DARPA (the Defense Advanced Research Projects Agency in the United States) claims that this single application more than paid back their thirty-year investment in AI.68 Airline reservation systems use sophisticated scheduling and pricing systems. Businesses make wide use of AI techniques in inventory control systems.
Traffic: Why We Drive the Way We Do (And What It Says About Us) by Tom Vanderbilt
Albert Einstein, autonomous vehicles, availability heuristic, Berlin Wall, call centre, cellular automata, Cesare Marchetti: Marchetti’s constant, cognitive dissonance, computer vision, congestion charging, Daniel Kahneman / Amos Tversky, DARPA: Urban Challenge, endowment effect, extreme commuting, fundamental attribution error, Garrett Hardin, Google Earth, hedonic treadmill, hindsight bias, hive mind, if you build it, they will come, impulse control, income inequality, Induced demand, invisible hand, Isaac Newton, Jane Jacobs, John Nash: game theory, Kenneth Arrow, lake wobegon effect, loss aversion, megacity, Milgram experiment, Nash equilibrium, Sam Peltzman, Silicon Valley, statistical model, the built environment, The Death and Life of Great American Cities, traffic fines, Tragedy of the Commons, ultimatum game, urban planning, urban sprawl, women in the workforce, working poor
If all this seems complicated enough, now consider doing all of it in the kind of environment in which most of us typically drive: not lonely desert passes but busy city and suburban streets. When I caught up with Thrun, this is exactly what was on his mind, for he was in the testing phase for DARPA’s next race, the Urban Challenge. This time the course would be in a city environment, with off-roading Stanley retired in favor of sensible Junior, a 2006 VW Passat Wagon. The goal, according to DARPA, would be “safe and correct autonomous driving capability in traffic at 20 mph,” including “merging into moving traffic, navigating traffic circles, negotiating busy intersections, and avoiding obstacles.”
twenty per mile: Leslie George Norman, “Road Traffic Accidents: Epidemiology, Control and Prevention” (World Health Organization, Public Health Papers no. 12, 1962), p. 51. 440 words, per minute: This figure comes from William Ewald, Street Graphics (Washington, D.C.: American Society of Landscape Architects Foundation), p. 32. “avoiding obstacles”: See Urban Challenge Rules (Arlington, Va.: Defense Advanced Research Projects Agency, July 10, 2007). in the future: The cognitive scientist Donald D. Hoffman points out that an average traffic scene of a tree-lined street with cars creates a multitude of problems for computer intelligence, as analysis by researcher Scott Richman has revealed. Hoffman notes, “Several problems that Richman faced are evident from this picture: clutter, trees moving in the wind, shadows dancing on the road, cars in front hiding cars behind.
The reason was that most of the course consisted of straight roads. Maintaining the highest average speed over these sections was more important than taking the relatively few turns (the most dangerous parts of the road) at the highest speed possible. “Driving smarter,” Montemerlo calls it. This is something he has thought a lot about for the Urban Challenge. “You might initially think, ‘I’ll take everything Junior does, and make it as fast as possible. I’ll make it accelerate from the stop sign as fast as possible. I’ll make it wait the minimum amount of time when it stops.’ But it turns out it doesn’t help that much. We all know it from traffic.
Beautiful Data: The Stories Behind Elegant Data Solutions by Toby Segaran, Jeff Hammerbacher
23andMe, airport security, Amazon Mechanical Turk, bioinformatics, Black Swan, business intelligence, card file, cloud computing, computer vision, correlation coefficient, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, DARPA: Urban Challenge, data acquisition, database schema, double helix, en.wikipedia.org, epigenetics, fault tolerance, Firefox, Hans Rosling, housing crisis, information retrieval, lake wobegon effect, longitudinal study, Mars Rover, natural language processing, openstreetmap, prediction markets, profit motive, semantic web, sentiment analysis, Simon Singh, social graph, SPARQL, speech recognition, statistical model, supply-chain management, text mining, Vernor Vinge, web application
Velodyne Lidar Velodyne is a company located just south of San Jose, California, that’s run by two guys who compete in robot combat events like Battle Bots and Robot Wars in their spare time. The company produces loudspeakers, stereo equipment, and (naturally) powerful laser scanning devices, including the HDL-64E Lidar we used to capture the landscape and party scenes in “House of Cards.” The HDL-64E’s real claim to fame is that it was used successfully by several of the 2007 DARPA Urban Challenge vehicles, including the winning team, to achieve environment and terrain vision. In some cases, it was these vehicles’ only vision system. Velodyne’s HDL-64E Lidar is a scanner with 64 laser emitters and 64 laser detectors. It spins in a circle, gathering data 360 degrees horizontally and 26.8 degrees vertically at a rate of over one million data points per second, which approximates to about 5 megabytes of raw data per second.
For a behind-the-scenes look at what the production was like, check out the “Making Of” video at the Google Code site I mentioned at the beginning of this chapter. The Outdoor Lidar Shoot The first thing we did on arrival in Florida was set the Lidar up on the back of an old van the production crew had rented. We used the van to capture the static landscape data you see in the video, such as the city and the cul-de-sac. Unlike the DARPA Urban Challenge vehicles, we did not put the Lidar on top of the vehicle. Instead, we tilted it 90 degrees and mounted it to the back of the van. This meant that the lasers would sweep the environment vertically. If picturing this is confusing, think of a lighthouse tipped on its side and sticking off the back of the vehicle, like a tail pipe.
Apposite calls have been made to the community to formally critique visualization work in an effort to consider aesthetics more collectively (e.g., Kosara 2007). However, until a usable body of knowledge is developed, we are reliant upon broad principles and rules of thumb when developing aesthetically pleasing graphics. A number of these are used in Beautiful Code (Oram and Wilson 2007) and can be usefully applied to data visualization. For example, Brian Kernighan (Kernighan 2007) identifies characteristics of beautiful code that include compactness, elegance, efficiency, and utility, and informally quantifies compactness by indicating, “Ideally the code would fit onto a single page.”
Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia by Anthony M. Townsend
1960s counterculture, 4chan, A Pattern Language, Airbnb, Amazon Web Services, anti-communist, Apple II, Bay Area Rapid Transit, Burning Man, business process, call centre, carbon footprint, charter city, chief data officer, clean water, cleantech, cloud computing, computer age, congestion charging, connected car, crack epidemic, crowdsourcing, DARPA: Urban Challenge, data acquisition, Deng Xiaoping, digital map, Donald Davies, East Village, Edward Glaeser, game design, garden city movement, Geoffrey West, Santa Fe Institute, George Gilder, ghettoisation, global supply chain, Grace Hopper, Haight Ashbury, Hedy Lamarr / George Antheil, hive mind, Howard Rheingold, interchangeable parts, Internet Archive, Internet of things, Jacquard loom, Jane Jacobs, jitney, John Snow's cholera map, Joi Ito, Khan Academy, Kibera, Kickstarter, knowledge worker, load shedding, M-Pesa, Mark Zuckerberg, megacity, mobile money, mutually assured destruction, new economy, New Urbanism, Norbert Wiener, Occupy movement, off grid, openstreetmap, packet switching, Panopticon Jeremy Bentham, Parag Khanna, patent troll, Pearl River Delta, place-making, planetary scale, popular electronics, RFC: Request For Comment, RFID, ride hailing / ride sharing, Robert Gordon, self-driving car, sharing economy, Shenzhen special economic zone , Silicon Valley, Skype, smart cities, smart grid, smart meter, social graph, social software, social web, special economic zone, Steve Jobs, Steve Wozniak, Stuxnet, supply-chain management, technoutopianism, Ted Kaczynski, telepresence, The Death and Life of Great American Cities, too big to fail, trade route, Tyler Cowen: Great Stagnation, undersea cable, Upton Sinclair, uranium enrichment, urban decay, urban planning, urban renewal, Vannevar Bush, working poor, working-age population, X Prize, Y2K, zero day, Zipcar
., “Downtime statistics of current cloud solutions,” International Working Group on Cloud Computing Resiliency website, n.d., accessed February 14, 2013, http://iwgcr.org/wp-content/uploads/2012/06/IWGCR-Paris.Ranking-002-en.pdf. 34Kathleen Hickey, “DARPA: Dump Passwords for Always-on Biometrics,” Government Computer News, March 21, 2012, http://gcn.com/articles/2012/03/21/darpa-dump-passwords-continuous-biometrics.aspx. 35Global Positioning System: Significant Challenges in Sustaining and Upgrading Widely Used Capabilities (US Government Accountability Office: Washington, DC), GAO-09-670T, May 7, 2009, http://www.gao.gov/products/GAO-09-670T. 36Global Navigation Space Systems: Reliance and Vulnerabilities (London: Royal Academy of Engineering, 2011), 3. 37“Scientists Warn of ‘Dangerous Over-reliance on GPS,’” The Raw Story, March 8, 2011, http://www.rawstory.com/rs/2011/03/08/scientists-warn-of-dangerous-over-reliance-on-gps/. 38“BufferBloat: What’s Wrong with the Internet?”
In chapter 3 we saw how the roots of modern city planning grew from Patrick Geddes’s evolutionary understanding of cities and his belief that the practical application of sociology was crucial to solving the fast-multiplying problems of industrial-era cities. Geddes would no doubt approve of how today’s smart-city builders are applying technology to urban challenges and seeking to develop a new, rigorous empirical science of cities. But he also understood the limits of science, and the need to view cities with eyes that see not only facts, but wonder as well. As biographer Helen Meller wrote, Geddes believed that “the city had to be seen as a whole, not as an amalgam of disparate elements each requiring specific treatment. . . .
ID=3352&sku=IN1104731WH. 40“Historical Figures in Telecommunications,” International Telecommunications Union, last modified February 11, 2010, http://www.itu.int/en/history/overview/Pages/figures.aspx. 41Urs Fitze, “No Longer A One-Way Street,” Pictures of the Future, Spring 2011, 22, http://www.siemens.com/innovation/pool/en/publikationen/ publications_pof/pof_spring_2011/pof_0111_strom_smartgrid_en.pdf. 42Edwin D. Hill, “New Challenges Demand New Solutions: IBEW Leader Charts Energy Future,” EnergyBiz, September/October 2007, http://energycentral.fileburst.com/EnergyBizOnline/2007-5-sep-oct/Financial_Front_New_Challenges.pdf. 43Martin Rosenberg, “Continental Grid Vision Needed,” RenewableEnergyWorld.com blog, last modified December 11, 2007, http://www.renewableenergyworld.com/rea/news/article/2007/12/continental-grid-vision-needed-50777. 44“Company development 1847–1865,” Siemens, n.d., http://www.siemens.com/history/en/history/1847_1865_beginnings_and_initial _expansion.htm. 45Jeff St.