robotic process automation

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pages: 347 words: 97,721

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

"World Economic Forum" Davos, AI winter, Amazon Robotics, Andy Kessler, Apollo Guidance Computer, artificial general intelligence, asset allocation, Automated Insights, autonomous vehicles, basic income, Baxter: Rethink Robotics, behavioural economics, business intelligence, business process, call centre, carbon-based life, Clayton Christensen, clockwork universe, commoditize, conceptual framework, content marketing, dark matter, data science, David Brooks, deep learning, deliberate practice, deskilling, digital map, disruptive innovation, Douglas Engelbart, driverless car, Edward Lloyd's coffeehouse, Elon Musk, Erik Brynjolfsson, estate planning, financial engineering, fixed income, flying shuttle, follow your passion, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, game design, general-purpose programming language, global pandemic, Google Glasses, Hans Lippershey, haute cuisine, income inequality, independent contractor, index fund, industrial robot, information retrieval, intermodal, Internet of things, inventory management, Isaac Newton, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joi Ito, Khan Academy, Kiva Systems, knowledge worker, labor-force participation, lifelogging, longitudinal study, loss aversion, machine translation, Mark Zuckerberg, Narrative Science, natural language processing, Nick Bostrom, Norbert Wiener, nuclear winter, off-the-grid, pattern recognition, performance metric, Peter Thiel, precariat, quantitative trading / quantitative finance, Ray Kurzweil, Richard Feynman, risk tolerance, Robert Shiller, robo advisor, robotic process automation, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, six sigma, Skype, social intelligence, speech recognition, spinning jenny, statistical model, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, superintelligent machines, supply-chain management, tacit knowledge, tech worker, TED Talk, the long tail, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, Works Progress Administration, Zipcar

“An Updated Survey of Health Insurance Claims Receipt and Processing Times,” AHIP Center for Policy and Research, February 2013, http://www.ahip.org/survey/Healthcare-January 2013/. 9. Mary Lacity, Leslie Willcocks, and Andrew Craig, “Robotic Process Automation at Telefonica O2,” London School of Economics case study, April 2015, http://www.umsl.edu/~lacitym/TelefonicaOUWP022015FINAL.pdf. 10. Interview with Paul Donaldson, then of Xchanging, and Leslie Willcocks, Mary Lacity, and Andrew Craig, “Robotic Process Automation at Xchanging,” London School of Economics case study, June 2015, https://www.xchanging.com/system/files/dedicated-downloads/robotic-process-automation.pdf. 11. Jordan Novet, “South Korea’s Team KAIST Wins the 2015 DARPA Robotics Challenge,” VentureBeat, June 6, 2015, http://venturebeat.com/2015/06/06/koreas-team-kaist-wins-the-2015-darpa-robotics-challenge/. 12.

In health insurance companies, for example, the automated processing of medical claims (known as “auto-adjudication”) went from 37 percent in 2002 to 79 percent in 2011—and it’s probably well over that figure now.8 While this sort of automated decision-making can be done with paper documents, it’s a lot easier if the information is all digitized. More recently, companies have begun to employ a technology related to business rules and BPM called “robotic process automation.” This technology has the following traits: It does not involve robots, contrary to its name; It makes use of workflow and business rules technology; It is easily configured and modified by business users; It deals with highly repetitive and transactional tasks; It doesn’t learn or improve its performance without human modification; It typically interfaces with multiple information systems as if it were a human user; this is called “presentation layer” integration.

It is probable that many people who today hold tiny monopolies on specialized tasks they have mastered will see computers come to threaten them. Indeed, we were reminded of this in a recent conversation with Alastair Bathgate, founder of Blue Prism, the company we mentioned in Chapter 2. He sells “robotic” process automation to businesses that enables them to automate routine back-office process tasks, even where the numbers of knowledge workers performing them are not vast. We put quotes around the word “robotic” there because in fact this is software; the human’s replacement in the process has no physical embodiment.


pages: 301 words: 89,076

The Globotics Upheaval: Globalisation, Robotics and the Future of Work by Richard Baldwin

agricultural Revolution, Airbnb, AlphaGo, AltaVista, Amazon Web Services, Apollo 11, augmented reality, autonomous vehicles, basic income, Big Tech, bread and circuses, business process, business process outsourcing, call centre, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, commoditize, computer vision, Corn Laws, correlation does not imply causation, Credit Default Swap, data science, David Ricardo: comparative advantage, declining real wages, deep learning, DeepMind, deindustrialization, deskilling, Donald Trump, Douglas Hofstadter, Downton Abbey, Elon Musk, Erik Brynjolfsson, facts on the ground, Fairchild Semiconductor, future of journalism, future of work, George Gilder, Google Glasses, Google Hangouts, Hans Moravec, hiring and firing, hype cycle, impulse control, income inequality, industrial robot, intangible asset, Internet of things, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, Kevin Roose, knowledge worker, laissez-faire capitalism, Les Trente Glorieuses, low skilled workers, machine translation, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, manufacturing employment, Mark Zuckerberg, mass immigration, mass incarceration, Metcalfe’s law, mirror neurons, new economy, optical character recognition, pattern recognition, Ponzi scheme, post-industrial society, post-work, profit motive, remote working, reshoring, ride hailing / ride sharing, Robert Gordon, Robert Metcalfe, robotic process automation, Ronald Reagan, Salesforce, San Francisco homelessness, Second Machine Age, self-driving car, side project, Silicon Valley, Skype, Snapchat, social intelligence, sovereign wealth fund, standardized shipping container, statistical model, Stephen Hawking, Steve Jobs, supply-chain management, systems thinking, TaskRabbit, telepresence, telepresence robot, telerobotics, Thomas Malthus, trade liberalization, universal basic income, warehouse automation

Peter Bright, “Moore’s Law Really Is Dead This Time,” ArsTechnica.com, November 2, 2016. 5. Daniel Robinson, “Moore’s Law Is Running Out—But Don’t Panic,” ComputerWeekly.com, November 19, 2017. 6. See Leslie Willcocks, Mary Lacity, and Andrew Craig, “Robotic Process Automation at Xchanging,” Outsourcing Unit Working Research Paper Series 15/03, London School of Economics and Political Science, June 2015. 7. Willcocks, Lacity, and Craig, “Robotic Process Automation at Xchanging.” 8. Quoted in Jesse Scardina, “Conversica Cloud AI Software Tackles Sales Leads,” TechTarget. com (blog), June 1, 2016. 9. Machine learning has been around for decades, but a lack of computer power and data limited the effectiveness of the algorithms it produced in the past. 10.

Poppy is part of the new digital workforce where the “digital” refers to the worker not the work. She is a white-collar robot where the “white collar” refers to the attire of the workers she is replacing not the clothing that the robot is wearing. Poppy is an example of a new form of artificial intelligence called robotic process automation (RPA) which draws on the new capacities created by machine learning. Barnes views Poppy as a co-worker despite the fact that “she” is really just a piece of software. Indeed, it was Barnes who gave the software a name. Perhaps this naming stems from the fact that the software does exactly what Barnes used to do, and in exactly the same way.

MEET WHITE-COLLAR AUTOMATION The sophisticated computer systems and machine learning algorithms that are behind Lex Machina and the like are very expensive and require PhD-level computer scientists to get them up and running. If these sophisticated AI platforms were restaurants, they’d have a Michelin star or two. This puts them out of the reach of the companies for which most people work, namely small-and medium-sized firms. There is, however, a “fast-food” version of white-collar robots. It’s called “robotic process automation” (RPA) software; Poppy, who we met in Chapter 4, is a good example. RPA is probably not what comes to mind when people speak of the “robot apocalypse,” but RPA will be a key part of the Globotics Transformation. It’s worth a closer look. RPAs are automating white-collar jobs in a very direct way.


pages: 523 words: 61,179

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

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

Seth Fletcher, “How Big Data Is Taking Teachers Out of the Lecturing Business,” Scientific American, August 1, 2013, https://www.scientificamerican.com/article/how-big-data-taking-teachers-out-lecturing-business. Moving Well Beyond RPA The Virgin Trains system is a relatively advanced form of back-office automation because it can analyze and adapt to unstructured data as well as the sudden influx of data. Such applications are called “robotic process automation” (RPA). Simply put, RPA is software that performs digital office tasks that are administrative, repetitive, and mostly transactional within a workflow. In other words, it automates existing processes. But in order to reimagine processes, firms must utilize more advanced technologies—namely, AI.

See personalization cybersecurity, 56–58, 59 Darktrace, 58 DARPA Cyber Grand Challenges, 57, 190 Dartmouth College conference, 40–41 dashboards, 169 data, 10 in AI training, 121–122 barriers to flow of, 176–177 customization and, 78–80 discovery with, 178 dynamic, real-time, 175–176 in enterprise processes, 59 exhaust, 15 in factories, 26–27, 29–30 leadership and, 180 in manufacturing, 38–39 in marketing and sales, 92, 98–99, 100 in R&D, 69–72 in reimagining processes, 154 on supply chains, 33–34 supply chains for, 12, 15 velocity of, 177–178 data hygienists, 121–122 data supply-chain officers, 179 data supply chains, 12, 15, 174–179 decision making, 109–110 about brands, 93–94 black box, 106, 125, 169 employee power to modify AI, 172–174 empowerment for, 15 explainers and, 123–126 transparency in, 213 Deep Armor, 58 deep learning, 63, 161–165 deep-learning algorithms, 125 DeepMind, 121 deep neural networks (DNN), 63 deep reinforcement learning, 21–22 demand planning, 33–34 Dennis, Jamie, 158 design at Airbus, 144 AI system, 128–129 Elbo Chair, 135–137 generative, 135–137, 139, 141 product/service, 74–77 Dickey, Roger, 52–54 digital twins, 10 at GE, 27, 29–30, 183–184, 194 disintermediation, brand, 94–95 distributed learning, 22 distribution, 19–39 Ditto Labs, 98 diversity, 52 Doctors Without Borders, 151 DoubleClick Search, 99 Dreamcatcher, 136–137, 141, 144 drones, 28, 150–151 drug interactions, 72–74 Ducati, 175 Echo, 92, 164–165 Echo Voyager, 28 Einstein, 85–86, 196 Elbo Chair, 136–137, 139 “Elephants Don’t Play Chess” (Brooks), 24 Elish, Madeleine Clare, 170–171 Ella, 198–199 embodied intelligence, 206 embodiment, 107, 139–140 in factories, 21–23 of intelligence, 206 interaction agents, 146–151 jobs with, 147–151 See also augmentation; missing middle empathy engines for health care, 97 training, 117–118, 132 employees agency of, 15, 172–174 amplification of, 138–139, 141–143 development of, 14 hiring, 51–52 job satisfaction in, 46–47 marketing and sales, 90, 92, 100–101 on-demand work and, 111 rehumanizing time and, 186–189 routine/repetitive work and, 26–27, 29–30, 46–47 training/retraining, 15 warehouse, 31–33 empowerment, 137 bot-based, 12, 195–196 in decision making, 15 of salespeople, 90, 92 workforce implications of, 137–138 enabling, 7 enterprise processes, 45–66 compliance, 47–48 determining which to change, 52–54 hiring and recruitment, 51–52 how much to change, 54–56 redefining industries with, 56–58 reimagining around people, 58–59 robotic process automation (RPA) in, 50–52 routine/repetitive, 46–47 ergonomics, 149–150 EstherBot, 199 ethical, moral, legal issues, 14–15, 108 Amazon Echo and, 164–165 explainers and, 123–126 in marketing and sales, 90, 100 moral crumple zones and, 169–172 privacy, 90 in R&D, 83 in research, 78–79 ethics compliance managers, 79, 129–130, 132–133 European Union, 124 Ewing, Robyn, 119 exhaust data, 15 definition of, 122 experimentation, 12, 14 cultures of, 161–165 in enterprise processes, 59 leadership and, 180 learning from, 71 in manufacturing, 39 in marketing and sales, 100 in process reimagining, 160–165 in R&D, 83 in reimagining processes, 154 testing and, 74–77 expert systems, 25, 41 definition of, 64 explainability strategists, 126 explaining outcomes, 107, 114–115, 179 black-box concerns and, 106, 125, 169 jobs in, 122–126 sustaining and, 130 See also missing middle extended intelligence, 206 extended reality, 66 Facebook, 78, 79, 95, 177–178 facial recognition, 65, 90 factories, 10 data flow in, 26–27, 29–30 embodiment in, 140 job losses and gains in, 19, 20 robotic arms in, 21–26 self-aware, 19–39 supply chains and, 33–34 third wave in, 38–39 traditional assembly lines and, 1–2, 4 warehouse management and, 30–33 failure, learning from, 71 fairness, 129–130 falling rule list algorithms, 124–125 Fanuc, 21–22, 128 feedback, 171–172 feedforward neural networks (FNN), 63 Feigenbaum, Ed, 41 financial trading, 167 first wave of business transformation, 5 Fletcher, Seth, 49 food production, 34–37 ForAllSecure, 57 forecasts, 33–34 Fortescue Metals Group, 28 Fraunhofer Institute of Material Flow and Logistics (IML), 26 fusion skills, 12, 181, 183–206, 210 bot-based empowerment, 12, 195–196 developing, 15–16 holistic melding, 12, 197, 200–201 intelligent interrogation, 12, 185, 193–195 judgment integration, 12, 191–193 potential of, 209 reciprocal apprenticing, 12, 201–202 rehumanizing time, 12, 186–189 relentless reimagining, 12, 203–205 responsible normalizing, 12, 189–191 training/retraining for, 211–213 Future of Work survey, 184–185 Garage, Capital One, 205 Gaudin, Sharon, 99 GE.

See research and development (R&D) reciprocal apprenticing, 12, 201–202 recommendation systems, 65, 92, 110–111 recurrent neural networks (RNN), 63 regulations, 213 reimagining, relentless, 12, 203–205 reinforcement learning, 62 repetitive/routine work, 26–27, 29–30, 46–47 process reimagination and, 52–54 in R&D, 69–72 reimagining around people, 58–59 research and development (R&D), 10, 67–83 customization and delivery in, 77–80 ethical/legal issues in, 78–79 hypotheses in, 72–74 MELDS in, 83 observation in, 69–72 risk management and, 80–81 scientific method in, 69–77 testing in, 74–77 resource management, 74–75 retail pricing, 193–194 Rethink Robotics, 22, 24 Reverse Engineering and Forward Simulation (REFS), 72–74 Revionics, 194 Riedl, Mark O., 130 right to explanation, 124 Rio Tinto, 7–8, 109–110 risk management, 80–81 robotic arms, 21–23 learning by, 24–26 robotic process automation (RPA), 50–52 Robotics, Three Laws of (Asimov), 128–129 Robotiq, 23 Roomba, 24 Rosenblatt, Frank, 62 Round Chair, 136–137 routine work. See repetitive/routine work Royal Dutch Shell, 192 Ruh, Bill, 194–195 “Runaround” (Asimov), 128–129 Russo, Daniel, 49 safety engineers, AI, 129 safety issues, 126–129 Sagan, Carl, 135 sales.


pages: 619 words: 177,548

Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity by Daron Acemoglu, Simon Johnson

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 4chan, agricultural Revolution, AI winter, Airbnb, airline deregulation, algorithmic bias, algorithmic management, Alignment Problem, AlphaGo, An Inconvenient Truth, artificial general intelligence, augmented reality, basic income, Bellingcat, Bernie Sanders, Big Tech, Bletchley Park, blue-collar work, British Empire, carbon footprint, carbon tax, carried interest, centre right, Charles Babbage, ChatGPT, Clayton Christensen, clean water, cloud computing, collapse of Lehman Brothers, collective bargaining, computer age, Computer Lib, Computing Machinery and Intelligence, conceptual framework, contact tracing, Corn Laws, Cornelius Vanderbilt, coronavirus, corporate social responsibility, correlation does not imply causation, cotton gin, COVID-19, creative destruction, declining real wages, deep learning, DeepMind, deindustrialization, Demis Hassabis, Deng Xiaoping, deskilling, discovery of the americas, disinformation, Donald Trump, Douglas Engelbart, Douglas Engelbart, Edward Snowden, Elon Musk, en.wikipedia.org, energy transition, Erik Brynjolfsson, European colonialism, everywhere but in the productivity statistics, factory automation, facts on the ground, fake news, Filter Bubble, financial innovation, Ford Model T, Ford paid five dollars a day, fulfillment center, full employment, future of work, gender pay gap, general purpose technology, Geoffrey Hinton, global supply chain, Gordon Gekko, GPT-3, Grace Hopper, Hacker Ethic, Ida Tarbell, illegal immigration, income inequality, indoor plumbing, industrial robot, interchangeable parts, invisible hand, Isaac Newton, Jacques de Vaucanson, James Watt: steam engine, Jaron Lanier, Jeff Bezos, job automation, Johannes Kepler, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph-Marie Jacquard, Kenneth Arrow, Kevin Roose, Kickstarter, knowledge economy, labor-force participation, land reform, land tenure, Les Trente Glorieuses, low skilled workers, low-wage service sector, M-Pesa, manufacturing employment, Marc Andreessen, Mark Zuckerberg, megacity, mobile money, Mother of all demos, move fast and break things, natural language processing, Neolithic agricultural revolution, Norbert Wiener, NSO Group, offshore financial centre, OpenAI, PageRank, Panopticon Jeremy Bentham, paperclip maximiser, pattern recognition, Paul Graham, Peter Thiel, Productivity paradox, profit maximization, profit motive, QAnon, Ralph Nader, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Robert Bork, Robert Gordon, Robert Solow, robotic process automation, Ronald Reagan, scientific management, Second Machine Age, self-driving car, seminal paper, shareholder value, Sheryl Sandberg, Shoshana Zuboff, Silicon Valley, social intelligence, Social Responsibility of Business Is to Increase Its Profits, social web, South Sea Bubble, speech recognition, spice trade, statistical model, stem cell, Steve Jobs, Steve Wozniak, strikebreaker, subscription business, Suez canal 1869, Suez crisis 1956, supply-chain management, surveillance capitalism, tacit knowledge, tech billionaire, technoutopianism, Ted Nelson, TED Talk, The Future of Employment, The Rise and Fall of American Growth, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, theory of mind, Thomas Malthus, too big to fail, total factor productivity, trade route, transatlantic slave trade, trickle-down economics, Turing machine, Turing test, Twitter Arab Spring, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, union organizing, universal basic income, Unsafe at Any Speed, Upton Sinclair, upwardly mobile, W. E. B. Du Bois, War on Poverty, WikiLeaks, wikimedia commons, working poor, working-age population

Prediction Machines: The Simple Economics of Artificial Intelligence. Cambridge, MA: Harvard Business Review Press. Agrawal, D. P. 2007. The Indus Civilization: An Interdisciplinary Perspective. New Delhi: Aryan. AIIM (Association for Intelligent Information Management). 2022. “What Is Robotic Process Automation?” www.aiim.org/what-is-robotic-process-automation. Alexander, Magnus W. 1929. “The Economic Evolution of the United States: Its Background and Significance.” Address presented at the World Engineering Congress, Tokyo, Japan, November 1929. National Industrial Conference Board, New York. Alexopoulos, Michelle, and Jon Cohen. 2016.

Sophisticated tax-preparation software can query users about expenses or items that look suspicious, and customers can be presented with voice-activated menus to categorize their problem (even if this often works imperfectly, ends up shifting some of the work to users, and causes longer delays as customers wait for a human to provide the necessary help). In robotic process automation (RPA), for example, software implements tasks after watching human actions in the application’s graphical user interface. RPA bots are now deployed in banking, lending decisions, e-commerce, and various software-support functions. Prominent examples include automated voice-recognition systems and chatbots that learn from remote IT-support practices.

“[T]he challenge is…,” “improving lives…,” “creative capitalism,” and “to take on…” are from Gates (2008). On various definitions of AI, see the leading textbook, Russell and Norvig (2009), which provides several different definitions. From the Field of AI Dreams. On Jacquard’s loom, see Essinger (2004). On robotic process automation, see AIIM (2022) and Roose (2021). On RPAs’ mixed results, see Trefler (2018). On the classification of routine tasks, see Autor, Levy, and Murnane (2003) and Acemoglu and Autor (2011). The prediction that AI can perform close to 50 percent of jobs is in Frey and Osborne (2013). Further discussion can be found in Susskind (2020).


AI 2041 by Kai-Fu Lee, Chen Qiufan

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

But what exactly are the new jobs—and will they satisfy many humans’ desire to feel productive and useful? Who is most at risk, and how can humans flourish in the post-automation era? I’ll give my thoughts on these questions in the commentary at the end of the chapter, describing how technologies like robotics and robotic process automation will continue to evolve to take over tasks for both white-collar and blue-collar workers. IN THE DARKENED training room, Jennifer Greenwood and twelve other trainees gazed attentively at the imagery scrolling in midair before them. Accompanying the visuals was a male voice, narrating softly, like an oracle pronouncing divinations.

The company required him to use internal software to manage client data. A semi-intelligent assistant program, powered with machine learning, would pop up from time to time, helping him crunch numbers or tidy up, fill in forms, and generate notices—junior grunt work. This system, called RPA (robotic process automation), became the last straw for her father. Slowly, he noticed this helper getting smarter and doing more, sometimes even correcting his own small human errors. When her father finally awoke to reality, it overwhelmed him. Every time the smart helper made a correction on a task, the AI noted the fix as a data point, helping it become ever smarter in the process, ever closer to replacing its human co-worker.

How far will this job displacement go, and what industries will it hit hardest? In AI Superpowers I estimated that about 40 percent of our jobs could be accomplished mostly by AI and automation technologies by 2033. It’s not going to happen overnight, of course. Jobs will be taken over by AI gradually, just as we saw with RPA (robotic process automation) and Jennifer’s father, the underwriter, in “The Job Savior.” RPA is a “software robot” installed on workers’ computers that can watch everything the workers do. Over time, by watching millions of people at work, RPA figures out how to do employees’ routine and repetitive tasks. At some point, a company will decide it’s better off letting the robot take over a given task entirely from the human.


pages: 251 words: 80,831

Super Founders: What Data Reveals About Billion-Dollar Startups by Ali Tamaseb

"World Economic Forum" Davos, 23andMe, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Anne Wojcicki, asset light, barriers to entry, Ben Horowitz, Benchmark Capital, bitcoin, business intelligence, buy and hold, Chris Wanstrath, clean water, cloud computing, coronavirus, corporate governance, correlation does not imply causation, COVID-19, cryptocurrency, data science, discounted cash flows, diversified portfolio, Elon Musk, Fairchild Semiconductor, game design, General Magic , gig economy, high net worth, hiring and firing, index fund, Internet Archive, Jeff Bezos, John Zimmer (Lyft cofounder), Kickstarter, late fees, lockdown, Lyft, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Max Levchin, Mitch Kapor, natural language processing, Network effects, nuclear winter, PageRank, PalmPilot, Parker Conrad, Paul Buchheit, Paul Graham, peer-to-peer lending, Peter Thiel, Planet Labs, power law, QR code, Recombinant DNA, remote working, ride hailing / ride sharing, robotic process automation, rolodex, Ruby on Rails, Salesforce, Sam Altman, Sand Hill Road, self-driving car, shareholder value, sharing economy, side hustle, side project, Silicon Valley, Silicon Valley startup, Skype, Snapchat, SoftBank, software as a service, software is eating the world, sovereign wealth fund, Startup school, Steve Jobs, Steve Wozniak, survivorship bias, TaskRabbit, telepresence, the payments system, TikTok, Tony Fadell, Tony Hsieh, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, ubercab, web application, WeWork, work culture , Y Combinator

Saving time and saving money were the most common needs addressed by billion-dollar companies, and startups working on those needs were more likely to become billion-dollar outcomes. Some startups can save their customers both time and money. When Romanian entrepreneurs built UiPath in 2005, they planned on outsourcing software projects for the world’s biggest companies. It wasn’t until 2012 that they realized the potential of robotic process automation (RPA) and pivoted their product to automate tasks that would typically require a human touch. RPA saves time in many ways: an insurance company can use RPA software to automate downloading a receipt from email and uploading it into a database; in the legal department of a large factory, RPA bots can help lawyers automatically send nondisclosure agreements.

Many bootstrapped for years before later raising money from growth-stage investors or private-equity funds to accelerate growth. Atlassian—an Australian multibillion-dollar software company best known for Jira, an issue-tracking application—bootstrapped for eight years before raising a growth round from Accel partners. UiPath, a robotic process-automation company started in Romania, bootstrapped for ten years before raising venture capital. Many such companies did consulting work and provided services to bring in revenues and sustain themselves in the beginning. Some simply didn’t know about or didn’t have access to investors and had to bootstrap out of necessity.


pages: 161 words: 39,526

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

Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, artificial general intelligence, autonomous vehicles, backpropagation, business intelligence, business process, call centre, chief data officer, cognitive load, computer vision, conceptual framework, data science, deep learning, DeepMind, en.wikipedia.org, fake news, future of work, Geoffrey Hinton, industrial robot, information security, Internet of things, iterative process, Jeff Bezos, job automation, machine translation, Marc Andreessen, natural language processing, new economy, OpenAI, pattern recognition, performance metric, price discrimination, randomized controlled trial, recommendation engine, robotic process automation, Salesforce, self-driving car, sentiment analysis, Silicon Valley, single source of truth, skunkworks, software is eating the world, source of truth, sparse data, speech recognition, statistical model, strong AI, subscription business, technological singularity, The future is already here

General Operations Most companies have tons of repetitive digital workflows. These workflows can be tedious to complete. Employees responsible for these tasks can easily become bored and inattentive, allowing errors to creep into your operations and your data. Fortunately, these tasks are well-suited for automation by Robotic Process Automation (RPA), which are software robots programmed to perform a specified sequence of actions. Even better, RPA deployment is relatively fast and low risk, so that problematic robots can quickly be removed without detriment to existing systems. Examples of workflows at which RPAs excel include performing regular diagnostics of your software or hardware, creating and updating accounting records (such as payroll), or automatically generating and delivering periodic reports to the relevant stakeholders.


Digital Transformation at Scale: Why the Strategy Is Delivery by Andrew Greenway,Ben Terrett,Mike Bracken,Tom Loosemore

Airbnb, behavioural economics, bitcoin, blockchain, butterfly effect, call centre, chief data officer, choice architecture, cognitive dissonance, cryptocurrency, data science, Diane Coyle, en.wikipedia.org, fail fast, G4S, hype cycle, Internet of things, Kevin Kelly, Kickstarter, loose coupling, M-Pesa, machine readable, megaproject, minimum viable product, nudge unit, performance metric, ransomware, robotic process automation, Silicon Valley, social web, The future is already here, the long tail, the market place, The Wisdom of Crowds, work culture

Fortunately, the technology hype cycle is ready to provide a stream of distractions. All too often, the word digital is conflated with whatever technology fad has made it into the colour supplements this month. Blockchain. Artificial intelligence. The Internet of Things and connected devices. Robotic Process Automation. The captains of industry, ministers and senior officials who read colour supplements during their brief periods of down time see these exciting things and commission policy papers to unpick their potential effect on the organisations they run. The papers are good. But there is a gap – sometimes a huge gap – between policy or business school smarts and technological literacy.


pages: 208 words: 57,602

Futureproof: 9 Rules for Humans in the Age of Automation by Kevin Roose

"World Economic Forum" Davos, adjacent possible, Airbnb, Albert Einstein, algorithmic bias, algorithmic management, Alvin Toffler, Amazon Web Services, Atul Gawande, augmented reality, automated trading system, basic income, Bayesian statistics, Big Tech, big-box store, Black Lives Matter, business process, call centre, choice architecture, coronavirus, COVID-19, data science, deep learning, deepfake, DeepMind, disinformation, Elon Musk, Erik Brynjolfsson, factory automation, fake news, fault tolerance, Frederick Winslow Taylor, Freestyle chess, future of work, Future Shock, Geoffrey Hinton, George Floyd, gig economy, Google Hangouts, GPT-3, hiring and firing, hustle culture, hype cycle, income inequality, industrial robot, Jeff Bezos, job automation, John Markoff, Kevin Roose, knowledge worker, Kodak vs Instagram, labor-force participation, lockdown, Lyft, mandatory minimum, Marc Andreessen, Mark Zuckerberg, meta-analysis, Narrative Science, new economy, Norbert Wiener, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, off-the-grid, OpenAI, pattern recognition, planetary scale, plutocrats, Productivity paradox, QAnon, recommendation engine, remote working, risk tolerance, robotic process automation, scientific management, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, social distancing, Steve Jobs, Stuart Kauffman, surveillance capitalism, tech worker, The Future of Employment, The Wealth of Nations by Adam Smith, TikTok, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, warehouse robotics, Watson beat the top human players on Jeopardy!, work culture

Back-office bots are the software programs that can do the kinds of menial, unsexy tasks that are necessary for any large organization to function. If you work in a big company, you can probably think of someone with a generic-sounding title like operations coordinator or benefits administrator—these are exactly the kinds of people back-office bots are designed to replace. Many of these apps fall into a category known as “robotic process automation,” or RPA. Automation Anywhere, the company whose conference was detailed in this book’s introduction, is a major RPA vendor, but there are others you’ve probably never heard of, with names like UiPath, Blue Prism, and Kryon. Collectively, these companies are worth billions of dollars, and they’ve been growing so quickly that even large tech companies have stepped into the RPA business.


pages: 241 words: 70,307

Leadership by Algorithm: Who Leads and Who Follows in the AI Era? by David de Cremer

"Friedman doctrine" OR "shareholder theory", algorithmic bias, algorithmic management, AlphaGo, bitcoin, blockchain, business climate, business process, Computing Machinery and Intelligence, corporate governance, data is not the new oil, data science, deep learning, DeepMind, Donald Trump, Elon Musk, fake news, future of work, job automation, Kevin Kelly, Mark Zuckerberg, meta-analysis, Norbert Wiener, pattern recognition, Peter Thiel, race to the bottom, robotic process automation, Salesforce, scientific management, shareholder value, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, Stephen Hawking, The Future of Employment, Turing test, work culture , workplace surveillance , zero-sum game

This is not simply a prediction for the future, it is something that is already happening today. Examples abound: IBM, for example, applies the algorithm Watson Talent to its own HR teams to promote speed, efficiency and the optimal use of their operations.⁹⁵ Another example of such automation is the use of Robotic Process Automation (RPA). RPA uses software algorithms to closely replicate repetitive tasks like moving data between two spreadsheets. And, finally, especially within the context of HR management, the employment of algorithms to conduct repetitive administrative tasks has already been proven to be effective.


pages: 260 words: 67,823

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

accounting loophole / creative accounting, Albert Einstein, AltaVista, Amazon Robotics, Amazon Web Services, Andy Rubin, anti-bias training, augmented reality, Automated Insights, autonomous vehicles, Bernie Sanders, Big Tech, Cambridge Analytica, Clayton Christensen, cloud computing, collective bargaining, computer vision, Donald Trump, drone strike, Elon Musk, fake news, Firefox, fulfillment center, gigafactory, Google Chrome, growth hacking, hive mind, income inequality, Infrastructure as a Service, inventory management, iterative process, Jeff Bezos, job automation, Jony Ive, Kiva Systems, knowledge economy, Lyft, Mark Zuckerberg, Menlo Park, new economy, Nick Bostrom, off-the-grid, Peter Thiel, QR code, ride hailing / ride sharing, robotic process automation, Salesforce, self-driving car, Sheryl Sandberg, Silicon Valley, Skype, Snapchat, SoftBank, Steve Ballmer, Steve Jobs, Steve Wozniak, super pumped, tech worker, Tim Cook: Apple, uber lyft, warehouse robotics, wealth creators, work culture , zero-sum game

“You can automate a good chunk of what it probably takes a human to do,” Venkat said, speaking with a slight hint of discomfort. “What used to take twelve days to do, in terms of processing a claim, now takes two days. It used to cost around two thousand dollars to go process something; now it costs three hundred.” UiPath is one of several “robotic process automation” companies currently surging to meet a growing demand for these capabilities. Less than two months after its Miami confab, one of UiPath’s main competitors, Automation Anywhere, raised $300 million from Softbank. And Google, for its part, isn’t the only company licensing AI decision-making power.


Four Battlegrounds by Paul Scharre

2021 United States Capitol attack, 3D printing, active measures, activist lawyer, AI winter, AlphaGo, amateurs talk tactics, professionals talk logistics, artificial general intelligence, ASML, augmented reality, Automated Insights, autonomous vehicles, barriers to entry, Berlin Wall, Big Tech, bitcoin, Black Lives Matter, Boeing 737 MAX, Boris Johnson, Brexit referendum, business continuity plan, business process, carbon footprint, chief data officer, Citizen Lab, clean water, cloud computing, commoditize, computer vision, coronavirus, COVID-19, crisis actor, crowdsourcing, DALL-E, data is not the new oil, data is the new oil, data science, deep learning, deepfake, DeepMind, Demis Hassabis, Deng Xiaoping, digital map, digital rights, disinformation, Donald Trump, drone strike, dual-use technology, Elon Musk, en.wikipedia.org, endowment effect, fake news, Francis Fukuyama: the end of history, future of journalism, future of work, game design, general purpose technology, Geoffrey Hinton, geopolitical risk, George Floyd, global supply chain, GPT-3, Great Leap Forward, hive mind, hustle culture, ImageNet competition, immigration reform, income per capita, interchangeable parts, Internet Archive, Internet of things, iterative process, Jeff Bezos, job automation, Kevin Kelly, Kevin Roose, large language model, lockdown, Mark Zuckerberg, military-industrial complex, move fast and break things, Nate Silver, natural language processing, new economy, Nick Bostrom, one-China policy, Open Library, OpenAI, PalmPilot, Parler "social media", pattern recognition, phenotype, post-truth, purchasing power parity, QAnon, QR code, race to the bottom, RAND corporation, recommendation engine, reshoring, ride hailing / ride sharing, robotic process automation, Rodney Brooks, Rubik’s Cube, self-driving car, Shoshana Zuboff, side project, Silicon Valley, slashdot, smart cities, smart meter, Snapchat, social software, sorting algorithm, South China Sea, sparse data, speech recognition, Steve Bannon, Steven Levy, Stuxnet, supply-chain attack, surveillance capitalism, systems thinking, tech worker, techlash, telemarketer, The Brussels Effect, The Signal and the Noise by Nate Silver, TikTok, trade route, TSMC

While Maven’s ability to move quickly, bring in commercial tech and connect it to real-world operational problems was a game-changer from a bureaucratic and cultural standpoint, many experts I spoke with who were familiar with Maven said it had not revolutionized intelligence analysis. While Maven’s tools were technically impressive, they were not (yet) delivering on AI’s hype. Shanahan said the most impactful work the JAIC had done was in “robotic process automation tools” (“I wouldn’t even call it AI,” he acknowledged). Rachael Martin, mission director for business process automation at the JAIC, explained that they were focused on using automation, analytics, and data augmentation to modernize business processes across the department to “either introduce efficiencies, or find cost savings, or find new insights, or be more predictive about the way that we conduct our business.”

., 44, 193 Pittsburgh Supercomputing Center, 44 PLA, See People’s Liberation Army Pluribus, 50, 51 poisonous animal recognition, 211 poker, 43–44, 46–48, 50, 266–67, 269–73, 335 Poland, 108 Police Audio Intelligent Service Platform, 95 Police Cloud, 89–90 policy analysis, automated, 206 Politiwatch, 124 pornography, 121, 130 Portman, Rob, 37 Poseidon, 289; See also Status-6 post-disaster assessment, 204 power metrics, 13 Prabhakar, Arati, 210 prediction systems, 287–88 predictive maintenance, 196–97, 201 Price, Colin “Farva,” 3 Primer (company), 224 Princeton University, 156, 157 Project Maven, 35–36, 52–53, 56–59, 194, 202, 205, 224; See also Google-Maven controversy Project Origin, 138 Project Voltron, 195–99 Putin, Vladimir, 9, 131, 304–5 Q*bert, 235 Quad summit, 76 Qualcomm Ventures, 157 Quantum Integrity, 132 quantum technology, 37 “rabbit hole” effect, 145 race to the bottom on safety, 286, 289, 304 radar, synthetic aperture, 210 Rahimi, Ali, 232 Raj, Devaki, 202, 207, 213, 224 Rambo (fictional character), 130 RAND Corporation, 252 ranking in government strategy, 40 Rao, Delip, 120, 123 Rather, Dan, 143 Raytheon, 211 reaction times, 272–73 real-time computer strategy games, 267–69 real-world battlefield environments, 264 situations, 230–36 Rebellion Defense, 224 Reddit, 140 reeducation, 81 Reface app, 130 reinforcement learning, 221, 232, 243, 250 repression, 81, 175–77 research and development funding, 35–39, 36f, 38f, 39f, 333–34 Research Center for AI Ethics and Safety, 172 Research Center for Brain-Inspired Intelligence, 172 research communities, 327 responsible AI guidelines, 252 Responsible Artificial Intelligence Strategy, 252 résumé-sorting model, 234 Reuters, 95, 139 Rise and Fall of the Great Powers, The (Kennedy), 12 risk, 271, 290–93 robotic nuclear delivery systems, 289 robotic process automation tools, 206 robotic vehicles, 266 robots, 92–94, 265–66, 286 Rockwell Automation, 162 Rockwell Collins, 193 Romania, 108 Root, Phil, 231 Roper, Will, 55–56, 214, 224, 225, 257 Rubik’s cube, 26 rule-based AI systems, 230, 236 Rumsfeld, Donald, 61 Russia, 12, 40, 52, 108, 110 bots, 142 cyberattacks of, 246 disinformation, 122 invasion of Ukraine, 129, 196, 219, 288 nuclear capabilities, 50 submarines, 255 Rutgers University Big Data Laboratory, 156 RYaN (computer program), 287, 445; See also Operation RYaN; VRYAN safe city technology, 107–8 safety of AI, 286, 289, 304 Samsung, 27–29, 179, 181 Sandholm, Tuomas, 43–51 Sasse, Ben, 184 satellite imagery, 56 Saudi Arabia, 40, 107, 109, 141–42 Scale AI, 224 scaling of innovation, 224 Schatz, Brian, 37 schedule pressures, 254–55 Schmidt, Eric, 39, 40, 71–73, 150, 164–65 Schumer, Chuck, 39 Science (journal), 123 Seagate, 156, 390 security applications, 110–11, 315 security dilemma, 50–51, 289 Sedol, Lee, 23, 266, 274–75, 298 self-driving cars, 23, 65 semiconductor industry; See also semiconductors in China, 178–79 chokepoints, 180–81 export controls, 181–86 global chokepoints in, 178–87 globalization of, 27–29 international strategy, 186–87 in Japan, 179 supply chains, 26, 76, 300 in U.S., 179–80 Semiconductor Manufacturing International Corporation (SMIC), 178, 181, 184 semiconductor(s) fabrication of, 32 foundries, 27–28 improvements in, 325 manufacturing equipment, 179 market, 27 as strategic asset, 300 Seminar on Cyberspace Management, 108–9 SenseNets, 91, 156, 357 SenseTime, 37, 88–89, 91, 156, 160, 169, 353–54, 357, 388 SensingTech, 88 Sensity, 130–33 Sentinel, 132 Sequoia, 157 Serbia, 107, 110 Serelay, 138 servicemember deaths, 255 Seven Sons of National Defense, 161–62 “shallow fakes,” 129 Shanahan, Jack on automated nuclear launch, 289 on international information sharing, 258, 291–92 and JAIC, 66, 201, 203, 205–6, 214 and Project Maven, 57–58 on risks, 254, 256 Sharp Eyes, 88, 91 Shenzhen, China, 37 Shield AI, 66, 196, 222, 224 shortcuts, 254–56 Silk Road, 110 SIM cards, 80, 89 Singapore, 106, 107, 158 singularity in warfare, 279–80 Skyeye, 99 Skynet, 87–88, 90, 91 Slashdot, 120 Slate, 120 smartphones, 26, 80 SMIC (Semiconductor Manufacturing International Corporation), 178, 181, 184 Smith, Brad, 159, 163, 166, 167 social app dominance, 149–50 social credit system, 99–100 social governance, 97–104 social media, 126, 141–51 socio-technical problems, 65 soft power, 317 SOFWERX (Special Operations Forces Works), 214 SolarWinds, 246 South Africa, 107 South China Sea militarization, 71, 74 South Korea, 27, 40, 182, 185, 187 Soviet Union, 287, 289, 447 Spain, 40, 107 SparkCognition, 66, 224 Spavor, Michael, 177 Special Operations Command, 218 Special Operations Forces Works (SOFWERX), 214 speech recognition, 91 “Spider-Man neuron,” 295 Springer Nature, 158 Sputnik, 33, 71–72 Stability AI, 125, 295 stability, international, 286–93 Stable Diffusion, 125, 139, 295 Stallone, Sylvester, 130 Stanford Internet Observatory, 139 Stanford University, 31, 32, 57, 162 Starbucks, 92 StarCraft, 180, 298 StarCraft II, 267, 271, 441 Status-6, 289; See also Poseidon Steadman, Kenneth A., 192 STEM talent, 30–34 sterilization and abortion, 81 Strategic Capabilities Office, 56 strategic reasoning, 49 Strategy Robot, 44–45, 49, 51 Strike Hard Campaign, 79–80 Stuxnet, 283 subsidies, government, 179–80 Sullivan, Jake, 186 Sun Tzu, 45 superhuman attentiveness, 269–70 superhuman precision, 270 superhuman reaction time, 277 superhuman speed, 269, 271 supervised learning, 232 supply chain(s), 300 attacks, 246 global, 76, 179, 183 “Surprising Creativity of Digital Evolution, The,” 235 surveillance, 79–90 cameras, 6, 86–87, 91 laws and policies for, 108–9 throughout China, 84–90 in Xinjiang, 79–83 Sutskever, Ilya, 210 Sutton, Rich, 299, 455 swarms and swarming, 277–79 autonomous systems, 50, 220 demonstrations, 257 Sweden, 108, 158, 187 Switch-C, 294 Synopsys, 162 synthetic aperture radar, 210 synthetic media, 127–34, 138–39 criminal use, 128–29 deepfake detectors, 132–33 deepfake videos, 130–32 geopolitical risks, 129–30 watermarks, digital, 138–39 Syria, 58 system integration, 91 tactics and strategies, 270 Taiwan, 27, 71, 76, 100, 175, 178, 185–86 Taiwan Semiconductor Manufacturing Company (TSMC), 27–28, 179, 181, 184 Taiwan Strait, 71, 75–76 talent, 30–34, 304 Tang Kun, 393 tanks, 192 Tanzania, 109 targeting cycle, 263 target recognition, 210 Target Recognition and Adaptation in Contested Environments (TRACE), 210–12 Tay, chatbot, 247 TDP (thermal design power), 454 TechCrunch, 120 technical standards Chinese, 171–75 international, 169–71 techno-authoritarianism, 79–110, 169 China’s tech ecosystem, 91–96 global export of, 105–10, 106f social governance, 97–104 throughout China, 83–90 in Xinjiang, 79–83 technology ecosystem, Chinese, 91–96 platforms, 35 and power, 11 transfer, 33, 163–64 Tektronix, 162 Tencent, 37, 143, 160, 169, 172 Tensor Processing Unit (TPU), 180 Terregator, 193 Tesla, 65, 180 TEVV (test and evaluation, verification and validation), 251–52 Texas Instruments, 162 text generation, 117–21, 123 text-to-image models, 125, 295 Thailand, 107, 109 thermal design power (TDP), 454 Third Offset Strategy, 53, 61 “Thirteenth Five-Year Science and Technology Military-Civil Fusion Special Projects Plan,” 73 Thousand Talents Plan, 32, 164 “Three-Year Action Plan to Promote the Development of New-Generation AI Industry,” 73 Tiananmen Square massacre, 68, 97–98, 103, 148, 160, 341, 359 tic-tac-toe, 47, 336 TikTok, 146–49 Tortoise Market Research, Inc., 15, 40 TPU (Tensor Processing Unit), 180 TRACE (Target Recognition and Adaptation in Contested Environments), 210–12 Trade and Technology Council (TTC), 187 training costs, 296–97 training datasets, 19–23 attacks on, 238–40, 244–45 of drone footage, 203 “radioactive,” 139 real world environments, vs., 58, 64, 233, 264 size of, 294–96 transistor miniaturization, 28 transparency among nations, 258–59, 288 Treasury Department, 246 Trump, Donald, and administration; See also “Donald Trump neuron” budget cuts, 39–40 and COVID pandemic, 74 and Entity List, 166 GPT-2 fictitious texts of, 117–19 graduate student visa revocation, 164 and Huawei, 182–84 and JEDI contract, 215–16 national strategy for AI, 73 relations with China, 71 and TikTok, 147 Twitter account, 150 trust, 249–53 Trusted News Initiative, 138–39 “truth,” 130 Tsinghua University, 31, 93, 173, 291 TSMC, See Taiwan Semiconductor Manufacturing Company (TSMC) TTC (Trade and Technology Council), 187 Turkey, 107, 108, 110 Turkish language, 234 Twitter, 139–40, 142, 144, 149, 247 Uganda, 108, 109 Uighurs; See also Xinjiang, China facial recognition, 88–89, 158, 353–55 genocide, 79, 304 mass detention, 74, 79–81, 102, 175 speech recognition, 94 surveillance, 82, 155–56 Ukraine, 108, 129, 196, 219, 288 United Arab Emirates, 107, 109 United Kingdom, 12, 76, 108, 122, 158, 187, 191–92 United States AI policy, 187 AI research of, 30 Chinese graduate students in, 31 competitive AI strategy, 185 United States Presidential election, 2016, 122 United States Presidential election, 2020, 128, 131, 134, 150 University of Illinois, 157 University of Richmond, 123 Uniview, 89, 355 unsupervised learning, 232 Ürümqi, 80, 84 Ürümqi Cloud Computing Center, 156 U.S.


pages: 416 words: 112,268

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

3D printing, Ada Lovelace, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Alfred Russel Wallace, algorithmic bias, AlphaGo, Andrew Wiles, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, basic income, behavioural economics, Bletchley Park, blockchain, Boston Dynamics, brain emulation, Cass Sunstein, Charles Babbage, Claude Shannon: information theory, complexity theory, computer vision, Computing Machinery and Intelligence, connected car, CRISPR, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deep learning, deepfake, DeepMind, delayed gratification, Demis Hassabis, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ernest Rutherford, fake news, Flash crash, full employment, future of work, Garrett Hardin, Geoffrey Hinton, Gerolamo Cardano, Goodhart's law, Hans Moravec, ImageNet competition, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the wheel, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Nash: game theory, John von Neumann, Kenneth Arrow, Kevin Kelly, Law of Accelerating Returns, luminiferous ether, machine readable, machine translation, Mark Zuckerberg, multi-armed bandit, Nash equilibrium, Nick Bostrom, Norbert Wiener, NP-complete, OpenAI, openstreetmap, P = NP, paperclip maximiser, Pareto efficiency, Paul Samuelson, Pierre-Simon Laplace, positional goods, probability theory / Blaise Pascal / Pierre de Fermat, profit maximization, RAND corporation, random walk, Ray Kurzweil, Recombinant DNA, recommendation engine, RFID, Richard Thaler, ride hailing / ride sharing, Robert Shiller, robotic process automation, Rodney Brooks, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, smart cities, smart contracts, social intelligence, speech recognition, Stephen Hawking, Steven Pinker, superintelligent machines, surveillance capitalism, Thales of Miletus, The Future of Employment, The Theory of the Leisure Class by Thorstein Veblen, Thomas Bayes, Thorstein Veblen, Tragedy of the Commons, transport as a service, trolley problem, Turing machine, Turing test, universal basic income, uranium enrichment, vertical integration, Von Neumann architecture, Wall-E, warehouse robotics, Watson beat the top human players on Jeopardy!, web application, zero-sum game

(In a 2018 competition, AI software outscored experienced law professors in analyzing standard nondisclosure agreements and completed the task two hundred times faster.25) Routine forms of computer programming—the kind that is often outsourced today—are also likely to be automated. Indeed, almost anything that can be outsourced is a good candidate for automation, because outsourcing involves decomposing jobs into tasks that can be parceled up and distributed in a decontextualized form. The robot process automation industry produces software tools that achieve exactly this effect for clerical tasks performed online. As AI progresses, it is certainly possible—perhaps even likely—that within the next few decades essentially all routine physical and mental labor will be done more cheaply by machines.


pages: 463 words: 115,103

Head, Hand, Heart: Why Intelligence Is Over-Rewarded, Manual Workers Matter, and Caregivers Deserve More Respect by David Goodhart

active measures, Airbnb, Albert Einstein, assortative mating, basic income, Berlin Wall, Bernie Sanders, Big Tech, big-box store, Black Lives Matter, Boris Johnson, Branko Milanovic, Brexit referendum, British Empire, call centre, Cass Sunstein, central bank independence, centre right, computer age, corporate social responsibility, COVID-19, data science, David Attenborough, David Brooks, deglobalization, deindustrialization, delayed gratification, desegregation, deskilling, different worldview, Donald Trump, Elon Musk, emotional labour, Etonian, fail fast, Fall of the Berlin Wall, Flynn Effect, Frederick Winslow Taylor, future of work, gender pay gap, George Floyd, gig economy, glass ceiling, Glass-Steagall Act, Great Leap Forward, illegal immigration, income inequality, James Hargreaves, James Watt: steam engine, Jeff Bezos, job automation, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, knowledge economy, knowledge worker, labour market flexibility, lockdown, longitudinal study, low skilled workers, Mark Zuckerberg, mass immigration, meritocracy, new economy, Nicholas Carr, oil shock, pattern recognition, Peter Thiel, pink-collar, post-industrial society, post-materialism, postindustrial economy, precariat, reshoring, Richard Florida, robotic process automation, scientific management, Scientific racism, Skype, social distancing, social intelligence, spinning jenny, Steven Pinker, superintelligent machines, TED Talk, The Bell Curve by Richard Herrnstein and Charles Murray, The Rise and Fall of American Growth, Thorstein Veblen, twin studies, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, upwardly mobile, wages for housework, winner-take-all economy, women in the workforce, young professional

Of course, the decline in this skill does not imply that there will be no authors, writers, or editors in the future—but as in many other occupations, some of the more basic aspects of the work will shift to machines.”40 And that bank manager whose judgment has been replaced with a loan approval algorithm is emblematic of a broader shift in lower-level financial service jobs, which is likely to have a big impact on heavily financialized economies like the United States and the United Kingdom. As the report says: “A range of back-office functions to be automated, include financial reporting, accounting, actuarial sciences, insurance claims processing, credit scoring, loan approval, and tax calculation. Computer algorithms and ‘robotic process automation’ can drastically reduce the time and manpower devoted to these activities.”41 Capitalism in the Age of Robots What does all this mean? The knowledge economy needs fewer knowledge workers than expected. The recent expansion of higher education in much of the West will stop or even go into reverse as the demand for the middling and lower-rung jobs of the knowledge economy will decline.