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

AI winter, Andy Kessler, artificial general intelligence, asset allocation, Automated Insights, autonomous vehicles, basic income, Baxter: Rethink Robotics, business intelligence, business process, call centre, carbon-based life, Clayton Christensen, clockwork universe, commoditize, conceptual framework, dark matter, David Brooks, deliberate practice, deskilling, digital map, disruptive innovation, Douglas Engelbart, Edward Lloyd's coffeehouse, Elon Musk, Erik Brynjolfsson, estate planning, fixed income, follow your passion, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, game design, general-purpose programming language, global pandemic, Google Glasses, Hans Lippershey, haute cuisine, income inequality, 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, knowledge worker, labor-force participation, lifelogging, longitudinal study, loss aversion, Mark Zuckerberg, Narrative Science, natural language processing, Norbert Wiener, nuclear winter, pattern recognition, performance metric, Peter Thiel, precariat, quantitative trading / quantitative finance, Ray Kurzweil, Richard Feynman, risk tolerance, Robert Shiller, Robert Shiller, 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, transaction costs, 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, 2013/. 9. Mary Lacity, Leslie Willcocks, and Andrew Craig, “Robotic Process Automation at Telefonica O2,” London School of Economics case study, April 2015, 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, 11. Jordan Novet, “South Korea’s Team KAIST Wins the 2015 DARPA Robotics Challenge,” VentureBeat, June 6, 2015, 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, AltaVista, Amazon Web Services, augmented reality, autonomous vehicles, basic income, 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, David Ricardo: comparative advantage, declining real wages, deindustrialization, deskilling, Donald Trump, Douglas Hofstadter, Downton Abbey, Elon Musk, Erik Brynjolfsson, facts on the ground, future of journalism, future of work, George Gilder, Google Glasses, Google Hangouts, hiring and firing, impulse control, income inequality, industrial robot, intangible asset, Internet of things, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, knowledge worker, laissez-faire capitalism, low skilled workers, 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, 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, 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, TaskRabbit, telepresence, telepresence robot, telerobotics, Thomas Malthus, trade liberalization, universal basic income

Peter Bright, “Moore’s Law Really Is Dead This Time,”, November 2, 2016. 5. Daniel Robinson, “Moore’s Law Is Running Out—But Don’t Panic,”, 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 trading, Amazon Mechanical Turk, augmented reality, autonomous vehicles, blockchain, business process, call centre, carbon footprint, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, digital twin, disintermediation, Douglas Hofstadter,, Erik Brynjolfsson, friendly AI, future of work, industrial robot, Internet of things, inventory management, iterative process, Jeff Bezos, job automation, job satisfaction, knowledge worker, Lyft, natural language processing, personalized medicine, precision agriculture, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, robotic process automation, Rodney Brooks, Second Machine Age, self-driving car, sensor fusion, sentiment analysis, Shoshana Zuboff, Silicon Valley, software as a service, speech recognition, telepresence, telepresence robot, text mining, the scientific method, uber lyft

Seth Fletcher, “How Big Data Is Taking Teachers Out of the Lecturing Business,” Scientific American, August 1, 2013, 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: 251 words: 80,831

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

"side hustle", 23andMe, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Anne Wojcicki, barriers to entry, Ben Horowitz, bitcoin, business intelligence, buy and hold, Chris Wanstrath, clean water, cloud computing, coronavirus, corporate governance, correlation does not imply causation, Covid-19, COVID-19, cryptocurrency, discounted cash flows, diversified portfolio, Elon Musk, game design, gig economy, high net worth, hiring and firing, index fund, Internet Archive, Jeff Bezos, Kickstarter, late fees, Lyft, Marc Andreessen, Mark Zuckerberg, Mitch Kapor, natural language processing, Network effects, nuclear winter, PageRank, Paul Buchheit, Paul Graham, peer-to-peer lending, Peter Thiel, QR code, remote working, ride hailing / ride sharing, robotic process automation, rolodex, Ruby on Rails, Sam Altman, Sand Hill Road, self-driving car, shareholder value, sharing economy, side project, Silicon Valley, Silicon Valley startup, Skype, Snapchat, 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, Tony Hsieh, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, ubercab, web application, WeWork, 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, Amazon Web Services, artificial general intelligence, autonomous vehicles, backpropagation, business intelligence, business process, call centre, chief data officer, computer vision, conceptual framework,, future of work, industrial robot, Internet of things, iterative process, Jeff Bezos, job automation, Marc Andreessen, natural language processing, new economy, pattern recognition, performance metric, price discrimination, randomized controlled trial, recommendation engine, robotic process automation, self-driving car, sentiment analysis, Silicon Valley, skunkworks, software is eating the world, source of truth, speech recognition, statistical model, strong AI, 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, bitcoin, blockchain, butterfly effect, call centre, chief data officer, choice architecture, cognitive dissonance, cryptocurrency, Diane Coyle,, G4S, Internet of things, Kevin Kelly, Kickstarter, loose coupling, M-Pesa, minimum viable product, nudge unit, performance metric, ransomware, robotic process automation, Silicon Valley, social web, The future is already here, the market place, The Wisdom of Crowds

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

Airbnb, Albert Einstein, algorithmic bias, Amazon Web Services, Atul Gawande, augmented reality, automated trading system, basic income, Bayesian statistics, big-box store, business process, call centre, choice architecture, coronavirus, Covid-19, COVID-19, disinformation, Elon Musk, Erik Brynjolfsson, factory automation, fault tolerance, Frederick Winslow Taylor, Freestyle chess, future of work, gig economy, Google Hangouts, hiring and firing, hustle culture, income inequality, industrial robot, Jeff Bezos, job automation, John Markoff, knowledge worker, Kodak vs Instagram, labor-force participation, Lyft, mandatory minimum, Marc Andreessen, Mark Zuckerberg, meta-analysis, Narrative Science, new economy, Norbert Wiener, pattern recognition, planetary scale, Plutocrats, plutocrats, Productivity paradox, QAnon, recommendation engine, remote working, risk tolerance, robotic process automation, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, Steve Jobs, surveillance capitalism, The Future of Employment, The Wealth of Nations by Adam Smith, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, Watson beat the top human players on Jeopardy!

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

algorithmic bias, bitcoin, blockchain, business climate, business process, corporate governance, Donald Trump, Elon Musk, future of work, job automation, Kevin Kelly, Mark Zuckerberg, meta-analysis, Norbert Wiener, pattern recognition, Peter Thiel, race to the bottom, robotic process automation, shareholder value, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, Stephen Hawking, The Future of Employment, Turing test, 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 Web Services, augmented reality, Automated Insights, autonomous vehicles, Bernie Sanders, Clayton Christensen, cloud computing, collective bargaining, computer vision, Donald Trump, drone strike, Elon Musk, Firefox, Google Chrome, hive mind, income inequality, Infrastructure as a Service, inventory management, iterative process, Jeff Bezos, job automation, Jony Ive, knowledge economy, Lyft, Mark Zuckerberg, Menlo Park, new economy, Peter Thiel, QR code, ride hailing / ride sharing, robotic process automation, self-driving car, Silicon Valley, Skype, Snapchat, Steve Ballmer, Steve Jobs, Steve Wozniak, super pumped, Tim Cook: Apple, uber lyft, wealth creators, 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.

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, Andrew Wiles, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, basic income, blockchain, brain emulation, Cass Sunstein, Claude Shannon: information theory, complexity theory, computer vision, connected car, crowdsourcing, Daniel Kahneman / Amos Tversky, delayed gratification, Elon Musk,, Erik Brynjolfsson, Ernest Rutherford, Flash crash, full employment, future of work, Garrett Hardin, Gerolamo Cardano, ImageNet competition, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the wheel, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Nash: game theory, John von Neumann, Kenneth Arrow, Kevin Kelly, Law of Accelerating Returns, Mark Zuckerberg, Nash equilibrium, Norbert Wiener, NP-complete, openstreetmap, P = NP, Pareto efficiency, Paul Samuelson, Pierre-Simon Laplace, positional goods, probability theory / Blaise Pascal / Pierre de Fermat, profit maximization, RAND corporation, random walk, Ray Kurzweil, recommendation engine, RFID, Richard Thaler, ride hailing / ride sharing, Robert Shiller, Robert Shiller, 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, Turing machine, Turing test, universal basic income, uranium enrichment, Von Neumann architecture, Wall-E, Watson beat the top human players on Jeopardy!, web application, zero-sum game

(In 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-box store, Boris Johnson, Branko Milanovic, British Empire, call centre, Cass Sunstein, central bank independence, centre right, computer age, corporate social responsibility, Covid-19, COVID-19, David Attenborough, David Brooks, deglobalization, deindustrialization, delayed gratification, desegregation, deskilling, different worldview, Donald Trump, Elon Musk, Etonian, Fall of the Berlin Wall, Flynn Effect, Frederick Winslow Taylor, future of work, gender pay gap, gig economy, glass ceiling, 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, longitudinal study, low skilled workers, Mark Zuckerberg, mass immigration, 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 racism, Skype, social intelligence, spinning jenny, Steven Pinker, superintelligent machines, The Bell Curve by Richard Herrnstein and Charles Murray, The Rise and Fall of American Growth, Thorstein Veblen, twin studies, 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.