g-f(2)82 THE BIG PICTURE OF THE DIGITAL AGE, MIT TASK FORCE ON THE WORK OF THE FUTURE, AI Today, and the General Intelligence of Work.

Extra-condensed knowledge

• The ability to adapt to entirely novel situations is still an enormous challenge for AI and robotics, a key reason for companies’ continued reliance on human workers for a variety of tasks.
• From a work perspective, these technologies tend to be task oriented, that is they execute limited sets of tasks, more than the full set of activities comprising an occupation.
• Most of the AI deployed today, while novel and impressive, still falls under a category of “specialized AI.”
• Artificial general intelligence (AGI), the idea of a truly artificial human-like brain, remains a topic of deep research interest but a goal that experts agree is far in the future.

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Condensed knowledge  

• The ability to adapt to entirely novel situations is still an enormous challenge for AI and robotics, a key reason for companies’ continued reliance on human workers for a variety of tasks.
• From a work perspective, these technologies tend to be task oriented, that is they execute limited sets of tasks, more than the full set of activities comprising an occupation.
• Artificial general intelligence (AGI), the idea of a truly artificial human-like brain, remains a topic of deep research interest but a goal that experts agree is far in the future.
• Most of the AI deployed today, while novel and impressive, still falls under a category of what Task Force member, AI pioneer, and Director of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) Daniela Rus calls “specialized AI.”
• That is, these systems can solve a limited number of specific problems.
• They look at vast amounts of data, extract patterns, and make predictions to guide future actions.
• “Narrow AI solutions exist for a wide range of specific problems,” write Rus and MIT Sloan School Professor Thomas Malone, “and can do a lot to improve efficiency and productivity within the work world.”
• The systems we will explore below in insurance and healthcare all belong to this class of narrow AI, though they vary in different classes of machine learning, computer vision, natural language processing, or others.
• By their reliance on largely human-generated data, they excel at producing behaviors that mimic human data on well-known tasks (potentially including human biases).
• A current point of debate around AGI highlights its relevance for work. MIT professor emeritus, robotics pioneer, and Task Force Research Advisory Board member Professor Rodney Brooks, argues that the traditional “Turing test” for artificial intelligence should be updated.
• The old standard was a computer behind a wall, with which a human could hold a textual conversation and find indistinguishable from another person. This goal was achieved long ago with simple chatbots which few argue represent AGI.
• In a world of robotics, as the digital world increasingly mixes with the physical world, Brooks argues for a new standard for artificial general intelligence: the ability to do complex work tasks that require other types of interactions with the world. 
• Brooks’s idea captures the sense that today’s intelligence challenges are problems of physical dexterity, social interaction, and judgment as much as they are of symbolic processing. 
• These dimensions remain out of reach for current AI, which has significant implications for work. Pushing Brooks’s idea further, the future of AI is the future of work.

Category 2: The Big Picture of the Digital Age

[genioux fact produced, deduced or extracted from MIT]

Type of essential knowledge of this “genioux fact”: Essential Deduced and Extracted Knowledge (EDEK).

Type of validity of the "genioux fact". 

• Inherited from sources + Supported by the knowledge of one or more experts + Supported by a research.

Authors of the genioux fact

Fernando Machuca

References

The Work of the Future: Building Better Jobs in an Age of Intelligent Machines, DAVID AUTOR, DAVID MINDELL, ELISABETH REYNOLDS, November 17, 2020, MIT Work of the Future, MIT.

ABOUT THE AUTHORS

DAVID AUTOR

CO-CHAIR, MIT TASK FORCE ON THE WORK OF THE FUTURE

Ford Professor of Economics

Labor Studies Program Director, National Bureau of Economic Research

David Autor is Ford Professor of Economics and associate head of the MIT Department of Economics. His scholarship explores the labor market impacts of technological change and globalization, earnings inequality, and disability insurance and labor supply. Autor has received several awards for his scholarship, including the National Science Foundation Career Award; an Alfred P. Sloan Foundation Fellowship; and the Sherwin Rosen Prize for outstanding contributions in the field of labor economics—and for his teaching, including MIT’s James A. and Ruth Levitan Award for excellence in teaching; the Undergraduate Economic Association Teaching Award; and the Faculty Appreciation Award from the MIT Technology and Policy Program. He was recognized by Bloomberg as one of the 50 people who defined global business in 2017. 

DAVID MINDELL

CO-CHAIR, MIT TASK FORCE ON THE WORK OF THE FUTURE

Dibner Professor of the History of Engineering and Manufacturing

Professor of Aeronautics and Astronautics

Founder and CEO, Humatics Corporation

David Mindell, an engineer and historian, is Professor of Aeronautics and Astronautics and Dibner Professor of the History of Engineering and Manufacturing at MIT. An expert in human relationships with robotics and autonomous systems, he has led or participated in more than 25 oceanographic expeditions. From 2005 to 2011, he was director of MIT’s Program in Science, Technology, and Society. He is co-founder of Humatics Corporation, which develops technologies to transform how robots and autonomous systems work in human environments. Mindell has a BS in electrical engineering and BA in literature, both from Yale University, and a PhD in the history of technology from MIT.

ELISABETH REYNOLDS

EXECUTIVE DIRECTOR, MIT TASK FORCE ON THE WORK OF THE FUTURE

Principal Research Scientist

Executive Director, MIT Industrial Performance Center

Lecturer, Department of Urban Studies and Planning

Elisabeth Reynolds is a principal research scientist and executive director of the MIT Industrial Performance Center, as well as a lecturer in MIT’s Department of Urban Studies and Planning (DUSP). Her research examines systems of innovation and economic development more broadly with a focus on advanced manufacturing, growing innovative companies to scale, and building innovation capacity in developed and developing countries. Prior to coming to MIT, Reynolds was the director of the City Advisory Practice at the Initiative for a Competitive Inner City (ICIC), a non-profit focused on job and business growth in urban areas. She has been actively engaged in efforts to rebuild manufacturing capabilities in the U.S., most recently as a member of the Massachusetts Advanced Manufacturing Collaborative. She is a graduate of Harvard College and holds a Master’s in Economics from the University of Montreal as well as a PhD from MIT DUSP.