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Author: Joseph Ganem Publisher: ISBN: 9783031310058 Category : Languages : en Pages : 0
Book Description
This book provides a novel framework for understanding and revising labor markets and education policies in an era of machine learning. It posits that while learning and knowing both require thinking, learning is fundamentally different than knowing because it results in cognitive processes that change over time. Learning, in contrast to knowing, requires time and agency. Therefore, "learning algorithms"-that enable machines to modify their actions based on real-world experiences-are a fundamentally new form of artificial intelligence that have potential to be even more disruptive to labor markets than prior introductions of digital technology. To explore the difference between knowing and learning, Turing's "Imitation Game,"-that he proposed as a test for machine thinking-is expanded to include time dependence. The arguments presented in the book introduce three novel concepts: (1) Comparative learning advantage: This is a concept analogous to comparative labor advantage but arises from the disparate times required to learn new knowledge bases/skillsets. It is argued that in the future, comparative learning advantages between humans and machines will determine their division of labor. (2) Two dimensions of job performance-expertise and interpersonal: Job tasks can be sorted into two broad categories. Tasks that require expertise have stable endpoints, which makes these tasks inherently repetitive and subject to automation. Tasks that are interpersonal are highly context-dependent and lack stable endpoints, which makes these tasks inherently non-routine. Humans compared to machines have a comparative learning advantage along the interpersonal dimension, which is increasing in value economically. (3) The Learning Game is a time-dependent version of Turing's "Imitation Game." It is more than a thought experiment. The "Learning Game" provides a mathematical framework with quantitative criteria for training and assessing comparative learning advantages. The book is highly interdisciplinary-presenting philosophical arguments in economics, artificial intelligence, and education. It also provides data, mathematical analysis, and testable criteria that researchers in these fields will find of practical use. The book calls for a rethinking of how labor markets operate and how the education system should prepare students for future jobs. It concludes with a list of counterintuitive recommendations for future education and labor policies that all stakeholders-employers, employees, educators, students, and political leaders-should heed.
Author: Joseph Ganem Publisher: ISBN: 9783031310058 Category : Languages : en Pages : 0
Book Description
This book provides a novel framework for understanding and revising labor markets and education policies in an era of machine learning. It posits that while learning and knowing both require thinking, learning is fundamentally different than knowing because it results in cognitive processes that change over time. Learning, in contrast to knowing, requires time and agency. Therefore, "learning algorithms"-that enable machines to modify their actions based on real-world experiences-are a fundamentally new form of artificial intelligence that have potential to be even more disruptive to labor markets than prior introductions of digital technology. To explore the difference between knowing and learning, Turing's "Imitation Game,"-that he proposed as a test for machine thinking-is expanded to include time dependence. The arguments presented in the book introduce three novel concepts: (1) Comparative learning advantage: This is a concept analogous to comparative labor advantage but arises from the disparate times required to learn new knowledge bases/skillsets. It is argued that in the future, comparative learning advantages between humans and machines will determine their division of labor. (2) Two dimensions of job performance-expertise and interpersonal: Job tasks can be sorted into two broad categories. Tasks that require expertise have stable endpoints, which makes these tasks inherently repetitive and subject to automation. Tasks that are interpersonal are highly context-dependent and lack stable endpoints, which makes these tasks inherently non-routine. Humans compared to machines have a comparative learning advantage along the interpersonal dimension, which is increasing in value economically. (3) The Learning Game is a time-dependent version of Turing's "Imitation Game." It is more than a thought experiment. The "Learning Game" provides a mathematical framework with quantitative criteria for training and assessing comparative learning advantages. The book is highly interdisciplinary-presenting philosophical arguments in economics, artificial intelligence, and education. It also provides data, mathematical analysis, and testable criteria that researchers in these fields will find of practical use. The book calls for a rethinking of how labor markets operate and how the education system should prepare students for future jobs. It concludes with a list of counterintuitive recommendations for future education and labor policies that all stakeholders-employers, employees, educators, students, and political leaders-should heed.
Author: Joseph Ganem Publisher: Springer Nature ISBN: 3031310047 Category : Philosophy Languages : en Pages : 88
Book Description
This book provides a novel framework for understanding and revising labor markets and education policies in an era of machine learning. It posits that while learning and knowing both require thinking, learning is fundamentally different than knowing because it results in cognitive processes that change over time. Learning, in contrast to knowing, requires time and agency. Therefore, “learning algorithms”—that enable machines to modify their actions based on real-world experiences—are a fundamentally new form of artificial intelligence that have potential to be even more disruptive to labor markets than prior introductions of digital technology. To explore the difference between knowing and learning, Turing’s “Imitation Game,”—that he proposed as a test for machine thinking—is expanded to include time dependence. The arguments presented in the book introduce three novel concepts: (1) Comparative learning advantage: This is a concept analogous to comparative labor advantage but arises from the disparate times required to learn new knowledge bases/skillsets. It is argued that in the future, comparative learning advantages between humans and machines will determine their division of labor. (2) Two dimensions of job performance—expertise and interpersonal: Job tasks can be sorted into two broad categories. Tasks that require expertise have stable endpoints, which makes these tasks inherently repetitive and subject to automation. Tasks that are interpersonal are highly context-dependent and lack stable endpoints, which makes these tasks inherently non-routine. Humans compared to machines have a comparative learning advantage along the interpersonal dimension, which is increasing in value economically. (3) The Learning Game is a time-dependent version of Turing’s “Imitation Game.” It is more than a thought experiment. The “Learning Game” provides a mathematical framework with quantitative criteria for training and assessing comparative learning advantages. The book is highly interdisciplinary—presenting philosophical arguments in economics, artificial intelligence, and education. It also provides data, mathematical analysis, and testable criteria that researchers in these fields will find of practical use. The book calls for a rethinking of how labor markets operate and how the education system should prepare students for future jobs. It concludes with a list of counterintuitive recommendations for future education and labor policies that all stakeholders—employers, employees, educators, students, and political leaders—should heed.
Author: Ajay Agrawal Publisher: University of Chicago Press ISBN: 0226833127 Category : Business & Economics Languages : en Pages : 172
Book Description
A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.
Author: UNESCO International Centre for Technical and Vocational Education and Training Publisher: UNESCO Publishing ISBN: 9231004468 Category : Political Science Languages : en Pages : 56
Author: Tavis D. Jules Publisher: Emerald Group Publishing ISBN: 178754852X Category : Education Languages : en Pages : 304
Book Description
This book examines, from a comparative perspective, the impact of the movement from the so-called knowledge-based economy towards the Intelligent Economy, which is premised upon the application of knowledge. This volume links the advent of this new technological revolution to the world of governance and policy formulation in education.
Author: George P. Shultz Publisher: Hoover Press ISBN: 0817924361 Category : Technology & Engineering Languages : en Pages : 255
Book Description
The world is at an inflection point. Advancing technologies are creating new opportunities and challenges. Great demographic changes are occurring rapidly, with significant consequences. Governance everywhere is in disarray. A new world is emerging. These are some of the key insights to emerge from a series of interdisciplinary roundtables and global expert contributions hosted by the Hoover Institution. In these pages, George P. Shultz and James Timbie examine a range of issues shaping our present and future, region by region. Concrete proposals address migration, reversing the decline of K–12 education, updating the social safety net, maintaining economic productivity, protecting our democratic processes, improving national security, and more. Meeting these transformational challenges will require international cooperation, constructive engagement, and strong governance. The United States is well positioned to ride this wave of change—and lead other nations in doing the same.
Author: Michael A. Peters Publisher: Springer ISBN: 9811362254 Category : Education Languages : en Pages : 354
Book Description
This book examines the challenge of accelerating automation, and argues that countering and adapting to this challenge requires new methodological, philosophical, scientific, sociological, economic, ethical, and political perspectives that fundamentally rethink the categories of work and education. What is required is political will and social vision to respond to the question: What is the role of education in a digital age characterized by potential mass technological unemployment? Today’s technologies are beginning to cost more jobs than they create – and this trend will continue. There have been many proposed solutions to this problem, and they invariably involve an educational vision. Yet, in a world that simply doesn’t offer enough work for everyone, education is clearly not a panacea for technological unemployment. This collection presents responses to this question from a wide spectrum of disciplines, including but not limited to education studies, philosophy, history, politics, sociology, psychology, and economics.
Author: Andrew McAfee Publisher: W. W. Norton & Company ISBN: 0393254305 Category : Business & Economics Languages : en Pages : 320
Book Description
“A clear and crisply written account of machine intelligence, big data and the sharing economy. But McAfee and Brynjolfsson also wisely acknowledge the limitations of their futurology and avoid over-simplification.” —Financial Times In The Second Machine Age, Andrew McAfee and Erik Brynjolfsson predicted some of the far-reaching effects of digital technologies on our lives and businesses. Now they’ve written a guide to help readers make the most of our collective future. Machine | Platform | Crowd outlines the opportunities and challenges inherent in the science fiction technologies that have come to life in recent years, like self-driving cars and 3D printers, online platforms for renting outfits and scheduling workouts, or crowd-sourced medical research and financial instruments.
Author: Francesca Lazzeri Publisher: John Wiley & Sons ISBN: 111968238X Category : Computers Languages : en Pages : 224
Book Description
Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting. Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to: Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models’ performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.
Author: Zia Qureshi Publisher: Brookings Institution Press ISBN: 081573901X Category : Business & Economics Languages : en Pages : 298
Book Description
Addressing the big questions about how technological change is transforming economies and societies Rapid technological change—likely to accelerate as a consequence of the COVID-19 pandemic—is reshaping economies and how they grow. But change also causes disruption, creates winners and losers, and produces social stress. This book examines the challenges of digital transformation and suggests how creative policies can make it more productive and inclusive. Shifting Paradigms is the second book on technological change produced by a joint research project of the Brookings Institution and the Korea Development Institute. Contributors are experts from the United States, Europe, and Korea. The first volume, Growth in a Time of Change, was published by Brookings in February 2020. The book's underlying thesis is that the future is arriving faster than expected. Long-accepted paradigms about economic growth are changing as digital technologies transform markets and nearly every aspect of business and work. Change will only intensify with advances in artificial intelligence and other innovations. Investors, business leaders, workers, and public officials face many questions. Is rising market concentration inevitable with the new technologies or can their benefits be more widely shared? How can the promise of FinTech be captured while managing risks? Should workers fear the new automation? Are technology-driven shifts in business and work causing income inequality to rise? How should public policy respond? Shifting Paradigms addresses these questions in an engaging manner for anyone interested in understanding how the economic and social agenda is being transformed by today's winds of change.
Author: Joseph Ganem
Author: Joseph Ganem
Author: Joseph Ganem
Author: Ajay Agrawal
Author: UNESCO International Centre for Technical and Vocational Education and Training
Author: Tavis D. Jules
Author: George P. Shultz
Author: Michael A. Peters
Author: Andrew McAfee
Author: Francesca Lazzeri
Author: Zia Qureshi