Download or read book Essays in the Economics of Artificial Intelligence written by Michael William Webb and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of three chapters. In the first chapter, I develop a new method to predict the impacts of any technology on occupations. I use the overlap between the text of job task descriptions and the text of patents to construct a measure of the exposure of tasks to automation. I first apply the method to historical cases such as software and industrial robots. I establish that occupations I measure as highly exposed to previous automation technologies saw declines in employment and wages over the relevant periods. I use the fitted parameters from the case studies to predict the impacts of artificial intelligence. I find that, in contrast to software and robots, AI is directed at high-skilled tasks. Under the assumption that historical patterns of long-run substitution will continue, I estimate that AI will reduce 90:10 wage inequality, but will not affect the top 1%. The second chapter presents work coauthored with Nick Short, Nick Bloom, and Josh Lerner. We show that patenting in software, cloud computing, and artificial intelligence has grown rapidly in recent years. Such patents are acquired primarily by large US technology firms such as IBM, Microsoft, Google, and HP, as well as by Japanese multinationals such as Sony, Canon, and Fujitsu. Chinese patenting in the US is small but growing rapidly, and world-leading for drone technology. Patenting in machine learning has seen exponential growth since 2010, although patenting in neural networks saw a strong burst of activity in the 1990s that has only recently been surpassed. In all technological fields, the number of patents per inventor has declined near-monotonically, except for large increases in inventor productivity in software and semiconductors in the late 1990s. In most high-tech fields, Japan is the only country outside the US with significant US patenting activity; however, whereas Japan played an important role in the burst of neural network patenting in the 1990s, it has not been involved in the current acceleration. Comparing the periods 1970-89 and 2000-15, patenting in the current period has been primarily by entrant assignees, with the exception of neural networks. The third chapter presents work coauthored with Nick Bloom, Chad Jones, and John Van Reenen. Long-run growth in many models is the product of two terms: the effective number of researchers and their research productivity. We present evidence from various industries, products, and firms showing that research effort is rising substantially while research productivity is declining sharply. A good example is Moore's Law. The number of researchers required today to achieve the famous doubling of computer chip density is more than 18 times larger than the number required in the early 1970s. More generally, everywhere we look we find that ideas -- and the exponential growth they imply -- are getting harder to find.

Download or read book Essays on Information Technology Intangible Capital and the Economics of Artificial Intelligence written by Daniel Ian Rock and published by . This book was released on 2019 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation contains four essays concerning the economics of information technology, intangible capital, and artificial intelligence. In the first essay, "Engineering Value: The Returns to Technological Talent and Investments in Artificial Intelligence" I describe how firms can appropriate some of the value of their employees' human capital by assigning firm-specific tasks. I then use a database of employment records to document dynamics in the valuation of publicly traded firms as they relate to different types of employment, focusing especially on AI skills. The second essay, "The Productivity J-Curve: How Intangibles Complement General Purpose Technologies" (coauthored with Erik Brynjolfsson and Chad Syverson) addresses the concern that new technologies with wide applicability throughout the economy can cause both underestimation and overestimation of total factor productivity. As capital is accumulated, intangible investment output, and therefore productivity growth, will be underestimated only to later generate a yield (at which point productivity growth will be overestimated). Presenting a theoretical description of how to use corporate valuations to recover hidden investment value, we discuss how productivity growth and levels can be adjusted to accommodate these changes. Implications for research and development, computer hardware, and computer software investments are considered. The third essay, "Machine Learning and Occupational Change" (coauthored with Erik Brynjolfsson and Tom Mitchell), develops and implements a method to measure the labor market impact potential of machine learning technologies. Tasks are evaluated for their Suitability for Machine Learning (SML). We find that few occupations can be fully automated with machine learning, but many occupations will potentially be redesigned. The final essay, "Do Labor Demand Shifts Occur Within Firms or Across Them? Non-Routine-Biased Technological Change 2000-2016" (coauthored with Seth Benzell and Guillermo Lagarda) decomposes labor share shifts of occupational groups into changes between firms, within firms, and due to entry and exit. We find that within-firm compositional shifts are an important component of changes in the overall labor market. We also find that the rate of within-firm shifts has declined in the period from 2000 to 2016. Together, these essays offer insights into how artificial intelligence technologies, particularly machine learning, will impact the U.S. economy.

Download or read book The Economics of Artificial Intelligence written by Ajay Agrawal and published by University of Chicago Press. This book was released on 2019-06-07 with total page 643 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in artificial intelligence (AI) highlight the potential of this technology to affect productivity, growth, inequality, market power, innovation, and employment. This volume seeks to set the agenda for economic research on the impact of AI. It covers four broad themes: AI as a general purpose technology; the relationships between AI, growth, jobs, and inequality; regulatory responses to changes brought on by AI; and the effects of AI on the way economic research is conducted. It explores the economic influence of machine learning, the branch of computational statistics that has driven much of the recent excitement around AI, as well as the economic impact of robotics and automation and the potential economic consequences of a still-hypothetical artificial general intelligence. The volume provides frameworks for understanding the economic impact of AI and identifies a number of open research questions. Contributors: Daron Acemoglu, Massachusetts Institute of Technology Philippe Aghion, Collège de France Ajay Agrawal, University of Toronto Susan Athey, Stanford University James Bessen, Boston University School of Law Erik Brynjolfsson, MIT Sloan School of Management Colin F. Camerer, California Institute of Technology Judith Chevalier, Yale School of Management Iain M. Cockburn, Boston University Tyler Cowen, George Mason University Jason Furman, Harvard Kennedy School Patrick Francois, University of British Columbia Alberto Galasso, University of Toronto Joshua Gans, University of Toronto Avi Goldfarb, University of Toronto Austan Goolsbee, University of Chicago Booth School of Business Rebecca Henderson, Harvard Business School Ginger Zhe Jin, University of Maryland Benjamin F. Jones, Northwestern University Charles I. Jones, Stanford University Daniel Kahneman, Princeton University Anton Korinek, Johns Hopkins University Mara Lederman, University of Toronto Hong Luo, Harvard Business School John McHale, National University of Ireland Paul R. Milgrom, Stanford University Matthew Mitchell, University of Toronto Alexander Oettl, Georgia Institute of Technology Andrea Prat, Columbia Business School Manav Raj, New York University Pascual Restrepo, Boston University Daniel Rock, MIT Sloan School of Management Jeffrey D. Sachs, Columbia University Robert Seamans, New York University Scott Stern, MIT Sloan School of Management Betsey Stevenson, University of Michigan Joseph E. Stiglitz. Columbia University Chad Syverson, University of Chicago Booth School of Business Matt Taddy, University of Chicago Booth School of Business Steven Tadelis, University of California, Berkeley Manuel Trajtenberg, Tel Aviv University Daniel Trefler, University of Toronto Catherine Tucker, MIT Sloan School of Management Hal Varian, University of California, Berkeley

Download or read book Artificial Intelligence and Economic Analysis written by Scott J. Moss and published by Edward Elgar Publishing. This book was released on 1992-01-01 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This important book presents new and original work at the frontiers of economics, namely the interface between artificial intelligence (AI) and neoclassical economics. Artificial Intelligence and Economic Analysis focuses on three quite distinct lines of AI orientated research in economics: applications intended to extend neoclassical theory, applications intended to undermine neoclassical theory and applications which ignore neoclassical theory in the quest for new modelling techniques and fields of analysis. The contributors - all of whom are well established in the field - do not simply report established results but seek to identify those areas where the science of artificial intelligence could enrich standard economic analysis. It includes material from mainstream economists who are willing to express their own views about the limits of mainstream economic modelling and AI based economic modelling. The book makes an important contribution to a new and exciting area of economics which holds much hope for the future.

Download or read book Reasoning Learning and Coordination written by Abhijit Chaudhury and published by . This book was released on 1992 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Prediction Machines written by Ajay Agrawal and published by Harvard Business Press. This book was released on 2018-04-17 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: "What does AI mean for your business? Read this book to find out." -- Hal Varian, Chief Economist, Google Artificial intelligence does the seemingly impossible, magically bringing machines to life--driving cars, trading stocks, and teaching children. But facing the sea change that AI will bring can be paralyzing. How should companies set strategies, governments design policies, and people plan their lives for a world so different from what we know? In the face of such uncertainty, many analysts either cower in fear or predict an impossibly sunny future. But in Prediction Machines, three eminent economists recast the rise of AI as a drop in the cost of prediction. With this single, masterful stroke, they lift the curtain on the AI-is-magic hype and show how basic tools from economics provide clarity about the AI revolution and a basis for action by CEOs, managers, policy makers, investors, and entrepreneurs. When AI is framed as cheap prediction, its extraordinary potential becomes clear: Prediction is at the heart of making decisions under uncertainty. Our businesses and personal lives are riddled with such decisions. Prediction tools increase productivity--operating machines, handling documents, communicating with customers. Uncertainty constrains strategy. Better prediction creates opportunities for new business structures and strategies to compete. Penetrating, fun, and always insightful and practical, Prediction Machines follows its inescapable logic to explain how to navigate the changes on the horizon. The impact of AI will be profound, but the economic framework for understanding it is surprisingly simple.

Download or read book AI in Business and Economics written by Isabel Lausberg, Michael Vogelsang and published by Walter de Gruyter GmbH & Co KG. This book was released on 2024-05-11 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Still Think Robots Can t Do Your Job written by Riccardo Campa and published by D Editore. This book was released on 2018-01-01 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: Is Artificial Intelligence qualitatively different from other means of economizing the use of labor? Are we on the edge of a jobless society? If yes, are we ready for it? These are a few of the questions discussed in this collection of academic works. This book traces a brief history of the concept of technological unemployment; proposes a short-term scenario analysis concerning the relations between automation, education, and unemployment; analyzes the most recent literature on social robotics; examines the possible futures generated by the development of intelligent machines; shows the relation between automation and unemployment in an Italian case study; considers the impact if machines become effective pursuers of knowledge or even conscious; and addresses the role of serendipity in the development of science and technology.

Download or read book Essays in Technological Innovation Financial Economics written by Abhimanyu Mukerji and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis examines the effects of technological innovation, particularly recent developments in machine learning and artificial intelligence (ML/AI), on firm growth, productivity, investment and competitiveness. It has two parts. The first chapter of my dissertation takes a broad view to ask a more fundamental question: do these technologies add value, and how can we quantify this? Academic literature is divided into two broad schools of thought. The first is that ML/AI represent general purpose technologies comparable to electricity or the steam engine, citing the extensive and expanding applications as supporting evidence. The second suggests that the utility of ML/AI is, in reality, more limited, and that the technological landscape is still evaluating added value while in the inflationary phases of a hype cycle. The major challenge associated with this literature is in measuring timing and intensity: what firms use ML/AI, and how extensively is it applied in business functions? The bulk of research in this field has focused on job postings data, which requires subjective feature construction by the researcher. Moreover, jobs data does not provide a precise time series of adoption and utilization intensity. My paper improves upon these approaches by developing a novel methodology based on cutting edge techniques from natural language processing. I adopt deep learning and topic modeling frameworks for unsupervised textual analysis to generate measures superior to more traditional scaled frequency-based approaches. I show that ML/AI utilization is associated with enhanced predictive capabilities and reduced cash flow volatility, with significantly more accurate earnings forecasts by firms. Firms using ML/AI show higher capital and labor productivity, as well as higher sales growth, profitability and market returns. My work helps shed light on the impact of ML/AI in a corporate setting, building on similar work focusing more granularly on labor markets. I show that the evidence is supportive of the general purpose technology hypothesis, and that the widespread adoption of ML/AI is correlated with positive outcomes across a range of industries and markets. Moreover, I show a substitution effect, with firms cutting back on employment and increasing investment in technological innovation. In the second chapter, I work towards understanding the effects of these new technologies on smaller firms. In particular, I study the role of democratized access to ML/AI technologies in encouraging productivity and innovation. Technological innovation has historically been a major driver of economic growth, with Schumpeterian creative destruction and subsequent resource reallocation supporting higher levels of equilibrium output. In recent decades, there has been evidence that suggests that these economic mechanisms may not be working well: increased barriers to entry, reduced business dynamism, asymmetric contributions to technological innovation, a widening gap between small and large firms, and reduced productivity growth. This has led to decreased industry competitiveness and new firm market entry, with risks of predatory pricing, reduced wage growth and consumer surplus, and diminished incentives to innovate. Larger firms have seen greatly increased R&D investment and growth in digital capital holdings, which has fueled high research productivity, product diversification and technological complements. I emphasize the role of open-source ML/AI technologies in reducing this disparity and leveling the playing field for smaller firms: specifically, I study the unexpected public release of TensorFlow. The open-source release of TensorFlow rep- resents an exogenous shock to the cost of ML/AI related digital capital: firms are able to enjoy the benefits of these technologies without prohibitive investments in high skill human capital and technological infrastructure. This natural experiment provides a unique setting to study the effect of open-source technology in supporting small firm growth. My main findings are consistent with the hypothesis that digital capital accumulation positively impacts firm growth. I show that small, TensorFlow user firms have higher ex-post sales growth, market returns, and profitability. These firms are also more likely to innovate, and the evidence is suggestive that a larger share of user firms is associated with subsequent declines in a range of industry concentration measures. My findings support the reasoning that digital capital encourages ML/AI utilization which allows for greater unstructured task automation leading to increased labor productivity. Firms are also able to better forecast demand and reduce volatility of uncertain future cash flows. My research emphasizes asymmetric gains from technological innovation as a driver of productivity slowdown and reduced wage growth. I show that open-source technologies supporting infrastructure may help enhance competition and the scope for future proprietary innovation. Finally, I relate ML/AI capital formation to a broader literature discussing the efficacy and applications of these new technologies, and their effects on labor markets and productivity growth.

Download or read book Three Essays on the Application of Machine Learning Methods in Economics written by Abdelaziz Lawani and published by . This book was released on 2018 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Scientific Models of Legal Reasoning written by Scott Brewer and published by Routledge. This book was released on 2013-10-28 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: First published in 1998. This five-volume series contains some of this century's most influential or thought provoking articles on the subject of legal argument that have appeared in Anglo-American philosophy journals and law reviews. This volume offers a collection of essays by philosophers and legal scholars on economics, artificial intelligence and the physical sciences.

Download or read book Artificial Intelligence AI An Approach to Assess the Impact on the Information Economy written by Henry Alexander Wittke and published by . This book was released on 2019-11-21 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research Paper (postgraduate) from the year 2019 in the subject Business economics - Economic Policy, grade: 1,0, Helmut Schmidt University - University of the Federal Armed Forces Hamburg, language: English, abstract: An ongoing and seemingly unstoppable digital transformation brings about new options, opportunities but also challenges to individuals, organizations, companies and societies alike. In the wake of exponentially rising data, also the field of AI is developing significantly. Recent breakthroughs in the field of deep learning algorithms or neural networks as a part of machine learning, increase AI's potential to disrupt the world's largest industries. Governments are alarmed, seeing the potential consequences on the work-force and thus societal change while also being seemingly helpless against uncontrollable and powerful digital players such as Google or Facebook. They are increasingly penetrating the so-called real economic sectors, also using more and more AI-applications and transforming the rules and fabric how actors engage in socio-economic relationships. From a scientific perspective consensus or clear-cut definitions what might constitute AI precisely as a base for a conceptual framework is missing. Further, only little research has been conducted. This paper attempts to provide a basic framework using concepts of weak and strong AI and thus to make a small contribution to the initial research available today. The concept of the Information Economy as the economic dimension of the information society shall form the generic base of the methodological approach. Further, it can be adjusted to the purpose of this investigation and enriched by the notion of AI. To then attempt to estimate the impact of AI on economies, we use segmentation of economies in the information society age. These segments shall be used as research objects. Based on the analysis of AI's effects on labor and employment, different questions shall be raised and conceivable sce

Download or read book Evolutionary Computing and Artificial Intelligence written by Fernando Koch and published by Springer. This book was released on 2019-03-13 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Festschrift volume is published in honor of Takao Terano on the occasion of his retirement. Takao Terano is a leading expert in the areas of agent-based modelling, knowledge systems, evolutionary computation, and service science.The contributions in this volume reflect the breadth and impact of his work. The volume contains 12 full papers related to Takao Terano’s research. They deal with various aspects of artificial intelligence, multi-agent systems, collaborative and social computing, social networks, ubiquitous computing.

Download or read book Robots and AI written by Lili Yan Ing and published by Taylor & Francis. This book was released on 2022-06-13 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robots and artificial intelligence (AI) are powerful forces that will likely have large impacts on the size, direction, and composition of international trade flows. This book discusses how industrial robots, automation, and AI affect international growth, trade, productivity, employment, wages, and welfare. The book explains new approaches on how robots and artificial intelligence affect the world economy by presenting detailed theoretical framework and country-specific as well as firm-product level-specific exercises. This book will be a useful reference for those researching on robots, automation, AI and their economic impacts on trade, industry, and employment. The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.

Download or read book Economics of the Fourth Industrial Revolution written by Nicholas Johnson and published by Routledge. This book was released on 2020-10-25 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book applies cutting-edge economic analysis and social science to unpack the rich complexities and paradoxes of the Fourth Industrial Revolution. The book takes the reader on a bold, refreshing, and informative tour through its technological drivers, its profound impact on human ecosystems, and its potential for sustainable human development. The overarching message to the reader is that the Fourth Industrial Revolution is not merely something to be feared or survived; rather, this dramatic collision of technologies, disciplines, and ideas presents a magnificent opportunity for a generation of new pioneers to rewrite "accepted rules" and find new avenues to empower billions of people to thrive. This book will help readers to discern the difference between disruption and transformation. The reader will come away from this book with a deeply intuitive and highly contextual understanding of the core technological advances transforming the world as we know it. Beyond this, the reader will clearly appreciate the future impacts on our economies and social structures. Most importantly, the reader will receive an insightful and actionable set of guidelines to assist them in harnessing the Fourth Industrial Revolution so that both they and their communities may flourish. The authors do not primarily seek to make prescriptions for government policy, but rather to speak directly to people about what they can do for themselves, their families, and their communities to be future-proofed and ready to adapt to life in a rapidly evolving world ecosystem.

Download or read book On Rationality Artificial Intelligence and Economics written by Daniel Muller and published by World Scientific Publishing Company. This book was released on 2022 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: The world we live in presents plenty of tricky, impactful, and hard-tomake decisions to be taken. Sometimes the available options are ample, at other times they are apparently binary, either way, they often confront us with dilemmas, paradoxes, and even denial of values.In the dawn of the age of intelligence, when robots are gradually taking over most decision-making from humans, this book sheds a bit of light on decision rationale. It delves into the limits of these decision processes (for both humans and machines), and it does so by providing a new perspective that is somehow opposed to orthodox economics. All Economics reflections in this book are underlined and linked to Artificial Intelligence.The authors hope that this comprehensive and modern analysis, firmly grounded in the opinions of various groundbreaking Nobel laureate economists, may be helpful to a broad audience interested in how decisions may lead us all to flourishing societies. That is, societies in which economic blunders (caused by over simplification of problems and super estimation of tools) are reduced substantially.

Download or read book Decision Economics Minds Machines and their Society written by Edgardo Bucciarelli and published by Springer Nature. This book was released on 2021-08-16 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the result of a multi-year research project led and sponsored by the University of Chieti-Pescara, National Chengchi University, University of Salamanca, and Osaka University. It is the fifth volume to emerge from that international project, held under the aegis of the United Nations Academic Impact in 2020. All the essays in this volume were (virtually) discussed at the University of L’Aquila―as the venue of the 2nd International Conference on Decision Economics, a three-day global gathering of approximately one hundred scholars and practitioners—and were subjected to thorough peer review by leading experts in the field. The essays reflect the extent, diversity, and richness of several research areas, both normative and descriptive, and are an invaluable resource for graduate-level and PhD students, academics, researchers, policymakers and other professionals, especially in the social and cognitive sciences. Given its interdisciplinary scope, the book subsequently delivers new approaches on how to contribute to the future of economics, providing alternative explanations for various socio-economic issues such as computable humanities; cognitive, behavioural, and experimental perspectives in economics; data analysis and machine learning as well as research areas at the intersection of computer science, artificial intelligence, mathematics, and statistics; agent-based modelling and the related. The editors are grateful to the scientific committee for its continuous support throughout the research project as well as to the many participants for their insightful comments and always probing questions. In any case, the collaboration involved in the project extends far beyond the group of authors published in this volume and is reflected in the quality of the essays published over the years.