What Is Artificial Intelligence?
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Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril
Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril Michael Matheny, Sonoo Thadaney Israni, Mahnoor Ahmed, and Danielle Whicher, Editors WASHINGTON, DC NAM.EDU PREPUBLICATION COPY - Uncorrected Proofs NATIONAL ACADEMY OF MEDICINE • 500 Fifth Street, NW • WASHINGTON, DC 20001 NOTICE: This publication has undergone peer review according to procedures established by the National Academy of Medicine (NAM). Publication by the NAM worthy of public attention, but does not constitute endorsement of conclusions and recommendationssignifies that it is the by productthe NAM. of The a carefully views presented considered in processthis publication and is a contributionare those of individual contributors and do not represent formal consensus positions of the authors’ organizations; the NAM; or the National Academies of Sciences, Engineering, and Medicine. Library of Congress Cataloging-in-Publication Data to Come Copyright 2019 by the National Academy of Sciences. All rights reserved. Printed in the United States of America. Suggested citation: Matheny, M., S. Thadaney Israni, M. Ahmed, and D. Whicher, Editors. 2019. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine. PREPUBLICATION COPY - Uncorrected Proofs “Knowing is not enough; we must apply. Willing is not enough; we must do.” --GOETHE PREPUBLICATION COPY - Uncorrected Proofs ABOUT THE NATIONAL ACADEMY OF MEDICINE The National Academy of Medicine is one of three Academies constituting the Nation- al Academies of Sciences, Engineering, and Medicine (the National Academies). The Na- tional Academies provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. -
Artificial Intelligence
TechnoVision The Impact of AI 20 18 CONTENTS Foreword 3 Introduction 4 TechnoVision 2018 and Artificial Intelligence 5 Overview of TechnoVision 2018 14 Design for Digital 17 Invisible Infostructure 26 Applications Unleashed 33 Thriving on Data 40 Process on the Fly 47 You Experience 54 We Collaborate 61 Applying TechnoVision 68 Conclusion 77 2 TechnoVision 2018 The Impact of AI FOREWORD We introduce TechnoVision 2018, now in its eleventh year, with pride in reaching a second decade of providing a proven and relevant source of technology guidance and thought leadership to help enterprises navigate the compelling and yet complex opportunities for business. The longevity of this publication has been achieved through the insight of our colleagues, clients, and partners in applying TechnoVision as a technology guide and coupling that insight with the expert input of our authors and contributors who are highly engaged in monitoring and synthesizing the shift and emergence of technologies and the impact they have on enterprises globally. In this edition, we continue to build on the We believe that with TechnoVision 2018, you can framework that TechnoVision has established further crystalize your plans and bet on the right for several years with updates on last years’ technology disruptions, to continue to map and content, reflecting new insights, use cases, and traverse a successful digital journey. A journey technology solutions. which is not about a single destination, but rather a single mission to thrive in the digital epoch The featured main theme for TechnoVision 2018 through agile cycles of transformation delivering is AI, due to breakthroughs burgeoning the business outcomes. -
The Importance of Generation Order in Language Modeling
The Importance of Generation Order in Language Modeling Nicolas Ford∗ Daniel Duckworth Mohammad Norouzi George E. Dahl Google Brain fnicf,duckworthd,mnorouzi,[email protected] Abstract There has been interest in moving beyond the left-to-right generation order by developing alter- Neural language models are a critical compo- native multi-stage strategies such as syntax-aware nent of state-of-the-art systems for machine neural language models (Bowman et al., 2016) translation, summarization, audio transcrip- and latent variable models of text (Wood et al., tion, and other tasks. These language models are almost universally autoregressive in nature, 2011). Before embarking on a long-term research generating sentences one token at a time from program to find better generation strategies that left to right. This paper studies the influence of improve modern neural networks, one needs ev- token generation order on model quality via a idence that the generation strategy can make a novel two-pass language model that produces large difference. This paper presents one way of partially-filled sentence “templates” and then isolating the generation strategy from the general fills in missing tokens. We compare various neural network design problem. Our key techni- strategies for structuring these two passes and cal contribution involves developing a flexible and observe a surprisingly large variation in model quality. We find the most effective strategy tractable architecture that incorporates different generates function words in the first pass fol- generation orders, while enabling exact computa- lowed by content words in the second. We be- tion of the log-probabilities of a sentence. -
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design Jeffrey Dean Google Research [email protected] Abstract The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks. This paper is a companion paper to a keynote talk at the 2020 International Solid-State Circuits Conference (ISSCC) discussing some of the advances in machine learning, and their implications on the kinds of computational devices we need to build, especially in the post-Moore’s Law-era. It also discusses some of the ways that machine learning may also be able to help with some aspects of the circuit design process. Finally, it provides a sketch of at least one interesting direction towards much larger-scale multi-task models that are sparsely activated and employ much more dynamic, example- and task-based routing than the machine learning models of today. Introduction The past decade has seen a remarkable series of advances in machine learning (ML), and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas [LeCun et al. 2015]. Major areas of significant advances include computer vision [Krizhevsky et al. 2012, Szegedy et al. 2015, He et al. 2016, Real et al. 2017, Tan and Le 2019], speech recognition [Hinton et al. -
AI Superpowers
Review of AI Superpowers: China, Silicon Valley and the New World Order by Kai-Fu Lee by Preston McAfee1 Let me start by concluding that AI Superpowers is a great read and well worth the modest investment in time required to read it. Lee has a lively, breezy style, in spite of discussing challenging technology applied to a wide-ranging, thought-provoking subject matter, and he brings great insight and expertise to one of the more important topics of this century. Unlike most of the policy books that are much discussed but rarely read, this book deserves to be read. I will devote my review to critiquing the book, but nonetheless I strongly recommend it. Lee is one of the world’s top artificial intelligence (AI) researchers and a major force in technology startups in China. He was legendary in Microsoft because of his role in Microsoft’s leadership in speech recognition. He created Google’s research office in China, and then started a major incubator that he still runs. Lee embodies a rare combination of formidable technical prowess and managerial success, so it is not surprising that he has important things to say about AI. The book provides a relatively limited number of arguments, which I’ll summarize here: AI will transform business in the near future, China’s “copycat phase” was a necessary step to develop technical and innovative capacity, Success in the Chinese market requires unique customization, which is why western companies were outcompeted by locals, Local Chinese companies experienced “gladiatorial” competition, with ruthless copying, and often outright fraud, but the companies that emerged were much stronger for it, The Chinese government helped promote internet companies with subsidies and encouragement but not favoritism as widely believed, Chinese companies “went heavy” by vertically integrating, e.g. -
Ai & the Sustainable Development Goals
1 AI & THE SUSTAINABLE DEVELOPMENT GOALS: THE STATE OF PLAY Global Goals Technology Forum 2 3 INTRODUCTION In 2015, 193 countries agreed to the United Nations (UN) 2030 Agenda for Sustainable Development, which provides a shared blueprint for peace and prosperity for people and the planet, now and into the future. At its heart are the 17 Sustainable Development Goals (SDGs), which are an urgent call for action by all countries – developed and developing – in a global partnership. Achieving the SDGs is not just a moral imperative, but an economic one. While the world is making progress in some areas, we are falling behind in delivering the SDGs overall. We need all actors – businesses, governments, academia, multilateral institutions, NGOs, and others – to accelerate and scale their efforts to deliver the SDGs, using every tool at their disposal, including artificial intelligence (AI). In December 2017, 2030Vision 2030Vision published its first report, Uniting to “WE ARE AT A PIVOTAL Deliver Technology for the Global ABOUT 2030VISION Goals, which addressed the role MOMENT. AS ARTIFICIAL of digital technology – big data, AI & The Sustainable Development Goals: The State of Play State Goals: The Development Sustainable AI & The Founded in 2017, 2030Vision INTELLIGENCE BECOMES robotics, internet of things, AI, and is a partnership of businesses, MORE WIDELY ADOPTED, other technologies – in achieving NGOs, and academia that aims to the SDGs. transform the use of technology to WE HAVE A TREMENDOUS support the delivery of the SDGs. OPPORTUNITY TO In this paper, we focus on AI for 2030Vision serves as a platform REEVALUATE WHAT WE’VE the SDGs. -
China's Current Capabilities, Policies, and Industrial Ecosystem in AI
June 7, 2019 “China’s Current Capabilities, Policies, and Industrial Ecosystem in AI” Testimony before the U.S.-China Economic and Security Review Commission Hearing on Technology, Trade, and Military-Civil Fusion: China’s Pursuit of Artificial Intelligence, New Materials, and New Energy Jeffrey Ding D.Phil Researcher, Center for the Governance of AI Future of Humanity Institute, University of Oxford Introduction1 This testimony assesses the current capabilities of China and the U.S. in AI, highlights key elements of China’s AI policies, describes China’s industrial ecosystem in AI, and concludes with a few policy recommendations. China’s AI Capabilities China has been hyped as an AI superpower poised to overtake the U.S. in the strategic technology domain of AI.2 Much of the research supporting this claim suffers from the “AI abstraction problem”: the concept of AI, which encompasses anything from fuzzy mathematics to drone swarms, becomes so slippery that it is no longer analytically coherent or useful. Thus, comprehensively assessing a nation’s capabilities in AI requires clear distinctions regarding the object of assessment. This section compares the current AI capabilities of China and the U.S. by slicing up the fuzzy concept of “national AI capabilities” into three cross-sections: 1) scientific and technological (S&T) inputs and outputs, 2) different layers of the AI value chain (foundation, technology, and application), and 3) different subdomains of AI (e.g. computer vision, predictive intelligence, and natural language processing). This approach reveals that China is not poised to overtake the U.S. in the technology domain of AI; rather, the U.S. -
Artificial Intelligence, China, Russia, and the Global Order Technological, Political, Global, and Creative Perspectives
AIR UNIVERSITY LIBRARY AIR UNIVERSITY PRESS Artificial Intelligence, China, Russia, and the Global Order Technological, Political, Global, and Creative Perspectives Shazeda Ahmed (UC Berkeley), Natasha E. Bajema (NDU), Samuel Bendett (CNA), Benjamin Angel Chang (MIT), Rogier Creemers (Leiden University), Chris C. Demchak (Naval War College), Sarah W. Denton (George Mason University), Jeffrey Ding (Oxford), Samantha Hoffman (MERICS), Regina Joseph (Pytho LLC), Elsa Kania (Harvard), Jaclyn Kerr (LLNL), Lydia Kostopoulos (LKCYBER), James A. Lewis (CSIS), Martin Libicki (USNA), Herbert Lin (Stanford), Kacie Miura (MIT), Roger Morgus (New America), Rachel Esplin Odell (MIT), Eleonore Pauwels (United Nations University), Lora Saalman (EastWest Institute), Jennifer Snow (USSOCOM), Laura Steckman (MITRE), Valentin Weber (Oxford) Air University Press Muir S. Fairchild Research Information Center Maxwell Air Force Base, Alabama Opening remarks provided by: Library of Congress Cataloging-in- Publication Data Brig Gen Alexus Grynkewich (JS J39) Names: TBD. and Lawrence Freedman (King’s College, Title: Artificial Intelligence, China, Russia, and the Global Order : Techno- London) logical, Political, Global, and Creative Perspectives / Nicholas D. Wright. Editor: Other titles: TBD Nicholas D. Wright (Intelligent Biology) Description: TBD Identifiers: TBD Integration Editor: Subjects: TBD Mariah C. Yager (JS/J39/SMA/NSI) Classification: TBD LC record available at TBD AIR UNIVERSITY PRESS COLLABORATION TEAM Published by Air University Press in October -
Testimony Before the House Permanent Select Committee on Intelligence
July 19, 2018 Testimony before the House Permanent Select Committee on Intelligence China’s Threat to American Government and Private Sector Research and Innovation Leadership Elsa B. Kania, Adjunct Fellow Technology and National Security Center for a New American Security Chairman Nunes, Ranking Member Schiff, distinguished members of the committee, thank you for the opportunity to discuss the challenge that China poses to American research and innovation leadership. In my remarks, I will discuss the near-term threat of China’s attempts to exploit the U.S. innovation ecosystem and also address the long-term challenge of China’s advances and ambitions in strategic technologies. In recent history, U.S. leadership in innovation has been a vital pillar of our power and predominance. Today, however, in this new era of strategic competition, the U.S. confronts a unique, perhaps unprecedented challenge to this primacy. Given China’s continued exploitation of the openness of the U.S. innovation ecosystem, from Silicon Valley to our nation’s leading universities, it is imperative to pursue targeted countermeasures against practices that are illegal or, at best, problematic. At the same time, our justified concerns about constraining the transfer of sensitive and strategic technologies to China must not distract our attention from the long-term, fundamental challenge— to enhance U.S. competitiveness, at a time when China is starting to become a true powerhouse and would-be superpower in science and technology (科技强国). China’s Quest for Indigenous Innovation China’s attempts to advance indigenous innovation (自主创新) have often leveraged and been accelerated by tech transfer that is undertaken through both licit and illicit means. -
2019 Edelman Ai Survey
2019 EDELMAN AI SURVEY SURVEY OF TECHNOLOGY EXECUTIVES AND THE GENERAL POPULATION SHOWS EXCITEMENT AND CURIOSITY YET UNCERTAINTY AND WORRIES THAT ARTIFICIAL INTELLIGENCE COULD BE A TOOL OF DIVISION March 2019 Contents 3 Executive Summary 4 Technology 13 Society 25 Business & Government 35 Conclusion 36 Key Takeaways 37 Appendix: Survey Methodology and Profile of Tech Executives 2019 EDELMAN AI SURVEY RESULTS REPORT | 2 Executive Summary Born in the 1950s, artificial intelligence (AI) is hardly AI while nearly half expect the poor will be harmed. new. After suffering an “AI Winter”* in the late 1980s, Approximately 80 percent of respondents expect recent advances with more powerful computers, AI to invoke a reactionary response from those who more intelligent software and vast amounts of “big feel threatened by the technology. Additionally, data” have led to breakneck advances over the last there are also worries about the dark side of AI, several years mostly based on the “deep learning” including concerns by nearly 70 percent about the breakthrough in 2012. Every day, there are headlines potential loss of human intellectual capabilities as extolling the latest AI-powered capability ranging AI-powered applications increasingly make decisions from dramatic improvements in medical diagnostics for us. Furthermore, 7 in 10 are concerned about to agriculture, earthquake prediction, endangered growing social isolation from an increased reliance wildlife protection and many more applications. on smart devices. Nevertheless, there are many voices warning about The survey reveals the many positive benefits but a runaway technology that could eliminate jobs and also the potential that AI can be a powerful tool pose an existential threat to humanity. -
AI Principles 2020 Progress Update
AI Principles 2020 Progress update AI Principles 2020 Progress update Table of contents Overview ........................................................................................................................................... 2 Culture, education, and participation ....................................................................4 Technical progress ................................................................................................................... 5 Internal processes ..................................................................................................................... 8 Community outreach and exchange .....................................................................12 Conclusion .....................................................................................................................................17 Appendix: Research publications and tools ....................................................18 Endnotes .........................................................................................................................................20 1 AI Principles 2020 Progress update Overview Google’s AI Principles were published in June 2018 as a charter to guide how we develop AI responsibly and the types of applications we will pursue. This report highlights recent progress in AI Principles implementation across Google, including technical tools, educational programs and governance processes. Of particular note in 2020, the AI Principles have supported our ongoing work to address -
User Experience + Research Scientist Collaboration in a Generative Machine Learning Interface
CHI 2019 Case Study CHI 2019, May 4–9, 2019, Glasgow, Scotland, UK Identifying the Intersections: User Figure 1: Google AI. http://ai.google Experience + Research Scientist Collaboration in a Generative Machine Learning Interface Claire Kayacik, Sherol Chen, Signe Noerly, Jess Holbrook, Adam Roberts, Douglas Eck Google, Inc. Mountain View, CA , 94043 USA (cbalgemann,sherol,noerly,jessh,adarob,deck)@google.com ABSTRACT Creative generative machine learning interfaces are stronger when multiple actors bearing different points of view actively contribute to them. User experience (UX) research and design involvement in the creation of machine learning (ML) models help ML research scientists to more effectively identify human needs that ML models will fulfill. The People and AI Research (PAIR) group within Google developed a novel program method in which UXers are embedded into an ML research group for three Figure 2: Magenta is a project within Google Brain that focuses on training ma- months to provide a human-centered perspective on the creation of ML models. The first full-time chines for creative expressivity, genera- cohort of UXers were embedded in a team of ML research scientists focused on deep generative models tive personalization, and intuitive inter- to assist in music composition. Here, we discuss the structure and goals of the program, challenges we faces. https://g.co/magenta faced during execution, and insights gained as a result of the process. We offer practical suggestions Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.