Making AI Great Again: Keeping the AI Spring
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CONTENT 03 2020 theme – Engines of Disruption 04 Chips go cold in AI winter 05 Stagflation rewards value over growth 06 ECB folds and hikes rates 07 In energy, green is not the new black 08 South Africa gets electrocuted by ESKOM debt 09 Trump announces America First Tax 10 Sweden breaks bad 11 Dems win clean sweep in 2020 election 12 Hungary leaves the EU 13 Asia launches digital reserve currency 2020 rent at twice the price of owning and maybe even slightly normalise viewed as plain fun, the principles Outrageous Predictions 2020 – a tax on the poor if ever there rates, while governments step into behind the Outrageous Predictions was one, and a driver of inequality. the breach with infrastructure and resonate very strongly with our This risks leaving an entire climate policy-linked spending. clients and the Saxo Group. Not - Engines of Disruption generation without the savings just in terms of what could happen STEEN JAKOBSEN, CHIEF INVESTMENT OFFICER needed to own their own house, The list of Outrageous Predictions to the portfolios and wealth typically the only major asset that this year all play to the theme of allocation, but also as an input to many medium- and lower-income disruption, because our current our respective areas of business, households will ever obtain. Thus paradigm is simply at the end of careers and life in general. Looking into the future is something of a fool’s game, but it remains a Already, there are a number of we are denying the very economic the road. Not because we want it to This is the spirit of Outrageous useful exercise — helping prepare for what lies ahead by considering cracks in the system that show mechanism that made the older end, but simply because extending Predictions: to create the very the full possible range of economic and political outcomes. -
Final Exam Review History of Science 150
Final Exam Review History of Science 150 1. Format of the Exam 90 minutes, on canvas 12:25pm December 18. You are welcome to bring notes to the exam, so you could start by filling out this sheet with notes from lectures and the readings! Like the mid-term, the final exam will have two kinds of questions. 1) Multiple choice questions examining your knowledge of key concepts, terms, historical developments, and contexts 2) Short answer questions in which ask you to draw on things you’ve learned in the course (from lecture, readings, videos) to craft a short argument in a brief essay expressing your informed issue on a historical question 2. Sample Questions Multiple Choice: Mina Rees was involved in (and wrote about) which of the following computing projects? A) Silicon Valley start-ups in the dot-com period B) Charles Babbage’s Difference Engine C) Works Projects Administration Tables Project D) Federal funding for computing research after WWII Short Answer: (Your answers should be between 100-200 words, and keep to specifics (events, machines, developments, people) that demonstrate your knowledge of materials covered from the course) A) What are two historical factors important to the development of Silicon Valley’s technology industry after World War II? B) In what ways did the field of programming change (in terms of its status and workers) between World War II and the late 1960s? 3. Topics to Review: Below, is a list of ideas to review for the final exam, which covers material through the entire course. You should review in particular, lecture notes, O’Mara’s The Code and other course readings provided on Canvas. -
Global-Bibliography.Pdf
Bibliography The following abbreviations are used for frequently cited conferences and journals: AAAI Proceedings of the AAAI Conference on Artificial Intelligence AAMAS Proceedings of the International Conference on Autonomous Agents and Multi-agent Systems ACL Proceedings of the Annual Meeting of the Association for Computational Linguistics AIJ Artificial Intelligence (Journal) AIMag AI Magazine AIPS Proceedings of the International Conference on AI Planning Systems AISTATS Proceedings of the International Conference on Artificial Intelligence and Statistics BBS Behavioral and Brain Sciences CACM Communications of the Association for Computing Machinery COGSCI Proceedings of the Annual Conference of the Cognitive Science Society COLING Proceedings of the International Conference on Computational Linguistics COLT Proceedings of the Annual ACM Workshop on Computational Learning Theory CP Proceedings of the International Conference on Principles and Practice of Constraint Programming CVPR Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition EC Proceedings of the ACM Conference on Electronic Commerce ECAI Proceedings of the European Conference on Artificial Intelligence ECCV Proceedings of the European Conference on Computer Vision ECML Proceedings of the The European Conference on Machine Learning ECP Proceedings of the European Conference on Planning EMNLP Proceedings of the Conference on Empirical Methods in Natural Language Processing FGCS Proceedings of the International Conference on Fifth Generation Computer -
Law, Sciences and New Technologies
Law, Sciences and New Technologies 2 Law, Sciences and New Technologies Open Access Series of the Interdepartmental Research Centre European Centre for Law, Science and New Technologies (ECLT) University of Pavia URL: <http://www.paviauniversitypress.it/collana/LSNT/17/> Series Editors Amedeo Santosuosso Carlo Alberto Redi Scientific Board Amedeo Santosuosso (University of Pavia - Court of Appeal of Milan) Carlo Alberto Redi (University of Pavia) Andrea Belvedere (University of Pavia) Ettore Dezza (University of Pavia) Silvia Garagna (University of Pavia) Oliver R. Goodenough (Vermont Law School) Associate Editor Barbara Bottalico (University of Pavia) Texts published in the series “Law, Sciences and New Technologies” have been peer-reviewed prior to acceptance. DATA-DRIVEN DECISION MAKING. LAW, ETHICS, ROBOTICS, HEALTH AMEDEO SANTOSUOSSO, GIULIA PINOTTI (Eds) Data-Driven Decision Making. Law, Ethics, Robotics, Health / [edited by] Amedeo Santosuosso, Giulia Pinotti (Eds). - Pavia : Pavia University Press, 2020. - 108 p. : ill. ; 22 cm. (Law, Sciences and New Technologies ; 2) http://archivio.paviauniversitypress.it/oa/9788869521348.pdf ISBN 9788869521331 (brossura) ISBN 9788869521348 (e-book PDF) © 2019 Pavia University Press – Pavia ISBN: 978-88-6952-134-8 Nella sezione Scientifica Pavia University Press pubblica esclusivamente testi scientifici valutati e approvati dal Comitato scientifico-editoriale. In copertina: L’isola ..., opera di Sabrina Mezzaqui, Galleria Massimo Minini per collezione Paolo Consolandi, fotografia di Alessandro Lui. Prima edizione: gennaio 2020 Pavia University Press – Edizioni dell’Università degli Studi di Pavia Via Luino, 12 – 27100 Pavia (PV) Italia http://www.paviauniversitypress.it – [email protected] Printed in Italy Table of Contents Introduction Science and Law in Big Data era: decisions, dilemmas and opportunities Amedeo Santosuosso, Giulia Pinotti............................................................................... -
AI Thinks Like a Corporation—And That's Worrying
Topics Current edition More Subscribe Log in or sign up Search Open Future Manage subscription Open Voices AI thinks like a corporation—and that’s worrying Arti!cial intelligence was born of organisational decision-making and state power; it needs human ethics, says Jonnie Penn of the University of Cambridge Open Future Nov 26th 2018 | by BY JONNIE PENN Arti!cial intelligence is everywhere but it is considered in a wholly ahistorical way. To understand the impact AI will have on our lives, it is vital to appreciate the context in which the !eld was established. After all, statistics and state control have evolved hand in hand for hundreds of years. Consider computing. Its origins have been traced not only to analytic philosophy, pure mathematics and Alan Turing, but perhaps surprisingly, to the history of public administration. In “The Government Machine: A Revolutionary History of the Computer” from 2003, Jon Agar of University College London charts the development of the British civil service as it ballooned from 16,000 employees in 1797 to 460,000 by 1999. He noticed an uncanny similarity between the functionality of a human bureaucracy and that of the digital electronic computer. (He confessed that he could not tell whether this observation was trivial or profound.) Get our daily newsletter Upgrade your inbox and get our Daily Dispatch and Editor's Picks. Email address Sign up now Both systems processed large quantities of information using a hierarchy of pre-set but adaptable rules. Yet one predated the other. This suggested a telling link between the organisation of human social structures and the digital tools designed to serve them. -
CAP 5636 - Advanced Artificial Intelligence
CAP 5636 - Advanced Artificial Intelligence Introduction This slide-deck is adapted from the one used by Chelsea Finn at CS221 at Stanford. CAP 5636 Instructor: Lotzi Bölöni http://www.cs.ucf.edu/~lboloni/ Slides, homeworks, links etc: http://www.cs.ucf.edu/~lboloni/Teaching/CAP5636_Fall2021/index.html Class hours: Tue, Th 12:00PM - 1:15PM COVID considerations: UCF expects you to get vaccinated and wear a mask Classes will be in-person, but will be recorded on Zoom. Office hours will be over Zoom. Motivating artificial intelligence It is generally not hard to motivate AI these days. There have been some substantial success stories. A lot of the triumphs have been in games, such as Jeopardy! (IBM Watson, 2011), Go (DeepMind’s AlphaGo, 2016), Dota 2 (OpenAI, 2019), Poker (CMU and Facebook, 2019). On non-game tasks, we also have systems that achieve strong performance on reading comprehension, speech recognition, face recognition, and medical imaging benchmarks. Unlike games, however, where the game is the full problem, good performance on a benchmark does not necessarily translate to good performance on the actual task in the wild. Just because you ace an exam doesn’t necessarily mean you have perfect understanding or know how to apply that knowledge to real problems. So, while promising, not all of these results translate to real-world applications Dangers of AI From the non-scientific community, we also see speculation about the future: that it will bring about sweeping societal change due to automation, resulting in massive job loss, not unlike the industrial revolution, or that AI could even surpass human-level intelligence and seek to take control. -
Artificial Intelligence the Next Digital Frontier?
ARTIFICIAL INTELLIGENCE THE NEXT DIGITAL FRONTIER? DISCUSSION PAPER JUNE 2017 Jacques Bughin | Brussels Eric Hazan | Paris Sree Ramaswamy | Washington, DC Michael Chui | San Francisco Tera Allas | London Peter Dahlström | London Nicolaus Henke | London Monica Trench | London Since its founding in 1990, the McKinsey Global Institute (MGI) has sought to develop a deeper understanding of the evolving global economy. As the business and economics research arm of McKinsey & Company, MGI aims to provide leaders in the commercial, public, and social sectors with the facts and insights on which to base management and policy decisions. The Lauder Institute at the University of Pennsylvania ranked MGI the world’s number-one private-sector think tank in its 2016 Global Think Tank Index for the second consecutive year. MGI research combines the disciplines of economics and management, employing the analytical tools of economics with the insights of business leaders. Our “micro-to-macro” methodology examines microeconomic industry trends to better understand the broad macroeconomic forces affecting business strategy and public policy. MGI’s in-depth reports have covered more than 20 countries and 30 industries. Current research focuses on six themes: productivity and growth, natural resources, labor markets, the evolution of global financial markets, the economic impact of technology and innovation, and urbanization. Recent reports have assessed the economic benefits of tackling gender inequality, a new era of global competition, Chinese innovation, and digital globalization. MGI is led by four McKinsey and Company senior partners: Jacques Bughin, James Manyika, Jonathan Woetzel, and Frank Mattern, MGI’s chairman. Michael Chui, Susan Lund, Anu Madgavkar, Sree Ramaswamy, and Jaana Remes serve as MGI partners. -
Global Artificial Intelligence Industry Whitepaper
Global artificial intelligence industry whitepaper Global artificial intelligence industry whitepaper | 4. AI reshapes every industry 1. New trends of AI innovation and integration 5 1.1 AI is growing fully commercialized 5 1.2 AI has entered an era of machine learning 6 1.3 Market investment returns to reason 9 1.4 Cities become the main battleground for AI innovation, integration and application 14 1.5 AI supporting technologies are advancing 24 1.6 Growing support from top-level policies 26 1.7 Over USD 6 trillion global AI market 33 1.8 Large number of AI companies located in the Beijing-Tianjin-Hebei Region, Yangtze River Delta and Pearl River Delta 35 2. Development of AI technologies 45 2.1 Increasingly sophisticated AI technologies 45 2.2 Steady progress of open AI platform establishment 47 2.3 Human vs. machine 51 3. China’s position in global AI sector 60 3.1 China has larger volumes of data and more diversified environment for using data 61 3.2 China is in the highest demand on chip in the world yet relying heavily on imported high-end chips 62 3.3 Chinese robot companies are growing fast with greater efforts in developing key parts and technologies domestically 63 3.4 The U.S. has solid strengths in AI’s underlying technology while China is better in speech recognition technology 63 3.5 China is catching up in application 64 02 Global artificial intelligence industry whitepaper | 4. AI reshapes every industry 4. AI reshapes every industry 68 4.1 Financial industry: AI enhances the business efficiency of financial businesses -
The Logicist Manifesto: at Long Last Let Logic-Based Artificial Intelligence Become a Field Unto Itself∗
The Logicist Manifesto: At Long Last Let Logic-Based Artificial Intelligence Become a Field Unto Itself∗ Selmer Bringsjord Rensselaer AI & Reasoning (RAIR) Lab Department of Cognitive Science Department of Computer Science Rensselaer Polytechnic Institute (RPI) Troy NY 12180 USA [email protected] version 9.18.08 Contents 1 Introduction 1 2 Background 1 2.1 Logic-Based AI Encapsulated . .1 2.1.1 LAI is Ambitious . .3 2.1.2 LAI is Based on Logical Systems . .4 2.1.3 LAI is a Top-Down Enterprise . .5 2.2 Ignoring the \Strong" vs. \Weak" Distinction . .5 2.3 A Slice in the Day of a Life of a LAI Agent . .6 2.3.1 Knowledge Representation in Elementary Logic . .8 2.3.2 Deductive Reasoning . .8 2.3.3 A Note on Nonmonotonic Reasoning . 12 2.3.4 Beyond Elementary Logical Systems . 13 2.4 Examples of Logic-Based Cognitive Systems . 15 3 Factors Supporting Logicist AI as an Independent Field 15 3.1 History Supports the Divorce . 15 3.2 The Advent of the Web . 16 3.3 The Remarkable Effectiveness of Logic . 16 3.4 Logic Top to Bottom Now Possible . 17 3.5 Learning and Denial . 19 3.6 Logic is an Antidote to \Cheating" in AI . 19 3.7 Logic Our Only Hope Against the Dark AI Future . 21 4 Objections; Rebuttals 22 4.1 \But you are trapped in a fundamental dilemma: your position is either redundant, or false." . 22 4.2 \But you're neglecting probabilistic AI." . 23 4.3 \But we now know that the mind, contra logicists, is continuous, and hence dynamical, not logical, systems are superior." . -
Rise of the Robots
Rise of the Robots Technology and the Threat of a Jobless Future MARTIN FORD A Member of the Perseus Books Group New York 2015 CONTENTS INTRODUCTION Introduction ix Sometime during the 1960s, the Nobel laureate economist Mil- Chapter 1 The Automation Wave 1 ton Friedman was consulting with the government of a developing Chapter 2 Is This Time Different? 29 Asian nation. Friedman was taken to a large-scale public works proj- Chapter 3 Information Technology: ect, where he was surprised to see large numbers of workers wielding An Unprecedented Force for Disruption 63 shovels, but very few bulldozers, tractors, or other heavy earth-moving equipment. When asked about this, the government official in charge Chapter 4 White-Collar Jobs at Risk 83 explained that the project was intended as a “jobs program.” Fried- Chapter 5 Transforming Higher Education 129 man’s caustic reply has become famous: “So then, why not give the Chapter 6 The Health Care Challenge 145 workers spoons instead of shovels?” Friedman’s remark captures the skepticism—and often outright Chapter 7 Technologies and Industries of the Future 175 derision—expressed by economists confronting fears about the pros- Chapter 8 Consumers, Limits to Growth . and Crisis? 193 pect of machines destroying jobs and creating long-term unemploy- Chapter 9 Super-Intelligence and the Singularity 229 ment. Historically, that skepticism appears to be well-founded. In the United States, especially during the twentieth century, advancing tech- Chapter 10 Toward a New Economic Paradigm 249 nology has consistently driven us toward a more prosperous society. Conclusion 281 There have certainly been hiccups—and indeed major disruptions— Acknowledgments 285 along the way. -
Deep Learning for Supernovae Detection
University of Cape Town Deep Learning for Supernovae Detection Deep Learning for Supernovae Detection Author: Supervisor: Gilad Amar Dr. Bruce Bassett Town A Minor Dissertation submitted in partial fulfilment of the requirements for the degree of aCape Master of Science ofin the Cosmology Group at AIMS Department of Mathematics and Applied Mathematics University August 2017 The copyright of this thesis vests in the author. No quotation from it or information derived from it is to be published without full acknowledgement of the source. The thesis is to be used for private study or non- commercial research purposes only. Published by the University of Cape Town (UCT) in terms of the non-exclusive license granted to UCT by the author. University of Cape Town Declaration of Authorship I, Gilad Amar, declare that this thesis titled, 'Deep Learning for Supernovae Detection' and the work presented in it are my own. I confirm that: This work was done wholly or mainly while in candidature for a research degree at this University. Where any part of this thesis has previously been submitted for a degree or any other qualification at this University or any other institution, this has been clearly stated. Where I have consulted the published work of others, this is always clearly at- tributed. Where I have quoted from the work of others, the source is always given. With the exception of such quotations, this thesis is entirely my own work. I have acknowledged all main sources of help. Where the thesis is based on work done by myself jointly with others, I have made clear exactly what was done by others and what I have contributed myself. -
Frameworks for Intelligent Systems
Frameworks for Intelligent Systems CompSci 765 Meeting 3 Pat Langley Department of Computer Science University of Auckland Outline of the Lecture • Computer science as an empirical discipline • Physical symbol systems • List structures and list processing • Reasoning and intelligence • Intelligence and search • Knowledge and intelligence • Implications for social cognition 2 Computer Science as an Empirical Discipline! In their Turing Award article, Newell and Simon (1976) make some important claims: • Computer science is an empirical discipline, rather than a branch of mathematics. • It is a science of the artificial, in that it constructs artifacts of sufficient complexity that formal analysis is not tractable. • We must study these computational artifacts as if they were natural systems, forming hypotheses and collecting evidence. They propose two hypotheses based on their founding work in list processing and artificial intelligence.! 3 Laws of Qualitative Structure! The authors introduce the idea of laws of qualitative structure, which are crucial for any scientific field’s development: • The cell doctrine in biology • Plate tectonics in geology • The germ theory of disease • The atomic theory of matter They propose two such laws, one related to mental structures and another and the other to mental processes. 4 Physical Symbol Systems! Newell and Simon’s first claim, the physical symbol system hypothesis, states that: • A physical symbol system has the necessary and sufficient means for general intelligent action. They emphasize general cognitive abilities, such as humans exhibit, rather than specialized ones. This is a theoretical claim that is subject to empirical tests, but the evidence to date generally supports it.! 5 More on Physical Symbol Systems! What do Newell and Simon mean by a physical symbol system? • Symbols are physical patterns that are stable unless modified.