2018 Annual Report
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NVIDIA CEO Jensen Huang to Host AI Pioneers Yoshua Bengio, Geoffrey Hinton and Yann Lecun, and Others, at GTC21
NVIDIA CEO Jensen Huang to Host AI Pioneers Yoshua Bengio, Geoffrey Hinton and Yann LeCun, and Others, at GTC21 Online Conference to Feature Jensen Huang Keynote and 1,300 Talks from Leaders in Data Center, Networking, Graphics and Autonomous Vehicles NVIDIA today announced that its CEO and founder Jensen Huang will host renowned AI pioneers Yoshua Bengio, Geoffrey Hinton and Yann LeCun at the company’s upcoming technology conference, GTC21, running April 12-16. The event will kick off with a news-filled livestreamed keynote by Huang on April 12 at 8:30 am Pacific. Bengio, Hinton and LeCun won the 2018 ACM Turing Award, known as the Nobel Prize of computing, for breakthroughs that enabled the deep learning revolution. Their work underpins the proliferation of AI technologies now being adopted around the world, from natural language processing to autonomous machines. Bengio is a professor at the University of Montreal and head of Mila - Quebec Artificial Intelligence Institute; Hinton is a professor at the University of Toronto and a researcher at Google; and LeCun is a professor at New York University and chief AI scientist at Facebook. More than 100,000 developers, business leaders, creatives and others are expected to register for GTC, including CxOs and IT professionals focused on data center infrastructure. Registration is free and is not required to view the keynote. In addition to the three Turing winners, major speakers include: Girish Bablani, Corporate Vice President, Microsoft Azure John Bowman, Director of Data Science, Walmart -
Lecture Note on Deep Learning and Quantum Many-Body Computation
Lecture Note on Deep Learning and Quantum Many-Body Computation Jin-Guo Liu, Shuo-Hui Li, and Lei Wang∗ Institute of Physics, Chinese Academy of Sciences Beijing 100190, China November 23, 2018 Abstract This note introduces deep learning from a computa- tional quantum physicist’s perspective. The focus is on deep learning’s impacts to quantum many-body compu- tation, and vice versa. The latest version of the note is at http://wangleiphy.github.io/. Please send comments, suggestions and corrections to the email address in below. ∗ [email protected] CONTENTS 1 introduction2 2 discriminative learning4 2.1 Data Representation 4 2.2 Model: Artificial Neural Networks 6 2.3 Cost Function 9 2.4 Optimization 11 2.4.1 Back Propagation 11 2.4.2 Gradient Descend 13 2.5 Understanding, Visualization and Applications Beyond Classification 15 3 generative modeling 17 3.1 Unsupervised Probabilistic Modeling 17 3.2 Generative Model Zoo 18 3.2.1 Boltzmann Machines 19 3.2.2 Autoregressive Models 22 3.2.3 Normalizing Flow 23 3.2.4 Variational Autoencoders 25 3.2.5 Tensor Networks 28 3.2.6 Generative Adversarial Networks 29 3.3 Summary 32 4 applications to quantum many-body physics and more 33 4.1 Material and Chemistry Discoveries 33 4.2 Density Functional Theory 34 4.3 “Phase” Recognition 34 4.4 Variational Ansatz 34 4.5 Renormalization Group 35 4.6 Monte Carlo Update Proposals 36 4.7 Tensor Networks 37 4.8 Quantum Machine Leanring 38 4.9 Miscellaneous 38 5 hands on session 39 5.1 Computation Graph and Back Propagation 39 5.2 Deep Learning Libraries 41 5.3 Generative Modeling using Normalizing Flows 42 5.4 Restricted Boltzmann Machine for Image Restoration 43 5.5 Neural Network as a Quantum Wave Function Ansatz 43 6 challenges ahead 45 7 resources 46 BIBLIOGRAPHY 47 1 1 INTRODUCTION Deep Learning (DL) ⊂ Machine Learning (ML) ⊂ Artificial Intelli- gence (AI). -
AI Computer Wraps up 4-1 Victory Against Human Champion Nature Reports from Alphago's Victory in Seoul
The Go Files: AI computer wraps up 4-1 victory against human champion Nature reports from AlphaGo's victory in Seoul. Tanguy Chouard 15 March 2016 SEOUL, SOUTH KOREA Google DeepMind Lee Sedol, who has lost 4-1 to AlphaGo. Tanguy Chouard, an editor with Nature, saw Google-DeepMind’s AI system AlphaGo defeat a human professional for the first time last year at the ancient board game Go. This week, he is watching top professional Lee Sedol take on AlphaGo, in Seoul, for a $1 million prize. It’s all over at the Four Seasons Hotel in Seoul, where this morning AlphaGo wrapped up a 4-1 victory over Lee Sedol — incidentally, earning itself and its creators an honorary '9-dan professional' degree from the Korean Baduk Association. After winning the first three games, Google-DeepMind's computer looked impregnable. But the last two games may have revealed some weaknesses in its makeup. Game four totally changed the Go world’s view on AlphaGo’s dominance because it made it clear that the computer can 'bug' — or at least play very poor moves when on the losing side. It was obvious that Lee felt under much less pressure than in game three. And he adopted a different style, one based on taking large amounts of territory early on rather than immediately going for ‘street fighting’ such as making threats to capture stones. This style – called ‘amashi’ – seems to have paid off, because on move 78, Lee produced a play that somehow slipped under AlphaGo’s radar. David Silver, a scientist at DeepMind who's been leading the development of AlphaGo, said the program estimated its probability as 1 in 10,000. -
Accelerators and Obstacles to Emerging ML-Enabled Research Fields
Life at the edge: accelerators and obstacles to emerging ML-enabled research fields Soukayna Mouatadid Steve Easterbrook Department of Computer Science Department of Computer Science University of Toronto University of Toronto [email protected] [email protected] Abstract Machine learning is transforming several scientific disciplines, and has resulted in the emergence of new interdisciplinary data-driven research fields. Surveys of such emerging fields often highlight how machine learning can accelerate scientific discovery. However, a less discussed question is how connections between the parent fields develop and how the emerging interdisciplinary research evolves. This study attempts to answer this question by exploring the interactions between ma- chine learning research and traditional scientific disciplines. We examine different examples of such emerging fields, to identify the obstacles and accelerators that influence interdisciplinary collaborations between the parent fields. 1 Introduction In recent decades, several scientific research fields have experienced a deluge of data, which is impacting the way science is conducted. Fields such as neuroscience, psychology or social sciences are being reshaped by the advances in machine learning (ML) and the data processing technologies it provides. In some cases, new research fields have emerged at the intersection of machine learning and more traditional research disciplines. This is the case for example, for bioinformatics, born from collaborations between biologists and computer scientists in the mid-80s [1], which is now considered a well-established field with an active community, specialized conferences, university departments and research groups. How do these transformations take place? Do they follow similar paths? If yes, how can the advances in such interdisciplinary fields be accelerated? Researchers in the philosophy of science have long been interested in these questions. -
On Recurrent and Deep Neural Networks
On Recurrent and Deep Neural Networks Razvan Pascanu Advisor: Yoshua Bengio PhD Defence Universit´ede Montr´eal,LISA lab September 2014 Pascanu On Recurrent and Deep Neural Networks 1/ 38 Studying the mechanism behind learning provides a meta-solution for solving tasks. Motivation \A computer once beat me at chess, but it was no match for me at kick boxing" | Emo Phillips Pascanu On Recurrent and Deep Neural Networks 2/ 38 Motivation \A computer once beat me at chess, but it was no match for me at kick boxing" | Emo Phillips Studying the mechanism behind learning provides a meta-solution for solving tasks. Pascanu On Recurrent and Deep Neural Networks 2/ 38 I fθ(x) = f (θ; x) ? F I f = arg minθ Θ EEx;t π [d(fθ(x); t)] 2 ∼ Supervised Learing I f :Θ D T F × ! Pascanu On Recurrent and Deep Neural Networks 3/ 38 ? I f = arg minθ Θ EEx;t π [d(fθ(x); t)] 2 ∼ Supervised Learing I f :Θ D T F × ! I fθ(x) = f (θ; x) F Pascanu On Recurrent and Deep Neural Networks 3/ 38 Supervised Learing I f :Θ D T F × ! I fθ(x) = f (θ; x) ? F I f = arg minθ Θ EEx;t π [d(fθ(x); t)] 2 ∼ Pascanu On Recurrent and Deep Neural Networks 3/ 38 Optimization for learning θ[k+1] θ[k] Pascanu On Recurrent and Deep Neural Networks 4/ 38 Neural networks Output neurons Last hidden layer bias = 1 Second hidden layer First hidden layer Input layer Pascanu On Recurrent and Deep Neural Networks 5/ 38 Recurrent neural networks Output neurons Output neurons Last hidden layer bias = 1 bias = 1 Recurrent Layer Second hidden layer First hidden layer Input layer Input layer (b) Recurrent -
Future of Research on Catastrophic and Existential Risk
ORE Open Research Exeter TITLE Working together to face humanity's greatest threats: Introduction to The Future of Research in Catastrophic and Existential Risk AUTHORS Currie, AM; Ó hÉigeartaigh, S JOURNAL Futures DEPOSITED IN ORE 06 February 2019 This version available at http://hdl.handle.net/10871/35764 COPYRIGHT AND REUSE Open Research Exeter makes this work available in accordance with publisher policies. A NOTE ON VERSIONS The version presented here may differ from the published version. If citing, you are advised to consult the published version for pagination, volume/issue and date of publication Working together to face humanity’s greatest threats: Introduction to The Future of Research on Catastrophic and Existential Risk. Adrian Currie & Seán Ó hÉigeartaigh Penultimate Version, forthcoming in Futures Acknowledgements We would like to thank the authors of the papers in the special issue, as well as the referees who provided such constructive and useful feedback. We are grateful to the team at the Centre for the Study of Existential Risk who organized the first Cambridge Conference on Catastrophic Risk where many of the papers collected here were originally presented, and whose multi-disciplinary expertise was invaluable for making this special issue a reality. We’d like to thank Emma Bates, Simon Beard and Haydn Belfield for feedback on drafts. Ted Fuller, Futures’ Editor-in-Chief also provided invaluable guidance throughout. The Conference, and a number of the publications in this issue, were made possible through the support of a grant from the Templeton World Charity Foundation (TWCF); the conference was also supported by a supplementary grant from the Future of Life Institute. -
ARCHITECTS of INTELLIGENCE for Xiaoxiao, Elaine, Colin, and Tristan ARCHITECTS of INTELLIGENCE
MARTIN FORD ARCHITECTS OF INTELLIGENCE For Xiaoxiao, Elaine, Colin, and Tristan ARCHITECTS OF INTELLIGENCE THE TRUTH ABOUT AI FROM THE PEOPLE BUILDING IT MARTIN FORD ARCHITECTS OF INTELLIGENCE Copyright © 2018 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. Acquisition Editors: Ben Renow-Clarke Project Editor: Radhika Atitkar Content Development Editor: Alex Sorrentino Proofreader: Safis Editing Presentation Designer: Sandip Tadge Cover Designer: Clare Bowyer Production Editor: Amit Ramadas Marketing Manager: Rajveer Samra Editorial Director: Dominic Shakeshaft First published: November 2018 Production reference: 2201118 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK ISBN 978-1-78913-151-2 www.packt.com Contents Introduction ........................................................................ 1 A Brief Introduction to the Vocabulary of Artificial Intelligence .......10 How AI Systems Learn ........................................................11 Yoshua Bengio .....................................................................17 Stuart J. -
Yoshua Bengio and Gary Marcus on the Best Way Forward for AI
Yoshua Bengio and Gary Marcus on the Best Way Forward for AI Transcript of the 23 December 2019 AI Debate, hosted at Mila Moderated and transcribed by Vincent Boucher, Montreal AI AI DEBATE : Yoshua Bengio | Gary Marcus — Organized by MONTREAL.AI and hosted at Mila, on Monday, December 23, 2019, from 6:30 PM to 8:30 PM (EST) Slides, video, readings and more can be found on the MONTREAL.AI debate webpage. Transcript of the AI Debate Opening Address | Vincent Boucher Good Evening from Mila in Montreal Ladies & Gentlemen, Welcome to the “AI Debate”. I am Vincent Boucher, Founding Chairman of Montreal.AI. Our participants tonight are Professor GARY MARCUS and Professor YOSHUA BENGIO. Professor GARY MARCUS is a Scientist, Best-Selling Author, and Entrepreneur. Professor MARCUS has published extensively in neuroscience, genetics, linguistics, evolutionary psychology and artificial intelligence and is perhaps the youngest Professor Emeritus at NYU. He is Founder and CEO of Robust.AI and the author of five books, including The Algebraic Mind. His newest book, Rebooting AI: Building Machines We Can Trust, aims to shake up the field of artificial intelligence and has been praised by Noam Chomsky, Steven Pinker and Garry Kasparov. Professor YOSHUA BENGIO is a Deep Learning Pioneer. In 2018, Professor BENGIO was the computer scientist who collected the largest number of new citations worldwide. In 2019, he received, jointly with Geoffrey Hinton and Yann LeCun, the ACM A.M. Turing Award — “the Nobel Prize of Computing”. He is the Founder and Scientific Director of Mila — the largest university-based research group in deep learning in the world. -
Wise Leadership & AI 3
Wise Leadership and AI Leadership Chapter 3 | Behind the Scenes of the Machines What’s Ahead For Artificial General Intelligence? By Dr. Peter VERHEZEN With the AMROP EDITORIAL BOARD Putting the G in AI | 8 points True, generalized intelligence will be achieved when computers can do or learn anything that a human can. At the highest level, this will mean that computers aren’t just able to process the ‘what,’ but understand the ‘why’ behind data — context, and cause and effect relationships. Even someday chieving consciousness. All of this will demand ethical and emotional intelligence. 1 We underestimate ourselves The human brain is amazingly general compared to any digital device yet developed. It processes bottom-up and top-down information, whereas AI (still) only works bottom-up, based on what it ‘sees’, working on specific, narrowly defined tasks. So, unlike humans, AI is not yet situationally aware, nuanced, or multi-dimensional. 2 When can we expect AGI? Great minds do not think alike Some eminent thinkers (and tech entrepreneurs) see true AGI as only a decade or two away. Others see it as science fiction — AI will more likely serve to amplify human intelligence, just as mechanical machines have amplified physical strength. 3 AGI means moving from homo sapiens to homo deus Reaching AGI has been described by the futurist Ray Kurzweil as ‘singularity’. At this point, humans should progress to the ‘trans-human’ stage: cyber-humans (electronically enhanced) or neuro-augmented (bio-genetically enhanced). 4 The real risk with AGI is not malice, but unguided brilliance A super-intelligent machine will be fantastically good at meeting its goals. -
Transcript of the Highlights of Elon Musk's
Powered by What do electric cars have in common with life on Mars? One man — Elon Musk. This multitalented inventor founded SpaceX, a company that develops and manufactures space vehicles, and Tesla Motors, which produces 100% electricity-powered luxury roadsters. How does one person balance diverse interests and manage successful endeavors in space technology, sustainable energy and the Internet? From his early days teaching himself to program computers, learn how Elon Musk has worked to improve the quality of life for everyone through technology. Powered by Elon Musk - www.OnInnovation.com In Thomas Edison’s Menlo Park complex, the machine shop served as a bridge between two worlds. One was a world of practicality — advancing the creation of Edison’s most commercially successful inventions. The other, a world of experimentation — exploring what was possible. A similar integration of experiment and practicality exists in Hawthorne, California, at a company known as SpaceX. Its business is developing rockets that place commercial satellites in orbit. But SpaceX has broader ambitions: creating technologically astounding rockets that, at some point in the future, could carry manned missions to Mars. At the helm of SpaceX is, an individual who came to prominence in the creation of PayPal . who is Chairman and CEO of the electric sports car manufacturer Tesla Motors . and who has a passion for doing things others only dream about. Bottom:The second floor of Thomas Edison’s Menlo Park Laboratory (lower right) in Greenfield Village served as the center of the experimental process. New ideas and solutions were developed and tested here. Edison inventions and patent models (left) were displayed in the laboratory as a reminder of Edison’s ingenuity. -
Global Catastrophic Risks 2016
Global Challenges Foundation Global Catastrophic Risks 2016 © Global Challenges Foundation/Global Priorities Project 2016 GLOBAL CATASTROPHIC RISKS 2016 THE GLOBAL CHALLENGES FOUNDATION works to raise awareness of the The views expressed in this report are those of the authors. Their Global Catastrophic Risks. Primarily focused on climate change, other en- statements are not necessarily endorsed by the affiliated organisations. vironmental degradation and politically motivated violence as well as how these threats are linked to poverty and rapid population growth. Against this Authors: background, the Foundation also works to both identify and stimulate the Owen Cotton-Barratt*† development of good proposals for a management model – a global gover- Sebastian Farquhar* nance – able to decrease – and at best eliminate – these risks. John Halstead* Stefan Schubert* THE GLOBAL PRIORITIES PROJECT helps decision-makers effectively prior- Andrew Snyder-Beattie† itise ways to do good. We achieve his both by advising decision-makers on programme evaluation methodology and by encouraging specific policies. We * = The Global Priorities Project are a collaboration between the Centre for Effective Altruism and the Future † = The Future of Humanity Institute, University of Oxford of Humanity Institute, part of the University of Oxford. Graphic design: Accomplice/Elinor Hägg Global Challenges Foundation in association with 4 Global Catastrophic Risks 2016 Global Catastrophic Risks 2016 5 Contents Definition: Global Foreword 8 Introduction 10 Catastrophic Risk Executive summary 12 1. An introduction to global catastrophic risks 20 – risk of events or 2. What are the most important global catastrophic risks? 28 Catastrophic climate change 30 processes that would Nuclear war 36 Natural pandemics 42 Exogenous risks 46 lead to the deaths of Emerging risks 52 Other risks and unknown risks 64 Our assessment of the risks 66 approximately a tenth of 3. -
Race in Academia
I, SOCIETY AI AND INEQUALITY: NEW DECADE, MORE CHALLENGES Mona Sloane, Ph.D. New York University [email protected] AI TECHNOLOGIES are becoming INTEGRAL to SOCIETY © DotEveryone SURVEILLANCE CAPITALISM TRADING in BEHAVIOURAL FUTURES © The Verge © The Innovation Scout TOTALIZING NARRATIVE of AI AI as a master technology = often either utopia or dystopia AI = human hubris? ”In 2014, the eminent scientists Stephen Hawking, Stuart Russell, Max Tegmark and Frank Wilczek wrote: ‘The potential benefits are huge; everything that civilisation has to offer is a product of human intelligence; we cannot predict what we might achieve when this intelligence is magnified by the tools that AI may provide, but the eradication of war, disease, and poverty would be high on anyone's list. Success in creating AI would be the biggest event in human history’.” Artificial General Intelligence? CORRELATION CAUSATION Race Race Gender Class INTERSECTIONAL INEQUALITY ETHICS EVERYWHERE EVERYWHERE EVERYWHERE EVERYWHERE EVERYWHERE EVERYWHERE EVERYWHERE ETHICS as POLICY APPROACH or as TECHNOLOGY APPROACH The ‘SOCIAL’ Problem ETHICS IS A SMOKESCREEN WHOSE HEAD – OR “INTELLIGENCE” – ARE TRYING TO MODEL, ANYWAY? THE FOUNDERS OF THE ”BIG 5 TECH COMPANIES”: AMAZON, FACEBOOK, ALPHABET, APPLE, MICROSOFT BIG TECH, BIG BUSINESS https://www.visualcapitalist.com/how-big-tech-makes-their-billions-2020/ 1956 DARTMOUTH CONFRENCE THE DIVERSITY ARGUMENT IS IMPORTANT. BUT IT IS INSUFFICIENT. EXTRACTION as a social super structure Prediction Dependency Extraction EXTRACTION Capitalism Racism White Supremacy Settler Colonialism Carcerality Heteronormativity … SCIENCE = ONTOLOGICAL AND EPISTEMOLOGICAL ENGINE Real world consequences of classification systems: racial classification in apartheid South Africa Ruha Benjamin, Alondra Nelson, Charlton McIlwain, Meredith Broussard, Andre Brock, Safiya Noble, Mutale Nkonde, Rediet Abebe, Timnit Gebru, Joy Buolamwini, Jessie Daniels, Sasha Costanza- Chock, Marie Hicks, Anna Lauren Hoffmann, Laura Forlano, Danya Glabau, Kadija Ferryman, Vincent Southerland – and more.