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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. -
Re-Excavated: Debunking a Fallacious Account of the JAFFE Dataset Michael Lyons
”Excavating AI” Re-excavated: Debunking a Fallacious Account of the JAFFE Dataset Michael Lyons To cite this version: Michael Lyons. ”Excavating AI” Re-excavated: Debunking a Fallacious Account of the JAFFE Dataset. 2021. hal-03321964 HAL Id: hal-03321964 https://hal.archives-ouvertes.fr/hal-03321964 Preprint submitted on 18 Aug 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives| 4.0 International License “Excavating AI” Re-excavated: Debunking a Fallacious Account of the JAFFE Dataset Michael J. Lyons Ritsumeikan University Abstract Twenty-five years ago, my colleagues Miyuki Kamachi and Jiro Gyoba and I designed and photographed JAFFE, a set of facial expression images intended for use in a study of face perception. In 2019, without seeking permission or informing us, Kate Crawford and Trevor Paglen exhibited JAFFE in two widely publicized art shows. In addition, they published a nonfactual account of the images in the essay “Excavating AI: The Politics of Images in Machine Learning Training Sets.” The present article recounts the creation of the JAFFE dataset and unravels each of Crawford and Paglen’s fallacious statements. -
Devops and Dataops
Getting DataOps Right Andy Palmer, Michael Stonebraker, Nik Bates-Haus, Liam Cleary, and Mark Marinelli Beijing Boston Farnham Sebastopol Tokyo Getting DataOps Right by Andy Palmer, Michael Stonebraker, Nik Bates-Haus, Liam Cleary, and Mark Marinelli Copyright © 2019 O’Reilly Media. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more infor‐ mation, contact our corporate/institutional sales department: 800-998-9938 or [email protected]. Development Editor: Jeff Bleiel Proofreader: Charles Roumeliotis Acquisitions Editor: Rachel Roumeliotis Interior Designer: David Futato Production Editor: Katherine Tozer Cover Designer: Karen Montgomery Copyeditor: Octal Publishing, Inc. Illustrator: Rebecca Demarest June 2019: First Edition Revision History for the First Edition 2019-05-22: First Release The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Getting DataOps Right, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the authors, and do not represent the publisher’s views. While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. -
Toward Machines with Emotional Intelligence
Toward Machines with Emotional Intelligence Rosalind W. Picard MIT Media Laboratory Abstract For half a century, artificial intelligence researchers have focused on giving machines linguistic and mathematical-logical reasoning abilities, modeled after the classic linguistic and mathematical-logical intelligences. This chapter describes new research that is giving machines (including software agents, robotic pets, desktop computers, and more) skills of emotional intelligence. Machines have long been able to appear as if they have emotional feelings, but now machines are also being programmed to learn when and how to display emotion in ways that enable the machine to appear empathetic or otherwise emotionally intelligent. Machines are now being given the ability to sense and recognize expressions of human emotion such as interest, distress, and pleasure, with the recognition that such communication is vital for helping machines choose more helpful and less aggravating behavior. This chapter presents several examples illustrating new and forthcoming forms of machine emotional intelligence, highlighting applications together with challenges to their development. 1. Introduction Why would machines need emotional intelligence? Machines do not even have emotions: they don’t feel happy to see us, sad when we go, or bored when we don’t give them enough interesting input. IBM’s Deep Blue supercomputer beat Grandmaster and World Champion Gary Kasparov at Chess without feeling any stress during the game, and without any joy at its accomplishment, -
Affectiva RANA EL KALIOUBY
STE Episode Transcript: Affectiva RANA EL KALIOUBY: A lip corner pull, which is pulling your lips outwards and upwards – what we call a smile – is action unit 12. The face is one of the most powerful ways of communicating human emotions. We're on this mission to humanize technology. I was spending more time with my computer than I did with any other human being – and this computer, it knew a lot of things about me, but it didn't have any idea of how I felt. There are hundreds of different types of smiles. There's a sad smile, there's a happy smile, there's an “I'm embarrassed” smile – and there's definitely an “I'm flirting” smile. When we're face to face, when we actually are able to get these nonverbal signals, we end up being kinder people. The algorithm tracks your different facial expressions. I really believe this is going to become ubiquitous. It's going to be the standard human machine interface. Any technology in human history is neutral. It's how we decide to use it. There's a lot of potential where this thing knows you so well, it can help you become a better version of you. But that same data in the wrong hands could be could be manipulated to exploit you. CATERINA FAKE: That was computer scientist Rana el Kaliouby. She invented an AI tool that can read the expression on your face and know how you’re feeling in real time. With this software, computers can read our signs of emotion – happiness, fear, confusion, grief – which paves the way for a future where technology is more human, and therefore serves us better. -
Affective Computing and Autism
Affective Computing and Autism a a RANA EL KALIOUBY, ROSALIND PICARD, AND SIMON BARON-COHENb aMassachusetts Institute of Technology, Cambridge, Massachusetts 02142-1308, USA bUniversity of Cambridge, Cambridge CB3 0FD, United Kingdom ABSTRACT: This article highlights the overlapping and converging goals and challenges of autism research and affective computing. We propose that a collaboration between autism research and affective computing could lead to several mutually beneficial outcomes—from developing new tools to assist people with autism in understanding and operating in the socioemotional world around them, to developing new computa- tional models and theories that will enable technology to be modified to provide an overall better socioemotional experience to all people who use it. This article describes work toward this convergence at the MIT Media Lab, and anticipates new research that might arise from the interaction between research into autism, technology, and human socioemotional intelligence. KEYWORDS: autism; Asperger syndrome (AS); affective computing; af- fective sensors; mindreading software AFFECTIVE COMPUTING AND AUTISM Autism is a set of neurodevelopmental conditions characterized by social interaction and communication difficulties, as well as unusually narrow, repeti- tive interests (American Psychiatric Association 1994). Autism spectrum con- ditions (ASC) comprise at least four subgroups: high-, medium-, and low- functioning autism (Kanner 1943) and Asperger syndrome (AS) (Asperger 1991; Frith 1991). Individuals with AS have average or above average IQ and no language delay. In the other three autism subgroups there is invariably some degree of language delay, and the level of functioning is indexed by overall IQ. Individuals diagnosed on the autistic spectrum often exhibit a “triad of strengths”: good attention to detail, deep, narrow interest, and islets of ability (Baron-Cohen 2004). -
Middle East Prepares for AI Acceleration
Research Insights Middle East prepares for AI acceleration Exploring AI commitment, ambitions and strategies By Ian Fletcher, Brian Goehring, Anthony Marshall, and Tarek Saeed Talking points Connectivity, big data drive AI use AI is the new space race While early investment in artificial Modern AI has been around since the 1950s. Since that time, there have been several AI false starts known as “AI intelligence (AI) was typically motivated by winters,” in which AI was not considered all that seriously. a desire to get ahead, the emphasis today Now, however, AI is being driven by the proliferation of is increasingly on achieving competitive ubiquitous connectivity, dramatically increased computing capability, unprecedented amounts of data, parity—making sure an organization is not and ever-more sophisticated systems of engagement. left behind. As a consequence, regional Today’s business leaders understand that AI is an leaders—especially those in Middle East increasingly important tool for future growth and prosperity and are investing accordingly. nations—are recognizing AI’s growing significance and are placing AI at the Forward-looking countries in the Middle East, such as the United Arab Emirates (UAE), Saudi Arabia, Egypt, and center of national economic strategies, Qatar are taking bold steps—through increased organization, and culture. investment across sectors and policy awareness and commitment—to prepare and position for dramatic AI creates jobs progress using AI. Smaller countries that develop a Despite intense media attention on AI’s significant edge in AI technology will punch above their weight class. possible impact on replacing workers, empirical evidence collected by the IBM AI investment is clearly on the rise. -
Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected In-The-Wild
Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected In-the-Wild Daniel McDuffyz, Rana El Kalioubyyz, Thibaud Senechalz, May Amrz, Jeffrey F. Cohnx, Rosalind Picardyz z Affectiva Inc., Waltham, MA, USA y MIT Media Lab, Cambridge, MA, USA x Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA [email protected], fkaliouby,thibaud.senechal,[email protected], [email protected], [email protected] Abstract ing research and media testing [14] to understanding non- verbal communication [19]. With the ubiquity of cameras Computer classification of facial expressions requires on computers and mobile devices, there is growing interest large amounts of data and this data needs to reflect the in bringing these applications to the real-world. To do so, diversity of conditions seen in real applications. Public spontaneous data collected from real-world environments is datasets help accelerate the progress of research by provid- needed. Public datasets truly help accelerate research in an ing researchers with a benchmark resource. We present a area, not just because they provide a benchmark, or a com- comprehensively labeled dataset of ecologically valid spon- mon language, through which researchers can communicate taneous facial responses recorded in natural settings over and compare their different algorithms in an objective man- the Internet. To collect the data, online viewers watched ner, but also because compiling such a corpus and getting one of three intentionally amusing Super Bowl commercials it reliably labeled, is tedious work - requiring a lot of effort and were simultaneously filmed using their webcam. They which many researchers may not have the resources to do. -
Getting Data Right Tackling the Challenges of Big Data Volume and Variety
Compliments of Getting Data Right Tackling the Challenges of Big Data Volume and Variety Jerry Held, Michael Stonebraker, Thomas H. Davenport, Ihab Ilyas, Michael L. Brodie, Andy Palmer & James Markarian Getting Data Right Tackling the Challenges of Big Data Volume and Variety Jerry Held, Michael Stonebraker, Thomas H. Davenport, Ihab Ilyas, Michael L. Brodie, Andy Palmer, and James Markarian Getting Data Right by Jerry Held, Michael Stonebraker, Thomas H. Davenport, Ihab Ilyas, Michael L. Brodie, Andy Palmer, and James Markarian Copyright © 2016 Tamr, Inc. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://safaribooksonline.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or [email protected]. Editor: Shannon Cutt Interior Designer: David Futato Production Editor: Nicholas Adams Cover Designer: Randy Comer Copyeditor: Rachel Head Illustrator: Rebecca Demarest Proofreader: Nicholas Adams January 2016: First Edition Revision History for the First Edition 2015-12-15: First Release The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Getting Data Right and related trade dress are trademarks of O’Reilly Media, Inc. While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. -
University of Waterloo SENATE EXECUTIVE COMMITTEE Notice of Meeting Date: Monday 1 February 2021 Time: 3:30 P.M. Place: Microsof
University of Waterloo SENATE EXECUTIVE COMMITTEE Notice of Meeting Date: Monday 1 February 2021 Time: 3:30 p.m. Place: Microsoft Teams Videoconference OPEN SESSION Action 1. Minutes of the 4 January 2021 Meeting Decision 2. Business Arising from the Minutes 3. Draft 22 February 2021 Senate Agenda Decision 4. Other Business CONFIDENTIAL SESSION 5. Chancellor Review Committee Information KJJ/ees Karen Jack 25 January 2021 University Secretary Secretary to the Executive Committee 1 of 48 University of Waterloo SENATE EXECUTIVE COMMITTEE Minutes of the 4 January 2021 Meeting Present: David Billedeau, Dan Brown, Jeff Casello, Joan Coutu, George Freeman, Feridun Hamdullahpur (chair), Karen Jack (secretary), Christiane Lemieux, William Power, Sam Rubin, James Rush, Abbie Simpson, Richard Staines, Johanna Wandel Regrets: Kofi Campbell 1. MINUTES OF THE 2 NOVEMBER 2020 MEETING Members heard a motion to approve the minutes of the 2 November 2020 meeting. Casello and Simpson. Carried unanimously. 2. BUSINESS ARISING FROM THE MINUTES There was no business arising. 3. APPOINTMENT OF CHANCELLOR REVIEW COMMITTEE The chair spoke to the historical practice of Senate appointing this committee to undertake the role of the nominating, or in this case, review committee for the role of chancellor. Following discussion, the committee heard a motion to recommend to Senate that it appoint the Senate Executive Committee as a chancellor review committee. Freeman and Staines. Carried unanimously. 4. DRAFT 18 JANUARY 2021 SENATE AGENDA The chair provided members with a brief review of the agenda including notice that in addition to his regular update from the president, the provost and vice-president, research and international jointly will present an update on the strategic plan working groups; that good news is coming regarding an award announcement; and he advised that the provost will offer commentary on current academic matters. -
2. Enterprise.Pdf
Enterprise 00 01 02 03 04 05 06 07 08 09 10 11 12 Artificial Intelligence Watch Closely Informs Strategy Act Now Enterprise Trends The Rise of MLOps Businesses can turn their unruly data- language processing collection and classi- As machine learning matures and new sets into structured data that can be fication. Trained to recognize keywords, applied business solutions emerge, devel- trained, and they can build and deploy special algorithms can rapidly sort, opers are shifting their focus from build- models with minimal skills. Create ML classify, and tag information to detect ing models to operating them. Within is Apple’s no-code, drag-and-drop tool patterns. For example, a model trained to software, a set of best practices known as that lets users build custom models such search for hate speech can detect bad ac- DevOps relies on tools, automation, and as recommendation engines, natural tors in social networks. Machine transla- workflows to reduce complexity so that processing systems, and text classifiers. tion generates training data for financial developers can focus on problems that Google’s AutoML includes image clas- crime classification; last year, it reduced need to be solved. This approach is now sification, object detection, translation, the amount of time needed for classifi- being used in machine learning. In 2020, and all sorts of pattern recognition tools. cation from 20 weeks (human analysts some of the fastest-growing GitHub MakeML creates object detection. Ap- working alone) to two weeks. projects were MLOps, or projects that plications have included tracking tennis dealt with tooling, infrastructure, and balls during matches and automatically Simulating Empathy and Emotion changing the colors of objects (such as operations. -
Affective Computing and Autism
Affective Computing and Autism RANA EL KALIOUBY,a ROSALIND PICARD,a AND SIMON BARON-COHENb aMassachusetts Institute of Technology, Cambridge, Massachusetts 02142-1308, USA bUniversity of Cambridge, Cambridge CB3 0FD, United Kingdom ABSTRACT: This article highlights the overlapping and converging goals and challenges of autism research and affective computing. We propose that a collaboration between autism research and affective computing could lead to several mutually beneficial outcomes—from developing new tools to assist people with autism in understanding and operating in the socioemotional world around them, to developing new computa- tional models and theories that will enable technology to be modified to provide an overall better socioemotional experience to all people who use it. This article describes work toward this convergence at the MIT Media Lab, and anticipates new research that might arise from the interaction between research into autism, technology, and human socioemotional intelligence. KEYWORDS: autism; Asperger syndrome (AS); affective computing; af- fective sensors; mindreading software AFFECTIVE COMPUTING AND AUTISM Autism is a set of neurodevelopmental conditions characterized by social interaction and communication difficulties, as well as unusually narrow, repeti- tive interests (American Psychiatric Association 1994). Autism spectrum con- ditions (ASC) comprise at least four subgroups: high-, medium-, and low- functioning autism (Kanner 1943) and Asperger syndrome (AS) (Asperger 1991; Frith 1991). Individuals with AS have average or above average IQ and no language delay. In the other three autism subgroups there is invariably some degree of language delay, and the level of functioning is indexed by overall IQ. Individuals diagnosed on the autistic spectrum often exhibit a “triad of strengths”: good attention to detail, deep, narrow interest, and islets of ability (Baron-Cohen 2004).