2018 ACM SIGAI Student Essay Contest on Artificial Intelligence Technologies
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Artificial Intelligence? 1 V2.0 © J
TU Darmstadt, WS 2012/13 Einführung in die Künstliche Intelligenz Einführung in die Künstliche Intelligenz Dozenten Prof. Johannes Fürnkranz (Knowledge Engineering) Prof. Ulf Brefeld (Knowledge Mining and Assessment) Homepage http://www.ke.informatik.tu-darmstadt.de/lehre/ki/ Termine: Dienstag 11:40-13:20 S202/C205 Donnerstag 11:40-13:20 S202/C205 3 VO + 1 UE Vorlesungen und Übungen werden in Doppelstunden abgehalten Übungen Terminplan wird auf der Web-Seite aktualisiert Ü voraussichtlich: 30.10., 13.11., 27.11., 11.12., 15.1., 29.1. Tafelübungen What is Artificial Intelligence? 1 V2.0 © J. Fürnkranz TU Darmstadt, WS 2012/13 Einführung in die Künstliche Intelligenz Text Book The course will mostly follow Stuart Russell und Peter Norvig: Artificial Intelligence: A Modern Approach. Prentice Hall, 2nd edition, 2003. Deutsche Ausgabe: Stuart Russell und Peter Norvig: Künstliche Intelligenz: Ein Moderner Ansatz. Pearson- Studium, 2004. ISBN: 978-3-8273-7089-1. 3. Auflage 2012 Home-page for the book: http://aima.cs.berkeley.edu/ Course slides in English (lecture is in German) will be availabe from Home-page What is Artificial Intelligence? 2 V2.0 © J. Fürnkranz TU Darmstadt, WS 2012/13 Einführung in die Künstliche Intelligenz What is Artificial Intelligence Different definitions due to different criteria Two dimensions: Thought processes/reasoning vs. behavior/action Success according to human standards vs. success according to an ideal concept of intelligence: rationality. Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally What is Artificial Intelligence? 3 V2.0 © J. Fürnkranz TU Darmstadt, WS 2012/13 Einführung in die Künstliche Intelligenz Definitions of Artificial Intelligence What is Artificial Intelligence? 4 V2.0 © J. -
A Century Long Commitment to Assessing Artificial Intelligence and Its Impact on Society1 Barbara J
A Century Long Commitment to Assessing Artificial Intelligence and Its Impact on Society1 Barbara J. Grosz, Harvard University, Inaugural Chair of the AI100 Standing Committee Peter Stone, University of Texas at Austin, Chair of the Inaugural AI100 Study Panel The Stanford One Hundred Year Study on Artificial Intelligence, a project that launched in December 2014, is designed to be a century-long periodic assessment of the field of Artificial Intelligence (AI) and its influences on people, their communities, and society. Colloquially referred to as "AI100", the project issued its first report in September 2016. A Standing Committee works with the Stanford Faculty Director of AI100 in overseeing the project and designing its activities. A little more than two years after the first report appeared, we reflect on the decisions made in shaping it, the process that produced it, its major conclusions, and reactions subsequent to its release. The inaugural AI100 report [4], which is titled “Artificial Intelligence and Life in 2030,” examines eight domains of human activity in which AI technologies are already starting to affect urban life. In scope, it encompasses domains with emerging products enabled by AI methods and ones raising concerns about technological impact generated by potential AI-enabled systems. The Study Panel members who authored the report and the AI100 Standing Committee, which is the body that directs the AI100 project, intend for it to act as a catalyst, spurring conversations on how we as a society might shape and share the potentially powerful technologies that AI could enable. In addition to influencing researchers and guiding decisions in industry and governments, the report aims to provide the general public with a scientifically and technologically accurate portrayal of the current state of AI and its potential. -
The Machine That Builds Itself: How the Strengths of Lisp Family
Khomtchouk et al. OPINION NOTE The Machine that Builds Itself: How the Strengths of Lisp Family Languages Facilitate Building Complex and Flexible Bioinformatic Models Bohdan B. Khomtchouk1*, Edmund Weitz2 and Claes Wahlestedt1 *Correspondence: [email protected] Abstract 1Center for Therapeutic Innovation and Department of We address the need for expanding the presence of the Lisp family of Psychiatry and Behavioral programming languages in bioinformatics and computational biology research. Sciences, University of Miami Languages of this family, like Common Lisp, Scheme, or Clojure, facilitate the Miller School of Medicine, 1120 NW 14th ST, Miami, FL, USA creation of powerful and flexible software models that are required for complex 33136 and rapidly evolving domains like biology. We will point out several important key Full list of author information is features that distinguish languages of the Lisp family from other programming available at the end of the article languages and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSL): languages which are specialized to a particular area and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the “programmable programming language.” We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and AI research in bioinformatics and computational biology. -
Backpropagation with Callbacks
Backpropagation with Continuation Callbacks: Foundations for Efficient and Expressive Differentiable Programming Fei Wang James Decker Purdue University Purdue University West Lafayette, IN 47906 West Lafayette, IN 47906 [email protected] [email protected] Xilun Wu Grégory Essertel Tiark Rompf Purdue University Purdue University Purdue University West Lafayette, IN 47906 West Lafayette, IN, 47906 West Lafayette, IN, 47906 [email protected] [email protected] [email protected] Abstract Training of deep learning models depends on gradient descent and end-to-end differentiation. Under the slogan of differentiable programming, there is an increas- ing demand for efficient automatic gradient computation for emerging network architectures that incorporate dynamic control flow, especially in NLP. In this paper we propose an implementation of backpropagation using functions with callbacks, where the forward pass is executed as a sequence of function calls, and the backward pass as a corresponding sequence of function returns. A key realization is that this technique of chaining callbacks is well known in the programming languages community as continuation-passing style (CPS). Any program can be converted to this form using standard techniques, and hence, any program can be mechanically converted to compute gradients. Our approach achieves the same flexibility as other reverse-mode automatic differ- entiation (AD) techniques, but it can be implemented without any auxiliary data structures besides the function call stack, and it can easily be combined with graph construction and native code generation techniques through forms of multi-stage programming, leading to a highly efficient implementation that combines the per- formance benefits of define-then-run software frameworks such as TensorFlow with the expressiveness of define-by-run frameworks such as PyTorch. -
Stuart Russell and Peter Norvig, Artijcial Intelligence: a Modem Approach *
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Artificial Intelligence ELSEVIER Artificial Intelligence 82 ( 1996) 369-380 Book Review Stuart Russell and Peter Norvig, Artijcial Intelligence: A Modem Approach * Nils J. Nilsson Robotics Laboratory, Department of Computer Science, Stanford University, Stanford, CA 94305, USA 1. Introductory remarks I am obliged to begin this review by confessing a conflict of interest: I am a founding director and a stockholder of a publishing company that competes with the publisher of this book, and I am in the process of writing another textbook on AI. What if Russell and Norvig’s book turns out to be outstanding? Well, it did! Its descriptions are extremely clear and readable; its organization is excellent; its examples are motivating; and its coverage is scholarly and thorough! End of review? No; we will go on for some pages-although not for as many as did Russell and Norvig. In their Preface (p. vii), the authors mention five distinguishing features of their book: Unified presentation of the field, Intelligent agent design, Comprehensive and up-to-date coverage, Equal emphasis on theory and practice, and Understanding through implementation. These features do indeed distinguish the book. I begin by making a few brief, summary comments using the authors’ own criteria as a guide. l Unified presentation of the field and Intelligent agent design. I have previously observed that just as Los Angeles has been called “twelve suburbs in search of a city”, AI might be called “twelve topics in search of a subject”. -
“Reflections on the Status and Future of Artificial Intelligence”
STATEMENT OF: ERIC HORVITZ TECHNICAL FELLOW AND DIRECTOR MICROSOFT RESEARCH—REDMOND LAB MICROSOFT CORPORATION BEFORE THE COMMITTEE ON COMMERCE SUBCOMMITTEE ON SPACE, SCIENCE, AND COMPETITIVENESS UNITED STATES SENATE HEARING ON THE DAWN OF ARTIFICIAL INTELLIGENCE NOVEMBER 30, 2016 “Reflections on the Status and Future of Artificial Intelligence” NOVEMBER 30, 2016 1 Chairman Cruz, Ranking Member Peters, and Members of the Subcommittee, my name is Eric Horvitz, and I am a Technical Fellow and Director of Microsoft’s Research Lab in Redmond, Washington. While I am also serving as Co-Chair of a new organization, the Partnership on Artificial Intelligence, I am speaking today in my role at Microsoft. We appreciate being asked to testify about AI and are committed to working collaboratively with you and other policymakers so that the potential of AI to benefit our country, and to people and society more broadly can be fully realized. With my testimony, I will first offer a historical perspective of AI, a definition of AI and discuss the inflection point the discipline is currently facing. Second, I will highlight key opportunities using examples in the healthcare and transportation industries. Third, I will identify the important research direction many are taking with AI. Next, I will attempt to identify some of the challenges related to AI and offer my thoughts on how best to address them. Finally, I will offer several recommendations. What is Artificial Intelligence? Artificial intelligence (AI) refers to a set of computer science disciplines aimed at the scientific understanding of the mechanisms underlying thought and intelligent behavior and the embodiment of these principles in machines that can deliver value to people and society. -
Pdf • Cynthia Breazeal
© copyright by Christoph Bartneck, Tony Belpaeime, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, and Selma Sabanovic 2019. https://www.human-robot-interaction.org Human{Robot Interaction An Introduction Christoph Bartneck, Tony Belpaeme, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, Selma Sabanovi´cˇ This material has been published by Cambridge University Press as Human Robot Interaction by Christoph Bartneck, Tony Belpaeime, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, and Selma Sabanovic. ISBN: 9781108735407 (http://www.cambridge.org/9781108735407). This pre-publication version is free to view and download for personal use only. Not for re-distribution, re-sale or use in derivative works. © copyright by Christoph Bartneck, Tony Belpaeime, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, and Selma Sabanovic 2019. https://www.human-robot-interaction.org This material has been published by Cambridge University Press as Human Robot Interaction by Christoph Bartneck, Tony Belpaeime, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, and Selma Sabanovic. ISBN: 9781108735407 (http://www.cambridge.org/9781108735407). This pre-publication version is free to view and download for personal use only. Not for re-distribution, re-sale or use in derivative works. © copyright by Christoph Bartneck, Tony Belpaeime, Friederike Eyssel, Takayuki Kanda, Merel Keijsers, and Selma Sabanovic 2019. https://www.human-robot-interaction.org Contents List of illustrations viii List of tables xi 1 Introduction 1 1.1 About this book 1 1.2 Christoph -
HC17.Computer History Museum Presentation
John Mashey Trustee www.computerhistory.org The Museum • Mission – To Preserve the Artifacts & Stories of the Information Age • Vision - Exploring the Computing Revolution and Its Impact on the Human Experience • Moving Forward: • Collecting over 25 years; started at Digital Equipment • Boston Artifacts moved to Silicon Valley in 1996 • Independent 501(c)(3) in July 1999 • New Home in Mountain View, CA in 2002 1401 N. Shoreline Dr, next to 101, with purple “on button” • June 2003: Phase 1 opened • Sept 2005: major new exhibit - Computer Chess www.computerhistory.org Our (Successive) Homes The Collection Behind the scenes: Largest collection of computing artifacts. Over 25 years of collecting! www.computerhistory.org The Collection Media Software Documentation Ephemera Hardware A World-Class Collection Lobby Exhibits: People & Innovation “Innovation 101”— Silicon Valley’s Contributions to Computer History www.computerhistory.org Exhibition Galleries: Now, Future Input/Output Now Processors Networking Visible Storage Software Storage Computing History Timeline Rotating Topical Exhibits www.computerhistory.org Virtual Visible Storage Activities • Speaker Series – History, history-in-the-making, special topics, exec briefs • Preservation Activities – Oral Histories - Active Collection of the Past – Videos & Photos - Proactive Collection for the Future • Restorations – Understand and restore environments of the past – IBM 1620; PDP-1; IBM 1401; – Many PC’s from study collection • Initial CyberMuseum – Virtual Visible Storage • Events – Seminars, -
The Dartmouth College Artificial Intelligence Conference: the Next
AI Magazine Volume 27 Number 4 (2006) (© AAAI) Reports continued this earlier work because he became convinced that advances The Dartmouth College could be made with other approaches using computers. Minsky expressed the concern that too many in AI today Artificial Intelligence try to do what is popular and publish only successes. He argued that AI can never be a science until it publishes what fails as well as what succeeds. Conference: Oliver Selfridge highlighted the im- portance of many related areas of re- search before and after the 1956 sum- The Next mer project that helped to propel AI as a field. The development of improved languages and machines was essential. Fifty Years He offered tribute to many early pio- neering activities such as J. C. R. Lick- leiter developing time-sharing, Nat Rochester designing IBM computers, and Frank Rosenblatt working with James Moor perceptrons. Trenchard More was sent to the summer project for two separate weeks by the University of Rochester. Some of the best notes describing the AI project were taken by More, al- though ironically he admitted that he ■ The Dartmouth College Artificial Intelli- Marvin Minsky, Claude Shannon, and never liked the use of “artificial” or gence Conference: The Next 50 Years Nathaniel Rochester for the 1956 “intelligence” as terms for the field. (AI@50) took place July 13–15, 2006. The event, McCarthy wanted, as he ex- Ray Solomonoff said he went to the conference had three objectives: to cele- plained at AI@50, “to nail the flag to brate the Dartmouth Summer Research summer project hoping to convince the mast.” McCarthy is credited for Project, which occurred in 1956; to as- everyone of the importance of ma- coining the phrase “artificial intelli- sess how far AI has progressed; and to chine learning. -
Curriculum Vitae
6/14/21 CURRICULUM VITAE Edward Hance Shortliffe, MD, PhD, MACP, FACMI, FIAHSI [work] Chair Emeritus and Adjunct Professor, Department of Biomedical Informatics Vagelos College of Physicians and Surgeons, Columbia University in the City of New York [email protected] – https://www.dbmi.columbia.edu/people/edward-shortliffe/ Adjunct Professor of Biomedical Informatics College of Health Solutions Arizona State University, Phoenix, AZ [email protected] – https://isearch.asu.edu/profile/1098580 Adjunct Professor, Department of Healthcare Policy and Research (Health Informatics) Weill Cornell Medical College, New York, NY http://hpr.weill.cornell.edu/divisions/health_informatics/ [home] 272 W 107th St #5B, New York, NY 10025-7833 Phone: 212-666-8440 — Mobile: 917-640-0933 [email protected] – http://www.shortliffe.net Born: Edmonton, Alberta, Canada Date of birth: 28 August 1947 Citizenship: U.S.A. (naturalized - 1962) Spouse: Vimla L. Patel, PhD Education From To School/Institution Major Subject, Degree, and Date 9/62 6/65 The Loomis School, Windsor, CT. High School 9/65 7/66 Gresham's School, Holt, Norfolk, U.K. Foreign Exchange Student 9/66 6/70 Harvard College, Cambridge, MA. Applied Math and Computer Science, A.B., June 1970 9/70 1/75 Stanford University, Stanford, CA PhD, Medical Information Sciences, January 1975 9/70 6/76 Stanford University School of Medicine MD, June 1976. 7/76 6/77 Massachusetts General Hospital, Boston, MA Internship in Internal Medicine 7/77 6/79 Stanford University Hospital, Stanford, CA Residency in Internal Medicine Honors Graduation Magna Cum Laude, Harvard College, June 1970 Medical Scientist Training Program (MSTP), NIH-funded Stanford Traineeship, September 1971 - June 1976 Grace Murray Hopper Award (Distinguished computer scientist under age 30), Association for Computing Machinery, October 1976 Research Career Development Award, National Library of Medicine, July 1979—June 1984 Henry J. -
Artificial Intelligence: a Modern Approach (3Rd Edition)
[PDF] Artificial Intelligence: A Modern Approach (3rd Edition) Stuart Russell, Peter Norvig - pdf download free book Free Download Artificial Intelligence: A Modern Approach (3rd Edition) Ebooks Stuart Russell, Peter Norvig, PDF Artificial Intelligence: A Modern Approach (3rd Edition) Popular Download, Artificial Intelligence: A Modern Approach (3rd Edition) Full Collection, Read Best Book Online Artificial Intelligence: A Modern Approach (3rd Edition), Free Download Artificial Intelligence: A Modern Approach (3rd Edition) Full Popular Stuart Russell, Peter Norvig, I Was So Mad Artificial Intelligence: A Modern Approach (3rd Edition) Stuart Russell, Peter Norvig Ebook Download, PDF Artificial Intelligence: A Modern Approach (3rd Edition) Full Collection, full book Artificial Intelligence: A Modern Approach (3rd Edition), online pdf Artificial Intelligence: A Modern Approach (3rd Edition), Download Free Artificial Intelligence: A Modern Approach (3rd Edition) Book, pdf Stuart Russell, Peter Norvig Artificial Intelligence: A Modern Approach (3rd Edition), the book Artificial Intelligence: A Modern Approach (3rd Edition), Download Artificial Intelligence: A Modern Approach (3rd Edition) E-Books, Read Artificial Intelligence: A Modern Approach (3rd Edition) Book Free, Artificial Intelligence: A Modern Approach (3rd Edition) PDF read online, Artificial Intelligence: A Modern Approach (3rd Edition) Ebooks, Artificial Intelligence: A Modern Approach (3rd Edition) Popular Download, Artificial Intelligence: A Modern Approach (3rd Edition) Free Download, Artificial Intelligence: A Modern Approach (3rd Edition) Books Online, Free Download Artificial Intelligence: A Modern Approach (3rd Edition) Books [E-BOOK] Artificial Intelligence: A Modern Approach (3rd Edition) Full eBook, CLICK HERE FOR DOWNLOAD The dorian pencil novels at the end of each chapter featured charts. makes us the meaning of how our trip and unk goes into trouble with others like land. -
Mastering Board Games
INSIGHTS | PERSPECTIVES COMPUTER SCIENCE community, with much analysis and com- mentary on the amazing style of play that Al- phaZero exhibited (see the figure). Note that Mastering board games neither the chess or shogi programs could take advantage of the TPU hardware that A single algorithm can learn to play three hard board games AlphaZero has been designed to use, making head-to-head comparisons more difficult. Chess, shogi, and Go are highly complex By Murray Campbell value in chess or shogi programs. The stron- but have a number of characteristics that gest programs in both games have relied on make them easier for AI systems. The game rom the earliest days of the computer variations of the alpha-beta algorithm, used state is fully observable; all the information era, games have been considered impor- in game-playing programs since the 1950s. needed to make a move decision is visible to tant vehicles for research in artificial in- Silver et al. demonstrated the power of the players. Games with partial observability, telligence (AI) (1). Game environments combining deep reinforcement learning such as poker, can be much more challenging, simplify many aspects of real-world with an MCTS algorithm to learn a variety although there have been notable successes problems yet retain sufficient complex- of games from scratch. The training method- in games like heads-up no-limit poker (11, 12). Fity to challenge humans and machines alike. ology used in AlphaZero is a slightly modi- Board games are also easy in other important Most programs for playing classic board fied version of that used in the predecessor dimensions.