PRINCIPLES of SYNTHETIC INTELLIGENCE

Total Page:16

File Type:pdf, Size:1020Kb

PRINCIPLES of SYNTHETIC INTELLIGENCE PRINCIPLES of SYNTHETIC INTELLIGENCE Building Blocks for an Architecture of Motivated Cognition Joscha Bach, Universität Osnabrück Cover artwork by Alex Guillotte (based on a painting by Leonardo da Vinci), used with friendly permission of the artist. Principles of Synthetic Intelligence Building Blocks for an Architecture of Motivated Cognition Dissertation submitted in partial fulfillment of the requirements for the doctoral degree (PhD) in Cognitive Science at the Fachbereich Humanwissenschaften, Universität Osnabrück by Joscha Bach 30th of March 2007 Advisors: Prof. Dr. Hans-Dieter Burkhard Prof. Dr. Dietrich Dörner Prof. Dr. Kai-Uwe Kühnberger Abstract Computational models of cognitive functioning have become a thriving paradigm in Cognitive Science, yet they usually only emphasize problem solving and reasoning, or treat perception and motivation as isolated modules. A very promising approach towards a broader architecture of cognition is the Psi theory of Dietrich Dörner. By dealing with the integration of motivation and emotion with perceptual and reasoning processes and including grounded neuro-symbolic representations, it contributes to an integrated understanding of the mind. The Psi theory supplies a conceptual framework highlighting the interrelations between perception and memory, language and mental representation, reasoning and motivation, emotion and cognition, autonomy and social behavior. However, its origin in psychology, its methodology and the lack of documentation have limited its impact to cognitive modeling. This work adapts the Psi theory to Cognitive Science and Artificial Intelligence, by summarizing and structuring it, by identifying its contribution to understanding cognition, and by providing a technical framework for implementing models of the theory. These goals are reflected in the three parts of this thesis. “Dörner’s Blueprint for a Mind” is a synopsis of the work of Dörner and his group. It reviews the available publications and implementations from the perspective of Cognitive Science and represents the statements and assumptions of the theory. “The Psi Theory as a Model of Cognition” briefly charts the domain of cognitive modeling and establishes the Psi theory’s position within this field, its contributions, limitations and some of its shortcomings. A special section focuses on computational models of emotion. One of the most interesting aspects of the theory is the suggestion of hierarchical neuro-symbolic representations which are grounded in a dynamic environment. These representations employ spreading activation mechanisms to act as associative memory, and they propose the combination of neural learning with symbolic reasoning and planning. “MicroPsi” is a comprehensive software framework designed to implement and execute models of the Psi theory as multi-agent systems. The final chapter introduces this framework and illustrates its application. Table of Contents 1 Dörner’s “blueprint for a mind” ...........................................................1 1.1 Introductory remarks ..........................................................................2 1.2 An Overview of the Psi Theory and Psi Agents .............................4 1.3 A simple autonomous vehicle .............................................................9 1.4 Representation of and for mental processes .................................12 1.4.1 Neural representation..........................................................................13 1.4.1.1 Neurons .....................................................................................13 1.4.1.2 Associators and dissociators......................................................14 1.4.1.3 Cortex fields, activators, inhibitors and registers ......................15 1.4.1.4 Sensor neurons and motor neurons............................................15 1.4.1.5 Sensors specific to cortex fields................................................15 1.4.1.6 Quads.........................................................................................16 1.4.2 Partonomies ........................................................................................19 1.4.3 Alternatives and subjunctions.............................................................20 1.4.4 Sensory schemas.................................................................................20 1.4.5 Effector/action schemas......................................................................21 1.4.6 Triplets................................................................................................22 1.4.7 Space and time....................................................................................23 1.4.8 Basic relationships..............................................................................24 1.5 Memory organization ........................................................................26 1.5.1 Episodic schemas................................................................................26 1.5.2 Behavior programs..............................................................................27 1.5.3 Protocol memory.................................................................................28 1.5.4 Abstraction and analogical reasoning .................................................31 1.5.5 Taxonomies.........................................................................................33 1.6 Perception .............................................................................................34 1.6.1 Expectation horizon............................................................................35 1.6.2 Orientation behavior ...........................................................................36 1.6.3 HyPercept ...........................................................................................36 1.6.3.1 How HyPercept works...............................................................36 1.6.3.2 Modification of HyPercept according to the Resolution Level .38 1.6.3.3 Generalization and Specialization.............................................39 1.6.3.4 Treating occlusions ...................................................................39 1.6.3.5 Assimilation of new objects into schemas.................................40 1.6.4 Situation image ...................................................................................41 1.6.5 Mental stage........................................................................................42 1.7 Managing knowledge .........................................................................42 1.7.1 Reflection (“Besinnung”)....................................................................42 iv Contents 1.7.2 Categorization (“What is it and what does it do?”).............................43 1.7.3 Symbol grounding...............................................................................44 1.8 Behavior control and action selection ............................................45 1.8.1 Appetence and aversion......................................................................46 1.8.2 Motivation...........................................................................................47 1.8.2.1 Urges.........................................................................................47 1.8.2.2 Motives......................................................................................47 1.8.2.3 Demands....................................................................................48 Fuel and water .........................................................................................48 Intactness (“Integrität”, integrity, pain avoidance) ..................................49 Certainty (“Bestimmtheit”, uncertainty reduction)..................................49 Competence (“Kompetenz”, efficiency, control).....................................50 Affiliation (“okayness”, legitimacy)........................................................51 1.8.2.4 Motive selection........................................................................53 1.8.3 Intentions ............................................................................................55 1.8.4 Action .................................................................................................55 1.8.4.1 Automatisms..............................................................................56 1.8.4.2 Simple Planning ........................................................................56 1.8.4.3 “What can be done?”—the Trial-and-error strategy..................58 1.8.5 Modulators..........................................................................................58 1.8.5.1 Activation/Arousal ....................................................................59 1.8.5.2 Selection threshold....................................................................59 1.8.5.3 Resolution level.........................................................................60 1.8.5.4 Sampling rate/securing behavior...............................................60 1.8.5.5 The dynamics of modulation.....................................................61 1.9 Emotion .................................................................................................63 1.9.1 Emotion as modulation.......................................................................64 1.9.2 Emotion and motivation......................................................................64 1.9.3 Emotional phenomena that are modeled by the Psi theory .................65 1.10 Language and Future
Recommended publications
  • Artificial General Intelligence and Classical Neural Network
    Artificial General Intelligence and Classical Neural Network Pei Wang Department of Computer and Information Sciences, Temple University Room 1000X, Wachman Hall, 1805 N. Broad Street, Philadelphia, PA 19122 Web: http://www.cis.temple.edu/∼pwang/ Email: [email protected] Abstract— The research goal of Artificial General Intelligence • Capability. Since we often judge the level of intelligence (AGI) and the notion of Classical Neural Network (CNN) are of other people by evaluating their problem-solving capa- specified. With respect to the requirements of AGI, the strength bility, some people believe that the best way to achieve AI and weakness of CNN are discussed, in the aspects of knowledge representation, learning process, and overall objective of the is to build systems that can solve hard practical problems. system. To resolve the issues in CNN in a general and efficient Examples: various expert systems. way remains a challenge to future neural network research. • Function. Since the human mind has various cognitive functions, such as perceiving, learning, reasoning, acting, I. ARTIFICIAL GENERAL INTELLIGENCE and so on, some people believe that the best way to It is widely recognized that the general research goal of achieve AI is to study each of these functions one by Artificial Intelligence (AI) is twofold: one, as certain input-output mapping. Example: various intelligent tools. • As a science, it attempts to provide an explanation of • Principle. Since the human mind seems to follow certain the mechanism in the human mind-brain complex that is principles of information processing, some people believe usually called “intelligence” (or “cognition”, “thinking”, that the best way to achieve AI is to let computer systems etc.).
    [Show full text]
  • Artificial Intelligence: Distinguishing Between Types & Definitions
    19 NEV. L.J. 1015, MARTINEZ 5/28/2019 10:48 AM ARTIFICIAL INTELLIGENCE: DISTINGUISHING BETWEEN TYPES & DEFINITIONS Rex Martinez* “We should make every effort to understand the new technology. We should take into account the possibility that developing technology may have im- portant societal implications that will become apparent only with time. We should not jump to the conclusion that new technology is fundamentally the same as some older thing with which we are familiar. And we should not hasti- ly dismiss the judgment of legislators, who may be in a better position than we are to assess the implications of new technology.”–Supreme Court Justice Samuel Alito1 TABLE OF CONTENTS INTRODUCTION ............................................................................................. 1016 I. WHY THIS MATTERS ......................................................................... 1018 II. WHAT IS ARTIFICIAL INTELLIGENCE? ............................................... 1023 A. The Development of Artificial Intelligence ............................... 1023 B. Computer Science Approaches to Artificial Intelligence .......... 1025 C. Autonomy .................................................................................. 1026 D. Strong AI & Weak AI ................................................................ 1027 III. CURRENT STATE OF AI DEFINITIONS ................................................ 1029 A. Black’s Law Dictionary ............................................................ 1029 B. Nevada .....................................................................................
    [Show full text]
  • Artificial Consciousness and the Consciousness-Attention Dissociation
    Consciousness and Cognition 45 (2016) 210–225 Contents lists available at ScienceDirect Consciousness and Cognition journal homepage: www.elsevier.com/locate/concog Review article Artificial consciousness and the consciousness-attention dissociation ⇑ Harry Haroutioun Haladjian a, , Carlos Montemayor b a Laboratoire Psychologie de la Perception, CNRS (UMR 8242), Université Paris Descartes, Centre Biomédical des Saints-Pères, 45 rue des Saints-Pères, 75006 Paris, France b San Francisco State University, Philosophy Department, 1600 Holloway Avenue, San Francisco, CA 94132 USA article info abstract Article history: Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to Received 6 July 2016 implement sophisticated forms of human intelligence in machines. This research attempts Accepted 12 August 2016 to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable Keywords: once we overcome short-term engineering challenges. We believe, however, that phenom- Artificial intelligence enal consciousness cannot be implemented in machines. This becomes clear when consid- Artificial consciousness ering emotions and examining the dissociation between consciousness and attention in Consciousness Visual attention humans. While we may be able to program ethical behavior based on rules and machine Phenomenology learning, we will never be able to reproduce emotions or empathy by programming such Emotions control systems—these will be merely simulations. Arguments in favor of this claim include Empathy considerations about evolution, the neuropsychological aspects of emotions, and the disso- ciation between attention and consciousness found in humans.
    [Show full text]
  • Computability and Complexity
    Computability and Complexity Lecture Notes Herbert Jaeger, Jacobs University Bremen Version history Jan 30, 2018: created as copy of CC lecture notes of Spring 2017 Feb 16, 2018: cleaned up font conversion hickups up to Section 5 Feb 23, 2018: cleaned up font conversion hickups up to Section 6 Mar 15, 2018: cleaned up font conversion hickups up to Section 7 Apr 5, 2018: cleaned up font conversion hickups up to the end of these lecture notes 1 1 Introduction 1.1 Motivation This lecture will introduce you to the theory of computation and the theory of computational complexity. The theory of computation offers full answers to the questions, • what problems can in principle be solved by computer programs? • what functions can in principle be computed by computer programs? • what formal languages can in principle be decided by computer programs? Full answers to these questions have been found in the last 70 years or so, and we will learn about them. (And it turns out that these questions are all the same question). The theory of computation is well-established, transparent, and basically simple (you might disagree at first). The theory of complexity offers many insights to questions like • for a given problem / function / language that has to be solved / computed / decided by a computer program, how long does the fastest program actually run? • how much memory space has to be used at least? • can you speed up computations by using different computer architectures or different programming approaches? The theory of complexity is historically younger than the theory of computation – the first surge of results came in the 60ties of last century.
    [Show full text]
  • What Technology Wants / Kevin Kelly
    WHAT TECHNOLOGY WANTS ALSO BY KEVIN KELLY Out of Control: The New Biology of Machines, Social Systems, and the Economic World New Rules for the New Economy: 10 Radical Strategies for a Connected World Asia Grace WHAT TECHNOLOGY WANTS KEVIN KELLY VIKING VIKING Published by the Penguin Group Penguin Group (USA) Inc., 375 Hudson Street, New York, New York 10014, U.S.A. Penguin Group (Canada), 90 Eglinton Avenue East, Suite 700, Toronto, Ontario, Canada M4P 2Y3 (a division of Pearson Penguin Canada Inc.) Penguin Books Ltd, 80 Strand, London WC2R 0RL, England Penguin Ireland, 25 St. Stephen's Green, Dublin 2, Ireland (a division of Penguin Books Ltd) Penguin Books Australia Ltd, 250 Camberwell Road, Camberwell, Victoria 3124, Australia (a division of Pearson Australia Group Pty Ltd) Penguin Books India Pvt Ltd, 11 Community Centre, Panchsheel Park, New Delhi - 110 017, India Penguin Group (NZ), 67 Apollo Drive, Rosedale, North Shore 0632, New Zealand (a division of Pearson New Zealand Ltd) Penguin Books (South Africa) (Pty) Ltd, 24 Sturdee Avenue, Rosebank, Johannesburg 2196, South Africa Penguin Books Ltd, Registered Offices: 80 Strand, London WC2R 0RL, England First published in 2010 by Viking Penguin, a member of Penguin Group (USA) Inc. 13579 10 8642 Copyright © Kevin Kelly, 2010 All rights reserved LIBRARY OF CONGRESS CATALOGING IN PUBLICATION DATA Kelly, Kevin, 1952- What technology wants / Kevin Kelly. p. cm. Includes bibliographical references and index. ISBN 978-0-670-02215-1 1. Technology'—Social aspects. 2. Technology and civilization. I. Title. T14.5.K45 2010 303.48'3—dc22 2010013915 Printed in the United States of America Without limiting the rights under copyright reserved above, no part of this publication may be reproduced, stored in or introduced into a retrieval system, or transmitted, in any form or by any means (electronic, mechanical, photocopying, recording or otherwise), without the prior written permission of both the copyright owner and the above publisher of this book.
    [Show full text]
  • Artificial Intelligence/Artificial Wisdom
    Artificial Intelligence/Artificial Wisdom - A Drive for Improving Behavioral and Mental Health Care (06 September to 10 September 2021) Department of Computer Science & Information Technology Central University of Jammu, J&K-181143 Preamble An Artificial Intelligence is the capability of a machine to imitate intelligent human behaviour. Machine learning is based on the idea that machines should be able to learn and adapt through experience. Machine learning is a subset of AI. That is, all machine learning counts as AI, but not all AI counts as machine learning. Artificial intelligence (AI) technology holds both great promises to transform mental healthcare and potential pitfalls. Artificial intelligence (AI) is increasingly employed in healthcare fields such as oncology, radiology, and dermatology. However, the use of AI in mental healthcare and neurobiological research has been modest. Given the high morbidity and mortality in people with psychiatric disorders, coupled with a worsening shortage of mental healthcare providers, there is an urgent need for AI to help identify high-risk individuals and provide interventions to prevent and treat mental illnesses. According to the publication Spectrum News, a form of AI called "deep learning" is sometimes better able than human beings to spot relevant patterns. This five days course provides an overview of AI approaches and current applications in mental healthcare, a review of recent original research on AI specific to mental health, and a discussion of how AI can supplement clinical practice while considering its current limitations, areas needing additional research, and ethical implications regarding AI technology. The proposed workshop is envisaged to provide opportunity to our learners to seek and share knowledge and teaching skills in cutting edge areas from the experienced and reputed faculty.
    [Show full text]
  • Alleys of Your Mind: Augmented Intelligence and Its Traumas
    [4] utin tiial ntelliene eonin it uin ests enain . atton aious antooenti allaies ae oled te deeloent o atiial intelliene as a oadl ased and idel undestood set o tenoloies. lan uins aous iitation ae as an inen- ious tout eeient ut also ie o in te thresholds of machine cognition according to its aaent siilait to a alse no o eela uan intelliene. o disao tat aile selee- tion is oee easie tan oosin altenatie oles o uan saiene indust and aen alon more heterogeneous spectrums. As various forms of machine intelligence become increasingly infrastruc- tual te iliations o tis diult ae eoolitial as ell as ilosoial. In Alleys of Your Mind: Augmented Intellligence and Its Traumas, edited by Matteo Pasquinelli, neurg: meson ress I: Alleys of Your Mind [One philosopher] asserted that he knew the whole secret . [H]e surveyed the two celestial strangers from top to toe, and maintained to their faces that their persons, their worlds, their suns, and their stars, were created solely for the use of man. At this assertion our two travelers let themselves fall against each other, seized with a ft of . inextinguishable laughter. — Voltaire, Micromegas: A Philosophical History (1752) Articial intelligence AI is aing a moment it cognoscenti from teen aing to lon Mus recently eiging in Positions are split as to whether AI ill sae us or ill destroy us ome argue tat AI can neer eist ile ot- ers insist that it is inevitable. In many cases, however, these polemics may be missing the real point as to what living and thinking with synthetic intelligence ery dierent from our on actually means In sort a mature AI is not an intelligence for us nor is its intelligence necessarily umanlie or our on sanity and safety e sould not as AI to retend to e uman To do so is self-defeating, unethical and perhaps even dangerous.
    [Show full text]
  • Inventing Computational Rhetoric
    INVENTING COMPUTATIONAL RHETORIC By Michael W. Wojcik A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Digital Rhetoric and Professional Writing — Master of Arts 2013 ABSTRACT INVENTING COMPUTATIONAL RHETORIC by Michael W. Wojcik Many disciplines in the humanities are developing “computational” branches which make use of information technology to process large amounts of data algorithmically. The field of computational rhetoric is in its infancy, but we are already seeing interesting results from applying the ideas and goals of rhetoric to text processing and related areas. After considering what computational rhetoric might be, three approaches to inventing computational rhetorics are presented: a structural schema, a review of extant work, and a theoretical exploration. Copyright by MICHAEL W. WOJCIK 2013 For Malea iv ACKNOWLEDGEMENTS Above all else I must thank my beloved wife, Malea Powell, without whose prompting this thesis would have remained forever incomplete. I am also grateful for the presence in my life of my terrific stepdaughter, Audrey Swartz, and wonderful granddaughter Lucille. My thesis committee, Dean Rehberger, Bill Hart-Davidson, and John Monberg, pro- vided me with generous guidance and inspiration. Other faculty members at Michigan State who helped me explore relevant ideas include Rochelle Harris, Mike McLeod, Joyce Chai, Danielle Devoss, and Bump Halbritter. My previous academic program at Miami University did not result in a degree, but faculty there also contributed greatly to my the- oretical understanding, particularly Susan Morgan, Mary-Jean Corbett, Brit Harwood, J. Edgar Tidwell, Lori Merish, Vicki Smith, Alice Adams, Fran Dolan, and Keith Tuma.
    [Show full text]
  • Machine Guessing – I
    Machine Guessing { I David Miller Department of Philosophy University of Warwick COVENTRY CV4 7AL UK e-mail: [email protected] ⃝c copyright D. W. Miller 2011{2018 Abstract According to Karl Popper, the evolution of science, logically, methodologically, and even psy- chologically, is an involved interplay of acute conjectures and blunt refutations. Like biological evolution, it is an endless round of blind variation and selective retention. But unlike biological evolution, it incorporates, at the stage of selection, the use of reason. Part I of this two-part paper begins by repudiating the common beliefs that Hume's problem of induction, which com- pellingly confutes the thesis that science is rational in the way that most people think that it is rational, can be solved by assuming that science is rational, or by assuming that Hume was irrational (that is, by ignoring his argument). The problem of induction can be solved only by a non-authoritarian theory of rationality. It is shown also that because hypotheses cannot be distilled directly from experience, all knowledge is eventually dependent on blind conjecture, and therefore itself conjectural. In particular, the use of rules of inference, or of good or bad rules for generating conjectures, is conjectural. Part II of the paper expounds a form of Popper's critical rationalism that locates the rationality of science entirely in the deductive processes by which conjectures are criticized and improved. But extreme forms of deductivism are rejected. The paper concludes with a sharp dismissal of the view that work in artificial intelligence, including the JSM method cultivated extensively by Victor Finn, does anything to upset critical rationalism.
    [Show full text]
  • How Empiricism Distorts Ai and Robotics
    HOW EMPIRICISM DISTORTS AI AND ROBOTICS Dr Antoni Diller School of Computer Science University of Birmingham Birmingham, B15 2TT, UK email: [email protected] Abstract rently, Cog has no legs, but it does have robotic arms and a head with video cameras for eyes. It is one of the most im- The main goal of AI and Robotics is that of creating a hu- pressive robots around as it can recognise various physical manoid robot that can interact with human beings. Such an objects, distinguish living from non-living things and im- android would have to have the ability to acquire knowl- itate what it sees people doing. Cynthia Breazeal worked edge about the world it inhabits. Currently, the gaining of with Cog when she was one of Brooks’s graduate students. beliefs through testimony is hardly investigated in AI be- She said that after she became accustomed to Cog’s strange cause researchers have uncritically accepted empiricism. appearance interacting with it felt just like playing with a Great emphasis is placed on perception as a source of baby and it was easy to imagine that Cog was alive. knowledge. It is important to understand perception, but There are several major research projects in Japan. A even more important is an understanding of testimony. A common rationale given by scientists engaged in these is sketch of a theory of testimony is presented and an appeal the need to provide for Japan’s ageing population. They say for more research is made. their robots will eventually act as carers for those unable to look after themselves.
    [Show full text]
  • Artificial Intelligence for Robust Engineering & Science January 22
    Artificial Intelligence for Robust Engineering & Science January 22 – 24, 2020 Organizing Committee General Chair: Logistics Chair: David Womble Christy Hembree Program Director, Artificial Intelligence Project Support, Artificial Intelligence Oak Ridge National Laboratory Oak Ridge National Laboratory Committee Members: Jacob Hinkle Justin Newcomer Research Scientist, Computational Sciences Manager, Machine Intelligence and Engineering Sandia National Laboratories Oak Ridge National Laboratory Frank Liu Clayton Webster Distinguished R&D Staff, Computational Distinguished Professor, Department of Sciences and Mathematics Mathematics Oak Ridge National Laboratory University of Tennessee–Knoxville Robust engineering is the process of designing, building, and controlling systems to avoid or mitigate failures and everything fails eventually. This workshop will examine the use of artificial intelligence and machine learning to predict failures and to use this capability in the maintenance and operation of robust systems. The workshop comprises four sessions examining the technical foundations of artificial intelligence and machine learning to 1) Examine operational data for failure indicators 2) Understand the causes of the potential failure 3) Deploy these systems “at the edge” with real-time inference and continuous learning, and 4) Incorporate these capabilities into robust system design and operation. The workshop will also include an additional session open to attendees for “flash” presentations that address the conference theme. AGENDA Wednesday, January 22, 2020 7:30–8:00 a.m. │ Badging, Registration, and Breakfast 8:00–8:45 a.m. │ Welcome and Introduction Jeff Nichols, Oak Ridge National Laboratory David Womble, Oak Ridge National Laboratory 8:45–9:30 a.m. │ Keynote Presentation: Dave Brooks, General Motors Company AI for Automotive Engineering 9:30–10:00 a.m.
    [Show full text]
  • Artificial Intelligence and National Security
    Artificial Intelligence and National Security Artificial intelligence will have immense implications for national and international security, and AI’s potential applications for defense and intelligence have been identified by the federal government as a major priority. There are, however, significant bureaucratic and technical challenges to the adoption and scaling of AI across U.S. defense and intelligence organizations. Moreover, other nations—particularly China and Russia—are also investing in military AI applications. As the strategic competition intensifies, the pressure to deploy untested and poorly understood systems to gain competitive advantage could lead to accidents, failures, and unintended escalation. The Bipartisan Policy Center and Georgetown University’s Center for Security and Emerging Technology (CSET), in consultation with Reps. Robin Kelly (D-IL) and Will Hurd (R-TX), have worked with government officials, industry representatives, civil society advocates, and academics to better understand the major AI-related national and economic security issues the country faces. This paper hopes to shed more clarity on these challenges and provide actionable policy recommendations, to help guide a U.S. national strategy for AI. BPC’s effort is primarily designed to complement the work done by the Obama and Trump administrations, including President Barack Obama’s 2016 The National Artificial Intelligence Research and Development Strategic Plan,i President Donald Trump’s Executive Order 13859, announcing the American AI Initiative,ii and the Office of Management and Budget’s subsequentGuidance for Regulation of Artificial Intelligence Applications.iii The effort is also designed to further June 2020 advance work done by Kelly and Hurd in their 2018 Committee on Oversight 1 and Government Reform (Information Technology Subcommittee) white paper Rise of the Machines: Artificial Intelligence and its Growing Impact on U.S.
    [Show full text]