Journal of Artificial General Intelligence
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AI and Education
AI and educationducation Guidance for policymakerspolicy-makers UNESCO Education Sector The Global Education 2030 Agenda Education is UNESCO’s top priority because UNESCO, as the United Nations’ specialized it is a basic human right and the foundation agency for education, is entrusted to lead and on which to build peace and drive sustainable coordinate the Education 2030 Agenda, which is development. UNESCO is the United Nations’ part of a global movement to eradicate poverty specialized agency for education and the through 17 Sustainable Development Goals by Education Sector provides global and 2030. Education, essential to achieve all of these regional leadership in education, strengthens goals, has its own dedicated Goal 4, which aims to national education systems and responds “ensure inclusive and equitable quality education to contemporary global challenges through and promote lifelong learning opportunities for education with a special focus on gender all.” The Education 2030 Framework for Action equality and Africa. provides guidance for the implementation of this ambitious goal and commitments. Published in 2021 by the United Nations Educational, Scientific and Cultural Organization 7, place de Fontenoy, 75352 Paris 07 SP, France © UNESCO 2021 ISBN 978-92-3-100447-6 This publication is available in Open Access under the Attribution-ShareAlike 3.0 IGO (CC-BY-SA 3.0 IGO) license (http://creativecommons.org/licenses/by-sa/3.0/igo/). By using the content of this publication, the users accept to be bound by the terms of use of the UNESCO Open Access Repository (http://www.unesco.org/open-access/terms-use-ccbysa-en). The designations employed and the presentation of material throughout this publication do not imply the expression of any opinion whatsoever on the part of UNESCO concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. -
Artificial Intelligence and Fundamental Physics Research Tommaso Dorigo, INFN – Padova
Artificial Intelligence and Fundamental Physics Research Tommaso Dorigo, INFN – Padova 5th USERN Congress, Workshop 2 November 8, 2020 - Tehran Contents • Artificial Intelligence: Where It Comes From, What It Is, Where It Is Going • Applications in Fundamental Physics Research • Quantum Computing: Physics Research for AI • The Future What is Intelligence ? Before we discuss artificial intelligence, we should agree on what intelligence is – and what isn’t. • The term «intelligence» comes from Latin “intelligo” to comprehend, to perceive …but that does not help much. We can consider the literature for help. We find that notable definitions differ significantly, also in relation to what can be general and what is specific of human beings: “The aggregate or global capacity of the individual to act purposefully, to think rationally, and to deal effectively with his environment” [Wechsler 1944] “The unique propensity of human beings to change or modify the structure of their cognitive functioning to adapt to the changing demands of a life situation” [Feuerstein 1990] But also, and more useful to us, are more abstract definitions such as: “Intelligence measures an agent's ability to achieve goals in a wide range of environments” [Legg 2007] “Intelligence is goal-directed adaptive behavior” [Sternberg 1982] What is Artificial Intelligence ? Curiously, a precise definition of Artificial Intelligence is not less challenging than that of Intelligence at large • AI: “Intelligence demonstrated by machines” – devices that have the ability to perceive -
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. -
Relative Study on Artificial Intelligence Interaction with Human Emotions
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 02 | Feb 2019 www.irjet.net p-ISSN: 2395-0072 Relative Study on Artificial Intelligence Interaction with Human Emotions Chandrika1, K Radha2 1M.Tech, I –Year, CSE, GITAM UNIVERSITY, RUDRARAM HYDERABAD, TELANAGANA, INDIA 2Asst.Professor, CSE, GITAM UNIVERSITY, RUDRARAM, HYDERABAD, TELANAGANA, INDIA ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract – Artificial Intelligence is a multidisciplinary field 1950 Alan Turing published “Computing Machinery and whose goal is to making software which thinks intelligently Intelligence” in which he proposes “the imitation game” similar to human activities and makes a lot easier to human to which will later become known as the “Turing Test.”[5] achieve their goal. There are some recent successes in Artificial Intelligence from computerized medical diagnosticians and It may be said that Alan Turing’s ideas for computational systems that automatically customize hardware to particular thinking lay the foundation for AI. user requirements. This Paper presents about, how does 1952 Arthur Samuel develops the first computer checkers- Artificial Intelligence interact with humans, Does Artificial playing program and the first computer program to learn on Intelligence can understand the human emotions, what can be its own. the impact of Artificial Intelligence on humans in upcoming years, and Does Artificial Intelligence can replace humans. August 31, 1955 The term “artificial intelligence” is coined in a proposal for a “2 month, 10 man study of artificial Key Words: Artificial Intelligence, computerized medical intelligence” submitted by John McCarthy (Dartmouth diagnosticians, Human Interaction, Human Emotions. College), Marvin Minsky (Harvard University), Nathaniel 1. What is Artificial Intelligence Rochester (IBM), and Claude Shannon (Bell Telephone Laboratories). -
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. -
Warren Mcculloch and the British Cyberneticians
Warren McCulloch and the British cyberneticians Article (Accepted Version) Husbands, Phil and Holland, Owen (2012) Warren McCulloch and the British cyberneticians. Interdisciplinary Science Reviews, 37 (3). pp. 237-253. ISSN 0308-0188 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/id/eprint/43089/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher’s version. Please see the URL above for details on accessing the published version. Copyright and reuse: Sussex Research Online is a digital repository of the research output of the University. Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available. Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. http://sro.sussex.ac.uk Warren McCulloch and the British Cyberneticians1 Phil Husbands and Owen Holland Dept. Informatics, University of Sussex Abstract Warren McCulloch was a significant influence on a number of British cyberneticians, as some British pioneers in this area were on him. -
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. -
Kybernetik in Urbana
Kybernetik in Urbana Ein Gespräch zwischen Paul Weston, Jan Müggenburg und James Andrew Hutchinson1 Paul Ernest Weston was born on February 3rd 1935 in Garland, Maine. After stu- dying physics at Wesleyan University in Middletown (Connecticut), 1952 through 1956, Paul Weston came to the University of Illinois in summer 1956, receiving his M.S. in August 1958. The following autumn, as a graduate student of physics, he attended a seminar led by Heinz von Foerster under the promising title „Cyberne- tics“.2 In June 1959 Paul joins the newly founded Biological Computer Laboratory (BCL) as a half time research assistant employed by the Electrical Engineering Department. Through roughly 1962, Weston’s primary emphasis was upon simulation of the processes of perception, with work in neuron models and pattern recognition. Together with Murray Babcock, Weston was one of the most prominent creators of prototypes built at the BCL. Already in 1961 Weston becomes widely known as the inventor of the NumaRete, a parallel operated pattern recognition machine, which played an important role in the public perception of the BCL through out the fol- lowing years.3 After 1964 Weston continues his research at BCL on the basis of a full position as a research assistant and starts to focus on information processing, particularly inte- rested in problems associated with natural-language interaction with machine. As a member of the Committee for Cognitive Studies Weston is also increasingly interes- ted in the potentials of how the new information technologies could shape society. In June 1970 Weston receives his PhD for developing a new and more efficient data structure called CYLINDER.4 After the BCL was closed in 1975 Weston continued to work as a Research Associate at the Department of Electrical Engineering and the Coordinated Science Laboratory of the University of Illinois. -
1011 Neurons } 1014 Synapses the Cybernetics Group
History 329/SI 311/RCSSCI 360 Computers and the Internet: A global history Week 6: Computing and Cybernetics in the Soviet Union Today } Review } Cybernetics: minds, brains, and machines } Soviet computing and cybernetics } Soviet economic planning } Next time Review: SAGE } Origin: Whirlwind digital computer project, MIT } SAGE = Semi-Automatic Ground Environment } Computer-controlled air defense of lower 48 states } Networked over telephone lines } Duplexed (two computers for high reliability) } Tube-based central processors made by IBM } Magnetic core memory } First truly large software development } Served as a pattern for many subsequent military projects } Major factor in US and IBM dominance of commercial computer markets by late1950s Cybernetics: minds, brains, and machines Key figures } Norbert Wiener } cybernetics } Warren McCulloch & Walter Pitts } neural nets } John von Neumann } brain modeling, cellular automata, biological metaphors } Frank Rosenblatt } perceptrons The Cybernetics Group } Norbert Wiener, MIT } WWII anti-aircraft problems } Servo/organism analogies } “Behavior, Purpose, and Teleology” (1943) } Information as measurable quantity } Feedback: circular self-corrective cycles BEHAVIOR, PURPOSE AND TELEOLOGY 21 Examples of predictions of higher order are shooting with a sling or with a bow and arrow. Predictive behavior requires the discrimination of at least two coordinates, a temporal and at least one spatial axis. Prediction will be more effective and flexible, however, if the behaving object can respond to changes in more than one spatial coordinate. The sensory receptors of an organism, or the corresponding elements of a machine, may therefore limit the predictive behavior. Thus, a bloodhound follows a trail, that is, it does not show any predictive behavior in trailing, because a chemical, olfactory input reports only spatial information: distance, as indicated by intensity. -
The Future of AI
Seeing the forest for the trees, and the forests beyond The future of AI A report by the Deloitte AI Institute The question of whether a computer can think is no more interesting than the question of whether a submarine can swim. —Edsger W. Dijkstra, computer science pioneer 2 Contents 01 Everything and 03 The machines 06 Guardrails nothing at all (or, a technological 25 4 history of the future) 8 07 Conclusion 02 Something old, 29 somehow new 04 Us and the machines 6 (or, an anthropological history of the future) 14 05 Us as the machines (or, a biological history of the future) 20 3 Everything and nothing at all 01 As futurists, my team and I secretly And so, when tech headlines begin to Like any gold rush, there’s hope beneath the spend the lion’s share of our time increasingly read as breathless brochures hype. To be sure, there is plenty of actual 02 studying the past. I like to say that for Artificial Intelligence, we grey-hairs can’t gold afoot insofar as we’re seeing a genuine, we’re closet historians. Specifically, help but be reminded of a certain upstart evidence-based phase shift from AI as “cherry- 03 we research the history of various technology category some 20 years ago called on-top” curiosity to “key ingredient” at leading technologies and how they’ve impacted, “the world wide web.” organizations. 61 percent of respondents to or failed to impact, the way the world a recent Deloitte Insights report say AI will 04 works and lives. -
The Evolution of Intelligence
Review From Homo Sapiens to Robo Sapiens: The Evolution of Intelligence Anat Ringel Raveh * and Boaz Tamir * Faculty of interdisciplinary studies, S.T.S. Program, Bar-Ilan University, Ramat-Gan 5290002, Israel; [email protected] (A.R.R.); [email protected] (B.T.) Received: 30 October 2018; Accepted: 18 December 2018; Published: 21 December 2018 Abstract: In this paper, we present a review of recent developments in artificial intelligence (AI) towards the possibility of an artificial intelligence equal that of human intelligence. AI technology has always shown a stepwise increase in its capacity and complexity. The last step took place several years ago with the increased progress in deep neural network technology. Each such step goes hand in hand with our understanding of ourselves and our understanding of human cognition. Indeed, AI was always about the question of understanding human nature. AI percolates into our lives, changing our environment. We believe that the next few steps in AI technology, and in our understanding of human behavior, will bring about much more powerful machines that are flexible enough to resemble human behavior. In this context, there are two research fields: Artificial Social Intelligence (ASI) and General Artificial Intelligence (AGI). The authors also allude to one of the main challenges for AI, embodied cognition, and explain how it can viewed as an opportunity for further progress in AI research. Keywords: Artificial Intelligence (AI); artificial general intelligence (AGI); artificial social intelligence (ASI); social sciences; singularity; complexity; embodied cognition; value alignment 1. Introduction 1.1. From Intelligence to Super-Intelligence In this paper we present a review of recent developments in AI towards the possibility of an artificial intelligence equals that of human intelligence. -
Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: a Review
information Review Ontology-Based Approach to Semantically Enhanced Question Answering for Closed Domain: A Review Ammar Arbaaeen 1,∗ and Asadullah Shah 2 1 Department of Computer Science, Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia 2 Faculty of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia; [email protected] * Correspondence: [email protected] Abstract: For many users of natural language processing (NLP), it can be challenging to obtain concise, accurate and precise answers to a question. Systems such as question answering (QA) enable users to ask questions and receive feedback in the form of quick answers to questions posed in natural language, rather than in the form of lists of documents delivered by search engines. This task is challenging and involves complex semantic annotation and knowledge representation. This study reviews the literature detailing ontology-based methods that semantically enhance QA for a closed domain, by presenting a literature review of the relevant studies published between 2000 and 2020. The review reports that 83 of the 124 papers considered acknowledge the QA approach, and recommend its development and evaluation using different methods. These methods are evaluated according to accuracy, precision, and recall. An ontological approach to semantically enhancing QA is found to be adopted in a limited way, as many of the studies reviewed concentrated instead on Citation: Arbaaeen, A.; Shah, A. NLP and information retrieval (IR) processing. While the majority of the studies reviewed focus on Ontology-Based Approach to open domains, this study investigates the closed domain.