DOCSLIB.ORG

Hello, World: Artificial Intelligence and Its Use in the Public Sector

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Hello, World: Artificial Intelligence and Its Use in the Public Sector Hello, World: Artificial intelligence and its use in the public sector Jamie Berryhill Kévin Kok Heang Rob Clogher Keegan McBride November 2019 | http://oe.cd/helloworld OECD Working Papers on Public Governance No. 36 Cover images are the European Parliament and the dome of Germany’s Reichstag building processed through Deep Learning algorithms to match the style of Van Gogh paintings. tw Hello, World: Artificial Intelligence and its Use in the Public Sector Authors: Jamie Berryhill, Kévin Kok Heang, Rob Clogher, Keegan McBride PUBE 2 This document and any map included herein are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. 1. Note by Tukey: The information in this document with reference to ‘Cyprus’ relates to the southern part of the island. There is no single authority representing both Turkish and Greek Cypriot people on the island. Turkey recognises the Turkish Republic of Northern Cyprus (TRNC). Until a lasting and equitable solution is found within the context of the United Nations, Turkey shall preserve its position concerning the ‘Cyprus issue’. 2. Note by all the European Union Member States of the OECD and the European Commission: The Republic of Cyprus is recognised by all members of the United Nations with the exception of Turkey. The information in this document relates to the area under the effective control of the Government of the Republic of Cyprus. HELLO, WORLD: ARTIFICIAL INTELLIGENCE AND ITS USE IN THE PUBLIC SECTOR © OECD 2019 3 Foreword Artificial Intelligence (AI) is an area of research and technology application that can have a significant impact on public policies and services in many ways. In just a few years, it is expected that the potential will exist to free up nearly one-third of public servants’ time, allowing them to shift from mundane tasks to high-value work. Governments can also use AI to design better policies and make better decisions, improve communication and engagement with citizens and residents, and improve the speed and quality of public services. While the potential benefits of AI are significant, attaining them is not an easy task. Government use of AI trails that of the private sector; the field is complex and has a steep learning curve; and the purpose of, and context within, government are unique and present a number of challenges. The OECD Observatory of Public Sector Innovation (OPSI) (https://oecd-opsi.org) has prepared this working paper, Hello, World: Artificial Intelligence and its Use in the Public Sector, to help government officials understand AI and navigate considerations specific to the public sector. “Hello, World!” is traditionally the very first computer program written by someone learning how to code, and this primer is intended to enable public officials to take their first steps in exploring AI. It builds on the work of the OECD’s Going Digital project, a forthcoming OECD AI Policy Observatory (http://oecd.ai), and the Working Party of Senior Digital Government Officials (E- Leaders). It is the second in a series of overviews on topics of interest for the public sector innovation community, following Blockchains Unchained, published in June 2018. At a time of increasing complexity, uncertainty and shifting demands, governments and public servants need to understand, test and embed new ways of doing things. OPSI helps them by shining a light on governments’ efforts to create more efficient, effective and responsive policies and services and by accompanying them in their exploration and implementation of innovative approaches. Government can use—and in many cases is already using—AI to innovate. For instance, a number of global leaders already have strategies to build AI capacity as a national priority. AI can be used to make existing processes more efficient and accurate. It can be used to consume and analyse unstructured information, such as tweets, to help governments gain insights into citizen opinions. Finally, in looking to the future, it will be important to consider and prepare for the implications of AI on society, work, and human purpose. HELLO, WORLD: ARTIFICIAL INTELLIGENCE AND ITS USE IN THE PUBLIC SECTOR © OECD 2019 4 Acknowledgements Hello, World: Artificial Intelligence and its use in the Public Sector was prepared by the Directorate for Public Governance (GOV), under the leadership of Marcos Bonturi. The primer was produced by the Observatory of Public Sector Innovation (OPSI), in collaboration with the Working Party of Senior Digital Government Officials (E- Leaders) and the Network of the Observatory National Contact Points. The primer has been drafted by Kévin Kok Heang and Jamie Berryhill of OPSI; Rob Clogher, Executive Masters in Public Administration candidate at New York University and University College London; and Keegan McBride, Manager of GovAiLab at the School of Information Technologies based in Tallinn University of Technology. Piret Tõnurist and Alex Roberts of OPSI made significant contributions to the future-focus and anticipatory innovation section of Chapter 4. The work was carried out under the co-ordination of Marco Daglio (Head of OPSI and Acting Head of Division, RPS). Colleagues from within the OECD, including Luis Aranda, Barbara Ubaldi, Natalia Nolan Flecha, Delphine Moretti, Alistair Nolan and Karine Perset, reviewed and made comments. Liv Gaunt and David McDonald provided editorial assistance. The OPSI team also wishes to acknowledge the contributions of numerous stakeholders who shared insights through interviews, discussions, and correspondence. In particular, the team thanks Coline Cuau and Wietse Van Ransbeeck from CitizenLab; Dietmar Gattwinkel, Georges Lobo and Fidel Santiago from the European Commission; Gregg Blakely, Ashley Casovan, Noel Corriveau, Benoit Deshaies, Chelsea Escott, Russel Gauthier, Cezary Gesikowski, Hubert Laferriere, Michael Karlin, Laura MacDonald, Stan Martens, Patrick McEvenue, Amanda McPherson, Mark Robbins, and Jeremiah Stanghini from the Government of Canada; Aleksi Kopponen and Niko Ruostetsaari from the Government of Finland; Enzo Maria Le Fevre from the Government of Italy, Kenji Hiramoto, Ken Tamaru, and Hiroki Yoshida from the Government of Japan, Farah Hussain and Sebastien Krier from the Government of the United Kingdom; Dan Chenok from the IBM Center for The Business of Government, Olivia Elson from Results for Development; and Cosmina Dorobantu, Pauline Kinniburgh and Florian Ostmann from The Alan Turing Institute. Finally, the team wishes to acknowledge the contributions made by individuals during the public consultation phase of this primer (1 August-15 September, 2019). The OPSI team has made significant revisions and improvements to the report based on the feedback received, and the authors sincerely thank all who participated. While OPSI is unable to list everyone who participated, particularly noteworthy feedback was provided by John Atkinson, Javier Barreiro, Sonia Castro, Lequanne Collins-Bacchus, Stefan Bergheim, Martine Delannoy, Shachee Doshi, Michael Greenwood, Alex Goncharov, Raed Mansour, Mind Senses Global, Maria Marques, Roland Pihlakas, Karmen Kern Pipan, the Portuguese Psychologists Association, Olivia Shen, Craig Thomler, Stefan Torges, Colin van Noordt, Samuel Witherspoon and Nathan Young. HELLO, WORLD: ARTIFICIAL INTELLIGENCE AND ITS USE IN THE PUBLIC SECTOR © OECD 2019 5 Table of Contents Foreword ....................................................................................................................................... 3 Acknowledgements ...................................................................................................................... 4 Executive Summary ..................................................................................................................... 7 1. Artificial Intelligence: Definitions and context ..................................................................... 9 Defining Artificial Intelligence ................................................................................................ 11 General AI vs. Narrow AI ........................................................................................................ 13 Renewed enthusiasm for AI ..................................................................................................... 17 What is next for AI? ................................................................................................................. 27 2. Understanding different AI approaches .............................................................................. 30 Data as fuel for AI .................................................................................................................... 30 Evolution of AI: Rules-based AI versus Machine Learning .................................................... 39 Applying Machine Learning .................................................................................................... 46 Different ways machines can learn........................................................................................... 48 Other AI subfields benefiting from Machine Learning ............................................................ 59 Machine Learning performance ............................................................................................... 63 Machine Learning: Risks and challenges ................................................................................
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • 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).
    [Show full text]
  • Artificial Development of Neural-Symbolic Networks
    University of Exeter Department of Computer Science Artificial Development of Neural-Symbolic Networks Joseph Paul Townsend March 2014 Supervised by Dr. Ed Keedwell and Dr. Antony Galton Submitted by Joseph Paul Townsend, to the University of Exeter as a thesis for the degree of Doctor of Philosophy in Computer Science , March 2014. This thesis is available for Library use on the understanding that it is copyright material and that no quotation from the thesis may be published without proper acknowledgement. I certify that all material in this thesis which is not my own work has been identi- fied and that no material has previously been submitted and approved for the award of a degree by this or any other University. (signature) ................................................................................................. 1 Abstract Artificial neural networks (ANNs) and logic programs have both been suggested as means of modelling human cognition. While ANNs are adaptable and relatively noise resis- tant, the information they represent is distributed across various neurons and is therefore difficult to interpret. On the contrary, symbolic systems such as logic programs are in- terpretable but less adaptable. Human cognition is performed in a network of biological neurons and yet is capable of representing symbols, and therefore an ideal model would combine the strengths of the two approaches. This is the goal of Neural-Symbolic In- tegration [4, 16, 21, 40], in which ANNs are used to produce interpretable, adaptable representations of logic programs and other symbolic models. One neural-symbolic model of reasoning is SHRUTI [89, 95], argued to exhibit biological plausibility in that it captures some aspects of real biological processes.
    [Show full text]
  • Safety of Artificial Superintelligence
    Environment. Technology. Resources. Rezekne, Latvia Proceedings of the 12th International Scientific and Practical Conference. Volume II, 180-183 Safety of Artificial Superintelligence Aleksejs Zorins Peter Grabusts Faculty of Engineering Faculty of Engineering Rezekne Academy of Technologies Rezekne Academy of Technologies Rezekne, Latvia Rezekne, Latvia [email protected] [email protected] Abstract—The paper analyses an important problem of to make a man happy it will do it with the fastest and cyber security from human safety perspective which is usu- cheapest (in terms of computational resources) without ally described as data and/or computer safety itself with- using a common sense (for example killing of all people out mentioning the human. There are numerous scientific will lead to the situation that no one is unhappy or the predictions of creation of artificial superintelligence, which decision to treat human with drugs will also make him could arise in the near future. That is why the strong neces- sity for protection of such a system from causing any farm happy etc.). Another issue is that we want computer to arises. This paper reviews approaches and methods already that we want, but due to bugs in the code computer will presented for solving this problem in a single article, analy- do what the code says, and in the case of superintelligent ses its results and provides future research directions. system, this may be a disaster. Keywords—Artificial Superintelligence, Cyber Security, The next sections of the paper will show the possible Singularity, Safety of Artificial Intelligence. solutions of making superintelligence safe to humanity.
    [Show full text]
  • Liability Law for Present and Future Robotics Technology Trevor N
    Liability Law for Present and Future Robotics Technology Trevor N. White and Seth D. Baum Global Catastrophic Risk Institute http://gcrinstitute.org * http://sethbaum.com Published in Robot Ethics 2.0, edited by Patrick Lin, George Bekey, Keith Abney, and Ryan Jenkins, Oxford University Press, 2017, pages 66-79. This version 10 October 2017 Abstract Advances in robotics technology are causing major changes in manufacturing, transportation, medicine, and a number of other sectors. While many of these changes are beneficial, there will inevitably be some harms. Who or what is liable when a robot causes harm? This paper addresses how liability law can and should account for robots, including robots that exist today and robots that potentially could be built at some point in the near or distant future. Already, robots have been implicated in a variety of harms. However, current and near-future robots pose no significant challenge for liability law: they can be readily handled with existing liability law or minor variations thereof. We show this through examples from medical technology, drones, and consumer robotics. A greater challenge will arise if it becomes possible to build robots that merit legal personhood and thus can be held liable. Liability law for robot persons could draw on certain precedents, such as animal liability. However, legal innovations will be needed, in particular for determining which robots merit legal personhood. Finally, a major challenge comes from the possibility of future robots that could cause major global catastrophe. As with other global catastrophic risks, liability law could not apply, because there would be no post- catastrophe legal system to impose liability.
    [Show full text]
  • A Novel Generative Encoding for Evolving Modular, Regular and Scalable Networks
    A Novel Generative Encoding for Evolving Modular, Regular and Scalable Networks Marcin Suchorzewski Jeff Clune Artificial Intelligence Laboratory Creative Machines Laboratory West Pomeranian University of Technology Cornell University [email protected] [email protected] ABSTRACT 1. INTRODUCTION In this paper we introduce the Developmental Symbolic En- Networks designed by humans and evolution have certain coding (DSE), a new generative encoding for evolving net- properties that are thought to enhance both their perfor- works (e.g. neural or boolean). DSE combines elements of mance and adaptability, such as modularity, regularity, hier- two powerful generative encodings, Cellular Encoding and archy, scalability, and being scale-free [12, 11, 1]. In this pa- HyperNEAT, in order to evolve networks that are modular, per we introduce a new generative representation called the regular, scale-free, and scalable. Generating networks with Developmental Symbolic Encoding that evolves networks these properties is important because they can enhance per- that possess these properties. We first review the mean- formance and evolvability. We test DSE’s ability to gener- ing of these terms before discussing them with respect to ate scale-free and modular networks by explicitly rewarding previous evolutionary algorithms. these properties and seeing whether evolution can produce Modularity is the encapsulation of function within mostly networks that possess them. We compare the networks DSE independently operating units, where the internal workings evolves to those of HyperNEAT. The results show that both of the module tend to only affect the rest of the structure encodings can produce scale-free networks, although DSE through the module’s inputs and outputs [12, 11].
    [Show full text]
  • Artificial Biochemical Networks : Evolving Dynamical Systems to Control Dynamical Systems
    This is a repository copy of Artificial Biochemical Networks : Evolving Dynamical Systems to Control Dynamical Systems. White Rose Research Online URL for this paper: https://eprints.whiterose.ac.uk/75277/ Version: Accepted Version Article: Lones, Michael Adam, Fuente, Luis Alberto, Turner, Alexander Phillip et al. (4 more authors) (2014) Artificial Biochemical Networks : Evolving Dynamical Systems to Control Dynamical Systems. IEEE Transactions on Evolutionary Computation. 6423886. pp. 145- 166. ISSN 1089-778X https://doi.org/10.1109/tevc.2013.2243732 Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, PREPRINT, ACCEPTED FOR PUBLICATION DECEMBER 2012 1 Artificial Biochemical Networks: Evolving Dynamical Systems to Control Dynamical Systems Michael A. Lones, Senior Member, IEEE, Luis A. Fuente, Alexander P. Turner, Leo S. D. Caves, Susan Stepney, Stephen L. Smith, Member, IEEE, and Andy M. Tyrrell, Senior Member, IEEE Abstract—Biological organisms exist within environments in different evolutionary algorithms have been used to design which complex, non-linear dynamics are ubiquitous.
    [Show full text]
  • 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.
    [Show full text]
  • Classification Schemas for Artificial Intelligence Failures
    Journal XX (XXXX) XXXXXX https://doi.org/XXXX/XXXX Classification Schemas for Artificial Intelligence Failures Peter J. Scott1 and Roman V. Yampolskiy2 1 Next Wave Institute, USA 2 University of Louisville, Kentucky, USA [email protected], [email protected] Abstract In this paper we examine historical failures of artificial intelligence (AI) and propose a classification scheme for categorizing future failures. By doing so we hope that (a) the responses to future failures can be improved through applying a systematic classification that can be used to simplify the choice of response and (b) future failures can be reduced through augmenting development lifecycles with targeted risk assessments. Keywords: artificial intelligence, failure, AI safety, classification 1. Introduction Artificial intelligence (AI) is estimated to have a $4-6 trillion market value [1] and employ 22,000 PhD researchers [2]. It is estimated to create 133 million new roles by 2022 but to displace 75 million jobs in the same period [6]. Projections for the eventual impact of AI on humanity range from utopia (Kurzweil, 2005) (p.487) to extinction (Bostrom, 2005). In many respects AI development outpaces the efforts of prognosticators to predict its progress and is inherently unpredictable (Yampolskiy, 2019). Yet all AI development is (so far) undertaken by humans, and the field of software development is noteworthy for unreliability of delivering on promises: over two-thirds of companies are more likely than not to fail in their IT projects [4]. As much effort as has been put into the discipline of software safety, it still has far to go. Against this background of rampant failures we must evaluate the future of a technology that could evolve to human-like capabilities, usually known as artificial general intelligence (AGI).
    [Show full text]
  • The Digital Economy
    House of Commons Business, Innovation and Skills Committee The Digital Economy Second Report of Session 2016–17 HC 87 House of Commons Business, Innovation and Skills Committee The Digital Economy Second Report of Session 2016–17 Report, together with formal minutes relating to the report Ordered by the House of Commons to be printed 12 July 2016 HC 87 Published on 18 July 2016 by authority of the House of Commons The Business, Innovation and Skills Committee The Business, Innovation and Skills Committee is appointed by the House of Commons to examine the expenditure, administration, and policy of the Department for Business, Innovation and Skills. Current membership Mr Iain Wright MP (Labour, Hartlepool) (Chair) Paul Blomfield MP (Labour, Sheffield Central) Richard Fuller MP (Conservative, Bedford) Peter Kyle MP (Labour, Hove) Amanda Milling MP (Conservative, Cannock Chase) Jonathan Reynolds MP (Labour (Co-op), Stalybridge and Hyde) Amanda Solloway MP (Conservative, Derby North) Michelle Thomson MP (Independent, Edinburgh West) Kelly Tolhurst MP (Conservative, Rochester and Strood) Craig Tracey MP (Conservative, North Warwickshire) Chris White MP (Conservative, Warwick and Leamington) Powers The Committee is one of the departmental select committees, the powers of which are set out in House of Commons Standing Orders, principally in SO No 152. These are available on the internet via www.parliament.uk. Publication Committee reports are published on the Committee’s website at www.parliament.uk/bis and in print by Order of the House. Evidence relating to this report is published on the inquiry publications page of the Committee’s website. Committee staff The current staff of the Committee are Chris Shaw (Clerk), Martin Adams (Second Clerk), Josephine Willows (Senior Committee Specialist), Duma Langton (Committee Specialist), James McQuade (Senior Committee Assistant), Jonathan Olivier Wright, (Committee Assistant) and Gary Calder (Media Officer).
    [Show full text]
  • Artificial Intelligence Vs (General) Artificial Intelligence 3
    ISSN 0798 1015 HOME Revista ESPACIOS ÍNDICES / Index A LOS AUTORES / To the ! ! AUTORS ! Vol. 40 (Number 4) Year 2019. Page 3 ¿Will machines ever rule the world? ¿Las máquinas dominarán el mundo? PEDROZA, Mauricio 1; VILLAMIZAR, Gustavo 2; MENDEZ, James 3 Received: 11/08/2018 • Approved: 16/12/2018 • Published 04/02/2019 Contents 1. Introduction 2. (Narrow) Artificial Intelligence vs (General) Artificial Intelligence 3. Machines designed as tools of man 4. Machines thought as similar to man 5. Conclusions Acknowledgments Bibliographic references ABSTRACT: RESUMEN: From ancient automatons, to the latest technologies Desde los antiguos autómatas hasta las últimas of robotics and supercomputing, the continued tecnologías de la robótica, el continuo progreso de la progress of mankind has led man to even question his humanidad ha llevado al hombre a cuestionar incluso future status as a dominant species. “Will machines su estatus futuro como especie dominante. "¿Alguna ever rule the world?” or more precisely: Do we want vez dominarán las máquinas el mundo?" O más machines to rule the world? Theoretical and precisamente: ¿Queremos que las máquinas dominen technological challenges involved in this choice will be el mundo? Los retos teóricos y tecnológicos que se considered both under the conservative approach of plantean en esta elección serán considerados tanto "machines designed as tools of man" and in the bajo el enfoque conservador de "máquinas que sirven scenario of "machines thought as similar to man". al hombre" como en el escenario de "máquinas Keywords: Artificial Intelligence, Cognitive Machines, equiparables al hombre" Narrow Artificial Intelligence Strong Artificial Palabras clave: Inteligencia Artificial, Máquinas Intelligence, Artificial General Intelligence.
    [Show full text]