On the Possibility of Robots Having Emotions
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Close Engagements with Artificial Companions: Key Social, Psychological, Ethical and Design Issues
OII / e-Horizons Forum Discussion Paper No. 14, January 2008 Close Engagements with Artificial Companions: Key Social, Psychological, Ethical and Design Issues by Malcolm Peltu Oxford Internet Institute Yorick Wilks Oxford Internet Institute OII / e-Horizons Forum Discussion Paper No. 14 Oxford Internet Institute University of Oxford 1 St Giles, Oxford OX1 3JS United Kingdom Forum Discussion Paper January 2008 © University of Oxford for the Oxford Internet Institute 2008 Close Engagements with Artificial Companions Foreword This paper summarizes discussions at the multidisciplinary forum1 held at the University of Oxford on 26 October 2007 entitled Artificial Companions in Society: Perspectives on the Present and Future, as well as an open meeting the previous day addressed by Sherry Turkle2. The event was organized by Yorick Wilks, Senior Research Fellow for the Oxford Internet Institute (OII)3, on behalf of the e-Horizons Institute4 and in association with the EU Integrated Project COMPANIONS. COMPANIONS is studying conversational software-based artificial agents that will get to know their owners over a substantial period. These could be developed to advise, comfort and carry out a wide range of functions to support diverse personal and social needs, such as to be ‘artificial companions’ for the elderly, helping their owners to learn, or assisting to sustain their owners’ fitness and health. The invited forum participants, including computer and social scientists, also discussed a range of related developments that use advanced artificial intelligence and human– computer interaction approaches. They examined key issues in building artificial companions, emphasizing their social, personal, emotional and ethical implications. This paper summarizes the main issues raised. -
Open Problems in Artificial Life
Open Problems in Artificial Life Mark A. Bedau,† John S. McCaskill‡ Norman H. Packard§ Steen Rasmussen Chris Adami†† David G. Green‡‡ Takashi Ikegami§§ Abstract This article lists fourteen open problems in Kunihiko Kaneko artificial life, each of which is a grand challenge requiring a Thomas S. Ray††† major advance on a fundamental issue for its solution. Each problem is briefly explained, and, where deemed helpful, some promising paths to its solution are indicated. Keywords artificial life, evolution, self-organi- zation, emergence, self-replication, autopoeisis, digital life, artificial chemistry, selection, evolutionary learning, ecosystems, biosystems, astrobiology, evolvable hardware, dynamical hierarchies, origin of life, simulation, cultural evolution, 1 Introduction information theory At the dawn of the last century, Hilbert proposed a set of open mathematical problems. They proved to be an extraordinarily effective guideline for mathematical research in the following century. Based on a substantial body of existing mathematical theory, the challenges were both precisely formulated and positioned so that a significant body of missing theory needed to be developed to achieve their solution, thereby enriching mathematics as a whole. In contrast with mathematics, artificial life is quite young and essentially interdis- ciplinary. The phrase “artificial life” was coined by C. Langton [11], who envisaged an investigation of life as it is in the context of life as it could be. Although artificial life is fundamentally directed towards both the origins of biology and its future, the scope and complexity of its subject require interdisciplinary cooperation and collabo- ration. This broadly based area of study embraces the possibility of discovering lifelike behavior in unfamiliar settings and creating new and unfamiliar forms of life, and its major aim is to develop a coherent theory of life in all its manifestations, rather than an historically contingent documentation bifurcated by discipline. -
Open-Endedness for the Sake of Open-Endedness
Open-Endedness for the Sake Arend Hintze Michigan State University of Open-Endedness Department of Integrative Biology Department of Computer Science and Engineering BEACON Center for the Study of Evolution in Action [email protected] Abstract Natural evolution keeps inventing new complex and intricate forms and behaviors. Digital evolution and genetic algorithms fail to create the same kind of complexity, not just because we still lack the computational resources to rival nature, but Keywords because (it has been argued) we have not understood in principle how to create open-ended evolving systems. Much effort has been Open-ended evolution, computational model, complexity, diversity made to define such open-endedness so as to create forms of increasing complexity indefinitely. Here, however, a simple evolving computational system that satisfies all such requirements is presented. Doing so reveals a shortcoming in the definitions for open-ended evolution. The goal to create models that rival biological complexity remains. This work suggests that our current definitions allow for even simple models to pass as open-ended, and that our definitions of complexity and diversity are more important for the quest of open-ended evolution than the fact that something runs indefinitely. 1 Introduction Open-ended evolution has been identified as a key challenge in artificial life research [4]. Specifically, it has been acknowledged that there is a difference between an open-ended system and a system that just has a long run time. Large or slow systems will eventually converge on a solution, but open- ended systems will not and instead keep evolving. Interestingly, a system that oscillates or that is in a dynamic equilibrium [21] would continuously evolve, but does not count as an open-ended evolving system. -
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. -
Artificial Intelligence: How Does It Work, Why Does It Matter, and What Can We Do About It?
Artificial intelligence: How does it work, why does it matter, and what can we do about it? STUDY Panel for the Future of Science and Technology EPRS | European Parliamentary Research Service Author: Philip Boucher Scientific Foresight Unit (STOA) PE 641.547 – June 2020 EN Artificial intelligence: How does it work, why does it matter, and what can we do about it? Artificial intelligence (AI) is probably the defining technology of the last decade, and perhaps also the next. The aim of this study is to support meaningful reflection and productive debate about AI by providing accessible information about the full range of current and speculative techniques and their associated impacts, and setting out a wide range of regulatory, technological and societal measures that could be mobilised in response. AUTHOR Philip Boucher, Scientific Foresight Unit (STOA), This study has been drawn up by the Scientific Foresight Unit (STOA), within the Directorate-General for Parliamentary Research Services (EPRS) of the Secretariat of the European Parliament. To contact the publisher, please e-mail [email protected] LINGUISTIC VERSION Original: EN Manuscript completed in June 2020. DISCLAIMER AND COPYRIGHT This document is prepared for, and addressed to, the Members and staff of the European Parliament as background material to assist them in their parliamentary work. The content of the document is the sole responsibility of its author(s) and any opinions expressed herein should not be taken to represent an official position of the Parliament. Reproduction and translation for non-commercial purposes are authorised, provided the source is acknowledged and the European Parliament is given prior notice and sent a copy. -
How Artificial Intelligence Works
BRIEFING How artificial intelligence works From the earliest days of artificial intelligence (AI), its definition focused on how its results appear to show intelligence, rather than the methods that are used to achieve it. Since then, AI has become an umbrella term which can refer to a wide range of methods, both current and speculative. It applies equally well to tools that help doctors to identify cancer as it does to self-replicating robots that could enslave humanity centuries from now. Since AI is simultaneously represented as high-risk, low-risk and everything in between, it is unsurprising that it attracts controversy. This briefing provides accessible introductions to some of the key techniques that come under the AI banner, grouped into three sections to give a sense the chronology of its development. The first describes early techniques, described as 'symbolic AI' while the second focuses on the 'data driven' approaches that currently dominate, and the third looks towards possible future developments. By explaining what is 'deep' about deep learning and showing that AI is more maths than magic, the briefing aims to equip the reader with the understanding they need to engage in clear-headed reflection about AI's opportunities and challenges, a topic that is discussed in the companion briefing, Why artificial intelligence matters. First wave: Symbolic artificial intelligence Expert systems In these systems, a human expert creates precise rules that a computer can follow, step by step, to decide how to respond to a given situation. The rules are often expressed in an 'if-then-else' format. Symbolic AI can be said to 'keep the human in the loop' because the decision-making process is closely aligned to how human experts make decisions. -
Biological Computation: a Road
918 Biological Computation: A Road to Complex Engineered Systems Nelson Alfonso Gómez-Cruz Modeling and Simulation Laboratory Universidad del Rosario Bogotá, Colombia [email protected] Carlos Eduardo Maldonado Research Professor Universidad del Rosario Bogotá, Colombia [email protected] Provided that there is no theoretical frame for complex engineered systems (CES) as yet, this paper claims that bio-inspired engineering can help provide such a frame. Within CES bio- inspired systems play a key role. The disclosure from bio-inspired systems and biological computation has not been sufficiently worked out, however. Biological computation is to be taken as the processing of information by living systems that is carried out in polynomial time, i.e., efficiently; such processing however is grasped by current science and research as an intractable problem (for instance, the protein folding problem). A remark is needed here: P versus NP problems should be well defined and delimited but biological computation problems are not. The shift from conventional engineering to bio-inspired engineering needs bring the subject (or problem) of computability to a new level. Within the frame of computation, so far, the prevailing paradigm is still the Turing-Church thesis. In other words, conventional 919 engineering is still ruled by the Church-Turing thesis (CTt). However, CES is ruled by CTt, too. Contrarily to the above, we shall argue here that biological computation demands a more careful thinking that leads us towards hypercomputation. Bio-inspired engineering and CES thereafter, must turn its regard toward biological computation. Thus, biological computation can and should be taken as the ground for engineering complex non-linear systems. -
What Was Life? Answers from Three Limit Biologies Author(S): Stefan Helmreich Source: Critical Inquiry, Vol
What Was Life? Answers from Three Limit Biologies Author(s): Stefan Helmreich Source: Critical Inquiry, Vol. 37, No. 4 (Summer 2011), pp. 671-696 Published by: The University of Chicago Press Stable URL: http://www.jstor.org/stable/10.1086/660987 . Accessed: 25/08/2011 13:15 Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. The University of Chicago Press is collaborating with JSTOR to digitize, preserve and extend access to Critical Inquiry. http://www.jstor.org What Was Life? Answers from Three Limit Biologies Stefan Helmreich “What was life? No one knew.” —THOMAS MANN, The Magic Mountain What is life? A gathering consensus in anthropology, science studies, and philosophy of biology suggests that the theoretical object of biology, “life,” is today in transformation, if not dissolution. Proliferating repro- ductive technologies, along with genomic reshufflings of biomatter in such practices as cloning, have unwound the facts of life.1 Biotechnology, bio- This paper grew from a presentation at “Vitalism Revisited: History, Philosophy, Biology,” at the Center for Interdisciplinary Studies in Science and Cultural Theory, Duke University, 22 Mar. 2008. I thank Barbara Herrnstein Smith for inviting me. The paper went through revision for “Extreme: Histories and Economies of Humanness Inside Outerspaces,” at the American Anthropological Association meeting, 2–6 Dec. -
Endowing a Robotic Tutor with Empathic Qualities: Design and Pilot Evaluation
Heriot-Watt University Research Gateway Endowing a Robotic Tutor with Empathic Qualities: Design and Pilot Evaluation Citation for published version: Obaid, M, Aylett, R, Barendregt, W, Basedow, C, Corrigan, LJ, Hall, L, Jones, A, Kappas, A, Kuester, D, Paiva, A, Papadopoulos, F, Serholt, S & Castellano, G 2018, 'Endowing a Robotic Tutor with Empathic Qualities: Design and Pilot Evaluation', International Journal of Humanoid Robotics, vol. 15, no. 6, 1850025. https://doi.org/10.1142/S0219843618500251 Digital Object Identifier (DOI): 10.1142/S0219843618500251 Link: Link to publication record in Heriot-Watt Research Portal Document Version: Early version, also known as pre-print Published In: International Journal of Humanoid Robotics Publisher Rights Statement: Pre-print General rights Copyright for the publications made accessible via Heriot-Watt Research Portal is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy Heriot-Watt University has made every reasonable effort to ensure that the content in Heriot-Watt Research Portal complies with UK legislation. If you believe that the public display of this file breaches copyright please contact [email protected] providing details, and we will remove access to the work immediately and investigate your claim. Download date: 06. Oct. 2021 October 17, 2018 14:36 WSPC/INSTRUCTION FILE output International Journal of Humanoid Robotics c World Scientific Publishing Company Endowing a Robotic Tutor with Empathic Qualities: Design and Pilot Evaluation Mohammad Obaid1, Ruth Aylett2, Wolmet Barendregt3, Christina Basedow4, Lee J. -
Artificial Intelligence in Computer Graphics: a Constructionist Approach
Artificial Intelligence in Computer Graphics: A Constructionist Approach Kristinn R.Thórisson,Christopher ology aims to help coordinate the effort. approach to building AI systems. The Pennock,Thor List,John DiPirro There is also a lack of incremental accumula- Constructionist AI Methodology (CAIM) is an Communicative Machines tion of knowledge in AI and related computer emerging set of principles for designing and graphics. By supporting reuse of prior work implementing interactive intelligences, Introduction we enable the building of increasingly speeding up implementation of relatively The ultimate embodiment of artificial intelli- powerful systems, as core system elements complex, multi-functional systems with full, gence (AI) has traditionally been seen as do not need to be built from scratch. real-time perception-action loop capabilities. physical robots. However, hardware is expen- Background and Motivation It helps novices as well as experts with the sive to construct and difficult to modify. The The creation of believable, interactive charac- creation of embodied, virtual agents that can connection between graphics and AI is ters is a challenge familiar to many game perceive and act in real time and interact with becoming increasingly strong, and, on the one developers. Systems that have to respond to virtual as well as real environments. The hand, it is now clear that AI research can asynchronous inputs in real time, where the embodiment of such humanoids can come in benefit tremendously from embodiment in inputs’ timing is largely unpredictable, can various levels of completeness, from just a virtual worlds [1, 4, 5]. On the other hand, pose a serious challenge to the system design. -
A Theoretical Approach for a Novel Model to Realizing Empathy
A Theoretical Approach for a Novel Model to Realizing Empathy Marialejandra García Corretjer GTM-Grup de recerca en Tecnologies Mèdia, La Salle-Universitat Ramon Llull, Barcelona, Spain Carrer Quatre Camins 30, Barcelona, Spain, 08022, [email protected] The focus of Marialejandra's current line of research is the result of a diverse industry & academic experiences converging into one challenge. Throughout her career, after two Masters degrees (Multimedia Project Management and Research for Design & Innovation) and a Bachelor degree in Industrial Design & Fine Arts, she was always interested in the relationships between people and their objects, as a form of communication that reveals preferences, expectations, and personalities. Now, she studies the impact of this relationship between humans and smart objects within the Human Computer Interactions discipline, from the perspective of the process of Realizing Empathy. David Miralles GTM-Grup de recerca en Tecnologies Mèdia, La Salle-Universitat Ramon Llull, Barcelona, Spain Carrer Quatre Camins 30, Barcelona, Spain, [email protected] He holds a degree on Theoretical Physics of the University of Barcelona (1995). From 1996 to 2001 he worked at the Fundamental Physics Department at the same university. In 2001 he obtained a PhD on Mathematical Physics. From 2001 to 2007 he worked at the Department of Communication and Signal Theory of Universitat Ramon Llull. Nowadays, he is member of the Grup de recerca en Tecnologies Media at La Salle-Universitat Ramon Llull. Raquel Ros GTM-Grup de recerca en Tecnologies Mèdia, La Salle-Universitat Ramon Llull, Barcelona, Spain Carrer Quatre Camins 30, Barcelona, Spain, [email protected] A Theoretical Approach for a Novel Model to Realizing Empathy 1. -
Building Machines That Learn and Think Like People
BEHAVIORAL AND BRAIN SCIENCES (2017), Page 1 of 72 doi:10.1017/S0140525X16001837, e253 Building machines that learn and think like people Brenden M. Lake Department of Psychology and Center for Data Science, New York University, New York, NY 10011 [email protected] http://cims.nyu.edu/~brenden/ Tomer D. Ullman Department of Brain and Cognitive Sciences and The Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA 02139 [email protected] http://www.mit.edu/~tomeru/ Joshua B. Tenenbaum Department of Brain and Cognitive Sciences and The Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA 02139 [email protected] http://web.mit.edu/cocosci/josh.html Samuel J. Gershman Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA 02138, and The Center for Brains, Minds and Machines, Massachusetts Institute of Technology, Cambridge, MA 02139 [email protected] http://gershmanlab.webfactional.com/index.html Abstract: Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like