The Evolution of Intelligence
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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. -
Knowledge Elicitation: Methods, Tools and Techniques Nigel R
This is a pre-publication version of a paper that will appear as Shadbolt, N. R., & Smart, P. R. (2015) Knowledge Elicitation. In J. R. Wilson & S. Sharples (Eds.), Evaluation of Human Work (4th ed.). CRC Press, Boca Raton, Florida, USA. (http://www.amazon.co.uk/Evaluation-Human-Work-Fourth- Wilson/dp/1466559616/). Knowledge Elicitation: Methods, Tools and Techniques Nigel R. Shadbolt1 and Paul R. Smart1 1Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK. Introduction Knowledge elicitation consists of a set of techniques and methods that attempt to elicit the knowledge of a domain expert1, typically through some form of direct interaction with the expert. Knowledge elicitation is a sub-process of knowledge acquisition (which deals with the acquisition or capture of knowledge from any source), and knowledge acquisition is, in turn, a sub-process of knowledge engineering (which is a discipline that has evolved to support the whole process of specifying, developing and deploying knowledge-based systems). Although the elicitation, representation and transmission of knowledge can be considered a fundamental human activity – one that has arguably shaped the entire course of human cognitive and social evolution (Gaines, 2013) – knowledge elicitation had its formal beginnings in the early to mid 1980s in the context of knowledge engineering for expert systems2. These systems aimed to emulate the performance of experts within narrowly specified domains of interest3, and it initially seemed that the design of such systems would draw its inspiration from the broader programme of research into artificial intelligence. In the early days of artificial intelligence, much of the research effort was based around the discovery of general principles of intelligent behaviour. -
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. -
Evolutionary Psychology As of September 15
Evolutionary Psychology In its broad sense, the term ‘evolutionary psychology’ stands for any attempt to adopt an evolutionary perspective on human behavior by supplementing psychology with the central tenets of evolutionary biology. The underlying idea is that since our mind is the way it is at least in part because of our evolutionary past, evolutionary theory can aid our understanding not only of the human body, but also of the human mind. In the narrow sense, Evolutionary Psychology (with capital ‘E’ and ‘P’, to distinguish it from evolutionary psychology in the broad sense) is an adaptationist program which regards our mind as an integrated collection of cognitive mechanisms that are adaptations , i.e., the result of evolution by natural selection. Adaptations are traits present today because they helped to solve recurrent adaptive problems in the past. Evolutionary Psychology is interested in those adaptations that have evolved in response to characteristically human adaptive problems like choosing and securing a mate, recognizing emotional expressions, acquiring a language, distinguishing kin from non-kin, detecting cheaters or remembering the location of edible plants. Its purpose is to discover and explain the cognitive mechanisms that guide current human behavior because they have been selected for as solutions to these adaptive problems in the evolutionary environment of our ancestors. 1. Historic and Systematic Roots 1a. The Computational Model of the Mind 1b. The Modularity of Mind 1c. Adaptationism 2. Key Concepts and Arguments 2a. Adaptation and Adaptivity 1 2b. Functional Analysis 2c. The Environment of Evolutionary Adaptedness 2d. Domain-specificity and Modularity 2e. Human Nature 3. -
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 -
Eye on the Prize
AI Magazine Volume 16 Number 2 (1995) (© AAAI) Articles Eye on the Prize Nils J. Nilsson ■ In its early stages, the field of AI had as its main sufficiently powerful to solve large problems goal the invention of computer programs having of real-world consequence. In their efforts to the general problem-solving abilities of humans. get past the barrier separating toy problems Along the way, a major shift of emphasis devel- from real ones, AI researchers became oped from general-purpose programs toward per- absorbed in two important diversions from formance programs, ones whose competence was their original goal of developing general, highly specialized and limited to particular areas intelligent systems. One diversion was toward of expertise. In this article, I claim that AI is now developing performance programs, ones whose at the beginning of another transition, one that competence was highly specialized and limit- will reinvigorate efforts to build programs of gen- eral, humanlike competence. These programs will ed to particular areas of expertise. Another use specialized performance programs as tools, diversion was toward refining specialized much like humans do. techniques beyond those required for general- purpose intelligence. In this article, I specu- ver 40 years ago, soon after the birth late about the reasons for these diversions of electronic computers, people began and then describe growing forces that are Oto think that human levels of intelli- pushing AI to resume work on its original gence might someday be realized in computer goal of building programs of general, human- programs. Alan Turing (1950) was among the like competence. -
Decision-Making with Artificial Intelligence in the Social Context Responsibility, Accountability and Public Perception with Examples from the Banking Industry
watchit Expertenforum 2. Veröffentlichung Decision-Making with Artificial Intelligence in the Social Context Responsibility, Accountability and Public Perception with Examples from the Banking Industry Udo Milkau and Jürgen Bott BD 2-MOS-Umschlag-RZ.indd 1 09.08.19 08:51 Impressum DHBW Mosbach Lohrtalweg 10 74821 Mosbach www.mosbach.dhbw.de/watchit www.digital-banking-studieren.de Decision-Making with Artificial Intelligence in the Social Context Responsibility, Accountability and Public Perception with Examples from the Banking Industry von Udo Milkau und Jürgen Bott Herausgeber: Jens Saffenreuther Dirk Saller Wolf Wössner Mosbach, im August 2019 BD 2-MOS-Umschlag-RZ.indd 2 09.08.19 08:51 BD 2 - MOS - Innen - RZ_HH.indd 1 02.08.2019 10:37:52 Decision-Making with Artificial Intelligence in the Social Context 1 Decision-Making with Artificial Intelligence in the Social Context Responsibility, Accountability and Public Perception with Examples from the Banking Industry Udo Milkau and Jürgen Bott Udo Milkau received his PhD at Goethe University, Frankfurt, and worked as a research scientist at major European research centres, including CERN, CEA de Saclay and GSI, and has been a part-time lecturer at Goethe University Frankfurt and Frankfurt School of Finance and Management. He is Chief Digital Officer, Transaction Banking at DZ BANK, Frankfurt, and is chairman of the Digitalisation Work- ing Group and member of the Payments Services Working Group of the European Association of Co- operative Banks (EACB) in Brussels. Jürgen Bott is professor of finance management at the University of Applied Sciences in Kaiserslau- tern. As visiting professor and guest lecturer, he has associations with several other universities and business schools, e.g. -
Artificial Intelligence [R18a1205] Lecture Notes B.Tech Iii Year
ARTIFICIAL INTELLIGENCE [R18A1205] LECTURE NOTES B.TECH III YEAR – I SEM (R18) (2020-2021) MALLA REDDY COLLEGE OF ENGINEERING & TECHNOLOGY (Autonomous Institution – UGC, Govt. of India) Recognized under 2(f) and 12 (B) of UGC ACT 1956 (Affiliated to JNTUH, Hyderabad, Approved by AICTE - Accredited by NBA & NAAC – ‘A’ Grade - ISO 9001:2015 Certified) Maisammaguda, Dhulapally (Post Via. Hakimpet), Secunderabad – 500100, Telangana State, India MALLA REDDY COLLEGE OF ENGINEERING & TECHNOLOGY III Year B. Tech CSE ‐ I Sem L T/P/D C 3 -/-/- 3 (R18A1205) ARTIFICIAL INTELLIGENCE OBJECTIVES: To Learn the significance of intelligence systems. To understand the concepts of heuristic search techniques &logic programming. To know the various knowledge representation techniques. UNIT - I Introduction: AI problems, Agents and Environments, Structure of Agents, Problem Solving Agents Basic Search Strategies: Problem Spaces, Uninformed Search (Breadth-First, Depth-First Search, Depth-first with Iterative Deepening), Heuristic Search (Hill Climbing, Generic Best-First, A*), Constraint Satisfaction (Backtracking, Local Search) UNIT - II Advanced Search: Constructing Search Trees, Stochastic Search, A* Search Implementation, Minimax Search, Alpha-Beta Pruning Basic Knowledge Representation and Reasoning: Propositional Logic, First-Order Logic, Forward Chaining and Backward Chaining, Introduction to Probabilistic Reasoning, Bayes Theorem UNIT – III Advanced Knowledge Representation and Reasoning: Knowledge Representation Issues, Nonmonotonic Reasoning, Other Knowledge Representation Schemes Reasoning Under Uncertainty: Basic probability, Acting Under Uncertainty, Bayes’ Rule, Representing Knowledge in an Uncertain Domain, Bayesian Networks UNIT - IV Learning: What Is Learning? Rote Learning, Learning by Taking Advice, Learning in Problem Solving, Learning from Examples, Winston’s Learning Program, Decision Trees. UNIT - V Expert Systems: Representing and Using Domain Knowledge, Shell, Explanation, Knowledge Acquisition. -
New Media Art and Embodiment
New Media Art and Embodiment: Encountering Artificial Intelligence Semeli Panagidou s1601806 [email protected] MA Arts and Culture: Art of the Contemporary World and World Art Studies First reader: Prof. Dr. R. Zwijnenberg Second reader: Dr. A.K.C. Crucq July 2018 Acknowledgments First and foremost, I would like to thank my supervisor Prof. Dr. Rob Zwijnenberg for his continuing support, trust and patience in advising me throughout the process of writing my thesis. I am truly grateful for his time and attention and for all the moments of encouragement he has provided. This would not have been possible otherwise. I would also like to dedicate this effort to my parents for providing me with opportunities to explore and grow in my perception of the world through various means of education and for being understanding and compassionate throughout. In this I have been privileged and I am eternally grateful. 2 Contents INTRODUCTION 4 CHAPTER 1: Being in the world Ideas of cognition in science, philosophy, culture and art 1.1 Postcognitivism, embodiment and the influence of computation. 10 1.2 Hybrid cognitive art and multi-level encounters in the performance Guy Ben-Ary’s cellF. 14 CHAPTER 2: Performative technologies in art Interactions between human and artificial intelligence 2.1 Posthumanist performativity towards empathic communication in a technoetic present. 23 2.2 Meet me halfway: AI as deconstructive co-creator in Oscar Sharp and Ross Goodwin’s Sunspring. 29 2.3 Affective encounters with the machine in Driessens & Verstappen’s Tickle Salon. 35 CONCLUSION 41 LIST OF ILLUSTRATIONS 44 BIBLIOGRAPHY 45 3 Introduction Artificial intelligence has rapidly made its mark as the technology of our generation. -
The 'General Problem Solver' Does Not
ARTICLE The “General Problem Solver” Does Not Exist: Mortimer Taube and the Art of AI Criticism Shunryu Colin Garvey, Stanford University Institute for Human-Centered AI, USA This article reconfigures the history of artificial intelligence (AI) and its accompanying tradition of criticism by excavating the work of Mortimer Taube, a pioneer in information and library sciences, whose magnum opus, Computers and Common Sense: The Myth of Thinking Machines (1961), has been mostly forgotten. To convey the essence of his distinctive critique, the article focuses on Taube’s attack on the general problem solver (GPS), the second major AI program. After examining his analysis of the social construction of this and other “thinking machines,” it concludes that, despite technical changes in AI, much of Taube’s criticism remains relevant today. Moreover, his status as an “information processing” insider who criticized AI on behalf of the public good challenges the boundaries and focus of most critiques of AI from the past half-century. In sum, Taube’s work offers an alternative model from which contemporary AI workers and critics can learn much. EXPOSED INTELLIGENCE RESEARCH (TAUBE, discovered Mortimer Taube in a footnote to the 1961). READERS WHO HAVE BEEN penultimate chapter of Computers and Thought EXPOSED TO THIS BOOK SHOULD (1963), the first collected volume of articles in the REFER TO REVIEWS OF IT BY RICHARD I 1 nascent field of “artificial intelligence” (AI). The chap- LAING (1962) AND WALTER R. REITMAN ter, “Attitudes toward Intelligent Machines,” by Paul (1962), PARTICULARLY THE FORMER.2 Armer, then head of the Computer Sciences Depart- ment of the RAND Corporation, is a partisan consider- ation of the social aspects of AI. -
Logic: Overview
• Think about how you solved this problem. You could treat it as a CSP with variables X1 and X2, and search through the set of candidate solutions, checking the constraints. • However, more likely, you just added the two equations, divided both sides by 2 to easily find out that X1 = 7. This is the power of logical inference, where we apply a set of truth-preserving rules to arrive at the answer. This is in contrast to what is called model checking (for reasons that will become clear), which tries to directly find assignments. • We'll see that logical inference allows you to perform very powerful manipulations in a very compact way. This allows us to vastly increase the representational power of our models. Course plan Search problems Constraint satisfaction problems Markov decision processes Markov networks Adversarial games Bayesian networks Reflex States Variables Logic "Low-level intelligence" "High-level intelligence" Machine learning CS221 4 • We are at the last stage of our journey through the AI topics of this course: logic. Before launching in, let's take a moment to reflect. Propositional logic Syntax Semantics formula models Inference rules CS221 26 [Warner, 1965] Privacy: Randomized response Do you have a sibling? Method: • Flip two coins. • If both heads: answer yes/no randomly • Otherwise: answer yes/no truthfully Analysis: 4 1 true-prob = 3 × (observed-prob − 8 ) CS221 136 Causality Goal: figure out the effect of a treatment on survival Data: For untreated patients, 80% survive For treated patients, 30% survive Does the treatment help? Sick people are more likely to undergo treatment..