Turing, Searle and the Contested Significance of Artificial Intelligence
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Artificial Intelligence Is Stupid and Causal Reasoning Won't Fix It
Artificial Intelligence is stupid and causal reasoning wont fix it J. Mark Bishop1 1 TCIDA, Goldsmiths, University of London, London, UK Email: [email protected] Abstract Artificial Neural Networks have reached `Grandmaster' and even `super- human' performance' across a variety of games, from those involving perfect- information, such as Go [Silver et al., 2016]; to those involving imperfect- information, such as `Starcraft' [Vinyals et al., 2019]. Such technological developments from AI-labs have ushered concomitant applications across the world of business, where an `AI' brand-tag is fast becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong - an autonomous vehicle crashes; a chatbot exhibits `racist' behaviour; automated credit-scoring processes `discriminate' on gender etc. - there are often significant financial, legal and brand consequences, and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that \... all the impressive achievements of deep learning amount to just curve fitting". The key, Pearl suggests [Pearl and Mackenzie, 2018], is to replace `reasoning by association' with `causal reasoning' - the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for the New York Times: \we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets { often using an approach known as \Deep Learning" { and start building computer systems that from the moment of arXiv:2008.07371v1 [cs.CY] 20 Jul 2020 their assembly innately grasp three basic concepts: time, space and causal- ity"[Marcus and Davis, 2019]. -
The Thought Experiments Are Rigged: Mechanistic Understanding Inhibits Mentalistic Understanding
Georgia State University ScholarWorks @ Georgia State University Philosophy Theses Department of Philosophy Summer 8-13-2013 The Thought Experiments are Rigged: Mechanistic Understanding Inhibits Mentalistic Understanding Toni S. Adleberg Georgia State University Follow this and additional works at: https://scholarworks.gsu.edu/philosophy_theses Recommended Citation Adleberg, Toni S., "The Thought Experiments are Rigged: Mechanistic Understanding Inhibits Mentalistic Understanding." Thesis, Georgia State University, 2013. https://scholarworks.gsu.edu/philosophy_theses/141 This Thesis is brought to you for free and open access by the Department of Philosophy at ScholarWorks @ Georgia State University. It has been accepted for inclusion in Philosophy Theses by an authorized administrator of ScholarWorks @ Georgia State University. For more information, please contact [email protected]. THE THOUGHT EXPERIMENTS ARE RIGGED: MECHANISTIC UNDERSTANDING INHIBITS MENTALISTIC UNDERSTANDING by TONI ADLEBERG Under the Direction of Eddy Nahmias ABSTRACT Many well-known arguments in the philosophy of mind use thought experiments to elicit intuitions about consciousness. Often, these thought experiments include mechanistic explana- tions of a systems’ behavior. I argue that when we understand a system as a mechanism, we are not likely to understand it as an agent. According to Arico, Fiala, Goldberg, and Nichols’ (2011) AGENCY Model, understanding a system as an agent is necessary for generating the intuition that it is conscious. Thus, if we are presented with a mechanistic description of a system, we will be very unlikely to understand that system as conscious. Many of the thought experiments in the philosophy of mind describe systems mechanistically. I argue that my account of consciousness attributions is preferable to the “Simplicity Intuition” account proposed by David Barnett (2008) because it is more explanatory and more consistent with our intuitions. -
Introduction to Philosophy Fall 2019—Test 3 1. According to Descartes, … A. What I Really Am Is a Body, but I Also Possess
Introduction to Philosophy Fall 2019—Test 3 1. According to Descartes, … a. what I really am is a body, but I also possess a mind. b. minds and bodies can’t causally interact with one another, but God fools us into believing they do. c. cats and dogs have immortal souls, just like us. d. conscious states always have physical causes, but never have physical effects. e. what I really am is a mind, but I also possess a body. 2. Which of the following would Descartes agree with? a. We can conceive of existing without a body. b. We can conceive of existing without a mind. c. We can conceive of existing without either a mind or a body. d. We can’t conceive of mental substance. e. We can’t conceive of material substance. 3. Substance dualism is the view that … a. there are two kinds of minds. b. there are two kinds of “basic stuff” in the world. c. there are two kinds of physical particles. d. there are two kinds of people in the world—those who divide the world into two kinds of people, and those that don’t. e. material substance comes in two forms, matter and energy. 4. We call a property “accidental” (as opposed to “essential”) when ... a. it is the result of a car crash. b. it follows from a thing’s very nature. c. it is a property a thing can’t lose (without ceasing to exist). d. it is a property a thing can lose (without ceasing to exist). -
Preston, John, & Bishop, Mark
Preston, John, & Bishop, Mark (2002), Views into the Chinese Room: New Essays on Searle and Artificial Intelligence (Oxford: Oxford University Press), xvi + 410 pp., ISBN 0-19-825057-6. Reviewed by: William J. Rapaport Department of Computer Science and Engineering, Department of Philosophy, and Center for Cognitive Science, State University of New York at Buffalo, Buffalo, NY 14260-2000; [email protected], http://www.cse.buffalo.edu/ rapaport ∼ This anthology’s 20 new articles and bibliography attest to continued interest in Searle’s (1980) Chinese Room Argument. Preston’s excellent “Introduction” ties the history and nature of cognitive science, computation, AI, and the CRA to relevant chapters. Searle (“Twenty-One Years in the Chinese Room”) says, “purely . syntactical processes of the implemented computer program could not by themselves . guarantee . semantic content . essential to human cognition” (51). “Semantic content” appears to be mind-external entities “attached” (53) to the program’s symbols. But the program’s implementation must accept these entities as input (suitably transduced), so the program in execution, accepting and processing this input, would provide the required content. The transduced input would then be internal representatives of the external content and would be related to the symbols of the formal, syntactic program in ways that play the same roles as the “attachment” relationships between the external contents and the symbols (Rapaport 2000). The “semantic content” could then just be those mind-internal -
Digital Culture: Pragmatic and Philosophical Challenges
DIOGENES Diogenes 211: 23–39 ISSN 0392-1921 Digital Culture: Pragmatic and Philosophical Challenges Marcelo Dascal Over the coming decades, the so-called telematic technologies are destined to grow more and more encompassing in scale and the repercussions they will have on our professional and personal lives will become ever more accentuated. The transforma- tions resulting from the digitization of data have already profoundly modified a great many of the activities of human life and exercise significant influence on the way we design, draw up, store and send documents, as well as on the means used for locating information in libraries, databases and on the net. These changes are transforming the nature of international communication and are modifying the way in which we carry out research, engage in study, keep our accounts, plan our travel, do our shopping, look after our health, organize our leisure time, undertake wars and so on. The time is not far off when miniaturized digital systems will be installed everywhere – in our homes and offices, in the car, in our pockets or even embedded within the bodies of each one of us. They will help us to keep control over every aspect of our lives and will efficiently and effectively carry out multiple tasks which today we have to devote precious time to. As a consequence, our ways of acting and thinking will be transformed. These new winds of change, which are blowing strongly, are giving birth to a new culture to which the name ‘digital culture’ has been given.1 It is timely that a study should be made of the different aspects and consequences of this culture, but that this study should not be just undertaken from the point of view of the simple user who quickly embraces the latest successful inno- vation, in reaction essentially to market forces. -
Artificial Intelligence: a Roadmap for California
Artificial Intelligence: A Roadmap for California Report #245, November 2018 Milton Marks Commission on California State Government Organization and Economy Little Hoover Commission Dedicated to Promoting Economy and Efficiency Pedro Nava Chairman in California State Government Sean Varner Vice Chairman/ Subcommitee Member The Little Hoover Commission, formally known as the Milton Marks “Little Hoover” David Beier Commission on California State Government Organization and Economy, is an Subcommitee Chair independent state oversight agency created in 1962. By statute, the Commission Iveta Brigis is bipartisan and composed of nine public members, two senators and two Subcommitee Member assemblymembers. Cynthia Buiza In creating the Commission, the Legislature declared its purpose: Anthony Cannella Senator [T]o secure assistance for the Governor and itself in promoting Chad Mayes economy, efficiency and improved services in the transaction of Assemblymember the public business in the various departments, agencies and Don Perata instrumentalities of the executive branch of the state government, Bill Quirk and in making the operation of all state departments, agencies and Assemblymember instrumentalities, and all expenditures of public funds, more directly Richard Roth responsive to the wishes of the people as expressed by their elected Senator representatives. Cathy Schwamberger The Commission fulfills this charge by holding public hearings, consulting with experts Janna Sidley and listening to stakeholders. During the course of its studies, the Commission may Former Commissioners Who create subcommittees and conduct site visits. Served During The Study Joshua LaFarga The findings and recommendations of the Commission are submitted to the Governor and the Legislature for their consideration. Recommendations often take the form of Helen Iris Torres legislation, which the Commission supports through the legislative process. -
AI for Autonomous Ships – Challenges in Design and Validation
VTT TECHNICAL RESEARCH CENTRE OF FINLAND LTD AI for Autonomous Ships – Challenges in Design and Validation ISSAV 2018 Eetu Heikkilä Autonomous ships - activities in VTT . Autonomous ship systems . Unmanned engine room . Situation awareness . Autonomous autopilot . Connectivity . Human factors of remote and autonomous systems . Safety assessment 22/03/2018 2 Contents . AI technologies for autonomous shipping . Design & validation challenges . Methodological . Technical . Acknowledgements: Olli Saarela, Heli Helaakoski, Heikki Ailisto, Jussi Martio 22/03/2018 3 AI technologies Definitions - Autonomy Level Name 1 Human operated Autonomy is the ability of a system to 2 Human assisted achieve operational goals in complex domains by making decisions and executing 3 Human delegated actions on behalf of or in cooperation with 4 Supervised humans. (NFA, 2012) 5 Mixed initiative . Target to increase productivity, cost 6 Fully autonomous efficiency, and safety . Not only by reducing human work, but also by enabling new business logic 22/03/2018 5 Definitions - Artificial Intelligence . The Turing test: Can a person tell which of the other parties is a machine . AI is a moving target . When a computer program is able to perform a task, people start to consider the task “merely” computational, not requiring actual intelligence after all. ”AI is all the software we don’t yet know how to write.” . More practically: AI is a collection of technologies facilitating “smart” operation of machines and systems. Conclusions and actions fitting the prevailing -
Overview of Artificial Intelligence
Updated October 24, 2017 Overview of Artificial Intelligence The use of artificial intelligence (AI) is growing across a Currently, AI technologies are highly tailored to particular wide range of sectors. Stakeholders and policymakers have applications or tasks, known as narrow (or weak) AI. In called for careful consideration of strategies to influence its contrast, general (or strong) AI refers to intelligent behavior growth, reap predicted benefits, and mitigate potential risks. across a range of cognitive tasks, a capability which is This document provides an overview of AI technologies unlikely to occur for decades, according to most analysts. and applications, recent private and public sector activities, Some researchers also use the term “augmented and selected issues of interest to Congress. intelligence” to capture AI’s various applications in physical and connected systems, such as robotics and the AI Technologies and Applications Internet of Things, and to focus on using AI technologies to Though definitions vary, AI can generally be thought of as enhance human activities rather than to replace them. computerized systems that work and react in ways commonly thought to require intelligence, such as solving In describing the course of AI development, the Defense complex problems in real-world situations. According to Advanced Research Projects Agency (DARPA) has the Association for the Advancement of Artificial identified three “waves.” The first wave focused on Intelligence (AAAI), researchers broadly seek to handcrafted knowledge that allowed for system-enabled understand “the mechanisms underlying thought and reasoning in limited situations, though lacking the ability to intelligent behavior and their embodiment in machines.” learn or address uncertainty, as with many rule-based systems from the 1980s. -
Is the Artificial Intelligent? a Perspective on AI-Based Natural Language Processors
Pobrane z czasopisma New Horizons in English Studies http://newhorizons.umcs.pl Data: 16/03/2020 15:10:20 New Horizons in English Studies 4/2019 LANGUAGE • Wojciech Błachnio Maria Curie-SkłodowSka univerSity, LubLin [email protected] Is the Artificial Intelligent? A Perspective on AI-based Natural Language Processors Abstract. The issue of the relation between AI and human mind has been riddling the scientific world for ages. Having been an innate and exclusive faculty of the human mind, language is now manifested in a countless number of ways, transcending beyond the human-only production. There are applica- tions that can not only understand what is meant by an utterance, but also engage in a quasi-humane discourse. The manner of their operating is perfectly organised and can be accounted for by incorporat- ing linguistic theories. The main theory used in this article is Fluid Construction Grammar, which has been developed by Luc Steels. It is concerned with parsing and the segmentation of any utterance – two processes that are pivotalUMCS in AI’s understanding and production of language. This theory, in addition to five main facets of languages (phonological, morphological, semantic, syntactic and pragmatic), provides valuable insight into discrepancies between the natural and artificial perceptions of language. Though there are similarities between them, the article shall conclude with what makes two adjacent capabilities different. The aim of this paper is to display the mechanisms of AI natural language proces- sors with the aid of contemporary linguistic theories, and present possible issues which may ensue from using artificial language-recognising systems. -
Artificial Intelligence in Health Care Islam Haythem Salama Osama Jumaa Alzway Supervisor : Dr
Artificial intelligence in Health Care Islam Haythem Salama Osama Jumaa Alzway Supervisor : Dr. Abdelmonem Abdelnabi Objectives 1. General View of Artificial Intelligence 2. Artificial Intelligence in Health Care 3. Artificial Intelligence in Dentistry General View of AI - Islam Salama So , What is the AI ? The theory and development of a computer systems that is able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision- making, translation between languages ,and concept making . AI Revelation AI Revelation Human Conscious Levels 1.Information's 2.Believe 3.Ideas 4.Thinking 5.Conscious Classification of AI : 1. Strong AI 2. Weak AI Strong AI Artificial general intelligence a multitask system or machine with skillful and flexible as humans do and with ability to apply intelligence to any problem, rather than just one specific problem IBM Watson • -Watson is a question answering computer system capable of answering questions posed in natural language, developed in IBM's DeepQA project. • -Won 2011 Jeopardy Google Deep Mind • Company in UK now under alphabet group • mimics the short-term memory of the human brain • Won in Go Game “ Alfa Go “ 2016 Weak AI “Narrow AI” is artificial intelligence that is focused on one narrow task. Weak AI is defined in contrast to either. All currently existing systems considered artificial intelligence of any sort are weak AI at most. e.g: Weak AI “Narrow AI” Intelligence levels of machines 1. Optimal AI = good at his application e.g: scaning method can scans every letter. 2. Super Strong = better than other humans. 3. Strong = at his app not all people but some of them = face rec. -
Zombie Mouse in a Chinese Room
Philosophy and Technology manuscript No. (will be inserted by the editor) Zombie mouse in a Chinese room Slawomir J. Nasuto · J. Mark Bishop · Etienne B. Roesch · Matthew C. Spencer the date of receipt and acceptance should be inserted later Abstract John Searle's Chinese Room Argument (CRA) purports to demon- strate that syntax is not sucient for semantics, and hence that because com- putation cannot yield understanding, the computational theory of mind, which equates the mind to an information processing system based on formal com- putations, fails. In this paper, we use the CRA, and the debate that emerged from it, to develop a philosophical critique of recent advances in robotics and neuroscience. We describe results from a body of work that contributes to blurring the divide between biological and articial systems: so-called ani- mats, autonomous robots that are controlled by biological neural tissue, and what may be described as remote-controlled rodents, living animals endowed with augmented abilities provided by external controllers. We argue that, even though at rst sight these chimeric systems may seem to escape the CRA, on closer analysis they do not. We conclude by discussing the role of the body- brain dynamics in the processes that give rise to genuine understanding of the world, in line with recent proposals from enactive cognitive science1. Slawomir J. Nasuto Univ. Reading, Reading, UK, E-mail: [email protected] J. Mark Bishop Goldsmiths, Univ. London, UK, E-mail: [email protected] Etienne B. Roesch Univ. Reading, Reading, UK, E-mail: [email protected] Matthew C. -
AI Today, Beyond the Myth
AI today, beyond the myth Presented by:Ray Walshe For: ETSI Summit on Artificial Intelligence 04.04.2019 A Short History of Artificial Intelligence © ETSI 2019 Early Days • 1950 Alan Turing published “Computing Machinery and Intelligence” • “Imitation Game” see also John Searle’s “Chinese Room” • 1956 John McCarty held the first academic conference on the subject • The term artificial intelligence was coined • Marvin Minsky "Within a generation [...] the problem of creating 'artificial intelligence' will substantially be solved," © ETSI 2019 3 AI Winter • 1974–80 Initial optimism fades as progress in AI is slow • government funding stops • interest in the field drops off • Winter has begun • Thaw begins in 1980 • British government funds AI research again • Winter returns from 1987 to 1993 • Market Crash © ETSI 2019 4 AI Breakthrough • 1997 IBM's Deep Blue • Computer beats chess grandmaster Garry Kasparov. • 2011 IBM Watson • Watson's question‐answering system won the American quiz show "Jeopardy!" • beats reigning champions Brad Rutter and Ken Jennings • 2014 Chatbot called Eugene Goostman passes Turing test? © ETSI 2019 5 AI today • Art • Data Centres • Music • Sentiment Analysis • Fraud • Customer behaviour • Healthcare • Cyberbullying • Autonomous Vehicles • News and Media • Recommendations • Fishing © ETSI 2019 6 AI Computing © ETSI 2019 AI Hardware Three types of AI computing • Centralized environment, all calculations are done on one particular computer system, such as a dedicated server for processing data. • the single server model bottleneck • Distributed computing, not all transactions are processed in the same location, but that the distributed processors are still under the control of a single entity. • SETI • Decentralized computing, on its end, entails that no one single entity has control over the processing.