The Chinese Room Argument in the Context of the Rational Action Theory
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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). -
Much Has Been Written About the Turing Test in the Last Few Years, Some of It
1 Much has been written about the Turing Test in the last few years, some of it preposterously off the mark. People typically mis-imagine the test by orders of magnitude. This essay is an antidote, a prosthesis for the imagination, showing how huge the task posed by the Turing Test is, and hence how unlikely it is that any computer will ever pass it. It does not go far enough in the imagination-enhancement department, however, and I have updated the essay with a new postscript. Can Machines Think?1 Can machines think? This has been a conundrum for philosophers for years, but in their fascination with the pure conceptual issues they have for the most part overlooked the real social importance of the answer. It is of more than academic importance that we learn to think clearly about the actual cognitive powers of computers, for they are now being introduced into a variety of sensitive social roles, where their powers will be put to the ultimate test: In a wide variety of areas, we are on the verge of making ourselves dependent upon their cognitive powers. The cost of overestimating them could be enormous. One of the principal inventors of the computer was the great 1 Originally appeared in Shafto, M., ed., How We Know (San Francisco: Harper & Row, 1985). 2 British mathematician Alan Turing. It was he who first figured out, in highly abstract terms, how to design a programmable computing device--what we not call a universal Turing machine. All programmable computers in use today are in essence Turing machines. -
Turing Test Does Not Work in Theory but in Practice
Int'l Conf. Artificial Intelligence | ICAI'15 | 433 Turing test does not work in theory but in practice Pertti Saariluoma1 and Matthias Rauterberg2 1Information Technology, University of Jyväskylä, Jyväskylä, Finland 2Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands Abstract - The Turing test is considered one of the most im- Essentially, the Turing test is an imitation game. Like any portant thought experiments in the history of AI. It is argued good experiment, it has two conditions. In the case of control that the test shows how people think like computers, but is this conditions, it is assumed that there is an opaque screen. On actually true? In this paper, we discuss an entirely new per- one side of the screen is an interrogator whose task is to ask spective. Scientific languages have their foundational limita- questions and assess the nature of the answers. On the other tions, for example, in their power of expression. It is thus pos- side, there are two people, A and B. The task of A and B is to sible to discuss the limitations of formal concepts and theory answer the questions, and the task of the interrogator is to languages. In order to represent real world phenomena in guess who has given the answer. In the case of experimental formal concepts, formal symbols must be given semantics and conditions, B is replaced by a machine (computer) and again, information contents; that is, they must be given an interpreta- the interrogator must decide whether it was the human or the tion. They key argument is that it is not possible to express machine who answered the questions. -
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 -
THE TURING TEST RELIES on a MISTAKE ABOUT the BRAIN For
THE TURING TEST RELIES ON A MISTAKE ABOUT THE BRAIN K. L. KIRKPATRICK Abstract. There has been a long controversy about how to define and study intel- ligence in machines and whether machine intelligence is possible. In fact, both the Turing Test and the most important objection to it (called variously the Shannon- McCarthy, Blockhead, and Chinese Room arguments) are based on a mistake that Turing made about the brain in his 1948 paper \Intelligent Machinery," a paper that he never published but whose main assumption got embedded in his famous 1950 paper and the celebrated Imitation Game. In this paper I will show how the mistake is a false dichotomy and how it should be fixed, to provide a solid foundation for a new understanding of the brain and a new approach to artificial intelligence. In the process, I make an analogy between the brain and the ribosome, machines that translate information into action, and through this analogy I demonstrate how it is possible to go beyond the purely information-processing paradigm of computing and to derive meaning from syntax. For decades, the metaphor of the brain as an information processor has dominated both neuroscience and artificial intelligence research and allowed the fruitful appli- cations of Turing's computation theory and Shannon's information theory in both fields. But this metaphor may be leading us astray, because of its limitations and the deficiencies in our understanding of the brain and AI. In this paper I will present a new metaphor to consider, of the brain as both an information processor and a producer of physical effects. -
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. -
The Turing Test and Other Design Detection Methodologies∗
Detecting Intelligence: The Turing Test and Other Design Detection Methodologies∗ George D. Montanez˜ 1 1Machine Learning Department, Carnegie Mellon University, Pittsburgh PA, USA [email protected] Keywords: Turing Test, Design Detection, Intelligent Agents Abstract: “Can machines think?” When faced with this “meaningless” question, Alan Turing suggested we ask a dif- ferent, more precise question: can a machine reliably fool a human interviewer into believing the machine is human? To answer this question, Turing outlined what came to be known as the Turing Test for artificial intel- ligence, namely, an imitation game where machines and humans interacted from remote locations and human judges had to distinguish between the human and machine participants. According to the test, machines that consistently fool human judges are to be viewed as intelligent. While popular culture champions the Turing Test as a scientific procedure for detecting artificial intelligence, doing so raises significant issues. First, a simple argument establishes the equivalence of the Turing Test to intelligent design methodology in several fundamental respects. Constructed with similar goals, shared assumptions and identical observational models, both projects attempt to detect intelligent agents through the examination of generated artifacts of uncertain origin. Second, if the Turing Test rests on scientifically defensible assumptions then design inferences become possible and cannot, in general, be wholly unscientific. Third, if passing the Turing Test reliably indicates intelligence, this implies the likely existence of a designing intelligence in nature. 1 THE IMITATION GAME gin et al., 2003), this paper presents a novel critique of the Turing Test in the spirit of a reductio ad ab- In his seminal paper on artificial intelligence (Tur- surdum. -
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. -
A Review on Neural Turing Machine
A review on Neural Turing Machine Soroor Malekmohammadi Faradounbeh, Faramarz Safi-Esfahani Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran. Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran. [email protected], [email protected] Abstract— one of the major objectives of Artificial Intelligence is to design learning algorithms that are executed on a general purposes computational machines such as human brain. Neural Turing Machine (NTM) is a step towards realizing such a computational machine. The attempt is made here to run a systematic review on Neural Turing Machine. First, the mind-map and taxonomy of machine learning, neural networks, and Turing machine are introduced. Next, NTM is inspected in terms of concepts, structure, variety of versions, implemented tasks, comparisons, etc. Finally, the paper discusses on issues and ends up with several future works. Keywords— NTM, Deep Learning, Machine Learning, Turing machine, Neural Networks 1 INTRODUCTION The Artificial Intelligence (AI) seeks to construct real intelligent machines. The neural networks constitute a small portion of AI, thus the human brain should be named as a Biological Neural Network (BNN). Brain is a very complicated non-linear and parallel computer [1]. The almost relatively new neural learning technology in AI named ‘Deep Learning’ that consists of multi-layer neural networks learning techniques. Deep Learning provides a computerized system to observe the abstract pattern similar to that of human brain, while providing the means to resolve cognitive problems. As to the development of deep learning, recently there exist many effective methods for multi-layer neural network training such as Recurrent Neural Network (RNN)[2]. -
A Logical Hole in the Chinese Room
Minds & Machines (2009) 19:229–235 DOI 10.1007/s11023-009-9151-9 A Logical Hole in the Chinese Room Michael John Shaffer Received: 27 August 2007 / Accepted: 18 May 2009 / Published online: 12 June 2009 Ó Springer Science+Business Media B.V. 2009 Abstract Searle’s Chinese Room Argument (CRA) has been the object of great interest in the philosophy of mind, artificial intelligence and cognitive science since its initial presentation in ‘Minds, Brains and Programs’ in 1980. It is by no means an overstatement to assert that it has been a main focus of attention for philosophers and computer scientists of many stripes. It is then especially interesting to note that relatively little has been said about the detailed logic of the argument, whatever significance Searle intended CRA to have. The problem with the CRA is that it involves a very strong modal claim, the truth of which is both unproved and highly questionable. So it will be argued here that the CRA does not prove what it was intended to prove. Keywords Chinese room Á Computation Á Mind Á Artificial intelligence Searle’s Chinese Room Argument (CRA) has been the object of great interest in the philosophy of mind, artificial intelligence and cognitive science since its initial presentation in ‘Minds, Brains and Programs’ in 1980. It is by no means an overstatement to assert that it has been a main focus of attention for philosophers and computer scientists of many stripes. In fact, one recent book (Preston and Bishop 2002) is exclusively dedicated to the ongoing debate about that argument 20 some years since its introduction.