Metadata of the Chapter That Will Be Visualized Online

Total Page:16

File Type:pdf, Size:1020Kb

Metadata of the Chapter That Will Be Visualized Online Metadata of the chapter that will be visualized online Chapter Title Connectionism Copyright Year 2011 Copyright Holder Springer Science+Business Media, LLC Corresponding Author Family Name Karaminis Particle Given Name Themis N. Suffix Division Department of Psychological Sciences, Birkbeck College Organization University of London Address Malet Street, London, WC1E 7HX, UK Email tkaram01@students.bbk.ac.uk Author Family Name Thomas Particle Given Name Michael S.C. Suffix Division Department of Psychological Sciences, Birkbeck College Organization University of London Address Malet Street, London, WC1E 7HX, UK Email m.thomas@bbk.ac.uk Comp. by: KArunKumar Stage: Proof Chapter No.: 397 Title Name: ESL Page Number: 0 Date:23/3/11 Time:17:22:10 1 C Theoretical Background 38 2 Connectionism Artificial Neural Networks are abstract models of 39 biological neural systems. They consist of a set of identical 40 3 THEMIS N. KARAMINIS,MICHAEL S.C. THOMAS processing units, which are referred to as artificial neurons 41 4 Department of Psychological Sciences, Birkbeck College, or processing units. Artificial neurons are interconnected 42 5 University of London, London, UK via weighted connections. 43 A great deal of biological complexity is omitted in 44 artificial neural network models. For example, artificial 45 6 Synonyms neurons perform the simple function of discriminating 46 7 (Artificial) Neural network modeling; Connectionist between different levels of input activation. The detector 47 8 modeling; Neural nets; Parallel Distributed Processing model of the neuron (Fig. 1) is a crude approximation of 48 9 (PDP) the role of dendrites and synaptic channels in biological 49 neurons. According to this model, each neuron receives 50 10 Definition a number of inputs from other neurons. The neuron 51 11 Connectionism is an interdisciplinary approach to the integrates the inputs by computing a weighted sum of 52 12 study of cognition that integrates elements from the fields sending activation. Based on the value of the total input 53 13 of artificial intelligence, neuroscience, cognitive psychology, activation, an activation function (e.g., a threshold 54 14 and philosophy of mind. As a theoretical movement in function) determines the level of the output activation of 55 15 cognitive science, connectionism suggests that cognitive the neuron. The output activation is propagated to 56 16 phenomena can be explained with respect to a set of general succeeding neurons. 57 17 information-processing principles, known as parallel The pattern of connectivity between the processing 58 18 distributed processing (Rumelhart et al. 1986a). From a units defines the architecture of the neural network and 59 19 methodological point of view, connectionism is the input–output functions that can be performed. 60 20 a framework for studying cognitive phenomena using The processing units are usually arranged in layers. It is 61 21 architectures of simple processing units interconnected notable that a layered structure has also been observed 62 22 via weighted connections. in neural tissues. Many different neural network 63 23 These architectures present analogies to biological architectures have been implemented in the connectionist 64 24 neural systems and are referred to as (Artificial) Neural literature. One that has been particularly common 65 25 Networks. Connectionist studies typically propose and is the three-layer feed-forward neural network (Fig. 2). 66 26 implement neural network models to explain various In this network, the units are arranged in three layers: 67 27 aspects of cognition. The term connectionism stems input, hidden, and output. The connectivity is feed-for- 68 28 from the proposal that cognition emerges in neural ward, which means that the connections are unidirec- 69 29 network models as a product of a learning process which tional, and connect the input to the hidden, and the 70 30 shapes the values of the weighted connections. hidden to the output layer. The connectivity is also full: 71 31 Connectionism supports the idea that knowledge is Every neuron of a given layer is connected to every neuron 72 32 represented in the weights of the connections between of the next layer. 73 33 the processing units in a distributed fashion. This means A key property of neural networks is their ability to 74 34 that knowledge is encoded in the structure of the learn. Learning in neural networks is based on altering the 75 35 processing system, in contrast to the symbolic approach extent to which a given neuron’s activity alters the activity 76 36 where knowledge is readily shifted between different of the neurons to which it is connected. Learning is 77 37 memory registers. performed by a learning algorithm which determines 78 appropriate changes in the weight values to perform 79 a set of input–output mappings. For example, the 80 Norbert Seel (ed.), Encyclopedia of the Sciences of Learning, DOI 10.1007/978-1-4419-1428-6, # Springer Science+Business Media, LLC 2011 Comp. by: KArunKumar Stage: Proof Chapter No.: 397 Title Name: ESL Page Number: 0 Date:23/3/11 Time:17:22:10 2 C Connectionism 81 Backpropagation of Error algorithm (Rumelhart et al. One of the most influential connectionist models is 130 82 1986b) can be used to train a feed-forward multilayered that of Rumelhart and McClelland (1986) for the acquisi- 131 83 network (Fig. 2) using supervised learning. For this type of tion of the English past tense (Fig. 3). The domain of the 132 84 learning, the learning algorithm presents the network with English past tense is of theoretical interest to psycholin- 133 85 pairs of input patterns and desired output patterns guists because it presents a predominant regularity, with 134 86 (or targets). The algorithm computes the output error, the great majority of verbs forming their past tenses 135 87 i.e., the difference between the actual output of the through a stem-suffixation rule (e.g., walk/walked). 136 88 network and the targets. Next, the algorithm propagates However, a significant group of verbs form their past 137 89 appropriate error signals back down through each layer of tenses irregularly (e.g., swim/swam, hit/hit, is/was). 138 90 the network. These error signals are used to determine Rumelhart and McClelland trained a two-layered 139 91 weight changes necessary to achieve the minimization feed-forward network (a pattern associator) on mappings 140 92 of the output error. For a more detailed discussion of between phonological representations of the stems and the 141 93 learning in neural networks, see connectionist theories corresponding past tense forms of English verbs. 142 94 of learning. Rumelhart and McClelland showed that both regular and 143 95 Other issues that are considered in neural network irregular inflections could be learned by this network. 144 96 modeling concern the representation of the learning Furthermore, they argued that their model reproduced 145 97 environment. For example, a localist or a distributed a series of well-established phenomena in empirical 146 98 scheme can be used to represent different entities. In the studies of language acquisition. For example, the past 147 99 former, a single unit is used to encode an entity, while in tense rule was generalized to novel stems, while the 148 100 the latter an entity is encoded by an activation learning of irregular verbs followed a U-shaped pattern 149 101 pattern across multiple units. Furthermore, the different (an initial period of error-free performance succeeded by 150 102 input–output patterns which compose the learning a period of increased occurrence of overgeneralization 151 103 environment can be presented in different ways (e.g., errors, e.g., think/thinked instead of thought). 152 104 sequentially, randomly with replacement, incrementally, The success of this model in simulating the acquisition 153 105 or based on a frequency structure). of the English past tense demonstrated that an explicit 154 representation of rules is not necessary for the acquisition 155 106 Important Scientific Research and Open of morphology. Instead, a rule-like behavior was the 156 107 Questions product of the statistical properties of input–output 157 108 The concept of neural network computation was initially mappings. The Rumelhart and McClelland (1986) model 158 109 proposed in the 1940s. However, the foundations for their posed a serious challenge to existing ‘symbolic’ views, 159 110 systematic application to the exploration of cognition which maintained that the acquisition of morphology 160 111 were laid several decades later by the influential volumes was supported by two separate mechanisms, also referred 161 112 of Rumelhart, McClelland, and colleagues. Following this to as the dual-route model. According to the dual-route 162 113 seminal work, a large number of studies proposed neural model, a rule-based system was involved in the learning of 163 114 network models to address various cognitive phenomena. regular mappings, while a rote-memory was involved in the 164 115 Although connectionist models are inspired by learning of irregular mappings. A vigorous debate, also 165 116 computation in biological neural systems, they present known as the ‘past tense debate,’ ensued in the field of 166 117 a high level of abstraction. Therefore, they could not language acquisition (c.f., Pinker and Prince 1988). By the 167 118 claim biological plausibility. Connectionist models are time this debate resided, connectionist studies had moved 168 119 usually seen as cognitive models, which explain cognition on to addressing many aspects of the acquisition of past 169 120 based on general information-processing principles. One tense and inflectional morphology in greater detail. 170 121 of the main strengths of connectionism is that the neural For example, Thomas and Karmiloff-Smith (2003) 171 122 network models are not verbally specified but incorporated phonological and lexical-semantics infor- 172 123 implemented.
Loading...
Loading...
Loading...
Loading...
Loading...

1 pages remaining, click to load more.

Recommended publications
  • Warren Mcculloch and the British Cyberneticians
    Warren McCulloch and the British cyberneticians Article (Accepted Version) Husbands, Phil and Holland, Owen (2012) Warren McCulloch and the British cyberneticians. Interdisciplinary Science Reviews, 37 (3). pp. 237-253. ISSN 0308-0188 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/id/eprint/43089/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher’s version. Please see the URL above for details on accessing the published version. Copyright and reuse: Sussex Research Online is a digital repository of the research output of the University. Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available. Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. http://sro.sussex.ac.uk Warren McCulloch and the British Cyberneticians1 Phil Husbands and Owen Holland Dept. Informatics, University of Sussex Abstract Warren McCulloch was a significant influence on a number of British cyberneticians, as some British pioneers in this area were on him.
    [Show full text]
  • Kybernetik in Urbana
    Kybernetik in Urbana Ein Gespräch zwischen Paul Weston, Jan Müggenburg und James Andrew Hutchinson1 Paul Ernest Weston was born on February 3rd 1935 in Garland, Maine. After stu- dying physics at Wesleyan University in Middletown (Connecticut), 1952 through 1956, Paul Weston came to the University of Illinois in summer 1956, receiving his M.S. in August 1958. The following autumn, as a graduate student of physics, he attended a seminar led by Heinz von Foerster under the promising title „Cyberne- tics“.2 In June 1959 Paul joins the newly founded Biological Computer Laboratory (BCL) as a half time research assistant employed by the Electrical Engineering Department. Through roughly 1962, Weston’s primary emphasis was upon simulation of the processes of perception, with work in neuron models and pattern recognition. Together with Murray Babcock, Weston was one of the most prominent creators of prototypes built at the BCL. Already in 1961 Weston becomes widely known as the inventor of the NumaRete, a parallel operated pattern recognition machine, which played an important role in the public perception of the BCL through out the fol- lowing years.3 After 1964 Weston continues his research at BCL on the basis of a full position as a research assistant and starts to focus on information processing, particularly inte- rested in problems associated with natural-language interaction with machine. As a member of the Committee for Cognitive Studies Weston is also increasingly interes- ted in the potentials of how the new information technologies could shape society. In June 1970 Weston receives his PhD for developing a new and more efficient data structure called CYLINDER.4 After the BCL was closed in 1975 Weston continued to work as a Research Associate at the Department of Electrical Engineering and the Coordinated Science Laboratory of the University of Illinois.
    [Show full text]
  • 1011 Neurons } 1014 Synapses the Cybernetics Group
    History 329/SI 311/RCSSCI 360 Computers and the Internet: A global history Week 6: Computing and Cybernetics in the Soviet Union Today } Review } Cybernetics: minds, brains, and machines } Soviet computing and cybernetics } Soviet economic planning } Next time Review: SAGE } Origin: Whirlwind digital computer project, MIT } SAGE = Semi-Automatic Ground Environment } Computer-controlled air defense of lower 48 states } Networked over telephone lines } Duplexed (two computers for high reliability) } Tube-based central processors made by IBM } Magnetic core memory } First truly large software development } Served as a pattern for many subsequent military projects } Major factor in US and IBM dominance of commercial computer markets by late1950s Cybernetics: minds, brains, and machines Key figures } Norbert Wiener } cybernetics } Warren McCulloch & Walter Pitts } neural nets } John von Neumann } brain modeling, cellular automata, biological metaphors } Frank Rosenblatt } perceptrons The Cybernetics Group } Norbert Wiener, MIT } WWII anti-aircraft problems } Servo/organism analogies } “Behavior, Purpose, and Teleology” (1943) } Information as measurable quantity } Feedback: circular self-corrective cycles BEHAVIOR, PURPOSE AND TELEOLOGY 21 Examples of predictions of higher order are shooting with a sling or with a bow and arrow. Predictive behavior requires the discrimination of at least two coordinates, a temporal and at least one spatial axis. Prediction will be more effective and flexible, however, if the behaving object can respond to changes in more than one spatial coordinate. The sensory receptors of an organism, or the corresponding elements of a machine, may therefore limit the predictive behavior. Thus, a bloodhound follows a trail, that is, it does not show any predictive behavior in trailing, because a chemical, olfactory input reports only spatial information: distance, as indicated by intensity.
    [Show full text]
  • Journal of Sociocybernetics
    JOURNAL OF SOCIOCYBERNETICS _________________________________________________________________ Volume 3 Number 1 Spring/Summer 2002 Official Journal of the Research Committee on Sociocybernetics (RC51) of the International Sociological Association JOURNAL OF SOCIOCYBERNETICS www.unizar.es/sociocybernetics/ Editor Richard E. Lee Newsletter Cor Van Dijkum Felix Geyer Editorial Board Mike Byron Tessaleno Devezas Jorge González Bernd R. Hornung Chaime Marcuello Vessela Misheva Philip Nikolopoulos Bernard Scott Mike Terpstra ____________________________________________________________________________ The JOURNAL OF SOCIOCYBERNETICS (ISSN 1607-8667) is an electronic journal published biannually--Spring/Summer and Fall/Winter--by the Research Committee on Sociocybernetics of the International Sociological Association. MANUSCRIPT submissions should be sent electronically (in MSWord or Rich Text File format) to each of the editors: Richard E. Lee rlee@binghamton.edu, Felix Geyer, geyer@xs4all.nl, and Cor van Dijkum, c.vandijkum@fss.uu.nl. In general, please follow the Chicago Manuel of Style; citations and bibliography should follow the current journal style (APA). Normally, articles should be original texts of no more than 6000 words, although longer articles will be considered in exceptional circumstances. The Journal looks for submissions that are innovative and apply principles of General Systems Theory and Cybernetics to the social sciences, broadly conceived. COPYRIGHT remains the property of authors. Permission to reprint must be obtained from the authors and the contents of JoS cannot be copied for commercial purposes. JoS does, however, reserve the right to future reproduction of articles in hard copy, portable document format (.pdf), or HTML editions of JoS. iii SOCIOCYBERNETICS traces its intellectual roots to the rise of a panoply of new approaches to scientific inquiry beginning in the 1940's.
    [Show full text]
  • A Defense of Pure Connectionism
    City University of New York (CUNY) CUNY Academic Works All Dissertations, Theses, and Capstone Projects Dissertations, Theses, and Capstone Projects 2-2019 A Defense of Pure Connectionism Alex B. Kiefer The Graduate Center, City University of New York How does access to this work benefit ou?y Let us know! More information about this work at: https://academicworks.cuny.edu/gc_etds/3036 Discover additional works at: https://academicworks.cuny.edu This work is made publicly available by the City University of New York (CUNY). Contact: AcademicWorks@cuny.edu A DEFENSE OF PURE CONNECTIONISM by ALEXANDER B. KIEFER A dissertation submitted to the Graduate Faculty in Philosophy in partial fulfillment of the requirements for the degree of Doctor of Philosophy, The City University of New York 2019 © 2019 ALEXANDER B. KIEFER All Rights Reserved ii A Defense of Pure Connectionism By Alexander B. Kiefer This manuscript has been read and accepted by the Graduate Faculty in Philosophy in satisfaction of the dissertation requirement for the degree of Doctor of Philosophy. _________________________ _________________________________________ 1/22/2019 Eric Mandelbaum Chair of Examining Committee _________________________ _________________________________________ 1/22/2019 Nickolas Pappas Executive Officer Supervisory Committee: David Rosenthal Jakob Hohwy Eric Mandelbaum THE CITY UNIVERSITY OF NEW YORK iii ABSTRACT A Defense of Pure Connectionism by Alexander B. Kiefer Advisor: David M. Rosenthal Connectionism is an approach to neural-networks-based cognitive modeling that encompasses the recent deep learning movement in artificial intelligence. It came of age in the 1980s, with its roots in cybernetics and earlier attempts to model the brain as a system of simple parallel processors.
    [Show full text]
  • Intelligence Without Representation: a Historical Perspective
    systems Commentary Intelligence without Representation: A Historical Perspective Anna Jordanous School of Computing, University of Kent, Chatham Maritime, Kent ME4 4AG, UK; a.k.jordanous@kent.ac.uk Received: 30 June 2020; Accepted: 10 September 2020; Published: 15 September 2020 Abstract: This paper reflects on a seminal work in the history of AI and representation: Rodney Brooks’ 1991 paper Intelligence without representation. Brooks advocated the removal of explicit representations and engineered environments from the domain of his robotic intelligence experimentation, in favour of an evolutionary-inspired approach using layers of reactive behaviour that operated independently of each other. Brooks criticised the current progress in AI research and believed that removing complex representation from AI would help address problematic areas in modelling the mind. His belief was that we should develop artificial intelligence by being guided by the evolutionary development of our own intelligence and that his approach mirrored how our own intelligence functions. Thus, the field of behaviour-based robotics emerged. This paper offers a historical analysis of Brooks’ behaviour-based robotics approach and its impact on artificial intelligence and cognitive theory at the time, as well as on modern-day approaches to AI. Keywords: behaviour-based robotics; representation; adaptive behaviour; evolutionary robotics 1. Introduction In 1991, Rodney Brooks published the paper Intelligence without representation, a seminal work on behaviour-based robotics [1]. This paper influenced many aspects of research into artificial intelligence and cognitive theory. In Intelligence without representation, Brooks described his behaviour-based robotics approach to artificial intelligence. He highlighted a number of points that he considers fundamental in modelling intelligence.
    [Show full text]
  • THE INTELLECTUAL ORIGINS of the Mcculloch
    JHBS—WILEY RIGHT BATCH Top of ID Journal of the History of the Behavioral Sciences, Vol. 38(1), 3–25 Winter 2002 ᭧ 2002 John Wiley & Sons, Inc. (PHYSIO)LOGICAL CIRCUITS: THE INTELLECTUAL ORIGINS OF THE Base of 1st McCULLOCH–PITTS NEURAL NETWORKS line of ART TARA H. ABRAHAM This article examines the intellectual and institutional factors that contributed to the col- laboration of neuropsychiatrist Warren McCulloch and mathematician Walter Pitts on the logic of neural networks, which culminated in their 1943 publication, “A Logical Calculus of the Ideas Immanent in Nervous Activity.” Historians and scientists alike often refer to the McCulloch–Pitts paper as a landmark event in the history of cybernetics, and funda- mental to the development of cognitive science and artificial intelligence. This article seeks to bring some historical context to the McCulloch–Pitts collaboration itself, namely, their intellectual and scientific orientations and backgrounds, the key concepts that contributed to their paper, and the institutional context in which their collaboration was made. Al- though they were almost a generation apart and had dissimilar scientific backgrounds, McCulloch and Pitts had similar intellectual concerns, simultaneously motivated by issues in philosophy, neurology, and mathematics. This article demonstrates how these issues converged and found resonance in their model of neural networks. By examining the intellectual backgrounds of McCulloch and Pitts as individuals, it will be shown that besides being an important event in the history of cybernetics proper, the McCulloch– Pitts collaboration was an important result of early twentieth-century efforts to apply mathematics to neurological phenomena. ᭧ 2002 John Wiley & Sons, Inc.
    [Show full text]
  • What Is Systems Theory?
    What is Systems Theory? Systems theory is an interdisciplinary theory about the nature of complex systems in nature, society, and science, and is a framework by which one can investigate and/or describe any group of objects that work together to produce some result. This could be a single organism, any organization or society, or any electro-mechanical or informational artifact. As a technical and general academic area of study it predominantly refers to the science of systems that resulted from Bertalanffy's General System Theory (GST), among others, in initiating what became a project of systems research and practice. Systems theoretical approaches were later appropriated in other fields, such as in the structural functionalist sociology of Talcott Parsons and Niklas Luhmann . Contents - 1 Overview - 2 History - 3 Developments in system theories - 3.1 General systems research and systems inquiry - 3.2 Cybernetics - 3.3 Complex adaptive systems - 4 Applications of system theories - 4.1 Living systems theory - 4.2 Organizational theory - 4.3 Software and computing - 4.4 Sociology and Sociocybernetics - 4.5 System dynamics - 4.6 Systems engineering - 4.7 Systems psychology - 5 See also - 6 References - 7 Further reading - 8 External links - 9 Organisations // Overview 1 / 20 What is Systems Theory? Margaret Mead was an influential figure in systems theory. Contemporary ideas from systems theory have grown with diversified areas, exemplified by the work of Béla H. Bánáthy, ecological systems with Howard T. Odum, Eugene Odum and Fritj of Capra , organizational theory and management with individuals such as Peter Senge , interdisciplinary study with areas like Human Resource Development from the work of Richard A.
    [Show full text]
  • Analysis of Artificial Intelligence Hypothesis from the Perspective of Philosophy
    2019 4th International Social Sciences and Education Conference (ISSEC 2019) Analysis of Artificial Intelligence Hypothesis from the Perspective of Philosophy Zhang Yanan Yinchuan University of Energy, Yinchuan, 750000, China Keywords: Artificial intelligence; Philosophy of artificial intelligence; Formal; Intentionality Abstract: Artificial intelligence is closely related to philosophy, algorithms in artificial intelligence are closely related to logic, intentionality of intelligent machines is closely related to philosophy of mind and so on. However, the beginners of the development of artificial intelligence are concerned about technical problems, and are weak in philosophical thinking. With the development of artificial intelligence, it is necessary to think deeply about its philosophical problems to clarify its philosophical basis and clear away the technical obstacles of the development of artificial intelligence, so as to be conducive to the development of artificial intelligence technology and promote the progress of philosophical research. 1. Introduction In 1950 Alan Turing opened his classic book Computing Machinery and Intelligence by asking: can machines think? But he did not think such questions were worth discussing, for it was difficult to define thinking precisely, so Turing proposed his imitation game, the Turing test[1]. At the heart of the Turing test is whether a computer can behave in a way indistinguishable from a human, the first time that humans have thought behavioristically about the intelligence of a robot. John McCarthy in 19556 for the first time to use “AI (Artificial Intelligence, or AI, for short)” concept, he thought that Human Intelligence, Human Intelligence) as Artificial Intelligence concept to determine the premise, the ultimate goal is to make the computer program control simulating Human ability to solve problems of Artificial Intelligence, was born a to study how to use the machine to simulate the emerging discipline of Human Intelligence.
    [Show full text]
  • Connectionist Models of Cognition Michael S. C. Thomas and James L
    Connectionist models of cognition Michael S. C. Thomas and James L. McClelland 1 1. Introduction In this chapter, we review computer models of cognition that have focused on the use of neural networks. These architectures were inspired by research into how computation works in the brain and subsequent work has produced models of cognition with a distinctive flavor. Processing is characterized by patterns of activation across simple processing units connected together into complex networks. Knowledge is stored in the strength of the connections between units. It is for this reason that this approach to understanding cognition has gained the name of connectionism. 2. Background Over the last twenty years, connectionist modeling has formed an influential approach to the computational study of cognition. It is distinguished by its appeal to principles of neural computation to inspire the primitives that are included in its cognitive level models. Also known as artificial neural network (ANN) or parallel distributed processing (PDP) models, connectionism has been applied to a diverse range of cognitive abilities, including models of memory, attention, perception, action, language, concept formation, and reasoning (see, e.g., Houghton, 2005). While many of these models seek to capture adult function, connectionism places an emphasis on learning internal representations. This has led to an increasing focus on developmental phenomena and the origins of knowledge. Although, at its heart, connectionism comprises a set of computational formalisms,
    [Show full text]
  • Chapter 1 Chapter 2
    Notes Chapter 1 1. These are my translations of Pascal’s self-produced pamphlet, the original of which can be seen here: http://history-computer. com/Library/PascalLettreToChancelier.pdf with a transcrip- tion here: http://www.fourmilab.ch/babbage/pascal.html Chapter 2 1. Of course, this glosses over some important metaphysical questions that have only recently arisen in conjunction with computer science; what is it to talk of ‘identity’ when concerning the entities – programs, files, folders, sub-routines etc. – of computer science? In what sense is a later revision of ‘mybook.doc’ the same file as an earlier draft? For some discussion of these deep issues, see Smith (1996). 2. See http://aturingmachine.com/ for this particular device; the video footage there is breathtaking. See also http://legoofdoom. blogspot.com/ for examples of what one can do with this sort of machine. 3. Randall Munroe’s excellent webcomic “xkcd” even has a graphical map of optimal moves for playing the perfect exhaustive game: http:// xkcd.com/832/ 4. Hodges (2008) suggests that this reference to “mistakes” gives Turing a way out of the kinds of Gödel-style objections that we will consider in the next chapter. Indeed, it does; see page 70. 5. For substantial, but accessible, detail on the former, see Campbell et al. (2002), for material on both aspects written by an IBM insider, see Hsu (2002). 6. IBM Deep Blue FAQs: http://www.research.ibm.com/deepblue/ meet/html/d.3.3a.html#ai 7. Ibid. 8. There are many online versions that one can play around with.
    [Show full text]
  • Constructions, Chunking, and Connectionism: the Emergence Of
    Constructions, chunking, and connectionism p. 1 Constructions, chunking, and connectionism: The emergence of second language structure Nick C. Ellis University of Wales, Bangor n.ellis@bangor.ac.uk Revised chapter for Doughty & Long (Eds.) Handbook of Second Language Acquisition. Oxford: Blackwell. 11 April, 2001 Acknowledgment: Thanks to the Chester Language Development Group for feedback on a draft of this chapter and for pointing me at the right stuff in the first place. Constructions, chunking, and connectionism p. 2 0. Introduction and Overview Constructivist views of language acquisition hold that simple learning mechanisms operating in and across the human systems for perception, motor-action and cognition as they are exposed to language data as part of a communicatively-rich human social environment by an organism eager to exploit the functionality of language is enough to drive the emergence of complex language representations. The various tribes of constructivism, connectionists (Christiansen & Chater , 2001; Christiansen, Chater & Seidenberg, 1999; McClelland & Rumelhart, 1986; Levy, Bairaktaris, Bullinaria & Cairns, 1995; Plunkett, 1998), functional linguists (Bates & MacWhinney, 1981; MacWhinney & Bates, 1989), emergentists (Elman et al., 1996; MacWhinney 1999a), cognitive linguists (Croft & Cruse, 1999; Lakoff, 1987; Langacker 1987, 1991; Ungerer & Schmid, 1996), constructivist child language researchers (Slobin, 1997; Tomasello, 1992; 1995; 1998a, 2000), applied linguists influenced by chaos/ complexity theory (Larsen-Freeman, 1997), and computational linguists who explore statistical approaches to grammar (Bod, 1998; Jurafsky, 1996), all share a functional- developmental usage-based perspective on language. They emphasize the linguistic sign as a set of mappings between phonological forms and conceptual meanings or communicative intentions; thus their theories of language function, acquisition and neurobiology attempt to unite speakers, syntax and semantics, the signifiers and the signifieds.
    [Show full text]