Kees van Deemter: Not Exactly: In Praise of Vagueness Content Type Journal Article Pages 41-45 DOI 10.1007/s11023-011-9251-1 Authors Patrick Allo, Postdoctoral Fellow of the Research Foundation (FWO) Centre for Logic and Philosophy of Science, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium Journal Minds and Machines Online ISSN 1572-8641 Print ISSN 0924-6495 Journal Volume Volume 22 Journal Issue Volume 22, Number 1.

The essays in this volume represent the first steps by philosophers and artificial intelligence researchers toward explaining why it is necessary to add an ...

In its forty years of existence, Artificial Intelligence has suffered both from the exaggerated claims of those who saw it as the definitive solution of an ancestral dream — that of constructing an intelligent machine-and from its detractors, who described it as the latest fad worthy of quacks. Yet AI is still alive, well and blossoming, and has left a legacy of tools and applications almost unequalled by any other field-probably because, as the heir of Renaissance thought, it represents a (...) possible bridge between the humanities and the natural sciences, philosophy and neurophysiology, psychology and integrated circuits-including systems that today are taken for granted, such as the computer interface with mouse pointer and windows. This writing describes a few results of AI that have modified the scientific world, as well as the way a layman sees computers: thetechnology of programming languages, such asLISP-witness the unique excellence of academic departments that have contributed to them-thecomputing workstations-of which our modern PC is but a vulgarised descendant-theapplications to the educational field-e.g., the realisation of some ideas of genetic epistemology-and tointerdisciplinary philosophy-such as Hofstadter's associations between the arts and mathematics-and the use ofAI techniques in music and musicology. All this has led to a generalisation of AI towards Negrotti's overallTheory of the Artificial, which encompasses further specialisation such asartificial reality, artificial life, and applications ofneural networks among others. (shrink)

No philosopher has worked harder than Dan Dennett to set the possibility of machine mentality on firm philosophical footing. Dennett’s defense of this possibility has both a positive and a negative thrust. On the positive side, he has developed an account of mental activity that is tailor-made for the attribution of intentional states to purely mechanical contrivances, while on the negative side, he pillories as mystery mongering and skyhook grasping any attempts to erect barriers to the conception of machine mentality (...) by excavating gulfs to keep us “bona fide” thinkers apart from the rest of creation. While I think he’s “won” the rhetorical tilts with his philosophical adversaries, I worry that Dennett’s negative side sometimes gets the better of him, and that this obscures advances that can be made on the positive side of his program. In this paper, I show that Dennett is much too dismissive of original intentionality in particular, and that this notion can be put to good theoretical use after all. Though deployed to distinguish different grades of mentality, it can (and should) be incorporated into a philosophical account of the mind that is recognizably Dennettian in spirit. (shrink)

The dominant assumptions -- throughout contemporary philosophy, psychology, cognitive science, and artificial intelligence -- about the ontology underlying intentionality, and its core of representationality, is that of encodings -- some sort of informational or correspondence or covariation relationship between the represented and its representation that constitutes that representational relationship. There are many disagreements concerning details and implementations, and even some suggestions about claimed alternative ontologies, such as connectionism (though none that escape what I argue is the fundamental flaw in these (...) dominant approaches). One assumption that seems to be held by all, however, usually without explication or defense, is that there is _one_ singular underlying ontology to representationality. In this paper, I argue that there are in fact quite a number of ontologies that manifest representationality -- levels of representationality -- and that _none_ of them are the standard "manipulations of encoded symbols" ontology, nor any other variation on the informational approach to representation. Collectively, these multiple representational ontologies constitute a framework for cognition, whether natural or artificial. (shrink)

In an earlier paper in this journal[1], I sought to defend the claims that (1) substantial probability should be assigned to the hypothesis that machines will outsmart humans within 50 years, (2) such an event would have immense ramifications for many important areas of human concern, and that consequently (3) serious attention should be given to this scenario. Here, I will address a number of points made by several commentators.

Abstract: In the course of seeking an answer to the question "How do you know you are not a zombie?" Floridi (2005) issues an ingenious, philosophically rich challenge to artificial intelligence (AI) in the form of an extremely demanding version of the so-called knowledge game (or "wise-man puzzle," or "muddy-children puzzle")—one that purportedly ensures that those who pass it are self-conscious. In this article, on behalf of (at least the logic-based variety of) AI, I take up the challenge—which is to (...) say, I try to show that this challenge can in fact be met by AI in the foreseeable future. (shrink)

Andrew Boucher (1997) argues that ``parallel computation is fundamentally different from sequential computation'' (p. 543), and that this fact provides reason to be skeptical about whether AI can produce a genuinely intelligent machine. But parallelism, as I prove herein, is irrelevant. What Boucher has inadvertently glimpsed is one small part of a mathematical tapestry portraying the simple but undeniable fact that physical computation can be fundamentally different from ordinary, ``textbook'' computation (whether parallel or sequential). This tapestry does indeed immediately imply (...) that human cognition may be uncomputable. (shrink)

The dominant scientific and philosophical view of the mind – according to which, put starkly, cognition is computation – is refuted herein, via specification and defense of the following new argument: Computation is reversible; cognition isn't; ergo, cognition isn't computation. After presenting a sustained dialectic arising from this defense, we conclude with a brief preview of the view we would put in place of the cognition-is-computation doctrine.

This book argues that (1) AI will continue to produce machines with the capacity to pass stronger and stronger versions of the Turing Test but that (2) the "Person Building Project" (the attempt by AI and Cognitive Science to build a machine which is a person) will inevitably fail. The defense of (2) rests in large part on a refutation of the proposition that persons are automata -- a refutation involving an array of issues, from free will to Godel to (...) introspection to Searle and beyond. The defense of. (shrink)

Artificial intelligence research has foundered on the issue of representation. When intelligence is approached in an incremental manner, with strict reliance on interfacing to the real world through perception and action, reliance on representation disappears. In this paper we outline our approach to incrementally building complete intelligent Creatures. The fundamental decomposition of the intelligent system is not into independent information processing units which must interface with each other via representations. Instead, the intelligent system is decomposed into independent and parallel activity (...) producers which all interface directly to the world through perception and action, rather than interface to each other particularly much. The notions of central and peripheral systems evaporateeverything is both central and peripheral. Based on these principles we have built a very successful series of mobile robots which operate without supervision as Creatures in standard office environments. (shrink)

The emergence of electronic computers in the last thirty years has given rise to many interesting questions. Many of these questions are technical, relating to a machine’s ability to perform complex operations in a variety of circumstances. While some of these questions are not without philosophical interest, the one question which above all others has stimulated philosophical interest is explicitly non-technical and it can be expressed crudely as follows: Can a machine be said to think and, if so, in what (...) sense? The issue has received much attention in the scholarly journals with articles and arguments appearing in great profusion, some resolutely answering this question in the affirmative, some, equally resolutely, answering this question in the negative, and others manifesting modified rapture. While the ramifications of the question are enormous I believe that the issue at the heart of the matter has gradually emerged from the forest of complications. (shrink)

What happens when machines become more intelligent than humans? One view is that this event will be followed by an explosion to ever-greater levels of intelligence, as each generation of machines creates more intelligent machines in turn. This intelligence explosion is now often known as the “singularity”. The basic argument here was set out by the statistician I.J. Good in his 1965 article “Speculations Concerning the First Ultraintelligent Machine”: Let an ultraintelligent machine be defined as a machine that can far (...) surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an “intelligence explosion”, and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make. The key idea is that a machine that is more intelligent than humans will be better than humans at designing machines. So it will be capable of designing a machine more intelligent than the most intelligent machine that humans can design. So if it is itself designed by humans, it will be capable of designing a machine more intelligent than itself. By similar reasoning, this next machine will also be capable of designing a machine more intelligent than itself. If every machine in turn does what it is capable of, we should expect a sequence of ever more intelligent machines. This intelligence explosion is sometimes combined with another idea, which we might call the “speed explosion”. The argument for a speed explosion starts from the familiar observation that computer processing speed doubles at regular intervals. Suppose that speed doubles every two years and will do so indefinitely. Now suppose that we have human-level artificial intelligence 1 designing new processors. Then faster processing will lead to faster designers and an ever-faster design cycle, leading to a limit point soon afterwards. The argument for a speed explosion was set out by the artificial intelligence researcher Ray Solomonoff in his 1985 article “The Time Scale of Artificial Intelligence”.1 Eliezer Yudkowsky gives a succinct version of the argument in his 1996 article “Staring at the Singularity”: “Computing speed doubles every two subjective years of work.. (shrink)

High-level perception--”the process of making sense of complex data at an abstract, conceptual level--”is fundamental to human cognition. Through high-level perception, chaotic environmen- tal stimuli are organized into the mental representations that are used throughout cognitive pro- cessing. Much work in traditional artificial intelligence has ignored the process of high-level perception, by starting with hand-coded representations. In this paper, we argue that this dis- missal of perceptual processes leads to distorted models of human cognition. We examine some existing artificial-intelligence models--”notably (...) BACON, a model of scientific discovery, and the Structure-Mapping Engine, a model of analogical thought--”and argue that these are flawed pre- cisely because they downplay the role of high-level perception. Further, we argue that perceptu- al processes cannot be separated from other cognitive processes even in principle, and therefore that traditional artificial-intelligence models cannot be defended by supposing the existence of a --œrepresentation module--� that supplies representations ready-made. Finally, we describe a model of high-level perception and analogical thought in which perceptual processing is integrated with analogical mapping, leading to the flexible build-up of representations appropriate to a given context. (shrink)

The term \synthetic phenomenology" refers to: 1) any attempt to characterize the phenomenal states possessed, or modeled by, an artefact (such as a robot); or 2) any attempt to use an artefact to help specify phenomenal states (independently of whether such states are possessed by a naturally conscious being or an artefact). The notion of synthetic phenomenology is clari¯ed, and distinguished from some related notions. It is argued that much work in machine consciousness would bene¯t from being more cognizant of (...) the need for synthetic phenomenology of the ¯rst type, and of the possible forms it may take. It is then argued that synthetic phenomenology of the second type looks set to resolve some problems confronted by standard, nonsynthetic attempts at characterizing phenomenal states. An example of the second form of synthetic phenomenology is given. (shrink)

While the recent special issue of JCS on machine consciousness (Volume 14, Issue 7) was in preparation, a collection of papers on the same topic, entitled Artificial Consciousness and edited by Antonio Chella and Riccardo Manzotti, was published. The editors of the JCS special issue, Ron Chrisley, Robert Clowes and Steve Torrance, thought it would be a timely and productive move to have authors of papers in their collection review the papers in the Chella and Manzotti book, and include these (...) reviews in the special issue of the journal. Eight of the JCS authors (plus Uziel Awret) volunteered to review one or more of the fifteen papers in Artificial Consciousness; these individual reviews were then collected together with a minimal amount of editing to produce a seamless chapter-by-chapter review of the entire book. Because the number and length of contributions to the JCS issue was greater than expected, the collective review of Artificial Consciousness had to be omitted, but here at last it is. Each paper's review is written by a single author, so any comments made may not reflect the opinions of all nine of the joint authors! (shrink)

We first discuss Michael Dummett’s philosophy of mathematics and Robert Brandom’s philosophy of language to demonstrate that inferentialism entails the falsity of Church’s Thesis and, as a consequence, the Computational Theory of Mind. This amounts to an entirely novel critique of mechanism in the philosophy of mind, one we show to have tremendous advantages over the traditional Lucas-Penrose argument.

My claim is clear and unambiguous: no machine will pass a well-designed Turing Test unless we find some means of embedding it in lived social life. We have no idea how to do this but my argument, and all our evidence, suggests that it will not be a necessary condition that the machine have more than a minimal body. Exactly how minimal is still being worked out.

Alan Turing anticipated many areas of current research incomputer and cognitive science. This article outlines his contributionsto Artificial Intelligence, connectionism, hypercomputation, andArtificial Life, and also describes Turing's pioneering role in thedevelopment of electronic stored-program digital computers. It locatesthe origins of Artificial Intelligence in postwar Britain. It examinesthe intellectual connections between the work of Turing and ofWittgenstein in respect of their views on cognition, on machineintelligence, and on the relation between provability and truth. Wecriticise widespread and influential misunderstandings of theChurch–Turing thesis (...) and of the halting theorem. We also explore theidea of hypercomputation, outlining a number of notional machines thatcompute the uncomputable. (shrink)

Applied Artificial Intelligence, 21, 2007, pp. 259-279.

Forms of justification for inductive machine learning techniques are discussed and classified into four types. This is done with a view to introduce some of these techniques and their justificatory guarantees to the attention of philosophers, and to initiate a discussion as to whether they must be treated separately or rather can be viewed consistently from within a single framework.

This note corrects an error in the statement and proof of Propositions 9 and 10 of [C. Cross, Nonmonotonic inconsistency, Artificial Intelligence 149 (2) (2003) 161–178]. Both results turn out to depend on the postulate of Consistency Preservation.

Turing’s Imitation Game is often viewed as a test for theorised machines that could ‘think’ and/or demonstrate ‘intelligence’. However, contrary to Turing’s apparent intent, it can be shown that Turing’s Test is essentially a test for humans only. Such a test does not provide for theorised artificial intellects with human-like, but not human-exact, intellectual capabilities. As an attempt to bypass this limitation, I explore the notion of shifting the goal posts of the Turing Test, and related tests such as the (...) Total Turing Test, away from the exact imitation of human capabilities, and towards communication with humans instead. While the continued philosophical relevance of such tests is open to debate, the outcome is a different class of tests which are, unlike the Turing Test, immune to failure by means of sub-cognitive questioning techniques. I suggest that attempting to instantiate such tests could potentially be more scientifically and pragmatically relevant to some Artificial Intelligence researchers, than instantiating a Turing Test, due to the focus on producing a variety of goal directed outcomes through communicative methods, as opposed to the Turing Test’s emphasis on ‘fooling’ an Examiner. (shrink)

Philosophical Psychology, Volume 25, Issue 1, Page 149-153, February 2012.

This paper examines the hypothesis that analogies may play a role in the generation of new ideas that are built into new explanatory theories. Methods of theory construction by analogy, by failed analogy, and by modular components from several analogies are discussed. Two different analyses of analogy are contrasted: direct mapping (Mary Hesse) and shared abstraction (Michael Genesereth). The structure of Charles Darwin's theory of natural selection shows various analogical relations. Finally, an "abstraction for selection theories" is shown to be (...) the structure of a number of theories. (shrink)

The confusion between cognitive states and the content of cognitive states that gives rise to psychologism also gives rise to reverse psychologism. Weak reverse psychologism says that we can study cognitive states by studying content – for instance, that we can study the mind by studying linguistics or logic. This attitude is endemic in cognitive science and linguistic theory. Strong reverse psychologism says that we can generate cognitive states by giving computers representations that express the content of cognitive states and (...) that play a role in causing appropriate behaviour. This gives us strong representational, classical AI (REPSCAI), and I argue that it cannot succeed. This is not, as Searle claims in his Chinese Room Argument, because syntactic manipulation cannot generate content. Syntactic manipulation can generate content, and this is abundantly clear in the Chinese Room scenano. REPSCAI cannot succeed because inner content is not sufficient for cognition, even when the representations that carry the content play a role in generating appropriate behaviour. (shrink)

This article deals with the links between the enaction paradigm and artificial intelligence. Enaction is considered a metaphor for artificial intelligence, as a number of the notions which it deals with are deemed incompatible with the phenomenal field of the virtual. After explaining this stance, we shall review previous works regarding this issue in terms of artificial life and robotics. We shall focus on the lack of recognition of co-evolution at the heart of these approaches. We propose to explicitly integrate (...) the evolution of the environment into our approach in order to refine the ontogenesis of the artificial system, and to compare it with the enaction paradigm. The growing complexity of the ontogenetic mechanisms to be activated can therefore be compensated by an interactive guidance system emanating from the environment. This proposition does not however, resolve that of the relevance of the meaning created by the machine (sense-making). Such reflections lead us to integrate human interaction into this environment in order to construct relevant meaning in terms of participative artificial intelligence. This raises a number of questions with regards to setting up an enactive interaction. The article concludes by exploring a number of issues, thereby enabling us to associate current approaches with the principles of morphogenesis, guidance, the phenomenology of interactions and the use of minimal enactive interfaces in setting up experiments which will deal with the problem of artificial intelligence in a variety of enaction-based ways. (shrink)

The emotions have been one of the most fertile areas of study in psychology, neuroscience, and other cognitive disciplines. Yet as influential as the work in those fields is, it has not yet made its way to the desks of philosophers who study the nature of mind. Passionate Engines unites the two for the first time, providing both a survey of what emotions can tell us about the mind, and an argument for how work in the cognitive disciplines can help (...) us develop new ways of understanding the mind as a whole. Craig DeLancey shows that our best philosophical and scientific understanding of the emotions provides essential insights on key issues in the philosophy of mind and artificial intelligence: intentionality, aesthetics, rationality, action theory, moral psychology, consciousness, ontology and autonomy. He provides an accessible overview of the science of emotion, explaining with minimal jargon the technical issues that arise. The book also offers new ways to understand the mind, suggesting that it is autonomy--and not cognition--that should be the core problem of the philosophy of mind, cognitive science, and artificial intelligence. DeLancey argues that the philosophy of mind has been held back by an impoverished view of naturalism, and that a proper appreciation of the complexity of the sciences of mind, readily demonstrated by the science of emotion, will overcome this. Passionate Engines provides a unique, contemporary view of the link between science and philosophy, offering a bold new way of looking at the mind for scholars in a range of disciplines. Its accessible and refreshing approach will appeal to philosophers, psychologists, computer scientists, others in the cognitive disciplines, and lay people interested in the mind. (shrink)

For two hundred years materialist philosophers have argued that man is some sort of machine. The claim began with French materialists of the Enlightenment such as Pierre Cabanis, Julien La Mettrie, and Baron d’Holbach (La Mettrie even wrote a book titled Man the Machine). Likewise contemporary materialists like Marvin Minsky, Daniel Dennett, and Patricia Churchland claim that the motions and modifications of matter are sufficient to account for all human experiences, even our interior and cognitive ones. Whereas the Enlightenment philosophes (...) might have thought of humans in terms of gear mechanisms and fluid flows, contemporary materialists think of humans in terms of neurological systems and computational devices. The idiom has been updated, but the underlying impulse to reduce mind to matter remains unchanged. (shrink)

The first robot homicide was committed in 1981, according to my files. I have a yellowed clipping dated 12/9/81 from the Philadelphia Inquirer--not the National Enquirer--with the headline: Robot killed repairman, Japan reports The story was an anti-climax: at the Kawasaki Heavy Industries plant in Akashi, a malfunctioning robotic arm pushed a repairman against a gearwheel-milling machine, crushing him to death. The repairman had failed to follow proper instructions for shutting down the arm before entering the workspace. Why, indeed, had (...) this industrial accident in Japan been reported in a Philadelphia newspaper? Every day somewhere in the world a human worker is killed by one machine or another. The difference, of course, was that in the public imagination at least, this was no ordinary machine; this was a robot, a machine that might have a mind, might have evil intentions, might be capable not just of homicide but of murder. (shrink)

The field of Artificial Intelligence has produced so many new concepts--or at least vivid and more structured versions of old concepts--that it would be surprising if none of them turned out to be of value to students of animal behavior. Which will be most valuable? I will resist the temptation to engage in either prophecy or salesmanship; instead of attempting to answer the question: "How might Artificial Intelligence inform the study of animal behavior?" I will concentrate on the obverse: "How (...) might the study of animal behavior inform research in Artificial Intelligence?". (shrink)

It is possible to survey humankind and be proud, even to smile, for we accomplish great things. Art and science are two notable worthy human accomplishments. Consonant with art and science are some of the ways we treat each other. Sacrifice and heroism are two admirable human qualities <span class='Hi'>that</span> pervade human interaction. But, as everyone knows, all this goodness is more than balanced by human depravity. Moral corruption infests our being. Why?

In mid-May of 2001, I attended a fascinating workshop at Cold Spring Harbor Labs. The conference was held at the lab's Banbury Center, an elegant mansion and its beautiful surrounding estate, located on Banbury Lane, in the outskirts of Lloyd Harbor, overlooking the north shore of Long Island in New York. The estate was formerly owned by Charles Sammis Robertson. In 1976, Robertson donated his estate, and an endowment for its upkeep, to the Lab. The donation included the Robertson's mansion, (...) now called Robertson House, and a large, seven-car garage that would become the actual conference center. The Center was opened on Sunday, June 14, 1977, by Francis Crick who gave a talk entitled "How Scientists Work." For us, Banbury was an idyllic location with great food where we could talk about the most difficult problem in all o f science: what is the nature and cause of consciousness? (shrink)

A recent issue of Time magazine (March 29, 1999) was devoted to the twenty greatest "thinkers" of the twentieth century -- scientists, inventors, and engineers. There is one interesting omission: there are no cognitive psychologists or cognitive scientists. (Cognitive science is an amalgam of cognitive, neuro, and developmental psychology, artificial intelligence, philosophy, linguistics, biology, and anthropology.) Freud is there, to be sure. But, while he was very influential, it is not even clear that he was a scientist, let alone a (...) cognitive scientist. There are those who regard Freud as somewhere between incompetent and a charlatan (see Glymour, 1988). In any case, though Freud's positive proposal for the mind's architecture -- namely, that it contains the unconscious -- seems correct as far as it goes, it does appear that all the details are wrong. For example: (1) there is a lot more to the mind than the mere unconscious; (2) it is doubtful that there is an id, ego, and superego; (3) most dreams may very likely be meaningless; and (4) human motivation, even unconscious motivation, is about a lot more than sex. In the end, because he was most interested in certain kinds of human mental malfunctioning, Freud is probably best thought of as a physician, a proponent and early explorer of human mental health; he was not an experimental cognitive psychologist. (shrink)

Good sciences have good metaphors. Indeed, good sciences are good because they have good metaphors. AI could use more good metaphors. In this editorial, I would like to propose a new metaphor to help us understand intelligence. Of course, whether the metaphor is any good or not depends on whether it actually does help us. (What I am going to propose is not something opposed to computationalism -- the hypothesis that cognition is computation. Noncomputational metaphors are in vogue these days, (...) and to date they have all been equally plausible and equally successful. And, just to be explicit, I do not mean “IQ” by “intelligence.” I am using “intelligence” in the way AI uses it: as a semi-techical term referring to a general property of all intelligent systems, animal (including humans), or machine, alike.). (shrink)

Under the Superstition Mountains in central Arizona toil those who would rob humankind o f its humanity. These gray, soulless monsters methodically tear away at our meaning, our subjectivity, our essence as transcendent beings. With each advance, they steal our freedom and dignity. Who are these denizens of darkness, these usurpers of all that is good and holy? None other than humanity’s arch-foe: The Cognitive Scientists -- AI researchers, fallen philosophers, psychologists, and other benighted lovers of computers. Unless they are (...) stopped, humanity -- you and I -- will soon be nothing but numbers and algorithms locked away on magnetic tape. (shrink)

Concern over the nature of AI is, for the tastes many AI scientists, probably overdone. In this they are like all other scientists. Working scientists worry about experiments, data, and theories, not foundational issues such as what their work is really about or whether their discipline is methodologically healthy. However, most scientists aren’t in a field that is approximately fifty years old. Even relatively new fields such as nonlinear dynamics or branches of biochemistry are in fact advances in older established (...) sciences and are therefore much more settled. Of course, by stretching things, AI can be said to have a history reaching back t o Charles Babbage, and possibly back beyond that to Leibnitz. However, all of that is best viewed as prelude. AI’s history is punctuated with the invention of the computer (and, if one wants t o stretch our history back to the 1930s, the development of the notion of computation by Turing, Church, and others). Hence, AI really began (or began in earnest) sometime in the late 1940s or early 1950s (some mark the conference a t Dartmouth in the summer of 1957 as the moment of our birth). And since those years we simply have not had time to settle into a routine science attacking reasonably well understood questions (for example, many of the questions some of us regard as supreme are regarded by others as inconsequential or mere excursions). (shrink)

It is argued that the notion of Umwelt is relevant for contemporary discussions within theoretical biology, biosemiotics, the study of Artificial Life, Autonomous Systems Research and philosophy of biology. Focus is put on the question of whether an artificial creature can have a phenomenal world in the sense of the Umwelt notion of Jakob von Uexküll, one of the founding figures of biosemiotics. Rather than vitalism, Uexküll's position can be interpreted as a version of qualitative organicism. A historical sketch of (...) Autonomous Systems Research (ASR) is presented to show its theoretical roots and fruitful opposition to traditional AI style robotics. It is argued that these artificial systems are only partly 'situated' because they do not in the full sense of the word experience an Umwelt. A deeper understanding of truly situated autonomous systems as being a kind of complex selforganizing semiotic agents with emergent qualitative properties must be gained, not only from the broad field of theoretical biology, but also from the perspective of biosemiotics in the Uexküll tradition. The paper is thus an investigation of a new notion of autonomy that includes a qualitative aspect of the organism. This indicates that the Umwelt concept is not reducible to purely functional notions. (shrink)

I critically examine some provocative arguments that John Searle presents in his book The Rediscovery of Mind to support the claim that the syntactic states of a classical computational system are "observer relative" or "mind dependent" or otherwise less than fully and objectively real. I begin by explaining how this claim differs from Searle's earlier and more well-known claim that the physical states of a machine, including the syntactic states, are insufficient to determine its semantics. In contrast, his more recent (...) claim concerns the syntax, in particular, whether a machine actually has symbols to underlie its semantics. I then present and respond to a number of arguments that Searle offers to support this claim, including whether machine symbols are observer relative because the assignment of syntax is arbitrary, or linked to universal realizability, or linked to the sub-personal interpretive acts of a homunculus, or linked to a person's consciousness. I conclude that a realist about the computational model need not be troubled by such arguments. Their key premises need further support. (shrink)

M. Gams et al. (Eds.) IOS Press, "Strong AI": an Adolescent Disorder Donald Michie Professor Emeritus, University of Edinburgh, UK Associate ...

Do computers have beliefs? I argue that anyone who answers in the affirmative holds a view that is incompatible with what I shall call the commonsense approach to the propositional attitudes. My claims shall be two. First,the commonsense view places important constraints on what can be acknowledged as a case of having a belief. Second, computers – at least those for which having a belief would be conceived as having a sentence in a belief box – fail to satisfy some (...) of these constraints. This second claim can best be brought out in the context of an examination of the idea of computer self-knowledge and self-deception, but the conclusion is perfectly general: the idea that computers are believers, like the idea that computers could have self-knowledge or be self-deceived, is incompatible with the commonsense view. The significance of the argument lies in the choice it forces on us: whether to revise our notion of belief so as to accommodate the claim that computers are believers, or to give up on that claim so as to preserve our pretheoretic notion of the attitudes. We cannot have it both ways. (shrink)

he question of the possibility of artificial consciousness is both very new and very old. It is new in the context of contemporary cognitive science and its concern with whether a machine can be conscious; it is old in the form of the mind/body problem and the "other minds" problem of philosophy. Contemporary enthusiasts proceed at their peril if they ignore or are ignorant of the false starts and blind alleys that the older thinkers have painfully worked through.

Recent studies of Wittgenstein's later writing have made clear that they stand as a defence of two main ideas: that scepticism about the possibility of interpersonal discussions about our subjective feelings is misplaced and, as a seemingly startling corollary; that a mind state account of most 'mental activities' is incoherent. This leads to a great emphasis on skills and practices which, a fortiori, are definable only relationally, by reference to targets. In this paper I try to show that the 'computer' (...) analogue for the mind f ails on both of Wittgenstein's dimensions. There are no physiognomic language games in the computer centre, while the 'target' aspect of skill and practice concepts ties them in to a wholly human world. (shrink)

Hauser considers John Searle's attempt to distinguish acts from movements. On Searle's account, the difference between me raising my arm and my arm's just going up (e.g., if you forcibly raise it), is the causal involvement of my intention to raise my arm in the former, but not the latter, case. Yet, we distinguish a similar difference between a robot's raising its arm and its robot arm just going up (e.g., if you manually raise it). Either robots are rightly credited (...) with intentions or it's not intention that distinguishes action from mere movement. In either case acts are attributable to robots. Since the truth of such attributions depends not on the speaker's "intentional stance" but on "intrinsic" features of the things they are not merely figurative "as if" attributions. Gunderson allows that internally propelled programmed devices (Hauser Robots) do act but denies that they have the mental properties such acts seem to indicate. Rather, given our intuitive conviction that these machines lack consciousness, such performances evidence the dementalizability of acts.Hauser replies that the performances in question provide prima facie warrant for attributions of mental properties that considerations of consciousness are insufficient to override. (shrink)

My pocket calculator (Cal) has certain arithmetical abilities: it seems Cal calculates. That calculating is thinking seems equally untendentious. Yet these two claims together provide premises for a seemingly valid syllogism whose conclusion -- Cal thinks -- most would deny. I consider several ways to avoid this conclusion, and find them mostly wanting. Either we ourselves can't be said to think or calculate if our calculation-like performances are judged by the standards proposed to rule out Cal; or the standards -- (...) e.g., autonomy and self-consciousness -- make it impossible to verify whether anything or anyone (save myself) meets them. While appeals to the intentionality of thought or the unity of minds provide more credible lines of resistance, available accounts of intentionality and mental unity are insufficiently clear and warranted to provide very substantial arguments against Cal's title to be called a thinking thing. Indeed, considerations favoring granting that title are more formidable than generally appreciated. (shrink)

It will be found that the great majority, given the premiss that thought is not distinct from corporeal motion, take a much more rational line and maintain that thought is the same in the brutes as in us, since they observe all sorts of corporeal motions in them, just as in us. And they will add that the difference, which is merely one of degree, does not imply any essential difference; from this they will be quite justified in concluding that, (...) although there may be a smaller degree of reason in the beasts than there is in us, the beasts possess minds which are of exactly the same type as ours. (Descartes 1642: 288–289.). (shrink)

Views into the Chinese Room: New Essays on Searle and Artificial Intelligence Content Type Journal Article Pages 97-106 DOI 10.1007/BF03210002 Authors Reese M. Heitner, Department of Psychology, University of British Columbia, V6T 1Z2 Vancouver, BC, Canada Journal Minds and Machines Online ISSN 1572-8641 Print ISSN 0924-6495 Journal Volume Volume 15 Journal Issue Volume 15, Number 1.

Artificial Intelligence has become big business in the military and in many industries. In spite of this growth there still remains no consensus about what AI really is. The major factor which seems to be responsible for this is the lack of agreement about the relationship between behavior and intelligence. In part certain ethical concerns generated from saying who, what and how intelligence is determined may be facilitating this lack of agreement.

This paper revisits the often debated question Can machines think? It is argued that the usual identification of machines with the notion of algorithm has been both counter-intuitive and counter-productive. This is based on the fact that the notion of algorithm just requires an algorithm to contain a finite but arbitrary number of rules. It is argued that intuitively people tend to think of an algorithm to have a rather limited number of rules. The paper will further propose a modification (...) of the above mentioned explication of the notion of machines by quantifying the length of an algorithm. Based on that it appears possible to reconcile the opposing views on the topic, which people have been arguing about for more than half a century. (shrink)

This paper describes a study of the effects of two acts of social intelligence, namely mimicry and social praise, when used by an artificial social agent. An experiment ( N = 50) is described which shows that social praise—positive feedback about the ongoing conversation—increases the perceived friendliness of a chat-robot. Mimicry—displaying matching behavior—enhances the perceived intelligence of the robot. We advice designers to incorporate both mimicry and social praise when their system needs to function as a social actor. Different ways (...) of implementing mimicry and praise by artificial social actors in an ambient persuasive scenario are discussed. (shrink)

We confront the following popular views: that mind or life are algorithms; that thinking, or more generally any process other than computation, is computation; that anything other than a working brain can have thoughts; that anything other than a biological organism can be alive; that form and function are independent of matter; that sufficiently accurate simulations are just as genuine as the real things they imitate; and that the Turing test is either a necessary or sufficient or scientific procedure (...) for evaluating whether or not an entity is intelligent. Drawing on the distinction between activities and tasks, and the fundamental scientific principles of ontological lawfulness, epistemological realism, and methodological skepticism, we argue for traditional scientific materialism of the emergentist kind in opposition to the functionalism, behaviourism, tacit idealism, and merely decorative materialism of the artificial intelligence and artificial life communities. (shrink)

This paper explores Church's Thesis and related claims madeby Turing. Church's Thesis concerns computable numerical functions, whileTuring's claims concern both procedures for manipulating uninterpreted marksand machines that generate the results that these procedures would yield. Itis argued that Turing's claims are true, and that they support (the truth of)Church's Thesis. It is further argued that the truth of Turing's and Church'sTheses has no interesting consequences for human cognition or cognitiveabilities. The Theses don't even mean that computers can do as much (...) as peoplecan when it comes to carrying out effective procedures. For carrying out aprocedure is a purposive, intentional activity. No actual machine does, orcan do, as much. (shrink)

The objective of research in the foundations of Al is to explore such basic questions as: What is a theory in Al? What are the most abstract assumptions underlying the competing visions of intelligence? What are the basic arguments for and against each assumption? In this essay I discuss five foundational issues: (1) Core Al is the study of conceptualization and should begin with knowledge level theories. (2) Cognition can be studied as a disembodied process without solving the symbol grounding (...) problem. (3) Cognition is nicely described in propositional terms. (4) We can study cognition separately from learning. (5) There is a single architecture underlying virtually all cognition. I explain what each of these implies and present arguments from both outside and inside Al why each has been seen as right or wrong. (shrink)

This paper addresses the problem of human–computer interactions when the computer can interpret and express a kind of human-like behavior, offering natural communication. A conceptual framework for incorporating emotions with rationality is proposed. A model of affective social interactions is described. The model utilizes the SAIBA framework, which distinguishes among several stages of processing of information. The SAIBA framework is extended, and a model is realized in human behavior detection, human behavior interpretation, intention planning, attention tracking behavior planning, and behavior (...) realization components. Two models of incorporating emotions with rationality into a virtual artifact are presented. The first one uses an implicit implementation of emotions. The second one has an explicit realization of a three-layered model of emotions, which is highly interconnected with other components of the system. Details of the model with implicit implementation of emotional behavior are shown as well as evaluation methodology and results. Discussions about the extended model of an agent are given in the final part of the paper. (shrink)

According to the conventional wisdom, Turing (1950) said that computing machines can be intelligent. I don''t believe it. I think that what Turing really said was that computing machines –- computers limited to computing –- can only fake intelligence. If we want computers to become genuinelyintelligent, we will have to give them enough initiative (Turing, 1948, p. 21) to do more than compute. In this paper, I want to try to develop this idea. I want to explain how giving computers (...) more ``initiative'''' can allow them to do more than compute. And I want to say why I believe (and believe that Turing believed) that they will have to go beyond computation before they can become genuinely intelligent. (shrink)

Since there isn't a computer that seems conscious at this time, the idea of machine consciousness is supported by thought experiments. Here's one old chestnut: "What if you replaced your neurons one by one with neuron sized and shaped substitutes made of silicon chips that perfectly mimicked the chemical and electric functions of the originals? If you just replaced one single neuron, surely you'd feel the same. As you proceed, as more and more neurons are replaced, you'd stay conscious. Why (...) wouldn't you still be conscious at the end of the process, when you'd reside in a brain shaped glob of silicon? And why couldn't the resulting replacement brain have been manufactured by some other means?". (shrink)

Nicholas Agar has recently argued that it would be irrational for future human beings to choose to radically enhance themselves by uploading their minds onto computers. Utilizing Searle’s argument that machines cannot think, he claims that uploading might entail death. He grants that Searle’s argument is controversial, but he claims, so long as there is a non-zero probability that uploading entails death, uploading is irrational. I argue that Agar’s argument, like Pascal’s wager on which it is modelled, fails, because the (...) principle that we (or future agents) ought to avoid actions that might entail death is not action guiding. Too many actions fall under its scope for the principle to be plausible. I also argue that the probability that uploading entails death is likely to be lower than Agar recognizes. (shrink)

Over three decades ago, in a brief but provocative essay, Paul Ziff argued for the thesis that robots cannot have feelings because they are "mechanisms, not organisms, not living creatures. There could be a broken-down robot but not a dead one. Only living creatures can literally have feelings."[i] Since machines are not living things they cannot have feelings.