L'informatique se définissant comme le traitement rationnel de l'information par machine automatique et l'intelligence se caractérisant par une même capacité de traitement rationnel, il était inévitable que l'on songe à associer l'intelligence au traitement automatique de l'information. C'est ce qu'a fait John McCarthy en forgeant le terme d'intelligence artificielle. Par «intelligence artificielle» on peut vouloir exprimer l'ambition de1. Recréer, transformer ou développer l'intelligence artificiellement2. Simuler l'intelligence en la reconstituant dans des modéles imitant certains aspects de notre intelligence dite naturelle.

If a brain is duplicated so that there are two brains in identical states, are there then two numerically distinct phenomenal experiences or only one? There are two, I argue, and given computationalism, this has implications for what it is to implement a computation. I then consider what happens when a computation is implemented in a system that either uses unreliable components or possesses varying degrees of parallelism. I show that in some of these cases there can be, in a (...) deep and intriguing sense, a fractional (non-integer) number of qualitatively identical phenomenal experiences. This, in turn, has implications for what lessons one should draw from neural replacement scenarios such as Chalmers. (shrink)

Symbol Grounding Problem is commonly considered one of the central challenges in the philosophy of artificial intelligence as its resolution is deemed necessary for bridging the gap between simple data processing and understanding of meaning and language. SGP has been addressed on numerous occasions with varying results, all resolution attempts having been severely, but for the most part justifiably, restricted by the Zero Semantic Commitment Condition. A further condition that demands explanatory power in terms of machine-to-human communication is the Non-Stupidity (...) Condition that demands an SG approach to be able to account for plausibility of higher-level language use and understanding, such as pragmatics. In this article, we undertake the endeavour of attempting to explain how merging certain early requirements for SG, such as embodiment, environmental interaction, and compliance with the Z-Condition with symbol emergence rather than direct attempts at symbol grounding can help emulate human language acquisition. Along with the presumption that mind and language are both symbolic and computational, we argue that some rather abstract aspects of language can be logically formalised and finally, that this melange of approaches can yield the explanatory power necessary to satisfy the Non-Stupidity Condition without breaking any previous conditions. (shrink)

If the Trade Descriptions Act were applied to academic labels, cognitive scientists would be in trouble. For what they do is much wider than the name suggests—and wider, too, than most philosophers assume. They give you more for your money than you may have expected.

What is creativity? One new idea may be creative, whereas another is merely new: What's the difference? And how is creativity possible? These questions about human creativity can be answered, at least in outline, using computational concepts. There are two broad types of creativity, improbabilist and impossibilist. Improbabilist creativity involves novel combinations of familiar ideas. A deeper type involves METCS: the mapping, exploration, and transformation of conceptual spaces. It is impossibilist, in that ideas may be generated which – with respect (...) to the particular conceptual space concerned – could not have been generated before. The more clearly conceptual spaces can be defined, the better we can identify creative ideas. Defining conceptual spaces is done by musicologists, literary critics, and historians of art and science. Humanist studies, rich in intuitive subtleties, can be complemented by the comparative rigour of a computational approach. Computational modelling can help to define a space, and to show how it may be mapped, explored, and transformed. Impossibilist creativity can be thought of in “classical” Al terms, whereas connectionism illuminates improbabilist creativity. Most Al models of creativity can only explore spaces, not transform them, because they have no self-reflexive maps enabling them to change their own rules. A few, however, can do so. A scientific understanding of creativity does not destroy our wonder at it, nor does it make creative ideas predictable. Demystification does not imply dehumanization. (shrink)

It’s sometimes said, and even more often assumed, that life is necessary for mind. If so, and if A-Life promises to throw light on the nature of life as such, then A-Life is in principle highly relevant to the philosophy of mind and cognitive science. However, very few philosophers have attempted to argue for the relation between life and mind. It’s usually taken for granted. Even those (mostly in the Continental tradition, including some with a following in A-Life) who have (...) insisted on the linkage have stated it rather than justified it. If an evolutionary account of intentionality is acceptable, then perhaps biological life ‘makes room’ for mind. But that claim is problematic, since it’s not clear that the type of self-organization involved in life-as-such must necessarily include evolution. Even if it does, it’s a further step to show that life is strictly necessary for mind. (shrink)

Metabolism is a criterion of life. Three senses are distinguished. The weakest allows strong A-Life: virtual creatures having physical existence in computer electronics, but not bodies, are classes as 'alive'. The second excludes strong A-Life but allows that some non-biochemical A-Life robots could be classed as alive. The third, which stresses the body's self-production by energy budgeting and self-equilibrating energy exchanges of some (necessary) complexity, excludes both strong A-Life and living non-biochemical robots.

We consider several puzzles of bounded rationality. These include the Allais- and Ellsberg paradox, the disjunction effect, and related puzzles. We argue that the present account of quantum cognition—taking quantum probabilities rather than classical probabilities—can give a more systematic description of these puzzles than the alternate treatments in the traditional frameworks of bounded rationality. Unfortunately, the quantum probabilistic treatment does not always provide a deeper understanding and a true explanation of these puzzles. One reason is that quantum approaches introduce additional (...) parameters which possibly can be fitted to empirical data but which do not necessarily explain them. Hence, the phenomenological research has to be augmented by responding to deeper foundational issues. In this article, we make the general distinction between foundational and phenomenological research programs, explaining the foundational issue of quantum cognition from the perspective of operational realism. This framework is motivated by assuming partial Boolean algebras. They are combined into a uniform system via a mechanism preventing the simultaneous realization of perspectives. Gleason’s theorem then automatically leads to a distinction between probabilities that are defined by pure states and probabilities arising from the statistical mixture of pure states. This formal distinction relates to the conceptual distinction between risk and ignorance. Another outcome identifies quantum aspects in dynamic macro-systems using the framework of symbolic dynamics. Finally, we discuss several ideas that are useful for justifying complementarity in cognitive systems. (shrink)

This is my first publication of the distinction between phenomenal consciousness and access consciousness, though not using quite those terms. It ends with this: "The upshot is this: If Searle is using the access sense of "consciousness," his argument doesn't get to first base. If, as is more likely, he intends the what-it-is-like sense, his argument depends on assumptions about issues that the cognitivist is bound to regard as deeply unsettled empirical questions." Searle replies: "He refers to what he calls (...) an "access sense of consciousness." On my account there is no such sense.". (shrink)

In addition to his famous Chinese Room argument, John Searle has posed a more radical problem for views on which minds can be understood as programs. Even his wall, he claims, implements the WordStar program according to the standard definition of implementation because there is some ‘‘pattern of molecule movements’’ that is isomorphic to the formal structure of WordStar. Program implementation, Searle charges, is merely observer-relative and thus not an intrinsic feature of the world. I argue, first, that analogous charges (...) involving other concepts (motion and meaning) lead to consequences no one accepts. Second, I show that Searle’s treatment of computation is incoherent, yielding the consequence that nothing computes anything: even our standard personal computers fail to run any programs on this account. I propose an alternative account, one that accords with the way engineers, programmers, and cognitive scientists use the concept of computation in their empirical work. This alternative interpretation provides the basis of a philosophical analysis of program implementation, one that may yet be suitable for a computational theory of the mind. (shrink)

Artificial Neural Networks have reached “grandmaster” and even “super-human” performance across a variety of games, from those involving perfect information, such as Go, to those involving imperfect information, such as “Starcraft”. Such technological developments from artificial intelligence (AI) labs have ushered concomitant applications across the world of business, where an “AI” brand-tag is quickly becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong—an autonomous vehicle crashes, a chatbot exhibits “racist” behavior, 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, as Pearl suggests, 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 theNew 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 their assembly innately grasp three basic concepts: time, space, and causality.” In this paper, foregrounding what in 1949 Gilbert Ryle termed “a category mistake”, I will offer an alternative explanation for AI errors; it is not so much that AI machinery cannot “grasp” causality, but that AI machinery (qua computation) cannot understand anything at all. (shrink)

When experimental philosophers carry out studies on thought experiments, some participants are excluded based on certain exclusion criteria, mirroring standard social science vignette methodology. This involves excluding people that do not pay attention or who miscomprehend the scenario presented in thought experiments. However, experimental philosophy studies sometimes exclude an alarmingly high number of participants. We argue that this threatens the external and internal validity of the conclusions being drawn and we show how a simple visualization of thought experiments can reduce (...) exclusion rates significantly. Furthermore, we argue that focus should not merely be on how many are excluded, but also why they are excluded, and we highlight the role of comprehension questions in this regard. Philosophical thought experiments often rely on the acceptance of certain key premises that may be regarded contestable, and asking comprehension questions involving such key assumptions could be problematic as that may result in some participants being inadvertently excluded from the study, potentially creating a selection bias. (shrink)

The article discusses the case of Microsoft's Twitter chatbot Tay that “turned into a Nazi” after less than 24 hours from its release on the Internet. The first section presents a brief recapitulation of Alan Turing's proposal for a test for artificial intelligence and the way it influenced subsequent discussions in the philosophy of mind. In the second section, I offer a few arguments appealing for caution regarding the identification of an accomplished chatbot as a thinking being. These are motivated (...) principally by Ludwig Wittgenstein's discussions of mind and soul and by some Wittgensteinian philosophers' criticisms of AI endeavours. I will try to show that it is very difficult, if not impossible, to make sense of AIs such as chatbots as thinking beings, rather independently of their technical perfection and accomplishment. In the third section, the case of the “Nazi chatbot” Tay will offer me material for some light to be shed on the peculiar character of our interconnected concepts of thinking, soul and person and on the importance of their further ramified connections. (shrink)

Ned Block. Psychologism and behaviorism. Philosophical Review, 90, 5-43.) argued that a behaviorist conception of intelligence is mistaken, and that the nature of an agent's internal processes is relevant for determining whether the agent has intelligence. He did that by describing a machine which lacks intelligence, yet can answer questions put to it as an intelligent person would. The nature of his machine's internal processes, he concluded, is relevant for determining that it lacks intelligence. I argue against Block that it (...) is not the nature of its processes but of its linguistic behavior which is responsible for his machine's lack of intelligence. As I show, not only has Block failed to establish that the nature of internal processes is conceptually relevant for psychology, in fact his machine example actually supports some version of behaviorism. As Wittgenstein has maintained, as far as psychology is concerned, there may be chaos inside. (shrink)

Searle's Chinese Room was supposed to prove that computers can't understand: the man in the room, following, like a computer, syntactical rules alone, though indistinguishable from a genuine Chinese speaker, doesn't understand a word. But such a room is impossible: the man won't be able to respond correctly to questions like What is the time?, even though such an ability is indispensable for a genuine Chinese speaker. Several ways to provide the room with the required ability are considered, and it (...) is concluded that for each of these the room will have understanding. Hence, Searle's argument is invalid. (shrink)

The object of the study is a special branch of scientific knowledge, formed at the intersection of interests of various humanitarian disciplines – the philosophy of artificial intelligence; the subject of the study is the problems it considers. The author identifies the main traditional questions that the philosophy of artificial intelligence tried to answer earlier. Such questions in the earlier stages of the development of science were concentrated around the possibility of artificial intelligence to become aware of itself, learn to (...) think, feel and create like a person. Scientists since the 1960s interested in the problems of equality of natural and artificial intelligence, the ability of a computer to show benevolence or hostility towards its creators. The research was carried out using a historical and philosophical analysis of issues in the philosophy of artificial intelligence, the method of interpretive synthesis, etc. Systemic-structural, dialectical, cultural-historical, value-based, interdisciplinary approaches to the problem were used. The scientific novelty of the research lies in identifying the problems of the philosophy of artificial intelligence that are relevant at this stage of development of this scientific field. It is noted that the dynamics of the philosophy of artificial intelligence are significantly influenced by the fact that “strong” artificial intelligence has not been created for several decades. This has caused a transformation in the research field of the philosophy of artificial intelligence, and at the moment this branch of scientific knowledge is considering new questions, including why it is not possible to create “strong” AI that completely replicates human thinking; can a computer think, albeit differently, than a human; what is the difference between human and machine thinking; who should be responsible for the decisions and actions of artificial intelligence, and etc. The issues facing the philosophy of artificial intelligence are constantly updated. (shrink)

We can readily identify goal-directed systems and distinguish them from non-goal-directed systems. A woodpecker hunting for grubs is the first, a pendulum returning to rest is the second. But what is it to be a goal-directed system? Perhaps the dominant answer to this question, inspired by systems theories such as cybernetics, is that goal-directed systems are distinguished by their tendency to seek, aim at, or maintain some more-or-less easily identifiable goal. Cybernetics and the like would hold that physical systems subject (...) only to physical laws can exhibit such behavior. If sound, this systems approach to teleology would unify a diverse range of goal-directed phenomena and neatly side-step many traditional bogey-men of teleology, such as anthropomorphism and future causation. Goal-directed phenomena would be a normal feature of the natural causal world that could be described in purely descriptive and quantitative terms, and receive ordinary causal explanations. Thus, the systems approach promises to provide a naturalistic-cum-descriptive account of teleology suitable for use in naturalistic accounts of other phenomena, including the intentionality of mental states and even self-consciousness. (shrink)

In this paper the author tries to disentangle some of the problems tied up in John Searle's famous Chinese-room-argument. In a first step to answer the question what it would be for a system to have not only syntax, but also semantics the author gives a brief account of the functioning of the language understanding systems (LUS) so far developed in the framework of AI research thereby making clear that systems like Winograd's SHRDLU are indeed doing little more than mere (...) number crunching. But things would be entirely different, the author argues, if the database of a LUS were built up by the system itself via some perceptual component-at least, if this perceptual component had the capacity to distinguish objects having a certain property F from objects which do not. For in this case the system could store an internal representation of the fact that the object has the property F in its database if and only if the object in fact has that property. And this would be a good basis for calling such a system a genuine LUS. But Searle has objected to a very similar account of J. Fodor that nothing could be further from true language understanding. The reason for this complaint seems to be that Searle holds the view that a true LUS must e.g., know that the word hamburgers refers to hamburgers and that he moreover claims that this knowledge must be explicit or that the system must be aware of the reference of hamburgers to hamburgers. The author argues that this is asking too much. For it seems plausible to say that a system is able to understand e.g., the word hamburger even if it has only implicit knowledge of the fact that hamburger refers to hamburgers in the sense that it has the capacity to tell hamburgers from non hamburgers and the capacity to bring the word hamburger together just with objects of the former kind. (shrink)

Many of the mathematicians and scientists who guided the development of digital computers in the late 1940s, such as Alan Turing and John von Neumann, saw these new devices not just as tools for calculation but as devices that might employ the same principles as are exhibited in rational human thought. Thus, a subfield of what came to be called computer science assumed the label artificial intelligence. The idea of building artificial systems which could exhibit intelligent behavior comparable to that (...) of humans was a heady prospect, and the claims made on behalf of AI during the 1950s and 1960s were impossibly ambitious. Despite some theoretical and applied successes within the field, serious problems soon became evident. Instead of fulfilling the goal of quickly producing artificial intelligent agents which could compete with or outperform human beings, by the 1970s and 1980s AI had settled into a pattern of slower but real progress in modeling or simulating aspects of human intelligence. (shrink)

In philosophy the term intentionality refers to the feature possessed by mental states of beingabout things others than themselves. A serious question has been how to explain the intentionality of mental states. This paper starts with linguistic representations, and explores how an organism might use linguistic symbols to represent other things. Two research projects of Sue Savage-Rumbaugh, one explicity teaching twopan troglodytes to use lexigrams intentionally, and the other exploring the ability of several members ofpan paniscus to learn lexigram use (...) and comprehension of English speech spontaneously when raised in an appropriate environment, are examined to explore the acquisition process. Although it is controversial whether intentionality of mental states or linguistic symbols is primary, it is argued that the intentionality of linguistic symbols is primary and that studying how organisms learn to use linguistic symbols provides an avenue to understanding how intentionality is acquired by cognitive systems. (shrink)

This paper reviews four significant advances on the feedforward architecture that has dominated discussions of connectionism. The first involves introducing modularity into networks by employing procedures whereby different networks learn to perform different components of a task, and a Gating Network determines which network is best equiped to respond to a given input. The second consists in the use of recurrent inputs whereby information from a previous cycle of processing is made available on later cycles. The third development involves developing (...) compressed representations of strings in which there is no longer an explicit encoding of the components but where information about the structure of the original string can be recovered and so is present functionally. The final advance entails using connectionist learning procedures not just to change weights in networks but to change the patterns used as inputs to the network. These advances significantly increase the usefulness of connectionist networks for modeling human cognitive performance by, among other things, providing tools for explaining the productivity and systematicity of some mental activities, and developing representations that are sensitive to the content they are to represent. (shrink)

Philosophy of science is positioned to make distinctive contributions to cognitive science by providing perspective on its conceptual foundations and by advancing normative recommendations. The philosophy of science I embrace is naturalistic in that it is grounded in the study of actual science. Focusing on explanation, I describe the recent development of a mechanistic philosophy of science from which I draw three normative consequences for cognitive science. First, insofar as cognitive mechanisms are information-processing mechanisms, cognitive science needs an account of (...) how the representations invoked in cognitive mechanisms carry information about contents, and I suggest that control theory offers the needed perspective on the relation of representations to contents. Second, I argue that cognitive science requires, but is still in search of, a catalog of cognitive operations that researchers can draw upon in explaining cognitive mechanisms. Last, I provide a new perspective on the relation of cognitive science to brain sciences, one which embraces both reductive research on neural components that figure in cognitive mechanisms and a concern with recomposing higher-level mechanisms from their components and situating them in their environments. (shrink)

Philosophy of science is positioned to make distinctive contributions to cognitive science by providing perspective on its conceptual foundations and by advancing normative recommendations. The philosophy of science I embrace is naturalistic in that it is grounded in the study of actual science. Focusing on explanation, I describe the recent development of a mechanistic philosophy of science from which I draw three normative consequences for cognitive science. First, insofar as cognitive mechanisms are information‐processing mechanisms, cognitive science needs an account of (...) how the representations invoked in cognitive mechanisms carry information about contents, and I suggest that control theory offers the needed perspective on the relation of representations to contents. Second, I argue that cognitive science requires, but is still in search of, a catalog of cognitive operations that researchers can draw upon in explaining cognitive mechanisms. Last, I provide a new perspective on the relation of cognitive science to brain sciences, one which embraces both reductive research on neural components that figure in cognitive mechanisms and a concern with recomposing higher‐level mechanisms from their components and situating them in their environments. (shrink)

In his paper "Machines and the Mental" Fred Dretske claims that there is a difference on principle between men on the one and animals and machines on the other side which arises from the fact that men are able to respond to the meaning of symbols whereas animals and machines can only respond to the symbols that have the meaning. In this paper it is argued that this claim does not bear closer scrutiny. Mainly for two reasons: 1. The mere (...) having of a certain meaning does not rank among the possible causal properties of a symbol, not even in the case of men; for it is not the having of the meaning that may have certain effects on the behavior of a person, but the fact that the person associates this meaning with the symbol. 2. Even animals are capable to associate a meaning with a symbol, i. e. even animals can learn to come to believe that p if they become aware of a symbol which has the meaning that p. (shrink)

I start with some of the early challenges to the widely-employed philosophical method of cases—the very challenges that originally prompted the new movement of experimental philosophy—and with some fundamental questions about the method that are yet to have been given satisfying answers. I then propose that what has allowed both ‘armchair’ and ‘experimental’ participants in the ongoing debates concerning the method to ignore or repress those early challenges—and in particular Robert Cummins’s ‘calibration objection’—and to discount fundamental disagreements about those questions, (...) is in large part ‘the claim of continuity’, which is the claim, or assumption, that there is no philosophically significant difference between whatever it is that we do when we give our answer to the theorist’s question of whether some philosophically interesting word ‘applies’ (positively or negatively) to some theoretically significant case, and our use of that same word in the course of ordinary, non-philosophical discourse. I then summarize the ordinary language philosophy (OLP) argument against the claim of continuity, and explain why answers to the theorist’s questions are vulnerable to the calibration objection in a way that our non-philosophical, everyday employment of the same words is not. This then leads me to question another widespread assumption, which is the assumption that the theorist’s questions—as raised in the theorist’s context—have clear enough sense and correct answers. I end by responding to a series of objections to my argument and to OLP more generally. (shrink)