Alan Turing devised his famous test (TT) through a slight modificationof the parlor game in which a judge tries to ascertain the gender of twopeople who are only linguistically accessible. Stevan Harnad hasintroduced the Total TT, in which the judge can look at thecontestants in an attempt to determine which is a robot and which aperson. But what if we confront the judge with an animal, and arobot striving to pass for one, and then challenge him to peg which iswhich? (...) Now we can index TTT to a particular animal and its syntheticcorrelate. We might therefore have TTTrat, TTTcat,TTTdog, and so on. These tests, as we explain herein, are abetter barometer of artificialintelligence (AI) than Turing's originalTT, because AI seems to have ammunition sufficient only to reach thelevel of artificial animal, not artificial person. (shrink)
1. WHAT IS ARTIFICIALINTELLIGENCE? One of the fascinating aspects of the field of artificialintelligence (AI) is that the precise nature of its subject ..
This book deals with the major philosophical issues in the theoretical framework of ArtificialIntelligence (AI) in particular and cognitive science in general.
Made-Up Minds addresses fundamental questions of learning and concept invention by means of an innovative computer program that is based on the cognitive ...
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 artificialintelligence: 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 artificialintelligence. 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)
The peculiarity of the relationship between philosophy and ArtificialIntelligence (AI) has been evidenced since the advent of AI. This paper aims to put the basis of an extended and well founded philosophy of AI: it delineates a multi-layered general framework to which different contributions in the field may be traced back. The core point is to underline how in the same scenario both the role of philosophy on AI and role of AI on philosophy must be considered. (...) Moreover, this framework is revised and extended in the light of the consideration of a type of multiagent system devoted to afford the issue of scientific discovery both from a conceptual and from a practical point of view. (shrink)
The aims of this paper are threefold: To show that game-playing (GP), the discipline of ArtificialIntelligence (AI) concerned with the development of automated game players, has a strong epistemological relevance within both AI and the vast area of cognitive sciences. In this context games can be seen as a way of securely reducing (segmenting) real-world complexity, thus creating the laboratory environment necessary for testing the diverse types and facets of intelligence produced by computer models. This paper (...) aims to promote the belief that games represent an excellent tool for the project of computational psychology (CP). To underline how, despite this, GP has mainly adopted an engineering-inspired methodology and in doing so has distorted the framework of cognitive functionalism. Many successes (i.e. chess, checkers) have been achieved refusing human-like reasoning. The AI has appeared to work well despite ignoring an intrinsic motivation, that of creating an explanatory link between machines and mind. To assert that substantial improvements in GP may be obtained in the future only by renewed interest in human-inspired models of reasoning and in other cognitive studies. In fact, if we increase the complexity of games (from NP-Completeness to AI-Completeness) in order to reproduce real-life problems, computer science techniques enter an impasse. Many of AI’s recent GP experiences can be shown to validate this. The lack of consistent philosophical foundations for cognitive AI and the minimal philosophical commitment of AI investigation are two of the major reasons that play an important role in explaining why CP has been overlooked. (shrink)
This article examines argument structures and strategies in pro and con argumentation about the possibility of human-level artificialintelligence (AI) in the near term future. It examines renewed controversy about strong AI that originated in a prominent 1999 book and continued at major conferences and in periodicals, media commentary, and Web-based discussions through 2002. It will be argued that the book made use of implicit, anticipatory refutation to reverse prevailing value hierarchies related to AI. Drawing on Perelman and (...) Olbrechts-Tyteca's (1969) study of refutational argument, this study considers points of contact between opposing arguments that emerged in opposing loci, dissociations, and casuistic reasoning. In particular, it shows how perceptions of AI were reframed and rehabilitated through metaphorical language, reversal of the philosophical pair artificial/natural, appeals to the paradigm case, and use of the loci of quantity and essence. Furthermore, examining responses to the book in subsequent arguments indicates the topoi characteristic of the rhetoric of technology advocacy. (shrink)
ArtificialIntelligence 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.
Recent work in artificialintelligence has increasingly turned to argumentation as a rich, interdisciplinary area of research that can provide new methods related to evidence and reasoning in the area of law. Douglas Walton provides an introduction to basic concepts, tools and methods in argumentation theory and artificialintelligence as applied to the analysis and evaluation of witness testimony. He shows how witness testimony is by its nature inherently fallible and sometimes subject to disastrous failures. At (...) the same time such testimony can provide evidence that is not only necessary but inherently reasonable for logically guiding legal experts to accept or reject a claim. Walton shows how to overcome the traditional disdain for witness testimony as a type of evidence shown by logical positivists, and the views of trial sceptics who doubt that trial rules deal with witness testimony in a way that yields a rational decision-making process. (shrink)
Focuses on distinguished quotations representing the best thinking in philosophy, psychology, and artificialintelligence from classical civilization to ...
This interdisciplinary collection of classical and contemporary readings provides a clear and comprehensive guide to the many hotly-debated philosophical issues at the heart of artificialintelligence.
The Turing Test (TT), as originally specified, centres on theability to perform a social role. The TT can be seen as a test of anability to enter into normal human social dynamics. In this light itseems unlikely that such an entity can be wholly designed in anoff-line mode; rather a considerable period of training insitu would be required. The argument that since we can pass the TT,and our cognitive processes might be implemented as a Turing Machine(TM), that consequently (...) a TM that could pass the TT could be built, isattacked on the grounds that not all TMs are constructible in a plannedway. This observation points towards the importance of developmentalprocesses that use random elements (e.g., evolution), but in these casesit becomes problematic to call the result artificial. This hasimplications for the means by which intelligent agents could bedeveloped. (shrink)
Considerations of personal identity bear on John Searle's Chinese Room argument, and on the opposed position that a computer itself could really understand a natural language. In this paper I develop the notion of a virtual person, modelled on the concept of virtual machines familiar in computer science. I show how Searle's argument, and J. Maloney's attempt to defend it, fail. I conclude that Searle is correct in holding that no digital machine could understand language, but wrong in holding that (...)artificial minds are impossible: minds and persons are not the same as the machines, biological or electronic, that realize them. (shrink)
In CyberPhilosophy: The Intersection of Philosophy and Computing, edited by James H. Moor and Terrell Ward Bynum (Oxford, UK: Blackwell, 2002), 66-77. Also in Metaphilosophy 33.1/2 (2002): 70-82.
Contributors: Rodney A. Brooks, Paul M. Churchland, Andy Clark, Daniel C. Dennett, Hubert L. Dreyfus, Jerry A. Fodor, Joseph Garon, John Haugeland, Marvin...
In this article the question is raised whether artificialintelligence has any psychological relevance, i.e. contributes to our knowledge of how the mind/brain works. It is argued that the psychological relevance of artificialintelligence of the symbolic kind is questionable as yet, since there is no indication that the brain structurally resembles or operates like a digital computer. However, artificialintelligence of the connectionist kind may have psychological relevance, not because the brain is a (...) neural network, but because connectionist networks exhibit operating characteristics which mimic operant behavior. Finally it is concluded that, since most of the work done so far in AI and Law is of the symbolic kind, it has as yet contributed little to our understanding of the legal mind. (shrink)
In current philosophical research the term 'philosophy of social action' can be used - and has been used - in a broad sense to encompass the following central research topics: 1) action occurring in a social context; this includes multi-agent action; 2) joint attitudes (or "we-attitudes" such as joint intention, mutual belief) and other social attitudes needed for the explication and explanation of social action; 3) social macro-notions, such as actions performed by social groups and properties of social groups such (...) as their goals and beliefs; 4) social norms and social institutions (see Tuomela, 1984, 1995). The theory of social action understood analogously in a broad sense would then involve not only philosophical but all other relevant theorizing about social action. Thus, in this sense, such fields of ArtificialIntelligence (AI) as Distributed AI (DAI) and the theory of Multi-Agent Systems (MAS) fall within the scope of the theory of social action. DAI studies the social side of computer systems and includes various well-known areas ranging from Human Computer Interaction, Computer-Supported Cooperative Work, Organizational Processing, Distributed Problem Solving to Simulation of Social Systems and Organizations. Even if I am a philosopher with low artificialintelligence I will below try to say something about what the scope of DAI should be taken to be on conceptual and philosophical grounds. (In the later sections of the paper the central notion of joint intention will be the main topic - in order to illustrate how philosophers and DAI-researchers approach this issue.) Let us now consider the relationship between philosophy - especially philosophy of social action - and DAI. Both are concerned with social matters and in this sense seem to have a connection to social science proper. What kinds of questions should these areas of study be concerned with? In principle, ordinary social science should study all aspects of social life (in various societies and cultures), try to describe it and create general theories to explain it. (shrink)
The idea that human cognitive capacities are explainable by computational models is often conjoined with the idea that, while the states postulated by such models are in fact realized by brain states, there are no type-type correlations between the states postulated by computational models and brain states (a corollary of token physicalism). I argue that these ideas are not jointly tenable. I discuss the kinds of empirical evidence available to cognitive scientists for (dis)confirming computational models of cognition and argue that (...) none of these kinds of evidence can be relevant to a choice among competing computational models unless there are in fact type-type correlations between the states postulated by computational models and brain states. Thus, I conclude, research into the computational procedures employed in human cognition must be conducted hand-in-hand with research into the brain processes which realize those procedures. (shrink)
Janusz Czelakowski Elements of Formal Action Theory 1. Elementary Action Systems 1.1 Introductory Remarks. In contemporary literature one may distinguish ...
This note corrects an error in the statement and proof of Propositions 9 and 10 of [C. Cross, Nonmonotonic inconsistency, ArtificialIntelligence 149 (2) (2003) 161–178]. Both results turn out to depend on the postulate of Consistency Preservation.
I argue here that sophisticated AI systems, with the exception of those aimed at the psychological modeling of human cognition, must be based on general philosophical theories of rationality and, conversely, philosophical theories of rationality should be tested by implementing them in AI systems. So the philosophy and the AI go hand in hand. I compare human and generic rationality within a broad philosophy of AI and conclude by suggesting that ultimately, virtually all familiar philosophical problems will turn out to (...) be at least indirectly relevant to the task of building an autonomous rational agent, and conversely, the AI enterprise has the potential to throw light at least indirectly on most philosophical problems. (shrink)
The field of artificialintelligence and law is remarkably diverse not just because it encompasses many areas of academic study but also because it attracts the interest of both the research and commercial worlds. While much of the research is no doubt too exploratory and tentative to be of direct relevance to practising lawyers, in other projects there is but a short step from the research laboratory to the marketplace.Given that most readers of this journal tend to be (...) involved with, or interested in, research findings in the field, it might well be asked to what extent there should also be coverage here of commercial projects in artificialintelligence and law. (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 artificialintelligence 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)
Searle's celebrated Chinese Room Argument has shaken the foundations of ArtificialIntelligence. Many refutations have been attempted, but none seem convincing. This paper is an attempt to sort out explicitly the assumptions and the logical, methodological and empirical points of disagreement. Searle is shown to have underestimated some features of computer modeling, but the heart of the issue turns out to be an empirical question about the scope and limits of the purely symbolic (computational) model of the mind. (...) Nonsymbolic modeling turns out to be immune to the Chinese Room Argument. The issues discussed include the Total Turing Test, modularity, neural modeling, robotics, causality and the symbol-grounding problem. (shrink)
In the Fall of 1983, I offered a junior/senior-level course in Philosophy of ArtificialIntelligence, in the Department of Philosophy at SUNY Fredonia, after returning there from a year’s leave to study and do research in computer science and artificialintelligence (AI) at SUNY Buffalo. Of the 30 students enrolled, most were computerscience majors, about a third had no computer background, and only a handful had studied any philosophy. (I might note that enrollments have subsequently increased (...) in the Philosophy Department’s AI-related courses, such as logic, philosophy of mind, and epistemology, and that several computer science students have added philosophy as a second major.) This article describes that course, provides material for use in such a course, and offers a bibliography of relevant articles in the AI, cognitive science, and philosophical literature. (shrink)
The notion of context arises in assorted areas of artificialintelligence (AI), including knowledge representation, natural language processing, intelligent information retrieval, etc. Although the term ‘context’ is frequently employed in descriptions, explanations, and analyses of computer programs in these areas, its meaning is frequently left to the reader’s understanding. In other words, it is used in an intuitive manner. In an influential paper, Clark and Carlson (1981) state that context has become a favourite word. They then complain that (...) the denotation of the word has become murkier as its uses have been extended in many directions, making context some sort of ‘conceptual garbage can.’. (shrink)
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 artificialintelligence (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)
It is well known that people from other disciplines have made significant contributions to philosophy and have influenced philosophers. It is also true (though perhaps not often realized, since philosophers are not on the receiving end, so to speak) that philosophers have made significant contributions to other disciplines and have influenced researchers in these other disciplines, sometimes more so than they have influenced philosophy itself. But what is perhaps not as well known as it ought to be is that researchers (...) in other disciplines, writing in those other disciplines' journals and conference proceedings, are doing philosophically sophisticated work, work that we in philosophy ignore at our peril. Work in cognitive science and artificialintelligence (AI) often overlaps such paradigmatic philosophical specialties as logic, the philosophy of mind, the philosophy of language, and the philosophy of action. This special issue offers a sampling of research in cognitive science and AI that is philosophically relevant and philosophically sophisticated. (shrink)
The association of Wittgenstein’s name with the notion of artificialintelligence is bound to cause some surprise both to Wittgensteinians and to people interested in artificialintelligence. After all, Wittgenstein died in 1951 and the term artificialintelligence didn’t come into use until 1956 so that it seems unlikely that one could have anything to do with the other. However, establishing a connection between Wittgenstein and artificialintelligence is not as insuperable a (...) problem as it might appear at first glance. While it is true that artificialintelligence as a quasi-distinct discipline is of recent vintage, some of its concerns, especially those of a philosophical nature, have been around for quite some time. At the birth of modern philosophy we find Descartes wondering whether it would be possible to create a machine that would be phenomenologically indistinguishable from.. (shrink)
The greatest rhetorical challenge to developers of creative artificialintelligence systems is convincingly arguing that their software is more than just an extension of their own creativity. This paper suggests that “creative autonomy,” which exists when a system not only evaluates creations on its own, but also changes its standards without explicit direction, is a necessary condition for making this argument. Rather than requiring that the system be hermetically sealed to avoid perceptions of human influence, developing creative autonomy (...) is argued to be more plausible if the system is intimately embedded in a broader society of other creators and critics. Ideas are presented for constructing systems that might be able to achieve creative autonomy. (shrink)
John Haugeland's Mind design and Mind design II are organized around the idea that the fundamental idea of cognitive science is that, “intelligent beings are semantic engines — in other words, automatic formal systems with interpretations under which they consistently make sense”. The goal of artificialintelligence research, or the problem of “mind design” as Haugeland calls it, is to develop computers that are in fact semantic engines. This paper canvasses the changes in artificialintelligence research (...) reflected in the different selections of essays found in each volume. While Mind design II is a worthy successor to Mind design, there are some notable developments in artificialintelligence which suggest that seemingly intelligent behavior need not be guided by semantic engines at all. (shrink)
The field of ArtificialIntelligence has been around for over 60 years now. Soon after its inception, the founding fathers predicted that within a few years an intelligent machine would be built. That prediction failed miserably. Not only hasn’t an intelligent machine been built, but we are not much closer to building one than we were some 50 years ago. Many reasons have been given for this failure, but one theme has been dominant since its advent in 1969: (...) The Frame Problem. What looked initially like an innocuous problem in logic, turned out to be a much broader and harder problem of holism and relevance in commonsense reasoning. Despite an enormous literature on the topic, there is still disagreement not only on whether the problem has been solved, but even what exactly the problem is. In this paper we provide a formal description of the initial problem, the early attempts at a solution, and its ramification both in AI as well as philosophy. (shrink)
A translation of the renowned French reference book, Vocabulaire de sciences cognitives , the Dictionary of Cognitive Science presents comprehensive definitions of more than 120 terms. The editor and advisory board of specialists have brought together 60 internationally recognized scholars to give the reader a comprehensive understanding of the most current and dynamic thinking in cognitive science. Topics range from Abduction to Writing, and each entry covers its subject from as many perspectives as possible within the domains of psychology, (...) class='Hi'>artificialintelligence, neuroscience, philosophy, and linguistics. This multidisciplinary work is an invaluable resource for all collections. (shrink)
It may seem strange to associate the name of Jan Patočka with artificialintelligence. Neither a mathematician nor a logician, the phenomenology he espoused, with its emphasis on lived experience, seems worlds apart from the formalism associated with the discipline. Yet, as I hope to show, the radicality and depth of Patočka’s thought is such that it casts a wide net. The reform of metaphysics that Patočka proposed in his asubjective phenomenology also affects artificialintelligence. It (...) shows that what philosophers take as its most difficult, yet primary problem may well be the result of a category mistake. (shrink)
Harry Collins interprets Hubert Dreyfus’s philosophy of embodiment as a criticism of all possible forms of artificialintelligence. I argue that this characterization is inaccurate and predicated upon a misunderstanding of the relevance of phenomenology for empirical scientific research.
Artificialintelligence, conceived either as an attempt to provide models of human cognition or as the development of programs able to perform intelligent tasks, is primarily interested in theuses of language. It should be concerned, therefore, withpragmatics. But its concern with pragmatics should not be restricted to the narrow, traditional conception of pragmatics as the theory of communication (or of the social uses of language). In addition to that, AI should take into account also the mental uses of (...) language (in reasoning, for example) and the existential dimensions of language as a determiner of the world we (and our computers) live in. In this paper, the relevance of these three branches of pragmatics-sociopragmatics, psychopragmatics, and ontopragmatics-for AI are explored. (shrink)
Though it''s difficult to agree on the exact date of their union, logic and artificialintelligence (AI) were married by the late 1950s, and, at least during their honeymoon, were happily united. What connubial permutation do logic and AI find themselves in now? Are they still (happily) married? Are they divorced? Or are they only separated, both still keeping alive the promise of a future in which the old magic is rekindled? This paper is an attempt to answer (...) these questions via a review of six books. Encapsulated, our answer is that (i) logic and AI, despite tabloidish reports to the contrary, still enjoy matrimonial bliss, and (ii) only their future robotic offspring (as opposed to the children of connectionist AI) will mark real progress in the attempt to understand cognition. (shrink)
We consider a special case of heuristics, namely numeric heuristic evaluation functions, and their use in artificialintelligence search algorithms. The problems they are applied to fall into three general classes: single-agent path-finding problems, two-player games, and constraint-satisfaction problems. In a single-agent path-finding problem, such as the Fifteen Puzzle or the travelling salesman problem, a single agent searches for a shortest path from an initial state to a goal state. Two-player games, such as chess and checkers, involve an (...) adversarial relationship between two players, each trying to win the game. In a constraint-satisfaction, problem, such as the 8-Queens problem, the task is to find a state that satisfies a set of constraints. All of these problems are computationally intensive, and heuristic evaluation functions are used to reduce the amount of computation required to solve them. In each case we explain the nature of the evaluation functions used, how they are used in search algorithms, and how they can be automatically learned or acquired. (shrink)
I report the findings of a comparative analysis of online Christian and Buddhist responses to artificialintelligence. I review the Buddhist response and compare it with the Christian response outlined in an earlier essay (Tamatea 2008). The discussion seeks to answer two questions: Which approach to imago Dei informs the online Buddhist response to artificialintelligence? And to what extent does the preference for a particular approach emerge from a desire to construct the Self? The conclusion (...) is that, like the Christian response, the Buddhist response is grounded not so much in the reality of AI as it is in the discursive constructions of AI made available through Buddhist cosmology, which (paradoxically), like the Christian response, are deployed in defense of the Self, despite claimed adherence to the notion of anatta, or non-Self. (shrink)
This article deals with the links between the enaction paradigm and artificialintelligence. Enaction is considered a metaphor for artificialintelligence, 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 artificialintelligence. 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 artificialintelligence in a variety of enaction-based ways. (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)
For over a decade John Searle's ingenious argument against the possibility of artificialintelligence has held a prominent place in contemporary philosophy. This is not just because of its striking central example and the apparent simplicity of its argument. As its appearance in Scientific American testifies, it is also due to its importance to the wider scientific community. If Searle is right, artificialintelligence in the strict sense, the sense that would claim that mind can be (...) instantiated through a formal program of symbol manipulation, is basically wrong. No set of formal conditions can provide us with the characteristic feature of mind which is the intentionally of its mental contents. Formally regarded, such intentionally is an irreducible primitive. It cannot be analyzed into non-intentional (purely syntactic, symbolic) components. This paper will argue that this objection is based on a misunderstanding. Intentionality is not simply something given which is incapable of further analysis. It only appears so when we mistakenly abstract it from time. When we regard its temporal structure, it shows itself as a rule-governed, synthetic process, one capable of being instantiated both by machines and men. (shrink)
In this essay we advance the view that analytical epistemology and artificialintelligence are complementary disciplines. Both fields study epistemic relations, but whereas artificialintelligence approaches this subject from the perspective of understanding formal and computational properties of frameworks purporting to model some epistemic relation or other, traditional epistemology approaches the subject from the perspective of understanding the properties of epistemic relations in terms of their conceptual properties. We argue that these two practices should not be (...) conducted in isolation. We illustrate this point by discussing how to represent a class of inference forms found in standard inferential statistics. This class of inference forms is interesting because its members share two properties that are common to epistemic relations, namely defeasibility and paraconsistency. Our modeling of standard inferential statistical arguments exploits results from both logical artificialintelligence and analytical epistemology. We remark how our approach to this modeling problem may be generalized to an interdisciplinary approach to the study of epistemic relation. (shrink)
The tension between rule and judgment is well known with respect to the meaning of substantive legal commands. The same conflict is present in fact finding. The law penetrates to virtually all aspects of human affairs; irtually any interaction can generate a legal conflict. Accurate fact finding about such disputes is a necessary condition for the appropriate application of substantive legal commands. Without accuracy in fact finding, the law is unpredictable, and thus individuals cannot efficiently accommodate their affairs to its (...) commands. The need for accuracy and predictability in legal fact finding has generated a search for formal tools to apply to the task. Among the tools that have been examined are Bayes' Theorem and expected utility theory (Bayesian or statistical decision theory). These tools do not map well onto trials, which in turn has generated an examination of alternative approaches, in particular the story model and the relative plausibility theory. This paper discusses these issues in turn. It elaborates the basic structure of trials in the American tradition; examines the uneasy relationship between trials and such formalisms as Bayes' Theorem and expected utility theory; and introduces the relative plausibility theory as an explanation of the nature of juridical proof. (shrink)
Although activity aimed at the construction of artificialintelligence started about 60 years ago however, contemporary intelligent systems are effective in very narrow domains only. One of the reasons for this situation appears to be serious problems in the theory of intelligence. Intelligence is a characteristic of goal-directed systems and two classes of goal-directed systems can be derived from observations on animals and humans, one class is systems with innately and jointly determined goals and means. The (...) other class contains systems that are able to construct arbitrary goals and means. It is suggested that the classes (that implicitly underlie most models of artificialintelligence) are insufficient to explain human goal-directed activity. A broader approach to goal-directed systems is considered. This approach suggests that humans are goal-directed systems that jointly synthesize arbitrary goals and means. Neural and psychological data favoring this hypothesis and its experimental validation are considered. A simple computer model based on the idea of joint synthesis to simulate goal-directed activity is presented. The usage of the idea of joint synthesis for the construction of artificialintelligence is discussed. (shrink)
One of the central factors influencing the process and the outcome of technology transfer is the nature of the technology being transferred. This paper identifies and discusses the main characteristics of ArtificialIntelligence (AI) technology from the point of view of international technology transfer. It attempts to indicate the peculiarities of AI in this context and move towards a framework to assist recipient decision makers in optimising the formulation of their policies on AI technology transfer.
