Syntactic semantics is a holistic, conceptual-role-semantic theory of how computers can think. But Fodor and Lepore have mounted a sustained attack on holistic semantic theories. However, their major problem with holism (that, if holism is true, then no two people can understand each other) can be fixed by means of negotiating meanings. Syntactic semantics and Fodor and Lepore’s objections to holism are outlined; the nature of communication, miscommunication, and negotiation is discussed; Bruner’s ideas about the negotiation of meaning are explored; (...) and some observations on a problem for knowledge representation in AI raised by Winston are presented. (shrink)

A recently developed model of general collective intelligence defines a method for organizing humans or artificially intelligent agents that is believed to create the potential to exponentially increase the general problem-solving ability of groups of such entities over that of any individual entity. An analysis based on this model suggests that many and perhaps all “wicked problems” are collective optimization problems that cannot reliably be addressed without a system of collective optimization, but that might be reliably addressed through such a (...) system. This paper briefly introduces the concept of GCI as well as why it is a system of collective optimization, and explores why “wicked problems” from the perspective of GCI are collective optimization problems, and therefore why such problems might require GCI to be reliably solvable. (shrink)

Economic value additions to knowledge and demand provide practical, embedded and extensible meaning to philosophizing cognitive systems. Evaluation of a cognitive system is an empirical matter. Thinking of science in terms of distributed cognition (interactionism) enlarges the domain of cognition. Anything that actually contributes to the specific quality of output of a cognitive system is part of the system in time and/or space. Cognitive science studies behaviour and knowledge structures of experts and categorized structures based on underlying structures. Knowledge representation (...) through understanding of ‘epistemic cultures’ is an evolutionary stage. But cognition goes beyond knowledge representation. Notwithstanding the importance of epistemology of phenomena, the practicability cum philosophical aspects of machine learning needs to be seen in dynamic behaviour in socio-economic-technical value additions if human machine interaction processes that are context specific are incorporated into strong artificial intelligent systems. Cognitive Science is also studied from both computational and biological angles. Evolution of interactive forms of reasoning through understanding of meta-language of computations or biological learning processes is possible. But the limitation of historical cultures predefines the role of interactive processes in user-networks beyond technology networks. Despite this limitation, inclusive development notions of a heterogeneous national society such as India or Europe can be tested and incorporated. (shrink)

A recently developed model of general collective intelligence defines a method for organizing humans or artificially intelligent agents that is believed to create the potential to exponentially increase the general problem-solving ability of groups of such entities over that of any individual entity. An analysis based on this model suggests that many and perhaps all “wicked problems” are collective optimization problems that cannot reliably be addressed without a system of collective optimization, but that might be reliably addressed through such a (...) system. This paper briefly introduces the concept of GCI as well as why it is a system of collective optimization, and explores why “wicked problems” from the perspective of GCI are collective optimization problems, and therefore why such problems might require GCI to be reliably solvable. (shrink)

Theoretical commentaries on AI often operate as a metadiscourse on the way in which science represents itself to a wider public. The sciences and humanities do the same kind of work but in different fields that encourage them to talk about their work differently: science refers to a natural world that does not talk back, and the humanities refer continually to a world with communicative people in it. This paper suggests that much AI commentary is misconceived because it models itself (...) on the way that science represents itself, rather than on the actual practice of science.AI theorists have become increasingly worried about the lack of evaluation in AI, the lack of reflexivity, and the lack of contact with society. Frequently these writers turn to concepts of tacit knowledge to work through these worries. In doing so they are recognising the problem of AI's second-order representation of science and trying to deal with it. However, this recognition of a problem with the representations of science simply turns back to the legitimation crisis of Western politics where many commentators use science precisely as a ‘model’ for western political institutions. They do so because science is one of the few areas of knowledge where it has been legitimate to use plausible methodology for representation that allows for arbitrary designations of authority as well as parallel systems of different authority. However, the plausible rejects any control on reflexivity, assumes an ethnocentric club culture and does not address social context.It is in this sense that the problems of legitimation in political liberalism are similar to those of legitimation in sciences, both are rooted in their uses of representation. AI's link with the representation of science places it in the heart of this debate about legitimacy. This paper suggests that AI does need to learn about reflexivity and that it might well do so by looking at the recent work on experimentation and representation by historians of science, and by looking to the debates about representation by historians of science, and by looking to the debates about representation within the humanities. However, reflexivity may not be enough. Devising rules of thumb for the appropriate halting of reflexivity, is also needed to address social context and take action. (shrink)

The proper treatment of computationalism, as the thesis that cognition is computable, is presented and defended. Some arguments of James H. Fetzer against computationalism are examined and found wanting, and his positive theory of minds as semiotic systems is shown to be consistent with computationalism. An objection is raised to an argument of Selmer Bringsjord against one strand of computationalism, namely, that Turing-Test± passing artifacts are persons, it is argued that, whether or not this objection holds, such artifacts will inevitably (...) be persons. (shrink)