This paper presents the main topics, arguments, and positions in the philosophy of AI at present (excluding ethics). Apart from the basic concepts of intelligence and computation, the main topics of artificial cognition are perception, action, meaning, rational choice, free will, consciousness, and normativity. Through a better understanding of these topics, the philosophy of AI contributes to our understanding of the nature, prospects, and value of AI. Furthermore, these topics can be understood more deeply through the discussion of AI; so (...) we suggest that “AI Philosophy” provides a new method for philosophy. (shrink)

Which notion of computation (if any) is essential for explaining cognition? Five answers to this question are discussed in the paper. (1) The classicist answer: symbolic (digital) computation is required for explaining cognition; (2) The broad digital computationalist answer: digital computation broadly construed is required for explaining cognition; (3) The connectionist answer: sub-symbolic computation is required for explaining cognition; (4) The computational neuroscientist answer: neural computation (that, strictly, is neither digital nor analogue) is required for explaining cognition; (5) The extreme (...) dynamicist answer: computation is not required for explaining cognition. The first four answers are only accurate to a first approximation. But the “devil” is in the details. The last answer cashes in on the parenthetical “if any” in the question above. The classicist argues that cognition is symbolic computation. But digital computationalism need not be equated with classicism. Indeed, computationalism can, in principle, range from digital (and analogue) computationalism through (the weaker thesis of) generic computationalism to (the even weaker thesis of) digital (or analogue) pancomputationalism. Connectionism, which has traditionally been criticised by classicists for being non-computational, can be plausibly construed as being either analogue or digital computationalism (depending on the type of connectionist networks used). Computational neuroscience invokes the notion of neural computation that may (possibly) be interpreted as a sui generis type of computation. The extreme dynamicist argues that the time has come for a post-computational cognitive science. This paper is an attempt to shed some light on this debate by examining various conceptions and misconceptions of (particularly digital) computation. (shrink)

This paper presents the main topics, arguments, and positions in the philosophy of AI at present (excluding ethics). Apart from the basic concepts of intelligence and computation, the main topics of ar-tificial cognition are perception, action, meaning, rational choice, free will, consciousness, and normativity. Through a better understanding of these topics, the philosophy of AI contributes to our understand-ing of the nature, prospects, and value of AI. Furthermore, these topics can be understood more deeply through the discussion of AI; so (...) we suggest that “AI Philosophy” provides a new method for philosophy. (shrink)

According to David Chalmers, the hard problem of consciousness consists of explaining how and why qualitative experience arises from physical states. Moreover, Chalmers argues that materialist and reductive explanations of mentality are incapable of addressing the hard problem. In this chapter, I suggest that Chalmers’ hard problem can be usefully distinguished into a ‘how question’ and ‘why question,’ and I argue that evolutionary biology has the resources to address the question of why qualitative experience arises from brain states. From this (...) perspective, I discuss the different kinds of evolutionary explanations (e.g., adaptationist, exaptationist, spandrel) that can explain the origins of the qualitative aspects of various conscious states. This argument is intended to clarify which parts of Chalmers’ hard problem are amenable to scientific analysis. (shrink)

The Noble Ape Simulation offers an account of the mind as something that can be observed, measured, and ultimately simulated through external effects. This version of the applied mind is not created through a single method but through layering three simulations relating to information chemistry, social constraints, and evolving narrative. As examples, additional simulation elements in Noble Ape are presented to offer the simulation methodology of Noble Ape. This chapter, rather than being a theoretical critique, is intended as a project (...) report relating to three distinct yet interoperating simulated models of the mind. These are presented both as individual simulations and also the simulations’ interactions. This produces a novel account of the applied mind. The methods used in creating such an applied mind provide an interesting insight into the possible origin of mind through pragmatic application rather than conjecture. (shrink)

I present and evaluate the prospects of the biosemantic program, understood as a philosophical attempt to explain the mind’s origins by appealing to something that non-minded organisms and minded organisms have in common: representational capacity. I develop an analogy with ancient attempts to account for the origins of change, clarify the biosemantic program’s aims and methods, and then distinguish two importantly different forms of objection, a priori and a posteriori. I defend the biosemantic program from a priori objections on the (...) grounds that the standard of explanation presupposed by them is inappropriate and leads to absurdities if consistently applied. Once the way is cleared of a priori objections, the success of biosemantics turns on the strength of a posteriori objections, that is, on the program’s empirical adequacy. Here, its prospects are less clear, but I offer reasons, by analogy with chemical combination and other everyday phenomena, to think that minded beings and their representational capacities might well have their origin and explanation in non-minded beings. An evolutionary origin and explanation of mind is plausible, at least as far as naturalistic accounts and explanations go. (shrink)