The philosophy of information (PI) is a new area of research with its own field of investigation and methodology. This article, based on the Herbert A. Simon Lecture of Computing and Philosophy I gave at Carnegie Mellon University in 2001, analyses the eighteen principal open problems in PI. Section 1 introduces the analysis by outlining Herbert Simon's approach to PI. Section 2 discusses some methodological considerations about what counts as a good philosophical problem. The discussion centers on Hilbert's famous analysis (...) of the central problems in mathematics. The rest of the article is devoted to the eighteen problems. These are organized into five sections: problems in the analysis of the concept of information, in semantics, in the study of intelligence, in the relation between information and nature, and in the investigation of values. (shrink)

A survey of the field of hypercomputation, including discussion of a variety of objections.

It has been argued that neural networks and other forms of analog computation may transcend the limits of Turing-machine computation; proofs have been offered on both sides, subject to differing assumptions. In this article I argue that the important comparisons between the two models of computation are not so much mathematical as epistemological. The Turing-machine model makes assumptions about information representation and processing that are badly matched to the realities of natural computation (information representation and processing in or inspired by (...) natural systems). This points to the need for new models of computation addressing issues orthogonal to those that have occupied the traditional theory of computation. (shrink)

G. Priest's anti-consistency argument (Priest 1979, 1984, 1987) and J. R. Lucas's anti-mechanist argument (Lucas 1961, 1968, 1970, 1984) both appeal to Gödel incompleteness. By way of refuting them, this paper defends the thesis of quartet compatibility, viz., that the logic of the mind can simultaneously be Gödel incomplete, consistent, mechanical, and recursion complete (capable of all means of recursion). A representational approach is pursued, which owes its origin to works by, among others, J. Myhill (1964), P. Benacerraf (1967), J. (...) Webb (1980, 1983) and M. Arbib (1987). It is shown that the fallacy shared by the two arguments under discussion lies in misidentifying two systems, the one for which the Gödel sentence is constructable and to be proved, and the other in which the Gödel sentence in question is indeed provable. It follows that the logic of the mind can surpass its own Gödelian limitation not by being inconsistent or non-mechanistic, but by being capable of representing stronger systems in itself; and so can a proper machine. The concepts of representational provability, representational maximality, formal system capacity, etc., are discussed. (shrink)

In his PhD thesis (1938) Turing introduced what he described as 'a new kind of machine'. He called these 'O-machines'. The present paper employs Turing's concept against a number of currently fashionable positions in the philosophy of mind.

Explaining the mind by building machines with minds runs into the other-minds problem: How can we tell whether any body other than our own has a mind when the only way to know is by being the other body? In practice we all use some form of Turing Test: If it can do everything a body with a mind can do such that we can't tell them apart, we have no basis for doubting it has a mind. But what is (...) "everything" a body with a mind can do? Turing's original "pen-pal" version (the TT) only tested linguistic capacity, but Searle has shown that a mindless symbol-manipulator could pass the TT undetected. The Total Turing Test (TTT) calls for all of our linguistic and robotic capacities; immune to Searle's argument, it suggests how to ground a symbol manipulating system in the capacity to pick out the objects its symbols refer to. No Turing Test, however, can guarantee that a body has a mind. Worse, nothing in the explanation of its successful performance requires a model to have a mind at all. Minds are hence very different from the unobservables of physics (e.g., superstrings); and Turing Testing, though essential for machine-modeling the mind, can really only yield an explanation of the body. (shrink)

_Behavioral and Brain Sciences_ , 15, 183-247, 1992. Reprinted in _The Philosopher's Annual_ , Grim, Mar and Williams, eds., vol. XV-1992, 1994, pp. 23-68; Noel Sheehy and Tony Chapman, eds., _Cognitive Science_ , Vol. I, Elgar, 1995, pp.210-274.

The point of departure of this paper is Penrose's definition of conscious action as that in which stimulus and response are linked by a non‐algorithmic relationship, which Penrose defines as ‘understanding’. My purpose is to explore the nature of this understanding by means of a two‐step process. The first step is provided by Tononi's Integrated Information Theory of consciousness. This theory provides us with a quantitative measure of conscious states that we need to transform into qualitative meaning. In the second (...) step, we obtain this qualitative meaning with the help of the structuralist theory of mind. From this perspective, meaning originates in the set of qualitative contrasts that define the alternative courses of action that might have been implemented. Work on the analogy between quantitative bits and qualitative binary oppositions does not solve the hard problem of consciousness, but it might help to reformulate it in a new, productive way. (shrink)

Scientific behavior is used as a benchmark to examine the truth status of computationalism (COMP) as a law of nature. A COMP-based artificial scientist is examined from three simple perspectives to see if they shed light on the truth or falsehood of COMP through its ability or otherwise, to deliver authentic original science on the a priori unknown like humans do. The first perspective (A) looks at the handling of ignorance and supports a claim that COMP is "trivially true" or (...) "pragmatically false" in the sense that you can simulate a scientist if you already know everything, which is a state that renders the simulation possible but pointless. The second scenario (B) is more conclusive and unusual in that it reveals that the COMP scientist can never propose/debate that COMP is a law of nature. This marked difference between the human and the artificial scientist in this single, very specific circumstance, means that COMP cannot be true as a general claim. The third scenario (C) examines the artificial scientist's ability to do science on itself/humans to uncover the "law of nature" which results in itself. This scenario reveals that a successful test for scientific behavior by a COMP-based artificial scientist supports a claim that COMP is true. Such a test is quite practical and can be applied to an artificial scientist based on any design principle, not merely COMP. Scenario (C) also reveals a practical example of the COMP scientist's inability to handle informal systems (in the form of liars), which further undermines COMP. Overall, the result is that COMP is false, with certainty in one very specific, critical place. This lends support to the claims (i) that artificial general intelligence will not succeed based on COMP principles, and (ii) computationally enacted abstract models of human cognition will never create a mind. (shrink)

Experiences of qualia, subjective sensory-like aspects of stimuli, are central to imagistic representation. Following Raffman , qualia are considered to reflect experiential knowledge distinct from descriptive, abstract, and propositional knowledge; following Jackendoff , objective neural activity is distinguished from subjective experience. It is argued that descriptive physical knowledge does not provide an adequate accounting of qualia, and philosophical scenarios such as the Turing test and the Chinese Room are adapted to demonstrate inadequacies of accounts of cognition that ignore subjective experience. (...) Arguments by Dennett and others that qualia do not exist or that qualia do not provide additional explanatory power are addressed, and it is suggested that consideration of qualia is necessary in order to explain objective behavior. The hypotheses of functional equivalence, second-order isomorphism, and psychophysical complementarity between imagery and perception are discussed, and the ability of analog and schematic models of imagery to account for qualia is examined. (shrink)

In the visual system, the represented features of individual objects (shape, colour, movement, and so on) are distributed both in space and time within the brain. Representations of inner and outer event sequences arrive through different sense organs at different times, and are likewise distributed. Objects are nevertheless perceived as integrated wholes - and event sequences are experienced to form a coherent "consciousness stream." In their thoughtful article, Dennett & Kinsbourne ask how this is achieved.