Philosophy

ABSTRACT. It is not widely realised that Turing was probably the first person to consider building computing machines out of simple, neuron-like elements connected together into networks in a largely random manner. Turing called his networks 'unorganised machines'. By the ...

ABSTRACT. This article traces the development of possible worlds semantics through the work of: Wittgenstein, 1913–1921; Feys, 1924; McKinsey, 1945; Carnap, 1945–1947; McKinsey, Tarski and Jónsson, 1947–1952; von Wright, 1951; Becker, 1952; Prior, 1953– 1954; Montague, ...

1. Johnson-Laird is surely right when he discerns three mutually exclusive positions in current thinking concerning the relationship between human mentality and computation (1987: 252). He suggests that the only alterna-tive to these three positions is that consciousness is not ...

CiteSeerX - Document Details (Isaac Councill, Lee Giles): >3 representations or whether, in some deep sense, the world in itself is vague. The outback and the maritime region of New Devon are certainly objects in the world but there is surely nothing in these anodyne examples ...

A myth has arisen concerning Turing's article of 1936, namely that Turing set forth a fundamental principle concerning the limits of what can be computed by machine—a myth that has passed into cognitive science and the philosophy of minds to wide and pernicious effect. This supposed principle, sometimes incorrectly termed the Church-Turing thesis, is the claim that the class of functions that can be computed by machines is identical to the class of functions that can be computed by Turing machines. In point of fact, Turing himself nowhere endorses or even states this claim (nor does Church). The author describes a number of notional machines, both analog and digital, that can compute more than a universal Turing machine. These machines are exemplars of the class of nonclassical computing machines. Nothing known at present rules out the possibility that machines in this class will one day be built or that the brain itself is such a machine. These theoretical considerations underc...

Alan Turing anticipated many areas of current research incomputer and cognitive science. This article outlines his contributionsto Artificial Intelligence, connectionism, hypercomputation, andArtificial Life, and also describes Turing's pioneering role in thedevelopment of electronic stored-program digital computers. It locatesthe origins of Artificial Intelligence in postwar Britain. It examinesthe intellectual connections between the work of Turing and ofWittgenstein in respect of their

Lenat's CYC is the severest test to date of the declarative paradigm of knowledge representation and of traditional AI's ‘physical symbol system hypothesis’. Lenat and Guha describe CYC as ‘mankind’s first foray into large-scale ontological engineering’. In designing CYC Lenat and Guha have attempted to solve traditional philosophical problems in ontology, epistemology, and logic. This paper reviews four areas that have been extensively documented by Lenat and Guha: the problem of the nature of substance; the problem of causality; the frame problem; and the problem of reasoning within an inconsistent theory or set of beliefs. The solutions to these problems that Lenat and Guha offer turn out to be disappointingly weak. Lenat's prediction that he will produce ‘a system with human-level breadth and depth of knowledge’ by the early years of next century betrays a failure to appreciate the sheer difficulty of ontological, logical and epistemological problems that he has t...

What are the limits of physical computation? In his 'Church's Thesis and Principles for Mechanisms', Turing's student Robin Gandy proved that any machine satisfying four idealised physical 'principles' is equivalent to some Turing machine. Gandy's four principles in effect define a class of computing machines ('Gandy machines'). Our question is: What is the relationship of this class to the class of all (ideal) physical computing machines? Gandy himself suggests that the relationship is identity. We do not share this view. We will point to interesting examples of (ideal) physical machines that fall outside the class of Gandy machines and compute functions that are not Turingmachine computable.

Accelerating Turing machines have attracted much attention in the last decade or so. They have been described as "the work-horse of hypercomputation" (Potgieter and Rosinger 2009). But do they really compute beyond the "Turing limit"-e.g., compute the halting function? We argue that the answer depends on what you mean by an accelerating Turing machine, on what you mean by computation, and even on what you mean by a Turing machine. We show first that in the current literature the term "accelerating Turing machine" is used to refer to two very different species of accelerating machine, which we call end-stage-in and end-stage-out machines, respectively. We argue that end-stage-in accelerating machines are not Turing machines at all. We then present two differing conceptions of computation, the internal and the external, and introduce the notion of the epistemic embedding of a computation. We argue that no accelerating Turing machine computes the halting function in the internal sense. Finally, we distinguish between two very different conceptions of the Turing machine, the purist conception and the realist conception; and we argue that Turing himself was no subscriber to the purist conception. We conclude that under the realist conception, but not under the purist conception, an accelerating Turing machine is able to compute the halting function in the external sense. We adopt a relatively informal approach throughout, since we take the key issues to be philosophical rather than mathematical.

Searle has recently used two adaptations of his Chinese room argument in an attack on connectionism. I show that these new forms of the argument are fallacious. First I give an exposition and rebuttal of the original Chinese room argument, and a brief introduction to the essentials of connectionism. Searle launched his now famous Chinese room argument in 1980. 1 His target was traditional program-writing, symbol-crunching AI. Since that time the connectionist revolution has taken place, leaving AI and cognitive science considerably altered. Searle has recently used two adaptations of the Chinese room argument in an attack on connectionism. 2 I shall show that these new forms of the argument are fallacious. First, I will give an exposition and rebuttal of the original Chinese room argument, and a brief introduction to the essentials of connectionism. The Chinese Room Argument Consider a hypothetical AI program that responds intelligently, in written Chinese, to an input of Chinese sentences. Suppose the program performs indistinguishably from a native Chinese speaker under all input conditions. Searle's argument is designed to show that, performance notwithstanding, the program cannot actually understand Chinese. (In his 1980 paper, Searle's stalking horse was Roger Schank's SAM, a program sometimes described-inappropriately-as being able to understand simple English stories. 3) The hero of the proceedings, call him Joe Soap, is locked in a room with a hard copy of the program. Joe understands no Chinese. His job is to 'handwork' the program-to carry out by hand all the bit manipulations that are performed by a computer running the program. (Conversion between the Chinese input/output and bit code is effected via a lookup table that pairs Chinese characters with, say, Pinyin ASCII.

It is not widely realised that Turing was probably the first person to consider building computing machines out of simple, neuron-like elements connected together into networks in a largely random manner. Turing called his networks 'unorganised machines'. By the application of what he described as 'appropriate interference, mimicking education' an unorganised machine can be trained to perform any task that a Turing machine can carry out, provided the number of 'neurons' is sufficient. Turing proposed simulating both the behaviour of the network and the training process by means of a computer program. We outline Turing's connectionist project of 1948.

This paper charts some early history of the possible worlds semantics for modal logic, starting with the pioneering work of Prior and Meredith. The contributions of Geach, Hintikka, Kanger, Kripke, Montague, and Smiley are also discussed. Prior was charmed by the useful and agreeable Dublin habit, contracted from Lukasiewicz, of referring to various key formulae by proper names. .. Thus CCCpqpp is called '