Susan G. Sterrett

Turing wrote that the “guiding principle” of his investigation into the possibility of intelligent machinery was “The analogy [of machinery that might be made to show intelligent behavior] with the human brain.” [10] In his discussion of the investigations that Turing said were guided by this analogy, however, he employs a more far-reaching analogy: he eventually expands the analogy from the human brain out to “the human community as a whole.” Along the way, he takes note of an obvious fact in the bigger scheme of things regarding human intelligence: grownups were once children; this leads him to imagine what a machine analogue of childhood might be. In this paper, I’ll discuss Turing’s child-machine, what he said about different ways of educating it, and what impact the “bringing up” of a child-machine has on its ability to behave in ways that might be taken for intelligent. I’ll also discuss how some of the various games he suggested humans might play with machines are related to this approach.

This paper investigates the following proposal about machine intelligence: that behaviour in which a habitual response that would have been inappropriate in a certain unfamiliar situation is overridden and replaced by a more appropriate response be considered evidence of intelligence. The proposal was made in an earlier paper (Sterrett 2000) and arose from an analysis of a neglected test for intelligence hinted at in Turing's legendary 'Computing Machinery and Intelligence'; it was also argued there that it was a more principled test of machine intelligence than straightforward comparisons with human behaviour. The present paper first summarizes the previous claim then looks at writings about intelligence, or the lack of it, in animals and machines by various writers (Descartes, Hume, Darwin and James). It is then shown that, despite their considerable differences regarding fundamental things such as what kinds of creatures are intelligent and the relationship between reason, instinct and behaviour, all of these writers would regard behaviour that meets the proposed criterion as evidence of intelligence. Finally, some recent work in employing logic and reinforcement learning in conjunction with 'behaviour-based' principles in the design of intelligent agents is described; the significance for the prospect of machine intelligence according to the proposed criterion is discussed

I survey a broad variety of models with an eye to asking what kind of model each is in the following sense: in virtue of what is each of them regarded as a model? It will be seen that when we classify models according to the answer to this question, it comes to light that the notion of model predominant in philosophy of science covers only some of the kinds of models used in scientific contexts. The notion of a model predominant in philosophy of science requires that a model be related to something formal, such as equations or statements. Not all the examples provided in the brief survey in this paper fit that notion of a model. I identify another kind of model that ought to be included in philosophical and foundational studies of scientific models, which I call a “piece of the world” kind of model, to contrast with a “realm of thought” kind of model. These models also have formal methodologies associated with them, and, hence, analytic philosophers of science can embrace them without abandoning the rigor that has characterized the discipline.

. Some disagreements have arisen in the last few years regarding the role played by material properties when modeling, simulating and experimenting on physical systems (Morrison 2008, Parker (forthcoming), Winsberg (forthcoming), Guala 2002, 2005; Morgan 2005). The question has proven more involved than it first appears. A number of significant and correct points have already been made, but some confusions remain. In this paper I attempt to sort them out. After pointing out the importance of some distinctions that need to be drawn, I explain the ways in which material properties matter in modeling and simulations.

The year that Ludwig Wittgenstein was born in Vienna, 1889, nearby developments already underway portended two major changes of the coming century: the advent of controlled heavier-than-air flight and the mass production of musical sound recordings. Before they brought about major social changes, though, these innovations appeared in Europe in the form of children’s toys. Both a rubber-band-powered model helicopter-like toy employing an ingenious solution to the problem of control, and a working toy gramophone with which music could be reproduced from hard discs, appeared in Europe in time for Ludwig’s childhood. And, both innovations reappear in his work as an adult. The relationship between the advent of heavier-than-air flight and Wittgenstein’s claim in Tractatus Logico-Philosophicus that a proposition is a picture or model is a topic in its own right, and I discuss it in separate works. 1 In this essay, I consider the way Wittgenstein employed the development of sound recordings in discussing logical form in the Tractatus