Science and religion have been described as the “two dominant forces in our culture”. As such, the relation between them has been a matter of intense debate, having profound implications for deeper understanding of our place in the universe. One position naturally associated with scientists of a materialistic outlook is that science and religion are contradictory, incompatible worldviews; however, a great deal of recent literature criticises this “conflict thesis” as simple-minded, essentially ignorant of the nature of religion and its philosophical (...) and theological underpinnings. In this first part of a two-part article, I set out the wide-ranging background required for a proper understanding of the debate as a preliminary for the second part, in which Ian Barbour ’s influential four-fold typology of science-religion relations is critically assessed, leading to the conclusion that the conflict model is not to be so easily dismissed. (shrink)

Following the “modularity” orthodoxy of some years ago, it has traditionally been assumed that there is a clear and obvious separation between perception and cognition. Close examination of this concept, however, fails to reveal the join. Ballard et al.'s contention that the two “cannot be easily separated” is consistent with nonmodular views of the way that symbol grounding might be achieved in situated systems. Indeed, the traditional separation is viewed as unhelpful.

Uninformed learning mechanisms will not discover “type- 2” regularities in their inputs, except fortuitously. Clark & Thornton argue that error back-propagation only learns the classical parity problem – which is “always pure type-2” – because of restrictive assumptions implicit in the learning algorithm and network employed. Empirical analysis showing that back-propagation fails to generalise on the parity problem is cited to support their position. The reason for failure, however, is that generalisation is simply not a relevant issue. Nothing can be (...) gleaned about back-propagation in particular, or learning in general, from this failure. (shrink)

Clark & Thornton take issue with my claim that parity is not a generalisation problem, and that nothing can be inferred about back-propagation in particular, or learning in general, from failures of parity generalisation. They advance arguments to support their contention that generalisation is a relevant issue. In this continuing commentary, I examine generalisation more closely in order to refute these arguments. Different learning algorithms will have different patterns of failure: back-propagation has no special status in this respect. This is (...) not to deny that a particular algorithm might fortuitously happen to produce the “intended” function in an parity-generalisation task. (shrink)

In the preceding Part 1 of this two-part paper, I set out the background necessary for an understanding of the current status of the debate surrounding the relationship between science and religion. In this second part, I will outline Ian Barbour’s influential four-fold typology of the possible relations, compare it with other similar taxonomies, and justify its choice as the basis for further detailed discussion. Arguments are then given for and against each of Barbour’s four models: conflict, independence, integration and (...) dialogue. In contradiction of the recent trend to dismiss the conflict model as overly “simplistic”, I conclude that it is the clear front-runner. Critical examination reveals that theology (the academic face of religion) typically proceeds by first affirming belief in God and then seeking rationalisations that protect this belief against contrary evidence. As this is the very antithesis of scientific endeavour, the two disciplines are in unavoidable and irreconcilable conflict. (shrink)

Locus equations offer promise for an understanding of at least some aspects of perceptual invariance in speech, but they were discovered almost fortuitously. With the present availability of powerful machine learning algorithms, ignorance -based automatic discovery procedures are starting to supplant knowledge-based scientific inquiry. Principles of self-learning and self-organization are powerful tools for speech research but remain somewhat under-utilized.