Multi-modal modelling with multi-module mechanics : Autonomy in a computational model of language learing
David M W Powers · RePEc: Research Papers in Economics · 1992
Autonomy has been the subject of many claims, and what is needed most urgently is a summary and analysis of the major claims. Only once we have established what is meant by ‘autonomy’, and what is claimed under this rubric, can we hope to decide a question which relate as much to the Philosophy of Science as to Empirical Science per se. Nonetheless, the philosophy which guides our science can have considerable impact on the results of our research, not to mention the short shrift which we may give, or meet from, those working within other paradigms. This is the context in which this volume has arisen. With this in mind it may be helpful to reveal the biases which lie behind this essay, and the directions in which it will seek to influence the field. The perspective is that of a (hopefully not too impertinent) Computer Scientist seeking the gems throughout Cognitive Science which might be useful in his ambition of building a Language Learning system (Powers: 1983, 84, 85, 89, 91). Machine Learning of Natural Language and Ontology (MLNLO), or simply Natural Language Learning (NLL), is pursued from two perspectives: the artificial intelligence or engineering perspective and the psycholinguistic or scientific perspective. In the present Chomskian era of linguistic claims of the autonomy of language, a new slant emerges: NLL may help provide answers to the philosophical, teleological and neurological questions of whether language is learnt,