Unsupervised Machine Learning and LinguisticsThe fundamental problem of linguistics is to find how knowledge of language is represented and how that knowledge is acquired by children learning their first language; understanding or solving this problem would open the door to a new generation of intelligent language processing systems. This is fundamentally a computational problem, which can be studied using the tools of formal language theory and computational learning. Solving it requires reconceptualising some basic concepts -- including the relationship between a grammar and the language it defines.

In this talk I will give an overview of this field (assuming no prior knowledge of linguistics or machine learning) and discuss some recent technical results in distributional learning that can potentially provide a solution to this problem. These techniques involve modelling the relationship between substrings and the contexts that they can appear in -- these give rise to algorithms for learning classes of context free and context sensitive languages that seem to be a good match for the properties of natural language.

Alex ClarkA concept we refer to as the biological constraint is shown to be able where id=11; to explain the effectiveness of mathematical descriptions of the universe, as well as accounting for the origin of life and our ability to think logically. The biological constraint, which can be studied systematically through the use of appropriate models, refers to selection in the biological realm in favour of mechanisms that have wide applicability, a subset of which have mathematical character that can evolve to ever subtler forms. The precise conformance of physical phenomena to precise mathematical laws is related to the enforcement of symmetry.