Partha Niyogi
			University of Chicago

			Friday, April 8, 2005
			      9-10:30 AM
			    4448 East Hall

     The Computational Nature of Language Learning and Evolution

Humans are distinguished by the ability to acquire and use
language. This ability allows us to transmit information in a
non-genetic manner across generations. As a result it becomes possible
for us to have a sense of history, culture, and tradition. Curiously
enough, language may be viewed as a formal object with words and
grammatical rules. Language learning may then be viewed as an
inductive inference procedure that infers these formal objects from
data. This allows one to take a computational view of language
acquisition and indeed, this view has dominated current thinking in
artificial intelligence, cognitive science, and linguistics.

    Now language learning is the mechanism by which language is
transmitted from one generation to the next --- children acquire the
language of the mature speakers in the population. In this talk, we
consider the interplay between learning by individuals and language
change and evolution by populations. By considering an ensemble of
language learners, one can derive various dynamical systems that show
how the population might evolve under those assumptions. We will
consider several such dynamical systems and see how they might shed
light on questions such as dialect formation, language evolution,
convergence on shared languages and so on. Along the way, the
mathematical framework will be elaborated and connections to other
disciplines will be emphasized.