Baggett Lectures: Paul Smolensky (Johns Hopkins)

This is the last in a series of three lectures by distinguished phonologist Paul Smolensky, Krieger-Eisenhower Professor of Cognitive Science at Johns Hopkins University, generously supported by Dave Baggett.

Overview of the lectures

A fundamental task of cognitive science is reconciling (i) the discrete, categorical character of mental states and knowledge — e.g., symbolic expressions governed by symbolic rules of grammar or logic — with (ii) the continuous, gradient character of neural states and processes. This year’s Baggett Lectures will present an approach to unifying discrete symbolic and continuous neural computation: Gradient Symbolic Computation (GSC). This unification leads to new grammatical theories as well as novel neural network architectures that realize these theories. The importance of reconciling symbolic and neural network computation now extends beyond basic science into applied Natural Language Processing, where the best-performing systems utilize neural networks, but it is not currently known how to construct networks that enable rapid instruction, human understanding of internal knowledge, and competence in a diversity of tasks — all properties that are characteristic of symbolic systems.

Lecture 3, Gradient symbols and graded universals in grammatical processing and learning

Gradient Symbolic Computation process models of incremental (word-by-word) syntactic parsing will be discussed, as well as process models of graded probabilistic biases in language learning and the potential role of such biases in explaining statistical typological universals.