Seminar by Geraint Wiggins on Computational Creativity
Geraint A. Wiggins, from Queen Mary University of London, gives a seminar on "Creativity, deep symbolic learning, and the information dynamics of thinking" on Thursday, March 2nd 2017, at 15:30h in room 55.309 of the Communication Campus of the UPF.
Abstract: I present a hypothetical theory of cognition which is based on the principle that mind/brains are information processors and compressors, that are sensitive to certain measures of information content, as defined by Shannon (1948). The model is intended to help explicate processes of anticipatory and creative reasoning in humans and other higher animals. The model is motivated by the evolutionary value of prediction in information processing in an information-overloaded world.
The Information Dynamics of Thinking (IDyOT) model brings together symbolic and non-symbolic cognitive architectures, by combining sequential modelling with hierarchical symbolic memory, in which symbols are grounded by reference to their perceptual correlates. This is achieved by a process of chunking, based on boundary entropy, in which each segment of an input signal is broken into chunks, each of which corresponds with a single symbol in a higher level model. Each chunk corresponds with a temporal trajectory in the complex Hilbert space given by a spectral transformation of its signal; each symbol above each chunk corresponds with a point in a higher space which is in turn a spectral representation of the lower space. Norms in the spaces admit measures of similarity, which allow grouping of categories of symbol, so that similar chunks are associated with the same symbol. This chunking process recurses “up” IDyOT’s memory, so that representations become more and more abstract.
It is possible to construct a Markov Model along a layer of this model, or up or down between layers. Thus, predictions may be made from any part of the structure, more or less abstract, and it is in this capacity that IDyOT is claimed to model creativity, at multiple levels, from the construction of sentences in everyday speech to the improvisation of musical melodies.
IDyOT’s learning process is a kind of deep learning, but it differs from the more familiar neural network formulation because it includes symbols that are explicitly grounded in the learned input, and its answers will therefore be explicable in these terms.
In this talk, I will explain and motivate the design of IDyOT with reference to various different aspects of music, language and speech processing, and to animal behaviour.