Juillet 2016

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Lundi 11 Juillet
Heure: 14:00 - 15:00
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: Abstraction, Entropy and Computing Formats
Description: Luis A. Pineda In this talk a theory for the diagrammatic representation and computation of
finite discrete functions and abstractions is presented. The theory is defined in
terms of two basic operations that are computed directly on tables: the func-
tional abstrac tion and the functional application or reduction. However, unlike
the analogous operations of the lamba-calculus, these operations are not fully re-
versible and the system has an inherent information loss. For this, abstractions
have an associated entropy value that measures their degree of indeterminacy
or information content. The theory is applied to the definition and construction
of an associative memory, where the information is accessed by content, with
its associated memory register, recognition and retrieval operations. A case
study in visual memory with very promising preliminary results is presented.
The overall theory suggests a comprehensive view or space of possible compu-
tations that is defined in relation to (1) the trade-off between extensional and
intensional forms of expressing information and (2) the formats employed in
computations. This trade-off underlies the knowledge representation trade-off
of articial intelligence and cognitive science.The computing formats, in
turn, range from the linguistic format, whose paradigmatic case is the Turing
Machine, to fully distributed formats including neural networks and the
diagrammatic format.The view suggests that the trade-off between extensions
and intensions is the manner in which the entropy of abstractions surface in the
linguistic format. It also supports the case of direct representation in AI and
the case of images in the imagery debate, and helps to clarify the opposition between
symbolic and sub-symbolic computations. Finally, the implications of the view for learning,
creativity, embodied and situated cognition, and for the distinction between "artificial" and
"natural” computations are briefly discussed.