Tobias SCHEER — CNRS, Université Côte d'Azur — Introduction Session Cognition

SESSION A.I. & COGNITION Chair : Tobias Scheer (CNRS, Université Côte d’Azur) José Luis Bermudez (University Texas A&M) Paul Smolensky (Johns Hopkins University) Xavier Vasques (IBM, Laboratoire de Recherche en Neurosciences Cliniques)

Every time I fire a linguist, the performance of our speech recognition system goes up.

1988 He was not a pioneer of speech recognition, he was the pioneer of speech recognition. says Steven Young from the Signal Processing Society in 2010

Fred Jelinek (1932 -2010) IBM Research, then Johns Hopkins

The mind and the machine

• linguists are supposed to have an idea of how language works.

• ...of how it works in the mind.

• so do we or don't we want machines that mimic the workings of natural systems?

• is the implementation of natural workings in a machine enhancing or impeding its performance?

• was Jelinek right back in 1988 because the technology was not ready for the implementation of natural systems?

Is it ready today? One day?

Does that depend on technology at all?

the mind and the brain • talking about the mind...

• material objects have nonmaterial properties: e.g. the center of gravity • the mind is a non-material property of the brain • if the center of gravity is real, so is the mind.

• the mind is the subject matter of Cognitive Science

connectionism

Computation in the mind should be brain-style.

1989 • leading ideas of connectionism, freshly introduced then • instruments: artificial neural networks and PDP (Parallel Distributed Processing)

The operations in our models then can best be characterized as 'neurally inspired.'

David Rumelhart (1942-2011) psychologist

Rumelhart, David 1989. The Architecture of Mind: A Connectionist Approach. Foundations of Cognitive Science, edited by Michael Posner, 133-159. Cam., Mass.: MIT Press.

Artificial neural networks – artefacts or natural objects in the guise of a machine?

• to which extent exactly, if any, has the connectionist promise come true?

• are current neural networks successfully mimicking the brain?

• what does brain-style computation tell us about the (human) cognitive system?

• do we expect the workings of the cognitive system (the mind) to be in any way similar or identical to what the brain does? If not, how and where exactly do the brain and the mind meet?

• is striving for functional efficiency and performance corrupting the ambition for bio-inspiration?

• how much bio-inspired is the next generation of machines?

Artificial neural networks – artefacts or natural objects in the guise of a machine?

without knowing anything of the internal workings of current machines, three external obsevations may be relevant: •energy •exposure needed for successful learning •error tolerance

talks How deep is Deep Learning?

José-Luis Bermúdez Texas A&M University

Philosophy of mind

talks Human language is profoundly shaped by its neural substrate

Paul Smolensky Microsoft Research AI Johns Hopkins University

Cognitive Science Linguistics

talks "Neural" or "brain-like"? A mutual learning experience

Xavier Vasques IBM, Laboratoire de Recherche en Neurosciences Cliniques

Neuroscience Computer science