Connectionists: Fwd: INNS: Register for Marco Gori's Webinar!

[Thomas Trappenberg]

7 November 2024 - 14:00 UTC

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INNS Webinar Series
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Towards lifelong learning intelligent agents capable of focusing attention
and taking conscious actions - Neural Propagation in the Framework of
Cognidynamics
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*7 November 2024 | **14:00 UTC*
Register Now
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The fields of Artificial Intelligence (AI) and Cognitive Science began
intersecting significantly during the Eighties when the Connectionist wave
strongly propelled studies on Artificial Neural Networks. The evolution of
AI over the last few decades, focusing on deep learning and, more recently,
generative AI, has produced spectacular results that were hardly
predictable even by the pioneers of the discipline.

However, when examining early studies on Connectionism, many aspirations
remain unrealized, as most successful outcomes rely on the brute force of
combining computational resources with large data collections. This stands
in contrast to nature, where cognition emerges from environmental
interactions
and the processing of temporal information. In order to capture those
natural processes and explore an alternative path to Machine Learning, in
this talk I introduce the framework of Cognidynamics that describes
cognitive systems whose environmental interactions are driven by the
minimization of a functional over time.

This functional, referred to as cognitive action, replaces the traditional
statistical functional risk of Machine Learning in the temporal dimension.
I employ the tools of Theoretical Physics and Optimal Control to derive
unified laws of cognition for learning and inference in recurrent neural
networks. I demonstrate that Hamiltonian equations, in their causal
dissipative form, lead to a novel neural propagation scheme that is local
in both space and time. This addresses the longstanding debate on the
biological plausibility of Backpropagation and offers a new framework for
developing lifelong learning intelligent agents capable of focusing
attention and taking conscious actions.


*Marco Gori*
Siena Artificial Intelligence Lab
University of Siena
Marco Gori received the Ph.D. degree in 1990 from Università di Bologna,
Italy, working partly at the School of Computer Science (McGill University,
Montreal). He is currently full professor of computer science at the
University of Siena, where he is leading the Siena Artificial Intelligence
Lab. He is mostly interested in Machine Learning with emphasis on Neural
Computation.

The impact of his research on neural networks emerged mainly from the
growing interest in Graph Neural Networks. He introduced the first ideas in
the paper “A New Model for Learning in Graph Domains”, by M. Gori, M.
Monfardini and F. Scarselli (IJCNN2005) where the keyword Graph Neural
Network was coined. A few years later, the most significant paper “Graph
Neural Networks,” IEEE-TNN, 2009 provided a more robust analysis and an
accurate experimental evaluation. To date, the paper has received about
9,000 citations (about 6-7 citations/day in the last months).

Professor Gori has been the chair of the Italian Chapter of the IEEE
Computation Intelligence Society and the President of the Italian
Association for Artificial Intelligence. He is a Fellow of IEEE, EurAI,
IAPR, and ELLIS.
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