Towards the Synergy Between AI and Neuroscience
par
Irina Rish
AI Foundations department
IBM T.J. Watson Research Center
Mercredi 26 juin 2019, 10:00-11:00, Salle 3195, Pavillon André-Aisenstadt
Université de Montréal, 2920 Chemin de la Tour
La conférence sera présentée en anglais
Résumé:
Despite its remarkable recent advances, AI is still far from achieving human-level intelligence, and making further progress in that direction may require developing fundamentally new approaches to move us from today’s mostly “narrow”/task-specific AI towards a “broad”/multi-functional, continually learning, and self-evolving AI, and eventually to general/human-level AI. One promising avenue of research which is believed to have a potential for revolutionizing the field is to explore more biologically-inspired mechanisms behind the human intelligence. This approach has already proved to be useful before, giving rise to modern deep learning and reinforcement learning, but there is a wealth of untapped knowledge about the brain functioning accumulated in neuroscience, psychology and related disciplines, that can help us bring AI to the next level. On the other hand, introducing AI ideas, models and techniques to neuroscience, psychology and mental health also has a potential of revolutionizing those fields, so there is a clear need for more synergy between the studies of artificial and natural intelligence. In this talk, I plan to provide an overview of several ongoing efforts in our lab on the intersection between those fields, from (1) developing more biologically plausible alternatives to backpropagation, (2) tackling continual learning with adaptive, neurogenetic architectures and novel learning algorithms, (3) reward-driven attention in online decision making and more bio-inspired reward models in reinforcement learning, to (4) novel dialog generation approach for mental health (therapy), as well as (5) coupled nonlinear dynamical models for both brain activity modeling in neuroimaging and for improving recurrent neural nets performance in applications with limited training data.
Bio :
Irina Rish is a researcher at the AI Foundations department of the IBM T.J. Watson Research Center. She received MS in Applied Mathematics from Moscow Gubkin Institute, Russia, and PhD in Computer Science from the University of California, Irvine. Her areas of expertise include artificial intelligence and machine learning, with a particular focus on probabilistic graphical models, sparsity and compressed sensing, active learning, and their applications to various domains, ranging from diagnosis and performance management of distributed computer systems (“autonomic computing”) to predictive modeling and statistical biomarker discovery in neuroimaging and other biological data. Irina has published over 80 research papers, several book chapters, two edited books, and a monograph on Sparse Modeling, taught several tutorials and organized multiple workshops at machine-learning conferences, including NIPS, ICML and ECML. She holds over 60 patents and several IBM awards. Irina currently serves on the editorial board of the Artificial Intelligence Journal (AIJ). As an adjunct professor at the EE Department of Columbia University, she taught several advanced graduate courses on statistical learning and sparse signal modeling.
