AI and Neuroscience: Bridging the Gap
Speaker: Irina Rish, IBM
Time: 11:00 am - 12:00 pm Apr 16, 2019
Location: 370 Jay, Room 1201, Brooklyn, NY
Abstract: The ultimate objective of understanding and modeling intelligent behavior is at the core of both neuroscience and artificial intelligence. Cross-fertilization between these fields has already proven to be extremely useful, both in terms of neuroscience informing AI, with the most prominent examples including deep learning and reinforcement learning, as well as AI helping to bring neuroscience to a new level. This talk is on overview of some of our projects on the intersection between these two disciplines. We start with a brief summary of "AI for Neuro" (e.g., statistical biomarker discovery from neuroimaging of mental disorders and an automated depression therapy models), and continue with an in-depth overview of "Neuro for AI", i.e. better AI algorithms development which draws inspirations from neuroscience. In particular, I will focus on the continual (lifelong) learning objective, and discuss several examples of more neuro-inspired approaches, including (1) neurogenetic online model adaptation in non-stationary environments, (2) more biologically plausible alternatives to back-propagation, e.g., local optimization for neural net learning via alternating minimization with auxiliary activation variables, and co-activation memory, (3) modeling reward-driven attention and attention-driven reward in contextual bandit setting, as well as (4) modeling and forecasting behavior of coupled nonlinear dynamical systems such as brain (from calcium imaging and fMRI) using a combination of analytical van der Pol model with LSTMs, especially in small-data regimes, where such hybrid approach outperforms both of its components used separately. However, besides bridging the gap between biological computation and AI algorithms, another important open question remains about how to bridge the second gap: between emerging novel AI algorithms and constraints/capabilities of analog neuromorphic hardware.
About the Speaker: 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 70 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 26 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.