Irina Rish: AI for Neuroscience & Neuroscience for AI скачать в хорошем качестве

Irina Rish: AI for Neuroscience & Neuroscience for AI

AI and neuroscience share the same age-old goal: to understand the essence of intelligence. Thus, despite different tools used and different questions explored by those disciplines, both have a lot to learn from each other. In this talk, I will summarize some of our recent projects which explore both directions, AI for neuro and neuro for AI. AI for neuro involves using machine learning to recognize mental states and identify statistical biomarkers of various mental disorders from heterogeneous data (neuroimaging, wearables, speech), as well as applications of our recently proposed hashing-based representation learning to dialog generation in depression therapy. Neuro for AI implies drawing inspirations from neuroscience to develop better machine learning algorithms. In particular, I will focus on the continual (lifelong) learning objective, and discuss several examples of neuro-inspired approaches, including (1) neurogenetic online model adaptation in nonstationary environments, (2) more biologically plausible alternatives to backpropagation, 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. 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.