Support us! https://www.patreon.com/mlst MLST Discord: https://discord.gg/aNPkGUQtc5 YT: https://youtu.be/Vbi288CKgis Michael Levin is a Distinguished Professor in the Biology department at Tufts University, and the holder of the Vannevar Bush endowed Chair. He is the Director of the Allen Discovery Center at Tufts and the Tufts Center for Regenerative and Developmental Biology. His research focuses on understanding the biophysical mechanisms of pattern regulation and harnessing endogenous bioelectric dynamics for rational control of growth and form. The capacity to generate a complex, behaving organism from the single cell of a fertilized egg is one of the most amazing aspects of biology. Levin' lab integrates approaches from developmental biology, computer science, and cognitive science to investigate the emergence of form and function. Using biophysical and computational modeling approaches, they seek to understand the collective intelligence of cells, as they navigate physiological, transcriptional, morphognetic, and behavioral spaces. They develop conceptual frameworks for basal cognition and diverse intelligence, including synthetic organisms and AI. Also joining us this evening is Irina Rish. Irina is a Full Professor at the Université de Montréal's Computer Science and Operations Research department, a core member of Mila - Quebec AI Institute, as well as the holder of the Canada CIFAR AI Chair and the Canadian Excellence Research Chair in Autonomous AI. She has a PhD in AI from UC Irvine. Her research focuses on machine learning, neural data analysis, neuroscience-inspired AI, continual lifelong learning, optimization algorithms, sparse modelling, probabilistic inference, dialog generation, biologically plausible reinforcement learning, and dynamical systems approaches to brain imaging analysis.  Interviewer: Dr. Tim Scarfe TOC: [00:00:00] Introduction [00:02:09] Emergence [00:13:16] Scaling Laws [00:23:12] Intelligence [00:44:36] Transhumanism Prof. Michael Levin https://en.wikipedia.org/wiki/Michael_Levin_(biologist) https://www.drmichaellevin.org/ https://twitter.com/drmichaellevin Prof. Irina Rish https://twitter.com/irinarish https://irina-rish.com/

Support the show to get full episodes, full archive, and join the Discord community. Irina is a faculty member at MILA-Quebec AI Institute and a professor at Université de Montréal. She has worked from both ends of the neuroscience/AI interface, using AI for neuroscience applications, and using neural principles to help improve AI. We discuss her work on biologically-plausible alternatives to back-propagation, using "auxiliary variables" in addition to the normal connection weight updates. We also discuss the world of lifelong learning, which seeks to train networks in an online manner to improve on any tasks as they are introduced. Catastrophic forgetting is an obstacle in modern deep learning, where a network forgets old tasks when it is trained on new tasks. Lifelong learning strategies, like continual learning, transfer learning, and meta-learning seek to overcome catastrophic forgetting, and we talk about some of the inspirations from neuroscience being used to help lifelong learning in networks. Irina's website.Twitter: @irinarishRelated papers:Beyond Backprop: Online Alternating Minimization with Auxiliary Variables.Towards Continual Reinforcement Learning: A Review and Perspectives.Lifelong learning video tutorial: DLRL Summer School 2021 - Lifelong Learning - Irina Rish. 0:00 - Intro 3:26 - AI for Neuro, Neuro for AI 14:59 - Utility of philosophy 20:51 - Artificial general intelligence 24:34 - Back-propagation alternatives 35:10 - Inductive bias vs. scaling generic architectures 45:51 - Continual learning 59:54 - Neuro-inspired continual learning 1:06:57 - Learning trajectories