Uploaded January 2022 | Updated September 2026, 16 hours ago
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Free Video Series: Open Questions in AI and Neuroscience:
braininspired.co/open
Show notes: braininspired.co/podcast/123
Apple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2
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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
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
Patreon for full episodes and Discord community:
patreon.com/braininspired
Free Video Series: Open Questions in AI and Neuroscience:
braininspired.co/open
Show notes: braininspired.co/podcast/123
Apple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2
Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLe
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
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
