Uploaded January 2021 | Updated September 2026, 1 day ago
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Free Video Series: Open Questions in AI and Neuroscience:
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Music by: The New Year: thenewyear.net
Show notes: braininspired.co/podcast/95
It's generally agreed machine learning and AI provide neuroscience with tools for analysis and theoretical principles to test in brains, but there is less agreement about what neuroscience can provide AI. Should computer scientists and engineers care about how brains compute, or will it just slow them down, for example? Chris, Sam, and I discuss how neuroscience might contribute to AI moving forward, considering the past and present. This discussion also leads into related topics, like the role of prediction versus understanding, explainable AI, value alignment, the fundamental conundrum that humans specify the ultimate values of the tasks AI will solve, and more. Plus, a question from previous guest Andrew Saxe.
0:00 - Intro
5:00 - Good ol' days
13:50 - AI for neuro, neuro for AI
24:25 - Intellectual diversity in AI
28:40 - Role of philosophy
30:20 - Operationalization and benchmarks
36:07 - Prediction vs. understanding
42:48 - Role of humans in the loop
46:20 - Value alignment
51:08 - Andrew Saxe question
53:16 - Explainable AI
58:55 - Generalization
1:01:09 - What has AI revealed about us?
1:09:38 - Neuro for AI
1:20:30 - Concluding remarks
Patreon for full episodes and Discord community:
patreon.com/braininspired
Free Video Series: Open Questions in AI and Neuroscience:
braininspired.co/open
Apple podcasts: itunes.apple.com/us/podcast/brain-inspired/id1428880766?mt=2
Spotify: open.spotify.com/show/2UZj8c8Ap5oc2gh2rJxLLe
Music by: The New Year: thenewyear.net
Show notes: braininspired.co/podcast/95
It's generally agreed machine learning and AI provide neuroscience with tools for analysis and theoretical principles to test in brains, but there is less agreement about what neuroscience can provide AI. Should computer scientists and engineers care about how brains compute, or will it just slow them down, for example? Chris, Sam, and I discuss how neuroscience might contribute to AI moving forward, considering the past and present. This discussion also leads into related topics, like the role of prediction versus understanding, explainable AI, value alignment, the fundamental conundrum that humans specify the ultimate values of the tasks AI will solve, and more. Plus, a question from previous guest Andrew Saxe.
0:00 - Intro
5:00 - Good ol' days
13:50 - AI for neuro, neuro for AI
24:25 - Intellectual diversity in AI
28:40 - Role of philosophy
30:20 - Operationalization and benchmarks
36:07 - Prediction vs. understanding
42:48 - Role of humans in the loop
46:20 - Value alignment
51:08 - Andrew Saxe question
53:16 - Explainable AI
58:55 - Generalization
1:01:09 - What has AI revealed about us?
1:09:38 - Neuro for AI
1:20:30 - Concluding remarks
