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, AGI, 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. Also, check out Sam’s previous appearance on the podcast.
- Chris’s lab: Human Information Processing lab.
- Sam’s lab: Computational Cognitive Neuroscience Lab.
- Twitter: @gershbrain; @summerfieldlab
- Papers we discuss or mention or are related:
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