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Tony Zador runs the Zador lab at Cold Spring Harbor Laboratory. You've heard him on Brain Inspired a few times in the past, most recently in a panel discussion I moderated at this past COSYNE conference - a conference Tony co-founded 20 years ago. As you'll hear, Tony's current and past interests and research endeavors are of a wide variety, but today we focus mostly on his thoughts on NeuroAI.
We're in a huge AI hype cycle right now, for good reason, and there's a lot of talk in the neuroscience world about whether neuroscience has anything of value to provide AI engineers - and how much value, if any, neuroscience has provided in the past.
Tony is team neuroscience. You'll hear him discuss why in this episode, especially when it comes to ways in which development and evolution might inspire better data efficiency, looking to animals in general to understand how they coordinate numerous objective functions to achieve their intelligent behaviors - something Tony calls alignment - and using spikes in AI models to increase energy efficiency.
Read the transcript.
0:00 - Intro
3:28 - "Neuro-AI"
12:48 - Visual cognition history
18:24 - Information theory in neuroscience
20:47 - Necessary steps for progress
24:34 - Neuro-AI models and cognition
35:47 - Animals for inspiring AI
41:48 - What we want AI to do
46:01 - Development and AI
59:03 - Robots
1:25:10 - Catalyzing the next generation of AI
