BI 198 Tony Zador: Neuroscience Principles to Improve AI

Nov 11, 2024

In this intriguing discussion, Tony Zador, head of the Zador lab at Cold Spring Harbor Laboratory, shares his insights on the synergy between neuroscience and artificial intelligence. He argues that biological principles can significantly improve AI efficiency, particularly through understanding animal behavior. The conversation dives into the evolution of NeuroAI, the pitfalls of current AI models, and the parallels between genetic coding and neural networks. Zador highlights the importance of incorporating developmental learning stages from humans and animals to create more adaptable AI systems.

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Exploring Neuro AI and Vision Science

03:50 • 21min

Navigating AI's Cognitive Landscape

24:34 • 21min

Interplay of Genes, Behavior, and Neural Networks

46:02 • 16min

Exploring Motor Development: Humans vs. Animals

01:01:50 • 4min

Curriculum Learning and Its Implications in AI

01:06:04 • 13min

Challenges of Self-Driving Technology

01:19:11 • 6min

AI and Neuroscience: Bridging the Gap

01:25:14 • 5min

Navigating Research Interest and Community Dynamics

01:29:53 • 2min

Exploring the Intersection of Development and Neuroscience

01:31:35 • 3min

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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.

Zador Lab
Twitter: @TonyZador
Previous episodes:
Related papers
- Catalyzing next-generation Artificial Intelligence through NeuroAI.
- Encoding innate ability through a genomic bottleneck.
Essays
- NeuroAI: A field born from the symbiosis between neuroscience, AI.
- What the brain can teach artificial neural networks.

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