How Can Findings About The Brain Improve AI Systems?

Shinji Nishimoto · Leila Wehbe · Alexander Huth · Javier Turek · Nicole Beckage · Vy Vo · Mariya Toneva · Hsiang-Yun Chien · Shailee Jain · Richard Antonello

The brain comprises billions of neurons organized into an intricate network of highly specialized functional areas. This biological cognitive system can efficiently process vast amounts of multi-modal data to perceive and react to its ever-changing environment. Unlike current AI systems, it does not struggle with domain adaptation, few-shot learning, or common-sense reasoning. Inspiration from neuroscience has benefited AI in the past: dopamine reward signals inspired TD learning, modern convolutional networks mimic the deep, nested information flow in visual cortex, and hippocampal replay of previous experiences has brought about experience replay in reinforcement learning. Recent work at the intersection of neuroscience and AI has made progress in directly integrating neuroscientific data with AI systems and has led to learned representations that are more robust to label corruptions, allow for better generalization in some language tasks, and provide new ways to interpret and evaluate what domain-relevant information is learned by deep neural networks. In this workshop, we aim to examine the extent to which insights about the brain can lead to better AI.

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