The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Why Deep Networks and Brains Learn Similar Features with Sophia Sanborn - #644
Aug 28, 2023
Sophia Sanborn, a postdoctoral scholar at UC Santa Barbara, blends neuroscience and AI in her groundbreaking research. She dives into the universality of neural representations, showcasing how both biological systems and deep networks can efficiently find consistent features. The conversation also highlights her innovative work on Bispectral Neural Networks, linking Fourier transforms to group theory, and explores the potential of geometric deep learning to transform CNNs. Sanborn reveals the striking similarities between artificial and biological neural structures, presenting a fascinating convergence of insights.
45:15
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Quick takeaways
- Understanding the distinction between living and non-living systems, and the importance of formal models in studying representation and information encoding.
- The incorporation of geometric and group structure into machine learning algorithms can lead to more efficient and accurate computations, reducing the need for extensive data augmentation.
Deep dives
Importance of Representation in Living Systems
The concept of representation is a driving factor in understanding the distinction between living and non-living systems. Living systems encode and store information from the external world through sensors, transforming it into electrical activity in the brain. This transformation allows for rich perceptual and cognitive experiences.
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