#214 Learning & Memory, For Brains & AI, with Kim Stachenfeld, Senior Research Scientist at Google DeepMind

Jun 10, 2024

In this conversation, Kim Stachenfeld, a Senior Research Scientist at Google DeepMind, dives into the fascinating intersection of neuroscience and AI. She discusses how our understanding of human memory can enhance AI development. Kim explains the complexities of learning techniques and emphasizes the role of storytelling in data interpretation. The chat also covers the importance of customizable AI models and their potential to unlock scientific insights, alongside the challenges of applying cognitive principles to improve AI outcomes.

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INSIGHT

Developmental Brain Learning

Brain learning is structured developmentally, with cognitive abilities emerging gradually.
This contrasts with language models, which receive the entire dataset at once.

INSIGHT

Specialized Learning Systems

Different brain regions specialize in various types of learning, like episodic and procedural memory.
Henry Molaison's case, where hippocampus removal affected episodic memory but not procedural, exemplifies this.

ADVICE

Effective Learning Tips

Improve learning by prioritizing sleep, exercise, social interaction, and exposure to novelty.
These activities promote neurogenesis, memory consolidation, and a positive learning state.

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Memory, the foundation of human intelligence, is still one of the most complex and mysterious aspects of the brain. Despite decades of research, we've only scratched the surface of understanding how our memories are formed, stored, and retrieved. But what if AI could help us crack the code on memory? How might AI be the key to unlocking problems that have evaded human cognition for so long?

Kim Stachenfeld is a Senior Research Scientist at Google DeepMind in NYC and Affiliate Faculty at the Center for Theoretical Neuroscience at Columbia University. Her research covers topics in Neuroscience and AI. On the Neuroscience side, she study how animals build and use models of their world that support memory and prediction. On the Machine Learning side, she works on implementing these cognitive functions in deep learning models. Kim’s work has been featured in The Atlantic, Quanta Magazine, Nautilus, and MIT Technology Review. In 2019, she was named one of MIT Tech Review’s Innovators under 35 for her work on predictive representations in hippocampus.

In the episode, Richie and Kim explore her work on Google Gemini, the importance of customizability in AI models, the need for flexibility and adaptability in AI models, retrieval databases and how they improve AI response accuracy, AI-driven science, the importance of augmenting human capabilities with AI and the challenges associated with this goal, the intersection of AI, neuroscience and memory and much more.

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