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Theoretical Neuroscience Podcast
On origins of computational neuroscience and AI as scientific fields - with Terrence Sejnowski (vintage) - #9
Mar 16, 2024
Delving into the origins of computational neuroscience and AI, the podcast explores the transition from rule-based to learning-based AI approaches. It highlights the unreasonable effectiveness of math in deep learning and the evolution of reinforcement learning in neural structures. The synergy of AI and neuroscience in medical diagnostics, advancements in self-driving technology, and the transformative impact of AI on society are also discussed.
01:55:27
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Quick takeaways
- The podcast explores the parallel development of computational neuroscience and AI from the 1960s, showcasing pioneers like Terry Sanovsky.
- Insights into the evolution of learning algorithms, from early perceptron models to advanced structures like Boltzmann machines, are discussed.
Deep dives
Origin of Computational Neuroscience and AI Pioneers
The podcast episode delves into the early days of computational neuroscience and artificial intelligence (AI) with a focus on pioneers like Terry Sanovsky. Terry's contributions in establishing computational neuroscience at UCSD in the 1980s are highlighted, showcasing his role in merging neuroscience and AI fields. Significant work like the parallel development of computational neuroscience and AI from the 1960s to present is discussed, emphasizing Terry's insights shared in his book 'The Deep Learning Revolution'.
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Discover
highlights
Listen to the best highlights from the podcasts you love and dive into the full episode