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Physics World Weekly Podcast

Deep connections: why two AI pioneers won the Nobel Prize for Physics

Oct 10, 2024
Anil Ananthaswamy, author of "Why Machines Learn: The Elegant Maths Behind Modern AI", dives into the recent Nobel Prize awarded to John Hopfield and Geoffrey Hinton for their pioneering work in machine learning. He explains their foundational contributions and the surprising intersection of AI and physics. The conversation also touches on Hinton's warnings about AI's potential risks and the growing need for regulation, while exploring the exciting role of Boltzmann machines in generative AI and their connection to physical principles.
28:04

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Quick takeaways

  • John Hopfield and Geoffrey Hinton's work on AI demonstrates the deep interconnections between physics and machine learning through foundational mathematical principles.
  • Hinton's recent resignation from Google highlights a shift among AI researchers towards addressing the ethical implications and potential dangers of artificial intelligence.

Deep dives

Nobel Prize in Physics for AI and Machine Learning

The 2024 Nobel Prize in Physics was awarded to John Hopfield and Jeffrey Hinton for their significant contributions to machine learning and artificial neural networks. While traditionally considered outside the realm of physics, the recognition highlights the deep interconnections between these fields, particularly through the mathematics employed in AI. Both winners have emphasized that concepts from physics, such as those found in condensed matter physics and statistical physics, strongly underpin the developments in AI, suggesting a symbiotic relationship. This award marks a pivotal moment in academia, urging a reevaluation of the boundaries between physics and technology as AI continues to transform various sectors.

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