John Hopfield is professor at Princeton, whose life's work weaved beautifully through biology, chemistry, neuroscience, and physics. Most crucially, he saw the messy world of biology through the piercing eyes of a physicist. He is perhaps best known for his work on associate neural networks, now known as Hopfield networks that were one of the early ideas that catalyzed the development of the modern field of deep learning.
EPISODE LINKS: Now What? article: http://bit.ly/3843LeU John wikipedia: https://en.wikipedia.org/wiki/John_Hopfield Books mentioned:
- Einstein's Dreams: https://amzn.to/2PBa96X
- Mind is Flat: https://amzn.to/2I3YB84
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Here's the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.
OUTLINE: 00:00 - Introduction 02:35 - Difference between biological and artificial neural networks 08:49 - Adaptation 13:45 - Physics view of the mind 23:03 - Hopfield networks and associative memory 35:22 - Boltzmann machines 37:29 - Learning 39:53 - Consciousness 48:45 - Attractor networks and dynamical systems 53:14 - How do we build intelligent systems? 57:11 - Deep thinking as the way to arrive at breakthroughs 59:12 - Brain-computer interfaces 1:06:10 - Mortality 1:08:12 - Meaning of life https://lexfridman.com/john-hopfield/?utm_source=rss&utm_medium=rss&utm_campaign=john-hopfield
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