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:
John wikipedia:
Books mentioned:
– Einstein’s Dreams:
– Mind is Flat:
This episode is presented by Cash App. Download it & use code “LexPodcast”:
Cash App (App Store):
Cash App (Google Play):
PODCAST INFO:
Podcast website:
Apple Podcasts:
Spotify:
RSS:
Full episodes playlist:
Clips playlist:
OUTLINE:
0:00 – Introduction
2:35 – Difference between biological and artificial neural networks
8:49 – 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
CONNECT:
– Subscribe to this YouTube channel
– Twitter:
– LinkedIn:
– Facebook:
– Instagram:
– Medium:
– Support on Patreon:
source
