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 This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon. This episode is presented by Cash App. Download it (App Store, Google Play), use code "LexPodcast". 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
The Cash App engineers have made trading more accessible for new investors through an easy interface that provides a step up the next layer of abstraction over the stock market, while a physicist applied theoretical physics concepts for new biological insights.
Pedestrians tend to sway a little bit while crossing bridges, and this behavior is due to the frequency at which bridges sway. Evolutionary biology works in a way that a quirk can be transformed into a useful feature for living creatures, like molecules that change slightly to code for different proteins.
The differences between adaptation and evolutionary time lie in the time frames of the learning process. Whereas adaptation takes place over several generations, evolutionary time involves learning that happens over an individual's lifetime.
The dynamics of the synapse change constantly and this presents a challenge for creating accurate models for neurobiology and computation. Understanding the synaptic dynamics is crucial to understanding the functioning of complex networks in the brain.
The exploration of feed-forward neural networks, recurrent neural networks and convolutional neural networks has aided in solving image recognition and natural language processing. These kind of networks led to the neural network advancements today.
This podcast explores the physics of emergent stability in high dimensional networks and the insights gained from studying the dynamics of such systems, particularly in the field of neurobiology.
Robotics and neuroscience advancements are being made to improve the control of thought patterns and connected limbs, with focus on recording from 10 to the four or 10 to the five at a time to better understand brain function.
The human condition and mortality are discussed along with the interconnected biological systems that make it difficult to define life as an individual entity. It is acknowledged that even after an individual's death, fragments of their thoughts continue to exist.