How a Subfield of Physics Led to Breakthroughs in AI – and From There to This Year's Nobel Prize
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How a subfield of physics led to breakthroughs in AI - and from there to this year's Nobel Prize:
We covered the announcement of the Nobel Prize here. This article is to introduce the subject of Statistical Mechanics, for which you will need your thinking caps and an understanding of some serious mathematics. Follow the links for much more detail.
John J. Hopfield and Geoffrey E. Hinton received the Nobel Prize in physics on Oct. 8, 2024, for their research on machine learning algorithms and neural networks that help computers learn. Their work has been fundamental in developing neural network theories that underpin generative artificial intelligence.
A neural network is a computational model consisting of layers of interconnected neurons. Like the neurons in your brain, these neurons process and send along a piece of information. Each neural layer receives a piece of data, processes it and passes the result to the next layer. By the end of the sequence, the network has processed and refined the data into something more useful.
While it might seem surprising that Hopfield and Hinton received the physics prize for their contributions to neural networks, used in computer science, their work is deeply rooted in the principles of physics, particularly a subfield called statistical mechanics.
Statistical Mechanics is the third pillar of modern physics, next to quantum theory and relativity theory. Its aim is to account for the macroscopic behaviour of physical systems in terms of dynamical laws governing the microscopic constituents of these systems and probabilistic assumptions. Like other theories in physics, statistical mechanics raises a number of foundational and philosophical issues. But philosophical discussions in statistical mechanics face an immediate difficulty because unlike other theories, statistical mechanics has not yet found a generally accepted theoretical framework or a canonical formalism.
Journal Reference:
J J Hopfield, Neural networks and physical systems with emergent collective computational abilities, PNAS 79 (8) 2554-2558, (DOI: 10.1073/pnas.79.8.2554)
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