DTLR cannot resist opining on this year's Nobel science prizes laureates, especially when several of the non-physics laureates have physics connections, while one of the physics laureates does not.
One of the two laureates for physiology or medicine is Gary Ruvkun, whose bachelor's and Ph.D. degrees are in biophysics. One of the three laureates for chemistry, David Baker, had done a postdoc in biophysics. He currently has an adjunct appointment in the physics department at his university, along with four other departments aside from his home department. A second chemistry laureate, John Jumper, had done a bachelor's degree in physics and math, and a master's in theoretical condensed matter physics, before earning a second master's and a Ph.D. both in theoretical chemistry.
It is perhaps also notable that David Baker's father was a physicist and his mother was a geophysicist. One of the physics laureates, John Hopfield, also had two physicist parents.
Artificial intelligence was the center of this year's physics and chemistry awards. Two of the three chemistry laureates were awarded for AI-based protein structure prediction, for their work with AlphaFold, which is owned by Google's parent company, Alphabet. There is little doubt that this achievement belongs to biological chemistry. I was pleased to see a share of the award go to employees of a private sector company instead of a university, not something that happens often nowadays.
The physics prize, however, has been extremely controversial, in many cases sparking outrage and cynicism, among both physicists and non-physicists.
Let's start with the laureates themselves before discussing their achievements. A number of commentators, including some who should know better (e.g., Sabine Hossenfelder) falsely claim that the award was not given to physicists, but to computer scientists. This is only half true. One of the laureates, Geoffrey Hinton, is not a physicist, by either education or employment. He is a cognitive scientist and computer scientist. (Apparently he is also a direct descendent of George Boole!) The other, John Hopfield, has superb physics bona fides. His education was in physics, he was a Buckley Prize winner for his contributions to condensed matter physics, and he is probably the greatest living biological physicist today. It is true that he is also considered a neuroscientist and a molecular biologist. In fact, his last academic position before retirement was on the Princeton University molecular biology faculty. He is a titan of both physics and interdisciplinary science, though the Nobel committee focused on his work on Hopfield Networks, a model of associative memory based on statistical physics (specifically, spin glasses). He has served as President of the APS.
Unlike many, I celebrate Hopfield's Nobel award. Geoffrey Hinton is really the puzzle here. The Nobel committee zeroed in on his stochastic versions of Hopfield Networks, which are called Boltzmann machines. However, his contributions to neural networks go well beyond that: he introduced the backpropagation algorithm and later was one of the founders of deep learning methodology. He is just as much a titan in the neural net community as Hopfield is in the biological physics community.
The history of neural networks is long, and boasts key contributions by a number of individuals. While Hinton in undoubtedly a key figure in this history, others have also been recognized (including by the Turing award). Perhaps the key point for the Nobel committee was Hinton's use of statistical physics ideas for the Boltzmann machine.
There is no doubt that neural networks are used in physics, as in so many other fields of science and engineering. There is also no doubt that ideas from physics have influenced neural network technology, and machine learning more generally. But are Hopfield Networks and Boltzmann machines worthy of a Nobel Prize in physics? This is a question that was hotly debated in the days after the awards were announced. It seems though, the debate has been lopsided, at least in the sources I've read, with few commentators being willing to defend the Nobel commitee's choice this year. This award was a huge surprise to nearly everyone who follows the Nobel Prize news every October, including me. I was even more shocked when the host of the Physics World podcast admitted he had never heard of John Hopfield until the announcement of the prize.
The American Physical Society, in its official new release, decided to get behind the award, but notably emphasized Hopfield as a longtime member of the APS community. This could be read as a veiled critique of Hinton's award; as far as I know, Hinton's one contribution to the physics literature is a co-authored paper that was published in a physics conference proceedings.
In principle there are several APS units that might choose to celebrate the award to either Hopfield or both Hopfield and Hinton: the Division of Biological Physics, the Division on Computational Physics, the Topical Group on Statistical and Nonlinear Physics, the Topical Group on Data Science, and the Forum on Industrial and Applied Physics. However, I have seen and heard of few who have actually embraced this year's award.
This is regrettable. I think Hopfield is deserving of the honor in the sense that James Peebles was in 2019 - a kind of lifetime career recognition. I'm not ready to render an opinion on Geoffrey Hinton's Nobel Prize in Physics. We need to take a step back from the knee-jerk, immediate, and highly emotional reactions. I'm hoping some thoughtful commentators will, in the coming year, weigh in with well considered opinions. As a physicist who has worked in machine learning (there are now many, many of us), I may choose to view this award as recognition of those working in this interdisciplinary space who actually use physics ideas, regardless of our formal education or employment in physics.
No comments:
Post a Comment