Is there anything to this?
https://www.independent.co.uk/life-style/gadgets-and-tech/news/ai-machine-learning-light-speed-artificial-intelligence-a9629976.html?amp

Sure, there's lots of interesting uses for physics-based methods of doing the computation in neural networks. Neural networks are basically information flow graphs, and flows can be liquids, photons, whatever. This is just inference (just getting the computation done), not training (learning what the computation should be).

Doing the computations, even fore training isn't what we're doing on this project.. its more characterizing and understanding the limits of the computations once trained.

https://phys.org/news/2020-07-machine-reveals-recipe-artificial-proteins.amp

Now I keep seeing related articles, probably because machine AI figures out what interests me!
” Though artificial intelligence revealed the design rules, Ranganathan and his collaborators still don't fully understand why the models work. Next they will work to understand just how the models came to this conclusion. "There is much more work to be done," he said.
...the studies in proteins might even help teach us how the deep neural networks behind modern machine learning actually work."

It just goes to show how widespread ML/AI is, and why its important to get it right.

https://phys.org/news/2020-07-machine-reveals-recipe-artificial-proteins.amp
”Though artificial intelligence revealed the design rules, Ranganathan and his collaborators still don't fully understand why the models work. Next they will work to understand just how the models came to this conclusion. "There is much more work to be done," he said.
...the studies in proteins might even help teach us how the deep neural networks behind modern machine learning actually work."

The most important point in this article is, "The model shows that just conservation at amino acid positions and correlations in the evolution of pairs of amino acids are sufficient to predict new artificial sequences that would have the properties of the protein family."
By conserved they mean it does not change. In nature this means that if highly conserved amino acids are changed then the function and/or structure changes so much that evolution will eliminate it. What they fail to tell us is what percentage of the chorismate mutase proteins are conserved in their experiment. Then they have a second rule "correlations in the evolution of pairs of amino acids" that further reduces the number of possibilities. This work is not as profound as the author implies it is. I'd enjoy reading the "An evolution-based model for designing chorismate mutase enzymes, Science (2020)" article but I'm not going to pay $15 for it.

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This work is not as profound as the author implies it is. I'd enjoy reading the "An evolution-based model for designing chorismate mutase enzymes, Science (2020)" article but I'm not going to pay $15 for it.

OK, but let us know if it is worth investing in their company. That is what we really need to know.

Lots of AI research going on!
https://techxplore.com/news/2020-10-math-idea-dataset-size-ai.html