Google is an excellent computational science company. To the extent that breakthroughs are possible in computer science, Google continues to push the envelope of knowledge to greater heights.
However, an award that was supposed to cement that position for good exposed Google at the foundation of science. Google is not the heavyweight, at all, in theoretical science, as it is in computational science.
There have been several criticisms of the award, which, aside from the possible limitations across solutions, questions of the origin of the actual science and other fundamental bases continue to cast doubt on what should have been a clear-cut victory.
There is a recent feature on Scientific American, AI Comes to the Nobels: Double Win Sparks Debate about Scientific Fields, with the quotes, “I don’t think AlphaFold involves any radical change in the underlying science that wasn’t already in place. It’s just how it was put together and conceived in such a seamless way that allowed AlphaFold to reach those heights.”
“For example, one key input AlphaFold uses is the sequences of related proteins from different organisms, which can identify amino acid pairs that have tended to co-evolve and therefore might be in close physical proximity in a protein’s 3D structure. Researchers were already using this insight to predict protein structures at the time AlphaFold was developed, and some even began embedding the idea in deep learning neural networks.”
There is another feature on Harvard Medicine Magazine, Did AI Solve the Protein-Folding Problem?, with the quotes, “Plus, it’s not even safe to say the protein-folding problem is solved. One important box is ticked off: Researchers can now predict protein structures from amino acid sequences. But two other key parts of the problem — understanding the underlying physics and how proteins fold so quickly — remain unsolved.”
“Structure is not the final story; we want to understand much more. A few years post-AlphaFold2, there hasn’t been as much progress in treating diseases as some might have anticipated. For drug discovery, a deeper understanding of protein dynamics is essential. That includes studying how proteins behave in their natural cellular environments and why certain proteins fold incorrectly — a factor linked to many neurodegenerative diseases.”
“If we can predict how proteins fold without understanding how they do it, are we even legitimately doing science anymore, or is it something different? In the past, what drove us was understanding. With this wave of AI, we’re maybe losing some of that. We’re able to get the practical benefits, but we’re not necessarily gaining intellectual benefits.”
These are not light questions. They do not cancel the use of computational approaches, but they are questions on the basics of the science for which understanding is built—where Google is null.
Aside from AlphaFold, Google Research, in part, is also working on quantum consciousness, with such a sham theory that there is no hope it would ever answer the question. Google is also using computational science to map the brain, in connectomics, with zero to negligible theory, without even daring to check, observe, infer, or question if the neuron is the basic unit of the nervous system.
Google is simply following what is possible computationally, without a lead of basic science with the potential for rounded advances. Of course, there would be progress, but Google, with its massive research teams, is weak and almost completely irrelevant if answers in pure science are sought.