3 ms·

A lot things in practice are using empirical methods and are not looking at F=ma like representations. The basic issue with going just empirical is that there

A lot things in practice are using empirical methods and are not looking at F=ma like representations. The basic issue with going just empirical is that there is no way of knowing if the relationship actually exists or if what is observed is a symptom of something else. (Or, in the extreme case: random. Some bits in finance have that problem, for example, finding an edge in markets). Most successful application of ML etc. are not relying in areas where there are no ideas about any relationships, but rather where there are a lot of confounders and the relationships are complex/difficult to formulate. Sticking with on of the examples, we know that proteins fold in somewhat predictable ways, because we do have theories around the "mechanics" there. So good applications are not strictly in the realm of scientific theory but rather in being able to apply and use such theories in previously inaccessible ways.

PaulDavisThe1st 5y ago
> Sticking with on of the examples, we know that proteins fold in somewhat predictable ways, because we do have theories around the "mechanics" there But we were broadly unable to leverage those theories and knowledge into a predictive system. AlphaFold largely dropped all that (IIUC), and works much better without it. (I was once a PhD candidate in protein folding, and left in part because I felt the field was unlikely to make any substantive progress. It didn't, for at least 30 years, until NN-based "modelling" started to replace all approaches up till then)
- dataangel 5y ago
  Well, but wasn't AlphaFold at least in part trained based on the existing mechanical models? Or was AlphaFold only trained on ground truth imaging of the folding process?
  - PaulDavisThe1st 5y ago
    It was trained on experimentally-determined physical structures. Not predicted models based on various folding engines.
- snowwrestler 5y ago
  The point is that given a solution to protein folding, we can then compare that solution with current theory and even physically test it. The search space is enormous but individual solutions are physically deterministic. This is a good application of ML since there is an independent “check” on its work. And it’s hard to call it “the end of theory” when theory is still tightly coupled with the system, just at the back end instead of the front end. That is not true of many applications of ML, like Facebook, which simply spit out solutions at scale with no way to independently check them. We should not think the same way about all ML systems; there is a lot more to it than just the neural network.
  - PaulDavisThe1st 5y ago
    How is theory still involved? You don't use theory to check the predictions AlphaFold makes, you use molecular structure determination (x-ray crystalography, MRI, other techniques). Of course, those involve "theory", but not theories about protein folding.
    - RandomLensman 5y ago
      For me, theory enables this to actually work in the sense that we know that structures are not random, i.e. there is some way to predict them and it is inherent in the composition (hand waving). If there is no theory linking input and output then predictions could just randomly stop working, for example.
      - PaulDavisThe1st 5y ago
        This is just simply incorrect. Theory is not involved in this. We know that a given protein will always (more or less) fold into the same configuration every time, generally on their own. We know this by doing experiments in which the protein is denatured (heated to break internal covalent and some ionic bonds, and then left to anneal (cool down), and then inspected at the atomic level for the resulting structure. They always fold back to the same shape. This is an experimental determination, not a theoretical one. Nobody was ever successful at devising any comprehensive theory that explained how they did this, though they of course identified many different elements that are likely involved in some way (hydrophobicity, local charge, possible compactness and many more). But there is (currently) no theory that connects protein amino acid sequence to structure, which is why AlphaFold was such a huge deal. It makes the connection (somehow), without any theory builtin, and without any theory to explain. In fact, there is not even a theory that requires that they always fold into the same shape.

PaulDavisThe1st 5y ago

> Sticking with on of the examples, we know that proteins fold in somewhat predictable ways, because we do have theories around the "mechanics" there But we were broadly unable to leverage those theories and knowledge into a predictive system. AlphaFold largely dropped all that (IIUC), and works much better without it. (I was once a PhD candidate in protein folding, and left in part because I felt the field was unlikely to make any substantive progress. It didn't, for at least 30 years, until NN-based "modelling" started to replace all approaches up till then)

dataangel 5y ago
Well, but wasn't AlphaFold at least in part trained based on the existing mechanical models? Or was AlphaFold only trained on ground truth imaging of the folding process?
- PaulDavisThe1st 5y ago
  It was trained on experimentally-determined physical structures. Not predicted models based on various folding engines.
snowwrestler 5y ago
The point is that given a solution to protein folding, we can then compare that solution with current theory and even physically test it. The search space is enormous but individual solutions are physically deterministic. This is a good application of ML since there is an independent “check” on its work. And it’s hard to call it “the end of theory” when theory is still tightly coupled with the system, just at the back end instead of the front end. That is not true of many applications of ML, like Facebook, which simply spit out solutions at scale with no way to independently check them. We should not think the same way about all ML systems; there is a lot more to it than just the neural network.
- PaulDavisThe1st 5y ago
  How is theory still involved? You don't use theory to check the predictions AlphaFold makes, you use molecular structure determination (x-ray crystalography, MRI, other techniques). Of course, those involve "theory", but not theories about protein folding.
  - RandomLensman 5y ago
    For me, theory enables this to actually work in the sense that we know that structures are not random, i.e. there is some way to predict them and it is inherent in the composition (hand waving). If there is no theory linking input and output then predictions could just randomly stop working, for example.
    - PaulDavisThe1st 5y ago
      This is just simply incorrect. Theory is not involved in this. We know that a given protein will always (more or less) fold into the same configuration every time, generally on their own. We know this by doing experiments in which the protein is denatured (heated to break internal covalent and some ionic bonds, and then left to anneal (cool down), and then inspected at the atomic level for the resulting structure. They always fold back to the same shape. This is an experimental determination, not a theoretical one. Nobody was ever successful at devising any comprehensive theory that explained how they did this, though they of course identified many different elements that are likely involved in some way (hydrophobicity, local charge, possible compactness and many more). But there is (currently) no theory that connects protein amino acid sequence to structure, which is why AlphaFold was such a huge deal. It makes the connection (somehow), without any theory builtin, and without any theory to explain. In fact, there is not even a theory that requires that they always fold into the same shape.