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Then we get the really fun question: if the experimentally determined structure is only 90% accurate, can machine learning actually reach 100%? Can you learn ex

by mabbo 6y ago

Then we get the really fun question: if the experimentally determined structure is only 90% accurate, can machine learning actually reach 100%? Can you learn exact truth from inexact examples? Which gets into the concept of whether the ML model has actually learned some deeper conceptual ideas than we have, some deeper truth about how this works. If so, can we somehow extract that truth, or is it truly a black box that does the thing we want? I'm reminded of a sci-fi book I read long ago in which humans are discussing the fact that the science they are utilizing is beyond the scope of a human mind to comprehend- only the AIs can intuitively deal with 12-dimensional manifolds (or something to that extent). Maybe we've reached the doorstep of that future.

carlmr 6y ago
If you have an experimental error that is somewhat normally distributed around the mean, the the AI should, with enough examples, learn what the rules are that are closest to the mean. Because it will minimize the sum of errors. So i do think the results could be more accurate than measurement.
- willj 6y ago
  I don’t think we can assume the errors are normally distributed. It’s possible researchers are biased in a particular “direction”, away from 0 on all dimensions of this problem.
  - bollu 6y ago
    That's fine. It's still a normal distribution with a different mean. The Gaussian is characterized by having only the first two moments: mean and variance.
root_axis 6y ago
> Which gets into the concept of whether the ML model has actually learned some deeper conceptual ideas than we have, some deeper truth about how this works. Well I think that the results speak for themselves; ultimately the question you raise is one of semantics. ML models don't think in terms of "conceptual ideas" like humans do, these models simply perform at such a massive statistical scale that they can identify patterns far beyond any human conception. Clearly, the model embodies some verifiably reliable information about the way the world works, but this is "just" a trick of statistics not anything resembling actual "understanding" in the way the word is typically used when referring to human understanding.
hoseja 6y ago
There is no 100%. Proteins are flexible. Curious how this deals with that even more fiendishly difficult fact.

by mabbo 6y ago

carlmr 6y ago

If you have an experimental error that is somewhat normally distributed around the mean, the the AI should, with enough examples, learn what the rules are that are closest to the mean. Because it will minimize the sum of errors. So i do think the results could be more accurate than measurement.

willj 6y ago
I don’t think we can assume the errors are normally distributed. It’s possible researchers are biased in a particular “direction”, away from 0 on all dimensions of this problem.
- bollu 6y ago
  That's fine. It's still a normal distribution with a different mean. The Gaussian is characterized by having only the first two moments: mean and variance.

root_axis 6y ago

> Which gets into the concept of whether the ML model has actually learned some deeper conceptual ideas than we have, some deeper truth about how this works. Well I think that the results speak for themselves; ultimately the question you raise is one of semantics. ML models don't think in terms of "conceptual ideas" like humans do, these models simply perform at such a massive statistical scale that they can identify patterns far beyond any human conception. Clearly, the model embodies some verifiably reliable information about the way the world works, but this is "just" a trick of statistics not anything resembling actual "understanding" in the way the word is typically used when referring to human understanding.

hoseja 6y ago

There is no 100%. Proteins are flexible. Curious how this deals with that even more fiendishly difficult fact.