6 ms·

Could you shed some light on the 66% part, too? As a layman and general sceptic of hype topics, this one is the biggest obstacle for me. How are we going to pro

Could you shed some light on the 66% part, too? As a layman and general sceptic of hype topics, this one is the biggest obstacle for me. How are we going to procede with models that are 33% wrong? In other engineering areas, uncertainty is usually covered by the modeler. But how would that work here?

dnautics 6y ago
who knows. You can't generalize 1 error out of 3 to 66%. Statistics of small numbers. Knowing how ML works, I'm surprised it didn't give any indication of low confidence. Not to give DeepMind too much credit, but it's also entirely possible that "both are correct". By the nature of how targets are selected for CASP, year over year there is higher likelihood that "pathologically difficult" proteins are presented for the competition, for example - a protein that exhibits polymorphic structure where the technique (say Xtal vs NMR) biases the "solved" fold in a huge way. I believe CASP tries to weed out proteins that we know are polymorphic, but you can never be sure, and again, as time marches on, those are the proteins that fundamentally are harder to solve, so it's likely they will be enriched in the test pool. An extreme example is insulin. The structure of insulin is has been solved for 60? I think years, but when it's in the environment of its receptor, it looks TOTALLY different (solved 5? years ago). Having said that, doubtful that DeepMind could ascertain that structure, since the environment is super-super different. I think that the modestly high error rate is an indication that deepmind is mostly interpolating and has solved the broad protein folding heuristic. It probably will get better.
- Voloskaya 6y ago
  > You can't generalize 1 error out of 3 to 66% Right but we also can't generalize from 2 out 3, and without knowing this figure it's really hard to say how useful this is, no? > Knowing how ML works, I'm surprised it didn't give any indication of low confidence. This is actually a hard problem in ML, for example in NLP, many people assume a high log prob score means a high confidence but it is not true at all.
  - dnautics 6y ago
    > for example in NLP, many people assume a high log prob score means a high confidence but it is not true at all Thank you for the clarification. I wasn't aware of this, as I'm most familiar with super-basic/old NLP techniques like BOW/RNN/LSTM/GRU techniques, where log prob scores seemed to me to be roughly correlated with result quality. I'm aware the landscape has changed recently with insights about high dimensional searches...
    - Voloskaya 6y ago
      An interesting paper on this subject from just last week if you want to know a bit more: http://phontron.com/paper/jiang20lmcalibration.pdf http://phontron.com/paper/jiang20lmcalibration.pdf
    - visarga 6y ago
      There is no difference in the log prob between LSTM and transformers. It just depends on the number of updates you make (batch size, epochs), if you train it to overfit, then your log probs will be pushed closer to 1 or 0. But after training you can recalibrate the temperature of the softmax on the test set and still get meaningful confidence scores (temperature calibration). Or you can use a variation of cross entropy called Focal Loss that will leave your logits un-squashed.
willj 6y ago
I’m not a specialist in the biology domain, but more generally, for some models 66% accuracy is amazing. For example, a model predicting the next word with 66% accuracy.
- kylegill 6y ago
  My machine learning professor would always remind us that 50% is analogous to random chance in a binary decision, but in a model where the prediction is not so black and white, 66% doesn't sound half bad!
  - LeifCarrotson 6y ago
    It seems like this protein folding problem is something like the binary decision "guesses the winning lotto numbers" vs. "does not guess the winning lotto numbers". If it got answer right 1 time in 100, that would be amazing and you'd be foolish not to use it!
    - gph 6y ago
      >If it got answer right 1 time in 100, that would be amazing and you'd be foolish not to use it! Except you have no way of knowing if the answer it gives you is one of the 66 right predictions vs one of the 33 wrong predictions. You could say it's likely correct, but not to a high enough degree of confidence that you could really trust it without verifying using the old established techniques.
      - LeifCarrotson 6y ago
        You missed my point - there are not 33 but billions (an infinite number, really) of wrong protein structures. There is only one right structure, not 66. Also important - verifying that a given model has a signature that matches the established techniques is far easier than using those techniques to generate the complete model from scratch.
        6y ago

dnautics 6y ago

who knows. You can't generalize 1 error out of 3 to 66%. Statistics of small numbers. Knowing how ML works, I'm surprised it didn't give any indication of low confidence. Not to give DeepMind too much credit, but it's also entirely possible that "both are correct". By the nature of how targets are selected for CASP, year over year there is higher likelihood that "pathologically difficult" proteins are presented for the competition, for example - a protein that exhibits polymorphic structure where the technique (say Xtal vs NMR) biases the "solved" fold in a huge way. I believe CASP tries to weed out proteins that we know are polymorphic, but you can never be sure, and again, as time marches on, those are the proteins that fundamentally are harder to solve, so it's likely they will be enriched in the test pool. An extreme example is insulin. The structure of insulin is has been solved for 60? I think years, but when it's in the environment of its receptor, it looks TOTALLY different (solved 5? years ago). Having said that, doubtful that DeepMind could ascertain that structure, since the environment is super-super different. I think that the modestly high error rate is an indication that deepmind is mostly interpolating and has solved the broad protein folding heuristic. It probably will get better.

Voloskaya 6y ago
> You can't generalize 1 error out of 3 to 66% Right but we also can't generalize from 2 out 3, and without knowing this figure it's really hard to say how useful this is, no? > Knowing how ML works, I'm surprised it didn't give any indication of low confidence. This is actually a hard problem in ML, for example in NLP, many people assume a high log prob score means a high confidence but it is not true at all.
- dnautics 6y ago
  > for example in NLP, many people assume a high log prob score means a high confidence but it is not true at all Thank you for the clarification. I wasn't aware of this, as I'm most familiar with super-basic/old NLP techniques like BOW/RNN/LSTM/GRU techniques, where log prob scores seemed to me to be roughly correlated with result quality. I'm aware the landscape has changed recently with insights about high dimensional searches...
  - Voloskaya 6y ago
    An interesting paper on this subject from just last week if you want to know a bit more: http://phontron.com/paper/jiang20lmcalibration.pdf http://phontron.com/paper/jiang20lmcalibration.pdf
  - visarga 6y ago
    There is no difference in the log prob between LSTM and transformers. It just depends on the number of updates you make (batch size, epochs), if you train it to overfit, then your log probs will be pushed closer to 1 or 0. But after training you can recalibrate the temperature of the softmax on the test set and still get meaningful confidence scores (temperature calibration). Or you can use a variation of cross entropy called Focal Loss that will leave your logits un-squashed.

willj 6y ago

I’m not a specialist in the biology domain, but more generally, for some models 66% accuracy is amazing. For example, a model predicting the next word with 66% accuracy.

kylegill 6y ago
My machine learning professor would always remind us that 50% is analogous to random chance in a binary decision, but in a model where the prediction is not so black and white, 66% doesn't sound half bad!
- LeifCarrotson 6y ago
  It seems like this protein folding problem is something like the binary decision "guesses the winning lotto numbers" vs. "does not guess the winning lotto numbers". If it got answer right 1 time in 100, that would be amazing and you'd be foolish not to use it!
  - gph 6y ago
    >If it got answer right 1 time in 100, that would be amazing and you'd be foolish not to use it! Except you have no way of knowing if the answer it gives you is one of the 66 right predictions vs one of the 33 wrong predictions. You could say it's likely correct, but not to a high enough degree of confidence that you could really trust it without verifying using the old established techniques.
    - LeifCarrotson 6y ago
      You missed my point - there are not 33 but billions (an infinite number, really) of wrong protein structures. There is only one right structure, not 66. Also important - verifying that a given model has a signature that matches the established techniques is far easier than using those techniques to generate the complete model from scratch.
      - 6y ago