3 ms·

The partial credit approach is fine, but if we want to train a model to get the right answer, then it does matter. What I've noticed is that my loss curves on

The partial credit approach is fine, but if we want to train a model to get the right answer, then it does matter. What I've noticed is that my loss curves on small models for arithmetic will reach a steady state where they're still getting the answer wrong even if some digits are right. Yes you can keep training but the number of training epochs necessary seems to be inversely correlated exponentially with the model size So a model with x parameters is going to take n^2 time as long as a model with 2x parameters At a certain parameter count this basically means it is nigh impossible to get the right answer via training using gradient descent The more parameters, the easier it is to drive to convergence, which is a real, important metric At some point the estimated time for the ability to emerge spontaneously becomes greater than a human lifetime of even the lifetime of all humanity. So in the sense that increasing model size makes it tractable... I think it's fair to say that the ability emerges suddenly enough.

naasking 3y ago
It seems like model learning is too optimized for continuity, eg. continuous variables can be subdivided infinitely, where logic and algorithms are firm rather than fuzzy like this. Learning agents arguably need to be able to generalize from fuzzy to firm concepts to properly learn logic and algorithms. Whether that can happen automatically by scaling or whether that requires a fundamental shift is unclear.
- TeMPOraL 3y ago
  Except if we take humans as example, we do not "generalize from fuzzy to firm". We don't handle firm, we approximate it with fuzzy. Which is why formal logic makes for a fine family of puzzle games, but fares extremely badly at describing reality and reasoning about reality. Better suited mathematical frameworks are probabilistic in nature, and so happen to be fundamentally continuous.
  - naasking 3y ago
    > We don't handle firm We absolutely do handle firm. Mathematics, logic, morality are all examples that have plenty of firm classifications, as is typical, everyday reasoning, eg. I would state that I am naasking in this thread and I am not TeMPOraL, and I would not state that I am 99.999% probably naasking but 0.001% possibly TeMPOraL. > Which is why formal logic makes for a fine family of puzzle games, but fares extremely badly at describing reality and reasoning about reality. The device you're reading this on would suggest quite strongly that this conclusion is wrong.
    - TeMPOraL 3y ago
      > Mathematics, logic, morality are all examples that have plenty of firm classifications Which tend to fail to apply to reality, unless relaxed or turned into continuities. > as is typical, everyday reasoning, eg. I would state that I am naasking in this thread and I am not TeMPOraL, and I would not state that I am 99.999% probably naasking but 0.001% possibly TeMPOraL. Everyday reasoning is a shorthand, not formal logic. You stating you're naasking and not TeMPOraL is not a formal logic statement, nor is a fully discrete one - not unless you really believe this to be 100% likely, and do not admit the possibility of any of things like: - You dreaming or imagining it; - You're being a brain in a vat; - You being delirious or otherwise mentally compromised; - Your senses actually working correctly, and not making shit up; All of which, in reality, have non-zero likelihood. Applying discrete logic to real life forces you to accept things as certain even if evidence for them is less likely than our understanding of reality being correct - or conversely, rounding possible states of the universe down to impossible.
yorwba 3y ago
I think the point is that if you want a model that gives the right answer, you should still use partial credit to figure out how far away you are from that goal instead of binary correct-or-not accuracy. If you use a metric where improvement happens suddenly and unpredictably, you can't even estimate how much longer you need to train, because the ability might emerge spontaneously. But if the partial credit metric improves smoothly and predictably, you have a chance of extrapolating your training progress to see when you might hit your accuracy target, better than if you extrapolated accuracy directly. And if you find that the estimated time is too long and you decide to train a bigger model, you can try to extrapolate across model sizes to estimate how big of a model you might need.
- necovek 3y ago
  To those still missing the point, it's easy to come up with a metric that's guaranteed to be a huge jump. Eg. imagine a big corpus of arithmetic operations (say 2500 additions, 2500 subtractions, 2500 multiplications and 2500 divisions): you can test when a model passes computing all of those correctly, and it's probably obvious that you'll have a long list of "didn't pass" until you suddenly get it passing.
intended 3y ago
Did you see this submission? https://news.ycombinator.com/item?id=39575264 https://news.ycombinator.com/item?id=39575264 They sound linked.

naasking 3y ago

It seems like model learning is too optimized for continuity, eg. continuous variables can be subdivided infinitely, where logic and algorithms are firm rather than fuzzy like this. Learning agents arguably need to be able to generalize from fuzzy to firm concepts to properly learn logic and algorithms. Whether that can happen automatically by scaling or whether that requires a fundamental shift is unclear.

TeMPOraL 3y ago
Except if we take humans as example, we do not "generalize from fuzzy to firm". We don't handle firm, we approximate it with fuzzy. Which is why formal logic makes for a fine family of puzzle games, but fares extremely badly at describing reality and reasoning about reality. Better suited mathematical frameworks are probabilistic in nature, and so happen to be fundamentally continuous.
- naasking 3y ago
  > We don't handle firm We absolutely do handle firm. Mathematics, logic, morality are all examples that have plenty of firm classifications, as is typical, everyday reasoning, eg. I would state that I am naasking in this thread and I am not TeMPOraL, and I would not state that I am 99.999% probably naasking but 0.001% possibly TeMPOraL. > Which is why formal logic makes for a fine family of puzzle games, but fares extremely badly at describing reality and reasoning about reality. The device you're reading this on would suggest quite strongly that this conclusion is wrong.
  - TeMPOraL 3y ago
    > Mathematics, logic, morality are all examples that have plenty of firm classifications Which tend to fail to apply to reality, unless relaxed or turned into continuities. > as is typical, everyday reasoning, eg. I would state that I am naasking in this thread and I am not TeMPOraL, and I would not state that I am 99.999% probably naasking but 0.001% possibly TeMPOraL. Everyday reasoning is a shorthand, not formal logic. You stating you're naasking and not TeMPOraL is not a formal logic statement, nor is a fully discrete one - not unless you really believe this to be 100% likely, and do not admit the possibility of any of things like: - You dreaming or imagining it; - You're being a brain in a vat; - You being delirious or otherwise mentally compromised; - Your senses actually working correctly, and not making shit up; All of which, in reality, have non-zero likelihood. Applying discrete logic to real life forces you to accept things as certain even if evidence for them is less likely than our understanding of reality being correct - or conversely, rounding possible states of the universe down to impossible.

yorwba 3y ago

I think the point is that if you want a model that gives the right answer, you should still use partial credit to figure out how far away you are from that goal instead of binary correct-or-not accuracy. If you use a metric where improvement happens suddenly and unpredictably, you can't even estimate how much longer you need to train, because the ability might emerge spontaneously. But if the partial credit metric improves smoothly and predictably, you have a chance of extrapolating your training progress to see when you might hit your accuracy target, better than if you extrapolated accuracy directly. And if you find that the estimated time is too long and you decide to train a bigger model, you can try to extrapolate across model sizes to estimate how big of a model you might need.

necovek 3y ago
To those still missing the point, it's easy to come up with a metric that's guaranteed to be a huge jump. Eg. imagine a big corpus of arithmetic operations (say 2500 additions, 2500 subtractions, 2500 multiplications and 2500 divisions): you can test when a model passes computing all of those correctly, and it's probably obvious that you'll have a long list of "didn't pass" until you suddenly get it passing.

intended 3y ago

Did you see this submission? https://news.ycombinator.com/item?id=39575264 https://news.ycombinator.com/item?id=39575264 They sound linked.