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The technical reason it can't be done (or would be very difficult to do) is that weights are typically very uninterpretable. There aren't specific clusters of n

The technical reason it can't be done (or would be very difficult to do) is that weights are typically very uninterpretable. There aren't specific clusters of neurons that map to one concept or another, everything kind of does everything.

Supply5411 3y ago
I wonder if an expert can "impose" weights onto a model and the model will opt to continue with them when it resumes training. For example, in the vision example, the expert may not know where "orangeness" currently exists, but if they impose their own collection of weight adjustments that represent orangeness, will the model continue to use these weights as the path of least resistance when continuing to optimize? Just spitballing, but if we can't pick out which neurons do what, the alternative would seem to be to encourage the model to adopt a control interface of neurons.
- astrange 3y ago
  That would make it less efficient - since learning is compression, a less compressed model will also learn less at the same size.
  - Jensson 3y ago
    Humans have much more compressed models, trying to transfer human learning to machine learning could potentially be a way to get more efficient models. The way we do that currently is by labeling data for training, but maybe there are better ways to do it. Like some semi code to write with hints for the model. Like, instead of labeled data, could have a series of "lectures" of labeled data that would lead to a good end state, instead of training on all the data in parallel. You don't teach a child calculus by showing them a million calculus problems after all, you ramp up starting with simple numbers and then slowly ramping up with more concepts. But to do that we would need to change how we train models. Edit: By doing it that way you could see the skill of the model after each lecture, and update the lecture to try to make the model learn better. Not sure how to do that, but such ways to work with parts of models is a potential way forward.
    - astrange 3y ago
      There are techniques for this called "curriculum learning" and "textbooks". I'm not sure exactly what is in the textbooks since I admit to not reading the papers yet. I'm personally wondering if you could increase the reliability of training on web data by labeling it with where each document came from, so it knows different authors disagree on things. But this brings back the issue where people don't like it if a model can write "in the style of Author Name"…
  - Supply5411 3y ago
    Might be a worthwhile tradeoff. Less efficient model but a clear control interface, versus more efficient model but it's a blackbox.
gbear0 3y ago
On the idea of interpreting the weights, I've been very interested if it's possible to compute basis vectors of the weights matrix to define the core concepts within the model and then do a change of basis to allow reorganizing the model to more human understood concepts? I think the inherent compression of a specific training set into a matrix makes this more difficult cause the basis vectors likely won't contain clean representations of human ideas, but I also wonder if starting a new training set with an initialized (or fixed) matrix of human defined concepts would help align the model's weights to something that can be interpretable

Supply5411 3y ago

I wonder if an expert can "impose" weights onto a model and the model will opt to continue with them when it resumes training. For example, in the vision example, the expert may not know where "orangeness" currently exists, but if they impose their own collection of weight adjustments that represent orangeness, will the model continue to use these weights as the path of least resistance when continuing to optimize? Just spitballing, but if we can't pick out which neurons do what, the alternative would seem to be to encourage the model to adopt a control interface of neurons.

astrange 3y ago
That would make it less efficient - since learning is compression, a less compressed model will also learn less at the same size.
- Jensson 3y ago
  Humans have much more compressed models, trying to transfer human learning to machine learning could potentially be a way to get more efficient models. The way we do that currently is by labeling data for training, but maybe there are better ways to do it. Like some semi code to write with hints for the model. Like, instead of labeled data, could have a series of "lectures" of labeled data that would lead to a good end state, instead of training on all the data in parallel. You don't teach a child calculus by showing them a million calculus problems after all, you ramp up starting with simple numbers and then slowly ramping up with more concepts. But to do that we would need to change how we train models. Edit: By doing it that way you could see the skill of the model after each lecture, and update the lecture to try to make the model learn better. Not sure how to do that, but such ways to work with parts of models is a potential way forward.
  - astrange 3y ago
    There are techniques for this called "curriculum learning" and "textbooks". I'm not sure exactly what is in the textbooks since I admit to not reading the papers yet. I'm personally wondering if you could increase the reliability of training on web data by labeling it with where each document came from, so it knows different authors disagree on things. But this brings back the issue where people don't like it if a model can write "in the style of Author Name"…
- Supply5411 3y ago
  Might be a worthwhile tradeoff. Less efficient model but a clear control interface, versus more efficient model but it's a blackbox.

gbear0 3y ago

On the idea of interpreting the weights, I've been very interested if it's possible to compute basis vectors of the weights matrix to define the core concepts within the model and then do a change of basis to allow reorganizing the model to more human understood concepts? I think the inherent compression of a specific training set into a matrix makes this more difficult cause the basis vectors likely won't contain clean representations of human ideas, but I also wonder if starting a new training set with an initialized (or fixed) matrix of human defined concepts would help align the model's weights to something that can be interpretable