3 ms·

SVMs, Kernels, Online Learning are pretty well understood. Neural networks are an abuse of the chain rule and makes your model dependent on a bunch of partial d

SVMs, Kernels, Online Learning are pretty well understood. Neural networks are an abuse of the chain rule and makes your model dependent on a bunch of partial derivatives that you don't necessarily understand why are important, but happen to be extremely effective when given lots of data. I think neural nets are probably what you're referring to. If you understand the intuition about your problem, then you can reason about what those partial derivatives might represent. Except a lot of the time we don't really have that level of understanding. What is your level of experience with machine learning? I'm a little surprised with your perception.

te_chris 11y ago
I'm not OP, but I think you're misreading their comment. Their point, if I read it correctly, is not about the technical correctness of the ML in place, but the sociological effects. You can see this happening already: I'm sure FB's machine learning algos that they use to filter the stream are 'correct' in some way, but their effect is insidious and polarising on the way we consume information.
- mrdrozdov 11y ago
  I think that's more a reflection of how Facebook chooses to use the results of an algorithm rather than the algorithms themselves.
- norswap 11y ago
  Both, actually! Point is they are related.
norswap 11y ago
Extreme beginner (I saw a couple presentations). Yes probably neural networks more specifically. I understand that's where most of the excitement is nowadays and what seems to hold promises for the future. Simpler classifiers are easier to grok, but people don't seem to project the same hopes on them that they do on neural networks. > Except a lot of the time we don't really have that level of understanding. Precisely my point. My work is in computer languages and I do a lot of parsing. People have a hard time conceptualizing it (truthfully, it is hard) despite the fact that it's fully deterministic. Visualizing a huge decision tree and its ramifications, and distant consequences within it seems something humans are bad at. Neural networks have this, except they feed back into themselves, are not exactly non-deterministic, but statistical, and use a bunch of encoded intuition.

te_chris 11y ago

I'm not OP, but I think you're misreading their comment. Their point, if I read it correctly, is not about the technical correctness of the ML in place, but the sociological effects. You can see this happening already: I'm sure FB's machine learning algos that they use to filter the stream are 'correct' in some way, but their effect is insidious and polarising on the way we consume information.

mrdrozdov 11y ago
I think that's more a reflection of how Facebook chooses to use the results of an algorithm rather than the algorithms themselves.
norswap 11y ago
Both, actually! Point is they are related.

norswap 11y ago

Extreme beginner (I saw a couple presentations). Yes probably neural networks more specifically. I understand that's where most of the excitement is nowadays and what seems to hold promises for the future. Simpler classifiers are easier to grok, but people don't seem to project the same hopes on them that they do on neural networks. > Except a lot of the time we don't really have that level of understanding. Precisely my point. My work is in computer languages and I do a lot of parsing. People have a hard time conceptualizing it (truthfully, it is hard) despite the fact that it's fully deterministic. Visualizing a huge decision tree and its ramifications, and distant consequences within it seems something humans are bad at. Neural networks have this, except they feed back into themselves, are not exactly non-deterministic, but statistical, and use a bunch of encoded intuition.