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I don't see how it makes my statement untrue. The most effective ML techniques are more effective the more x/y pairs you have. Sure, you can get a leg up if you
by Quarrelsome 8y ago
I don't see how it makes my statement untrue. The most effective ML techniques are more effective the more x/y pairs you have. Sure, you can get a leg up if you partially define F yourself but then you're no longer being as hands off as before. In practice the WHOLE of F() will likely be a blend of machine learning as well as heuristics as a product of various layers that are one or the other.
- gugagore 8y agoNot the poster you're replying to, but it's just that you said "that's not how it works", and then suggested that the only way to get F is through data, even if it means generating synthetic data. So that's wrong and untrue, as you recognize in this latest comment by saying you can partially define F. To make a concrete example, the convolution operator, the number of layers, their connectivity--all of that partially defines F. Finding a structure is what allows you to learn from the x/y pairs instead of just storing them in a look-up table. It makes sense, then, that finding a better structure may allow you to learn from fewer x/y pairs. This is Apple's purported approach. It also means that if you have a huge number of x/y pairs, you might benefit from (or get away with) using a simpler structure. This is Google's approach in the domains where they have a lot of data. It is true that an important lesson has been "when designers build structure into machine learning systems, they often build in the wrong things, and they end up making commitments that the data cannot undo". But there's still a lot of structure that we put into machine learning system, and we're still learning about what good structures are for different applications, and different regimes of data.