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Perhaps a key distinction is whether the algorithm is mostly learned from data or whether the algorithm is mostly hand-engineered. In a broad view, I don't thi

Perhaps a key distinction is whether the algorithm is mostly learned from data or whether the algorithm is mostly hand-engineered. In a broad view, I don't think there's any meaningful metric for the "mostly" you're talking about. Sure, "mostly" seems to make sense in the context of laboriously trained deep networks. But if the training process is improved, drawing a line between training and "looking and understanding" become hard/purposeless. At the limit, suppose some crazy genius created a small, "hand crafted" program, from GOFAI or whatever principles and this program "knew how to learn". If you fed it Wikipedia or whatever, it understood that and by it's content, it would then be "mostly trained on data" or would it?

YeGoblynQueenne 6y ago
Well that's how neural networks er work. They are hand-crafted systems with a human-devised training algorithm, backprop. Their output is a model trained on data. But the algorithms that train the model, themselves, are coded manually. Same goes for basically all machine learning algorithms. They are hand-crafted systems that train models from data.
- mjburgess 6y ago
  Indeed, it is impossible to learn anything "from data". Data is just measurement of observable variables. A prior model of the meaning of those measurements is required first to parse them into a coherent "observational model"; and then many prior models is required to parse into a representational model. Few, if any extant "AI" systems are able to take the latter step, not least, as it requires more than measurements of the target system which are always ambiguous. (In particular, it requires a coordinated body).