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Comment by etangent | Hacker News Reader

Sure, but for someone who does not know what an SVM is, the big-picture takeaway is that a particular version of "soft" statistical learning (deep learning) had succeeded where "hard logic" approach failed.

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Just asking: hard logic failed in what area?

Computer Vision would the the most likely example. It’s quite hard to come up with “hard rules” for distinguishing cats and dogs.

Speech recognition is one such case. We thought that by breaking down the incoming speech into phonemes we would be able to get words from the bottom up. That never worked too well, whereas statistical speech recognition has been much more effective.

Carrots are vegetables. Vegetables are edible. Is this a picture of something edible? Having a bunch of propositions in a database is not much help if you can't relate them to messy sensory input. It turns out that making sense out of mess was a hard problem. Eventually, logic* will have to be brought in again, but at a higher level, to help out.

* Ofc Hinton's approach is based on logic too, but in the form of probability, calculus etc.

but with probe metrics and other experiments we are retroactively explaining why the big breaktroughs actually work, and these can inspire and fix the big breakthrough by putting them on more rigorous foundations. At the end you get a formal explanation of the conditional probability that given a carrot with 6DoF and such and such parameters (age, size, ...) in such and such lighting conditions results in that probability distribution for pixel values.

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