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Comment by aub3bhat | Hacker News Reader

Humanity used fire for a long time before combustion was understood. Even today Anesthesia is not well understood at biological/physiological level that has not stopped its safe use and innovation through Clinical Trials. Maybe competitions and empiricism are the best approaches to building intelligent systems. Why get caught up in Physics/Math envy?

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I took his point not to criticize those early stages, but simply to acknowledge them as such. Early fire users could not have built a rocket no matter how many experiments they performed until they understood combustion (and some other sciences).

In AI, we're not building rockets yet, but we have some really awesome and really powerful bonfires or whatever.

At least that's how I understood his point.

(And in anesthesia, when we do understand those things, we may very well look on our use today as barbaric or dangerous.)

Hinton isn't saying "Let's stop using fire," but "Let's understand the principles behind fire so we can use them in more sophisticated, informed and powerful ways."

The ML community did take the Theory approach trying to prove bounds, SLT/SRM, PAC, etc. and that was an excercise in futility. While I don't deny that there is value to looking under the hood but for a long while the community abandoned any empirical results that didn't fit their paradigm. Between rigorously validating their methods and writing yet another 4 page long proof. A lot of researchers would prefer latter, effectively locking out empirical approaches from most dissemination venues and eventually funding.

Because an improved theoretical understanding of how complex systems work can be incredibly valuable. Anesthesia is a good example - we get a lot of value from it, yes, but it would be way better if we could tailor dosages to individuals based on an understanding of how that individual will experience pain. There would be fewer severe complications, but also maybe you could wake up refreshed an hour after surgery instead of in a stupor.

If this could work with computer-trained models, that would be incredible too. What could a great speech understanding system teach us about language? What tricks from a facial-expression classifier could help autistic kids understand their friends?

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