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Well, no. A bunch of mechanism have models of lower complexity that have almost exactly the same predictive power but a completely different structures. Those h
by freemint 4y ago
Well, no. A bunch of mechanism have models of lower complexity that have almost exactly the same predictive power but a completely different structures. Those higher order structures are the "why".
Why did does a cube on a inclined plane start to slide? You act like the correct answer is "because the subatomic particles and space time in the light cone of the experiment made it that way" when one should expect "because the sin of the incline angle times mass times local gravity became bigger then the static friction between a cos(incline angle) times the original cube weight and the surface at no incline" which is a lot simpler.
- visarga 4y agoWhen the process being studied has more moving parts than humans can grasp in working memory there comes a limit after which we can't understand it anymore. Maybe the nice clean abstract concepts we expect are not there. It could be perfectly correct and yet complicated like front-end spaghetti code.
- tsimionescu 4y agoThat is possible, yes. But that doesn't make the question of "why?" meaningless, as the GGP was suggesting.
- visarga 4y agoI see neural nets as extensions of human thought - they explore the fuzzy depths where we can't grasp directly, like a microscope or a telescope enhance vision. They are enhanced correlation engines.
- freemint 4y agoThere explainable AI approaches using neural networks. Those approaches can answer why questions. I am trying to parse what you are saying but it is utterly meaningless in the context of the previous conversation.
- visarga 4y agoHumans have limited working memory but problems don't care about that and can be much more complex than we can handle. In these situations using the neural net as a "microscope" to zoom into the data might bring some insights from that complexity region that is out of reach for us.