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I agree with you. I was in the Michael Jordan camp, as in: Machine Learning is just statistical inference rebranded and with better marketing. There are lots of
by cambalache 6y ago
I agree with you. I was in the Michael Jordan camp, as in: Machine Learning is just statistical inference rebranded and with better marketing. There are lots of cool applications for sure but there is no "magic sauce" beyond your usual models obtained by error minimization. But lately, especially seeing the progress with the Attention/Transformer models I have been slowly letting myself convinced that maybe that is all there is behind human intelligence: Lots of priors encoded by evolution in the brain, and general quasi-brute-force mechanism for inference, deduction and imagination. Of course this is probably a naive view, but I think the main point will remain: Intelligence can be created or simulated by giving an agent the capability to extrapolate in a way that "makes sense" in the environment the agent will live.
- ieatpies 6y agoAttention based models are still extremely data inefficient and fail to generalize. I recommend reading the "On the Measure of Intelligence" paper by Chollet if this topic interests you.
- cambalache 6y agoI read the paper, I like Chollet, but he comes across as too pesimistic in that paper. I agree that current models are still very data inefficient, but, despite this, I think they are not that far away from the mark if you consider 2 things. 1)The brain probably has hardcoded a lot information. Things like an universal grammar a la Chomsky, the concept of continuity of time and space.Just add water and boom a 2 year old understands. The other thing is that humans are great extrapolating from few data points for a limited set of cognitive tasks, not all.
- tshoaib 6y agoAdd that to the third Michael Jordan I've heard of