[dreamlessfate]

This doesn't even feel worth learning.

Feels like Economics in undergrad, listening to professors repeat broken, oversimplified models that are so hilariously wrong in their assumptions that they have to invent entirely new definitions to deal with their own failings.

Who put the statistics nerds in charge of AI? Is this really the best we got? Chained probabilities? Gradient descent?

[dreamlessfate]

Like did the evolution of AI & ML research go like this?

> We're stuck. After decades of research, we've hit a dead end. All we're left with is a byzantine maze of IF/THEN statements. We cannot simulate intelligence using pure logic. We have failed.

> Ok but what if we throw in PROBABILITIES into a byzantine maze of IF/THEN statements??????

>GENIUS!

[jstx1]

It doesn't look good when you're dismissing things you don't understand at all.

[stealthcat]

So far sadly yes? http://www.incompleteideas.net/IncIdeas/BitterLesson.html

tl;dr So far things that enable faster search and faster learning win over long run.

Recursive things like backprop in NN and optimizing reward over long trees of states, seem to win despite huge compute requirements.

Personally I think we are still on the right track of trying to do the right thing, then do the thing right, then do the thing faster.

You cannot refute the things you do not understand.