Artificial Intelligence Is Already Weirdly Inhuman

Artificial Intelligence Is Already Weirdly Inhuman
nautil.us

I think this is because neural nets are not actually "intelligent" in terms of the commonly accepted definition of intelligence. They are dumb. They try, usually with simple probabilistic techniques and input element-wise transforms, to mimic some function that produces approximations for a given set of inputs and outputs. The training data and the test data will always have underlying differences which will create gaps in the data generating distribution, assuming a reasonably large-sized set of data. This is contrary to the assumptions that (every?) machine learning algorithm makes, usually referred to as the i.i.d assumptions. The test and training data are assumed to be independent and identically distributed. Since practically no real world data sets are perfectly identically distributed, there will always be gaps in the learned model and the real model.

Beyond that, the set of training data can never fully encompass the entire domain of possible input/outputs, else what need would we have for a machine to predict new ones? The oddities that researchers find are actually problems in the relationship of their training data to their testing data because the i.i.d assumptions are never actually true. We can only try to get as close as possible.

The solution to this problem is just nigh impossible, so we try to reduce it as much as possible.

It was fascinating to watch the changes in chess theory through that period as the machines validated or invalidated concepts and ideas that GM's had posited but been unable to prove one way or the other.

John Speelman wrote an intro to The Mammoth Book of Chess (that thing was awesome,I think I wore out three copies) which (paraphrasing horribly) that Chess is a realm of pure thought and that computers play it the way an alien might.

The best engines on commodity hardware now require offering pawn odds for a top human to even have a chance and their strength is accelerating[1] (stockfish now has a distributed test architecture to test for regressions which is incredibly cool[2])

[1] http://www.chess.com/article/view/how-rybka-and-i-tried-to-b... (Rybka was/is one of the strongest engines and in concert with a human it still couldn't beat Stockfish).

[2] https://stockfishchess.org/get-involved/

A solution to this I think would be splitting up the taks, like "finding eyes", "is this fur", etc ... freeze these networks, and use these as input. This would prevent most errors given in these examples.

A lot of animals know what eyes look like regardless of the species, not saying nature always has the best solution. But it probably had a good reason to have a specialized-build-in "head/eyes" detector.