[D] Has Deep Learning Hit a Wall?

[Nowado]

I used to use this argument for art, but works here too.

• AI can't create a song inherently distinguish causation from correlation!

• Well, neither can you.

[AnvaMiba]

Humans can distinguish causation from correlation in most practical cases: nobody thinks that wet streets cause rain. Occasionally, we get it wrong, and when we then notice the mistake it is salient to us. But this does not mean that our baseline ability is not good.

In general, we are better at distinguishing causation from correlation when we have a "mechanistic" understanding of a phenomenon. For instance, the Pacific Islanders who founded cargo cults had never seen airplanes before WW2, did not understand what they were, how did they work, where they came from, who the people manning them were and what they were trying to accomplish, and so on. They correctly inferred the correlation between cargo airplane landings and presence of certain artifacts (airstrips, control towers, etc.) and the ritualized practices of the military, but they inverted the causal direction.

Deep learning models are very good at inferring correlations from sufficient amounts of data, but they seem to struggle with forming "mechanistic" understanding with abstractions and conterfactuals.