[frobbin]

AI research, including speech recognition and machine vision, are currently ENGINEERING disciplines trying to make artifacts that do interesting things. Success is an artifact that works.

Several basic science disciplines are trying to understand how brains work. There is mostly tremendous amounts of experimental facts, difficult to put together, and some theory and modelling to go with it.

Norvig would be confused if he thinks that engineering AI systems automatically counts as models useful for understanding the brain. If there is application to understanding brains it is a welcome accident. It happens that there are signals in basal ganglia that look like the temporal difference error signal from reinforcement learning. So maybe RL research can help understand some brain circuitry in that case.

But in general the engineers are trying to get stuff to work, and they are deluded if they think they are simultaneously making progress in understanding how brains work.

EDIT:

For example: why does speech recognition use hidden markov models and N-gram language models? Because they're the best model of how brains understand speech? No! Not at all. HMMs and N-gram models are above all computationally tractable. Easy to implement, not too slow to run.

We have algorithms (such as baum-welch and N-gram smoothing techniques) to get them work work well in engineering applications. Nothing more. Might they help us understand brains? Maybe, but not at all necessarily so.