How Similar Are AI Models Trained Years Apart?

Emmanuel Ameisen says the structure inside artificial intelligence models is surprisingly similar even when the models were trained years apart, on different data, by different people. For some problems that are important, there is just one efficient way to do things.

He has studied many models and open source work. What the models learn, the structure inside them, and the way they solve problems overlap heavily. He compares the pattern to math. There is one way to do math efficiently that fits the structure of the brain, and people learn it. Very few people do it a crazy different way. Everybody works in base 10.

The overlap makes him think the models are picking up a fundamental structure of information that, in some ways, people are picking up on too. He leaves a stronger idea unsettled: a simulation-level reading in which the pattern is regularity in the world.