> GPT-3 contains an understanding of the world, or at least the text world. So does Wikipedia, so does a dictionary. The contents of the dictionary are meaningful. But nobody would say that the dictionary understands the world.

What differs GPT-3 from a database of text is that it seems like GPT-3 contains some representations of concepts that make sense outside of a text domain. It's that ability to create generalizable representations of concepts from sensory input that constitutes understanding.

> I think if you fed a human nonsense information since birth, the person would withdraw from everything and become catatonic. Bombarding them with random sensory experiences which didn't match their actions would result in them carrying out no actions at all.

Maybe my analogy wasn't clear. The point I was trying to make was that if your argument is:

GPT-3 holds no understanding because you can feed it data with patterns not representative of the world, and it'll learn those incorrect patterns.

Then my counter is:

People being fed incorrect data (i.e. incorrect sensory input) would also learn incorrect patterns. e.g. someone who feels cold things as hot and hot things as cold is being given incorrect sensory patterns (ones that aren't representative of real-world temperature), and forming an incorrect idea of what "hot" and "cold" things are as a result, i.e. not properly understanding the world.

My point being that it's the learned representations that determine understanding, not the architecture itself. Of course, if you gave a model completely random data with no correlations at all, then the model would not train either.