I thought it was a really interesting interview. If an ML system has been trained to identify whether or not a picture has a cat in it, it is very tempting to anthropomorphize this and say “the system has learned what cats are” but this is not what is really happening from the model’s point of view. The system doesn’t know what the difference between a picture of cat is vs a real cat or how cats behave or what mammals are or anything. It just knows the data in and yes/no out. Modern AI that can generate text, pictures or videos are truly phenomenal accomplishments but adding larger training sets, structural complexity and output capabilities does not really seem to be getting any closer to a General AI. Something with the framework for a level of agency that could say “wait, what exactly are these cat things I keep getting asked about?” The argument goes that, without the ability to self construct that higher level framework and then question the integrity of that mental model, current generation AI’s will always act erratically (at least from a human perspective).