> in that studies of machine language performance can already falsify some theories of human language cognition. They do not. > Language competency requires

> in that studies of machine language performance can already falsify some theories of human language cognition. They do not. > Language competency requires component X... I assume X is something like "can't be learned statistically". LLMs haven't shown that in humans (which is what we are talking about): Beside the fact that humans use much less data LLMs can learn arbitrary patterns, including patterns humans can't learn (just as humans can't "learn" to understand a 56k modem). > The reason is that models in general yield insights. Prediction machines don't generate insights by default, that isn't even their objective, so if they did it would just be by chance. You might as well say that by mixing random chemicals you might get a discovery in chemistry. It's possible but absurd. > Where there is already an understood rational process, you no longer need the insight. The rational process I'm talking about is the process of inquiry not the process under investigation. > Sometimes the big leaps of scientific understanding come from looking at the data You could imagine something like this happening abstractly but it hasn't happened, no one has proposed specifically how it might happen and no one is pursuing anything like this because there isn't any obvious thing to pursue. It's just wishful thinking. Maybe if I do random stuff I'll make a scientific breakthrough. Maybe! But extremely improbable and silly to go even further and assume that you would.