Yes exactly. All of the dismissive ‘it’s just a fancy next word predictor’ articles can’t see the woods for the trees. Just because the function of the model is

Yes exactly. All of the dismissive ‘it’s just a fancy next word predictor’ articles can’t see the woods for the trees. Just because the function of the model is to predict the next word tells us almost nothing about what internal representation of the world it has built in order to generalise. I don’t for a second think it’s currently of comparable complexity to the world model we all have in our brains, but I also don’t think there’s any clearly defined theoretical limit on what could be learned, beyond the need for the internal model to make better predictions and the optimiser to find this more optimal set of weights (which might be a limit in practice as of now).