That said, there’s an important further question that isn’t determined by the loss function alone—does the model do its most useful cognition in order to predict what a human would say, or via predicting what a human would say?
I don’t think there’s a major difference between the two, so the answer is, I guess, “yes.” I suppose a different way of framing this question, as I interpret it, is whether or not simulation and prediction are considered the same thing. I see those two things as largely similar. If they were not, wouldn’t there be some simulations we consider “faithful” that would not be very good at making predictions?
In physics, for example, models are generally considered useful for both simulation and prediction. Of course, if we have two different words for something, it implies there must be some difference. But prediction seems to refer to the output of a model, not necessarily the mechanics or the process that generates the final output. But given that the final output is a selected, chosen member of the full set of output, which includes everything, a prediction is a member of the set of simulated entities.
Shouldn’t GPT-4 be considered both a simulator and a predictor, then?
Well, consider the task of simulating a coin flip vs. predicting a coin flip. A simulation of a coin flip is satisfactory if it is heads 50% of the time, which is easier than predicting the outcome of some actual coin.
He asks:
I don’t think there’s a major difference between the two, so the answer is, I guess, “yes.” I suppose a different way of framing this question, as I interpret it, is whether or not simulation and prediction are considered the same thing. I see those two things as largely similar. If they were not, wouldn’t there be some simulations we consider “faithful” that would not be very good at making predictions?
In physics, for example, models are generally considered useful for both simulation and prediction. Of course, if we have two different words for something, it implies there must be some difference. But prediction seems to refer to the output of a model, not necessarily the mechanics or the process that generates the final output. But given that the final output is a selected, chosen member of the full set of output, which includes everything, a prediction is a member of the set of simulated entities.
Shouldn’t GPT-4 be considered both a simulator and a predictor, then?
Well, consider the task of simulating a coin flip vs. predicting a coin flip. A simulation of a coin flip is satisfactory if it is heads 50% of the time, which is easier than predicting the outcome of some actual coin.