>Machine learning produces models which are much better predictors, but it does not seem to follow that they have a superior understanding of the weather. Fair

>Machine learning produces models which are much better predictors, but it does not seem to follow that they have a superior understanding of the weather. Fair points, and I agree. I don't recall if I made this point in the linked piece, but I think the extra function is a model embedded within some dynamic such that the capacity for modelling is in service to some goal. The goal can be simple like answering questions or something more elaborate. But the point is to engage the model as to influence the dynamic in a semantically rich way. The model itself doesn't represent understanding, but a process that understands will have a model that can be queried and manipulated in various ways corresponding to the process' goals. >then it seems likely that the productions of a good predictor would unavoidably have that feature as well Yeah, assessment is hard because of the sheer size of the training data. We can't be sure that some seemingly intelligent response isn't just recalling a similar query from training. One of the requirements for understanding is the counterfactual capacity, being able to report accurate information that is derivative of the training data but not explicitly in the training data. The Sparks of AGI paper, assuming it can be believed, demonstrates this capacity IMO. Particularly where GPT-4 draws a graph of a room after having been given navigation instructions. But its hard to make a determination in particular cases.