> natural language used to be one of the metrics of AGI
what if we have chosen a wrong metric there?
what if we have chosen a wrong metric there?
But they do close a big gap - they're capable of "understanding" fuzzy ill-defined sentences and "infer" the context, insofar as they can help formalize it into a format parsable by another system.
But that’s it. Nothing here has justified the huge amount of money that are still being invested here. It’s nowhere near useful as mainframes computing or as attractive as mobile phones.
There's no reason to assume that models trained to predict a plausible next sequence of tokens wouldn't eventually develop "understanding" if it was the most efficient way to predict them.