> GPT-3 is just better at obfuscating that the model has no clue what it's talking about. There's an interesting angle to this as well, which is that it makes

> GPT-3 is just better at obfuscating that the model has no clue what it's talking about. There's an interesting angle to this as well, which is that it makes the models "unfalsifiable" in a way. You can never prove whether the data is a straight compression lookup or whether the network has generated an insight, because the model can't tell you (to anthropomorphize). This, more than anything else, would be the value of having explainable models. I don't blame the ML community for this gap, but it puts them in the unenviable position of not being scientific in the Popperian sense. There's a great element of "trust us, the intelligence is in there" or "the intelligence will get there", but when everything's a mashup of more hardware and data without a known structure, we ultimate have to take that on faith. We can do empirical measurements after the fact, but the guiding projections for how an experiment should behave is lacking. (I don't think anyone in any community has a satisfactory answer to this btw.)