> However I think what's missing here is our benchmarks (a la Turing test) are about negation as opposed to affirmation.
I would question the value of the Turing test, and maybe think that's not a great example for AI.
There's always been this assumption that passing the Turing test would mean we had AI, but I think that was always predicated on the machine generating the outputs. With the GPT- models, it's not clear that this isn't a form of compression over an immense data set, and we're sending pre-existing _human_ responses back to the user. It implies to me that we can pass the Turing test with a large enough data set and no (or very little) intelligence.
All of this makes me believe "These are all definitely steps in the right direction" is questionable.
I would question the value of the Turing test, and maybe think that's not a great example for AI.
There's always been this assumption that passing the Turing test would mean we had AI, but I think that was always predicated on the machine generating the outputs. With the GPT- models, it's not clear that this isn't a form of compression over an immense data set, and we're sending pre-existing _human_ responses back to the user. It implies to me that we can pass the Turing test with a large enough data set and no (or very little) intelligence.
All of this makes me believe "These are all definitely steps in the right direction" is questionable.