It is definitely misleading, in the same sense that the performance of a model on the training data is misleading. The interesting question w.r.t. GPT-3 is “how well does it perform in novel settings?”. And we can’t really know that, because apparently even publicly available interfaces are inside the training loop.
Now, there’s nothing wrong with training an AI like that! But the results then need to be interpreted with more care.
P.S.: sometimes children do parrot their parents to an alarming degree, e.g., about political positions they couldn’t possibly have the context to truly understand.
The interesting question w.r.t. GPT-3 is “how well does it perform in novel settings?”. And we can’t really know that, because apparently even publicly available interfaces are inside the training loop.
OpenAI still lets you use older versions of GPT-3, if you want to experiment with ones that haven’t had additional training.
P.S.: sometimes children do parrot their parents to an alarming degree, e.g., about political positions they couldn’t possibly have the context to truly understand.
It’s much better for children to parrot the political positions of their parents than to select randomly from the total space of political opinions. The vast majority of possible-political-opinion-space is unaligned.
If they’re randomly picking from a list of possible political positions, I’d agree. However, I suspect that is not the realistic alternative to parroting their parents political positions.
Maybe ideally it’d be rational reflection to the best of their ability on values and whatnot. However, if we had a switch to turn off parroting-parents-political-positions we’d be in a weird space...children wouldn’t even know about most political positions to even choose from.
Right, but we wouldn’t then use this as proof that our children are precocious politicians!
In this discussion, we need to keep separate the goals of making GPT-3 as useful a tool as possible, and of investigating what GPT-3 tells us about AI timelines.
It is definitely misleading, in the same sense that the performance of a model on the training data is misleading. The interesting question w.r.t. GPT-3 is “how well does it perform in novel settings?”. And we can’t really know that, because apparently even publicly available interfaces are inside the training loop.
Now, there’s nothing wrong with training an AI like that! But the results then need to be interpreted with more care.
P.S.: sometimes children do parrot their parents to an alarming degree, e.g., about political positions they couldn’t possibly have the context to truly understand.
OpenAI still lets you use older versions of GPT-3, if you want to experiment with ones that haven’t had additional training.
It’s much better for children to parrot the political positions of their parents than to select randomly from the total space of political opinions. The vast majority of possible-political-opinion-space is unaligned.
If they’re randomly picking from a list of possible political positions, I’d agree. However, I suspect that is not the realistic alternative to parroting their parents political positions.
Maybe ideally it’d be rational reflection to the best of their ability on values and whatnot. However, if we had a switch to turn off parroting-parents-political-positions we’d be in a weird space...children wouldn’t even know about most political positions to even choose from.
Right, but we wouldn’t then use this as proof that our children are precocious politicians!
In this discussion, we need to keep separate the goals of making GPT-3 as useful a tool as possible, and of investigating what GPT-3 tells us about AI timelines.
It doesn’t follow that a subset of well known political opinions is aligned, even with itself.