For a long time, "AI alignment" was a purely theoretical field, making very slow progress of questionable relevance, due to lack of anything interesting to experiment on. Now, we have things to experiment on, and the field is exploding, and we're finally learning things about how to align these systems. But not fast enough. I really don't want to overstate the capabilities of current-generation AI systems; they're no…
A lot of people seem to take the rapid improvement of LLMs from GPT-2 through GPT-4 and their brethren, and extrapolate that trendline to infinity. But that's not logically sound. The advances that have allowed this aren't arbitrarily scalable. Sure, we may see some more advances in AI tech that take us a few more jumps forward—but that doesn't imply that we will keep advancing at this pace until we hit AGI/superinte…
> But that's not logically sound.
Yup, five years ago I asked "Do we definitely already know it's going to be possible to deploy self-driving cars in an economically meaningful way?" and got the answer "yes", on a story titled "GM says it will put fleets of self-driving cars in cities in 2019"!
https://news.ycombinator.com/item?id=15824953
I just have no idea how people are making the extrapolations they are making about the power of future large language models.