Maybe this is off-base, but this seems mostly in-line with previous expectations?
I think the primary point of interest is that we really don’t need to re-pay the initial training cost for knowledge-possessing models, and that whenever these end up on the internet, it will take very little work to repurpose them as far as they can be straightforwardly repurposed.
(Maybe goal-directness/RL itself continues to require way more data, since our RL algorithms are still weirdly close to random search. I don’t really know.)
Yep, the last two years have basically gone about how people expected. Specifically, the people at leading AGI companies like OpenAI and DeepMind whose AGI timelines (based on public comments in interviews and talks, though I can’t be bothered to dredge them up right now*) are “roughly in the second half of the 20′s.” They totally called all this stuff.
[ETA: There have also been people with 30-year timelines who called it. I’m thinking of Ajeya Cotra and Paul Christiano primarily.]
*I feel a bit bad about not having sources here & if someone cares push me on this and I can try to find them again. I think I’m referring to something Kaplan said in a Q&A about the scaling laws paper, and something Sam Altman said on Twitter or in an interview, and maybe something Demis Hassabis said in an interview also, and an old 2009 blog post from Shane Legg.
Right, it’s not clear to me that this news should make you update against the Bio Anchors timelines vs what you previously did. (Most of my update has just been that the human-lifetime bio anchor seems more likely, and that I’d reduce the size of the preemptive update against it.)
Surely the current trend of fast-paced, groundbreaking capabilities improvements will slow down soon.
Any time now…
Starting to get a little concerned. Maybe we should reconsider the short timelines fire alarm.
Maybe this is off-base, but this seems mostly in-line with previous expectations?
I think the primary point of interest is that we really don’t need to re-pay the initial training cost for knowledge-possessing models, and that whenever these end up on the internet, it will take very little work to repurpose them as far as they can be straightforwardly repurposed.
(Maybe goal-directness/RL itself continues to require way more data, since our RL algorithms are still weirdly close to random search. I don’t really know.)
Yep, the last two years have basically gone about how people expected. Specifically, the people at leading AGI companies like OpenAI and DeepMind whose AGI timelines (based on public comments in interviews and talks, though I can’t be bothered to dredge them up right now*) are “roughly in the second half of the 20′s.” They totally called all this stuff.
[ETA: There have also been people with 30-year timelines who called it. I’m thinking of Ajeya Cotra and Paul Christiano primarily.]
*I feel a bit bad about not having sources here & if someone cares push me on this and I can try to find them again. I think I’m referring to something Kaplan said in a Q&A about the scaling laws paper, and something Sam Altman said on Twitter or in an interview, and maybe something Demis Hassabis said in an interview also, and an old 2009 blog post from Shane Legg.
Right, it’s not clear to me that this news should make you update against the Bio Anchors timelines vs what you previously did. (Most of my update has just been that the human-lifetime bio anchor seems more likely, and that I’d reduce the size of the preemptive update against it.)
Agreed.
Some relevant Altman tweets: 1, 2, 3
I think it is.
But take a moment to appreciate how insane that is.
2018-me just called and wanted to know how that is not AGI.
I told him something about “cached intelligence” and minimal extrapolation, but he didn’t quite buy it.
Thoughts and prayers for the “deep learning is hitting a wall” crowd 🙏
(This is not my joke, it was quoted by Sam Altman on twitter)