This was also on my mind after seeing Jesse’s short form yesterday. Ryan’s “this is good” comment was above Louis’ thorough explanation of an alternative formal motivation for IFs. That would still be the case if I hadn’t heavy upvoted and weak downvoted.
I personally cast my comment up/downvotes as an expression of my preference ordering for visibility. I would encourage others to also do so. For instance, I suggest Ryan’s comment should’ve been agreement voted rather than upvoted by others. This stance has as a corollary to not vote if you haven’t read the other comments whose rankings you are affecting—or rather vote with any other marker of which LW has many.
This ‘upvotes as visibility preferences’ policy isn’t tractable for posts, so I suspect the solution there—if one is needed—would have to be done on the backend by normalization. Not sure whether this is worth attempting.
Link here since I don’t particularly want to call out Ryan, his comment was fine. https://www.lesswrong.com/posts/7X9BatdaevHEaHres/jesse-hoogland-s-shortform
Thought provoking, thanks! First off, whether or not humanity (or current humans, or some distribution over humans) has already achieved escape velocity does not directly undercut the utility of escape velocity as a useful concept for AI takeoff. Certainly, I'd say the collective of humans has achieved some leap to universality in a sense that the bag of current near-SotA LLMs has not! And in particular, it's perfectly reasonable to take humans as our reference to define a unit dh/dt point for AI improvement.
Ok, now on to the hard part (speculating somewhat beyond the scope of my original post). Is there a nice notion of time horizon that generalizes METRs and lets us say something about when humanity has achieved escape velocity? I can think of two versions.
The easy way out is to punt to some stronger reference class of beings to get our time horizons, and measure dh/dt for humanity against this baseline. Now the human team is implicitly time limited by the stronger being's bound, and we count false positives against the humans even if humanity could eventually self correct.
Another idea is to notice that there's some class of intractable problems on which current human progress looks like either (1) random search or (2) entirely indirect, instrumental progress--e.g. self-improvement, general tool building etc. In these cases, there may be a sense in which we're exponentially slower than task-capable agent(s). We should be considered incapable of completing such tasks. I imagine some millenium problems, astrological engineering, etc. would be reasonably considered beyond us on this view.
Overall, I'm not particularly happy with these generalizations. But I still like having 'escape velocity for AI auto-R&D' as a threshold!
My perception is that Trump 2 is on track to be far worse (e.g. in terms of eroding democratic practices and institutions) than Trump 1. My vague understanding is that a main driver of this difference is how many people gave up on the "working with bad guys to make them less bad" plan--though probably this was not directly because they changed their view on such reasoning.
Should this update us on the working for net-negative AGI companies case?
2(Thanks for expanding! Will return to write a proper response to this tomorrow)
I've never been compelled by talk about continual learning, but I do like thinking in terms of time horizons. One notion of singularity that we can think of in this context is
Escape velocity: The point at which models improve by more than unit dh/dt i.e. horizon h per wall-clock t.
Then by modeling some ability to regenerate, or continuously deploy improved models you can predict this point. Very surprised I haven't seen this mentioned before, has someone written about this? The closest thing that comes to mind is T Davidson's ASARA SIE.
Of course, the METR methodology is likely to break down well before this point, so it's empirically not very useful. But, I like this framing! Conceptually there will be some point where models can robustly take over R&D work--including training, inventing of new infrastructure (skills, MCPs, cacheing protocols etc.). If they also know when to revisit their previous work they can then work productively over arbitrary time horizons. Escape velocity is a nice concept to have in our toolbox for thinking about R&D automation. It's a weaker notion than Davidson SIE.
It can both be the case that "a world in which the U.S. government took von Neumann's advice would likely be a much darker, bleaker, more violent one" and that JvN was correct ex ante. In particular, I find it plausible that we're living in quite a lucky timeline--one in which the Cuban missile crisis and other coinflips landed in our favor.
UK AISI is a government agency, so the pie chart is probably misleading on that segment!
I like the concept; on the other hand the flag feels strongly fascist to me.
Ran it by the AIs and 2 out of 3 had "authoritarian" as their first descriptor responding to "What political alignment does the aesthetic of this flag evoke?" FWIW.
From a skim, seems you should be using the 6.25x value rather than the 2.5x in B2 of your sheet. If I'm skimming it correctly, 6.25x is the estimate for replacing a hypothetical all median lab with a hypothetical all top researcher lab. This is what occurs when you improve your ASARA model. Whereas, 2.5x is the estimate for replacing the actual lab with an all top lab.
This still gives a lower than 4x value, but I think if you plug in reasonable log-normals 4x will be within your 90% CI, and so it seems fine.
Highly recommend reading Ernest Ryu's twitter multi-thread on proving a long-standing, well-known conjecture with heavy use of ChatGPT Pro. Ernest even includes the chatGPT logs! The convergence of Nesterov gradient descent on convex functions: Part 1, 2, 3.
Ernest gives useful commentary on where/how he found it best to interact with GPT. Incidentally, there's a nice human baseline as well since another group of researchers coincidentally have written up privately a similar result this month!
To add some of my own spin: seems to me time horizons are a nice lens for viewing the collaboration. Ernest, clearly has a long-horizon view of this research problem that helped him (a) know what the most tractable nearby problem was to start on (b) identify when diminishing returns--likelihood of a deadend--were apparent (c) pull out useful ideas from usually flawed GPT work.
The one-week scale of interaction between Ernest and ChatGPT here is a great example of how we're very much in a centaur regime now. We really need to be conceptualizing and measuring AI+human capabilities rather than single-AI capability. It also seems important to be thinking about what safety concerns arise in this regime.