Daniel Kokotajlo

Philosophy PhD student, worked at AI Impacts, then Center on Long-Term Risk, now OpenAI Futures/Governance team. Views are my own & do not represent those of my employer. Research interests include acausal trade, timelines, takeoff speeds & scenarios, decision theory, history, and a bunch of other stuff. I subscribe to Crocker's Rules and am especially interested to hear unsolicited constructive criticism. http://sl4.org/crocker.html


Agency: What it is and why it matters
AI Timelines
Takeoff and Takeover in the Past and Future

Wiki Contributions


Two-year update on my personal AI timelines

Thanks so much for this update! Some quick questions:

  1. Are you still estimating that the transformative model uses probably about 1e16 parameters & 1e16 flops? IMO something more like 1e13 is more reasonable.
  2. Are you still estimating that algorithmic efficiency doubles every 2.5 years (for now at least, until R&D acceleration kicks in?) I've heard from thers (e.g. Jaime Sevilla) that more recent data suggests it's doubling every 1 year currently.
  3. Do you still update against the lower end of training FLOP requirements, on the grounds that if we were 1-4 OOMs away right now the world would look very different?
  4. Is there an updated spreadsheet we can play around with?
«Boundaries», Part 1: a key missing concept from utility theory

For the games that matter most, the amounts of money-equivalent involved are large enough that utility is not roughly linear in it. (Example: Superintelligences deciding what to do with the cosmic endowment.) Or so it seems to me, I'd love to be wrong about this.

AGI ruin scenarios are likely (and disjunctive)

(This seems to me to be what many people imagine will happen to the pieces of the AGI puzzle other than the piece they’re most familiar with, via some sort of generalized Gell-Mann amnesia: the tech folk know that the technical arena is in shambles, but imagine that policy has the ball, and vice versa on the policy side. But whatever.)

Just wanted to say that this is also my impression, and has been for months. Technical & Policy both seem to be planning on the assumption that the other side will come through in the end, but the things they imagine this involving seem to be things that the other side thinks is pretty unlikely.

Gradations of Agency

Well said; I agree it should be split up like that.

Gradations of Agency

Good point re learning cognitive algorithms from imagined experience, that does seems pretty hard. From imitation though? We do it all the time. Here's an example of me doing both:

I read books about decision theory & ethics, and learn about expected utility maximization & the bounded variants that humans can actually do in practice (back of envelope calculations, etc.) I immediately start implementing this algorithm myself on a few occasions. (Imitation)

Then I read more books and learn about "pascal's mugging" and the like. People are arguing about whether or not it's a problem for expected utility maximization. I think through the arguments myself and come up with some new arguments of my own. This involves imagining how the expected utility maximization algorithm would behave in various hypothetical scenarios, and also just reasoning analytically about the properties of the algorithm. I end up concluding that I should continue using the algorithm but with some modifications. (Learning from imagined experience.)

Would you agree with this example, or are you thinking about the hierarchy somewhat differently than me? I'm keen to hear more if the latter.

Examples of AI Increasing AI Progress

Note that those who endorse Mslow don't think exponential growth will cut it; it'll be much faster than that (in line with the long-term trends in human history, which are faster than exponential). I'm thinking of e.g. Paul Christiano and Ajeya Cotra here who I'm pretty sure agree growth has been and will continue to be superexponential (the recent trend of apparent exponential growth being an aberration).

My complaining about the term "continuous takeoff" was a response to Matthew Barnett and others' usage of the term, not Yitz', since as you say Yitz didn't use it.

Anyhow, to the meat: None of the "hard takeoff people" or hard takeoff models predicted or would predict that the sorts of minor productivity advancements we are starting to see would lead to a FOOM by now. Ergo, it's a mistake to conclude from our current lack of FOOM that those models made incorrect predictions.

Gradations of Agency

Thanks! Hmm, I would have thought humans were at Level 6, though of course most of their cognition most of the time is at lower levels.

Examples of AI Increasing AI Progress

On behalf of hard takeoff people (and as someone who is like 50% one of them) the hard takeoff model predicts this stuff pretty much just as well as the "continuous models," i.e. is pretty much zero surprised by these data points.

(I put continuous in scare quotes because IMO it's a rhetorical weasel word that invites motte-and-bailey tactics -- the motte being "surely the burden of proof should be on whoever thinks the straight line on a graph will suddenly break or bend" and the bailey being "therefore the burden of proof is on whoever thinks that there won't be a multi-year period in which the world is going crazy due to powerful AGIs transforming the economy while still humans are in control because the AGIs aren't superhuman yet." I prefer the slow vs. fast takeoff terminology, or soft vs. hard.)

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