There is a harder second-order question of "what sorts of videos maximize watch time, and will those be bad for my child?" Hastings's evidence points toward "yes", but I don't think the answer is obvious a priori. (The things YouTube thinks I want to watch are almost all good or neutral for me; YMMV.)
For posterity, I would just like to make it clear that if I were ever cloned, I would treat my clone as an equal, and I wouldn't make him do things I wouldn't do—in fact I wouldn't try to make him do anything at all, we'd make decisions jointly.
(But of course my clone would already know that, because he's me.)
(I've spent an unreasonable amount of time thinking about how to devise a fair decision procedure between me and my clone to allocate tasks and resources in a perfectly egalitarian way.)
FWIW I think Habryka was right to call out that some parts of my comment were bad, and the scolding got me to think more carefully about it.
2Are you also lifting weights? I'm quite confident that you can gain muscle while taking retatrutide if you lift weights.
IIRC GLP-1 agonists cause more muscle loss than "old-fashioned" dieting, but the effect of resistance training far outweighs the extra muscle loss.
My question is, how do you make AI risk known while minimizing the risk of paradoxical impacts? "Never talk about it" is the wrong answer, but I expect there's a way to do better than we've done so far. This seems like an important thing to try to understand.
I don't do this on purpose but I feel like 90% of what I write about AI is something Eliezer already said at some point.
Yeah I pretty much agree with what you're saying. But I think I misunderstood your comment before mine, and the thing you're talking about was not captured by the model I wrote in my last comment; so I have some more thinking to do.
I didn't mean "can be trusted to take AI risk seriously" as "indeterminate trustworthiness but cares about x-risk", more like "the conjunction of trustworthy + cares about x-risk".
1ETA: I think this comment is missing some important things and I endorse Habryka's reply more than I endorse this comment
Like, the most important thing to estimate when evaluating a political candidate is their trustworthiness and integrity! It's the thing that would flip the sign on whether supporting someone is good or bad for the world.
I agree that this is an important thing that deserved more consideration in Eric's analysis (I wrote a note about it on Oct 22 but then I forgot to include it in my post yesterday). But I don't think it's too hard to put into a model (although it's hard to find the right numbers to use). The model I wrote down in my note is
- 30% chance Bores would oppose an AI pause / strong AI regulations (b/c it's too "anti-innovation" or something)
- 40% chance Bores would support strong regulations
- 30% chance he would vote for strong regulations but not advocate for them
- 90% chance Bores would support weak/moderate AI regulations
My guess is that 2/3 of the EV comes from strong regulations and 1/3 from weak regulations (which I just came up with a justification for earlier today but it's too complicated to fit in this comment), so these considerations reduce the EV to 37% (i.e., roughly divide EV by 3).
FWIW I wouldn't say "trustworthiness" is the most important thing, more like "can be trusted to take AI risk seriously", and my model is more about the latter. (A trustworthy politician who is honest about the fact that they don't care about AI safety will not be getting any donations from me.)
For constant-relative-risk-aversion (CRRA) utility functions, the Kelly criterion is optimal iff you have logarithmic utility. For proof, see Samuelson (1971), The "Fallacy" of Maximizing the Geometric Mean in Long Sequences of Investing or Gambling.
I think there is only a fixed-proportion betting rule (i.e. "bet P% of your bankroll" for fixed P) for CRRA utility functions, because if risk aversion varies then the betting rule must also vary. But I'm not sure how to prove that.
ETA: Actually I think it shouldn't be too hard to prove that using the definition of CRRA. You could do something like, assume a fixed-proportion betting rule exists for some constant P, and then calculate the implied relative risk aversion and show that it must be a constant.