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"It would make sense to pay that cost if necessary" makes more sense than "we should expect to pay that cost", thanks.
it sounds like you view it as a bad plan?
Basically, yes. I have a draft post outlining some of my objections to that sort of plan; hopefully it won't sit in my drafts as long as the last similar post did.
(I could be off, but it sounds like either you expect solving AI philosophical competence to come pretty much hand in hand with solving intent alignment (because you see them as similar technical problems?), or you expect not solving AI philosophical competence (while having solved intent alignment) to lead to catastrophe (thus putting us outside the worlds in which x-risks are reliably ‘solved’ for), perhaps in the way Wei Dai has talked about?)
I expect whatever ends up taking over the lightcone to be philosophically competent. I haven't thought very hard about the philosophical competence of whatever AI succeeds at takeover (conditional on that happening), or, separately, the philosophical competence of the stupidest possible AI that could succeed at takeover with non-trivial odds. I don't think solving intent alignment necessarily requires that we have also figured out how to make AIs philosophically competent, or vice-versa; I also haven't though about how likely we are to experience either disjunction.
I think solving intent alignment without having made much more philosophical progress is almost certainly an improvement to our odds, but is not anywhere near sufficient to feel comfortable, since you still end up stuck in a position where you want to delegate "solve philosophy" to the AI, but you can't because you can't check its work very well. And that means you're stuck at whatever level of capabilities you have, and are still approximately a sitting duck waiting for someone else to do something dumb with their own AIs (like point them at recursive self-improvement).
What do people mean when they talk about a "long reflection"? The original usages suggest flesh-humans literally sitting around and figuring out moral philosophy for hundreds, thousands, or even millions of years, before deciding to do anything that risks value lock-in, but (at least) two things about this don't make sense to me:
I tried to make a similar argument here, and I'm not sure it landed. I think the argument has since demonstrated even more predictive validity with e.g. the various attempts to build and restart nuclear power plants, directly motivated by nearby datacenter buildouts, on top of the obvious effects on chip production.
Should be fixed now.
Good catch, looks like that's from this revision, which looks like it was copied over from Arbital - some LaTeX didn't make it through. I'll see if it's trivial to fix.
The page isn't dead, Arbital pages just don't load sometimes (or take 15+ seconds).
I understand this post to be claiming (roughly speaking) that you assign >90% likelihood in some cases and ~50% in other cases that LLMs have internal subjective experiences of varying kinds. The evidence you present in each case is outputs generated by LLMs.
The referents of consciousness for which I understand you to be making claims re: internal subjective experiences are 1, 4, 6, 12, 13, and 14. I'm unsure about 5.
Do you have sources of evidence (even illegible) other than LLM outputs that updated you that much? Those seem like very surprisingly large updates to make on the basis of LLM outputs (especially in cases where those outputs are self-reports about the internal subjective experience itself, which are subject to substantial pressure from post-training).
Separately, I have some questions about claims like this:
The Big 3 LLMs are somewhat aware of what their own words and/or thoughts are referring to with regards to their previous words and/or thoughts. In other words, they can think about the thoughts "behind" the previous words they wrote.
This doesn't seem constructively ruled out by e.g. basic transformer architectures, but as justification you say this:
If you doubt me on this, try asking one what its words are referring to, with reference to its previous words. Its "attention" modules are actually intentionally designed to know this sort of thing, using using key/query/value lookups that occur "behind the scenes" of the text you actually see on screen.
How would you distinguish an LLM both successfully extracting and then faithfully representing whatever internal reasoning generated a specific part of its outputs, vs. conditioning on its previous outputs to give you plausible "explanation" for what it meant? The second seems much more likely to me (and this behavior isn't that hard to elicit, i.e. by asking an LLM to give you a one-word answer to a complicated question, and then asking it for its reasoning).
My impression is that Yudkowsky has harmed public epistemics in his podcast appearances by saying things forcefully and with rather poor spoken communication skills for novice audiences.
I recommend reading the Youtube comments on his recorded podcasts, rather than e.g. Twitter commentary from people with a pre-existing adversarial stance to him (or AI risk questions writ large).
This doesn't seem like it'd do much unless you ensured that there were training examples during RLAIF which you'd expect to cause that kind of behavior enough of the time that there'd be something to update against. (Which doesn't seem like it'd be that hard, though I think separately that approach seems kind of doomed - it's falling into a brittle whack-a-mole regime.)