Sequences
Posts
Wiki Contributions
If you had to put a rough number on how likely it is that a misaligned superintelligence would primarily value "small molecular squiggles" versus other types of misaligned goals, would it be more like 1000:1 or 1:1 or 1000:1 or something else?
Value them primarily? Uhhh... maybe 1:3 against? I admit I have never actually pondered this question before today; but 1 in 4 uncontrolled superintelligences spending most of their resources on tiny squiggles doesn't sound off by, like, more than 1-2 orders of magnitude in either direction.
Clocks are not actually very complicated; how plausible is it on your model that these goals are as complicated as, say, a typical human's preferences about how human civilization is structured?
It wouldn't shock me if their goals end up far more complicated than human ones; the most obvious pathway for it is (a) gradient descent turning out to produce internal preferences much faster than natural selection + biological reinforcement learning and (b) some significant fraction of those preferences being retained under reflection. (Where (b) strikes me as way less probable than (a), but not wholly forbidden.) The second most obvious pathway is if a bunch of weird detailed noise appears in the first version of the reflective process and then freezes.
Not obviously stupid on a very quick skim. I will have to actually read it to figure out where it's stupid.
(I rarely give any review this positive on a first skim. Congrats.)
By "dumb player" I did not mean as dumb as a human player. I meant "too dumb to compute the pseudorandom numbers, but not too dumb to simulate other players faithfully apart from that". I did not realize we were talking about humans at all. This jumps out more to me as a potential source of misunderstanding than it did 15 years ago, and for that I apologize.
I don't always remember my previous positions all that well, but I doubt I would have said at any point that sufficiently advanced LDT agents are friendly to each other, rather than that they coordinate well with each other (and not so with us)?
Actually, to slightly amend that: The part where squiggles are small is a more than randomly likely part of the prediction, but not a load-bearing part of downstream predictions or the policy argument. Most of the time we don't needlessly build our own paperclips to be the size of skyscrapers; even when having fun, we try to do the fun without vastly more resources, than are necessary to that amount of fun, because then we'll have needlessly used up all our resources and not get to have more fun. We buy cookies that cost a dollar instead of a hundred thousand dollars. A very wide variety of utility functions you could run over the outside universe will have optima around making lots of small things because each thing scores one point, and so to score as many points as possible, each thing is as small as it can be as still count as a thing. Nothing downstream depends on this part coming true and there are many ways for it to come false; but the part where the squiggles are small and molecular is an obvious kind of guess. "Great giant squiggles of nickel the size of a solar system would be no more valuable, even from a very embracing and cosmopolitan perspective on value" is the loadbearing part.
The part where squiggles are small and simple is unimportant. They could be bigger and more complicated, like building giant mechanical clocks. The part that matters is that squiggles/paperclips are of no value even from a very cosmopolitan and embracing perspective on value.
2I think that the AI's internal ontology is liable to have some noticeable alignments to human ontology w/r/t the purely predictive aspects of the natural world; it wouldn't surprise me to find distinct thoughts in there about electrons. As the internal ontology goes to be more about affordances and actions, I expect to find increasing disalignment. As the internal ontology takes on any reflective aspects, parts of the representation that mix with facts about the AI's internals, I expect to find much larger differences -- not just that the AI has a different concept boundary around "easy to understand", say, but that it maybe doesn't have any such internal notion as "easy to understand" at all, because easiness isn't in the environment and the AI doesn't have any such thing as "effort". Maybe it's got categories around yieldingness to seven different categories of methods, and/or some general notion of "can predict at all / can't predict at all", but no general notion that maps onto human "easy to understand" -- though "easy to understand" is plausibly general-enough that I wouldn't be unsurprised to find a mapping after all.
Corrigibility and actual human values are both heavily reflective concepts. If you master a requisite level of the prerequisite skill of noticing when a concept definition has a step where its boundary depends on your own internals rather than pure facts about the environment -- which of course most people can't do because they project the category boundary onto the environment, but I have some credit that John Wentworth might be able to do it some -- and then you start mapping out concept definitions about corrigibility or values or god help you CEV, that might help highlight where some of my concern about unnatural abstractions comes in.
Entirely separately, I have concerns about the ability of ML-based technology to robustly point the AI in any builder-intended direction whatsoever, even if there exists some not-too-large adequate mapping from that intended direction onto the AI's internal ontology at training time. My guess is that more of the disagreement lies here.
12
3
2What the main post is responding to is the argument: "We're just training AIs to imitate human text, right, so that process can't make them get any smarter than the text they're imitating, right? So AIs shouldn't learn abilities that humans don't have; because why would you need those abilities to learn to imitate humans?" And to this the main post says, "Nope."
The main post is not arguing: "If you abstract away the tasks humans evolved to solve, from human levels of performance at those tasks, the tasks AIs are being trained to solve are harder than those tasks in principle even if they were being solved perfectly." I agree this is just false, and did not think my post said otherwise.
Unless I'm greatly misremembering, you did pick out what you said was your strongest item from Lethalities, separately from this, and I responded to it. You'd just straightforwardly misunderstood my argument in that case, so it wasn't a long response, but I responded. Asking for a second try is one thing, but I don't think it's cool to act like you never picked out any one item or I never responded to it.
EDIT: I'm misremembering, it was Quintin's strongest point about the Bankless podcast. https://www.lesswrong.com/posts/wAczufCpMdaamF9fy/my-objections-to-we-re-all-gonna-die-with-eliezer-yudkowsky?commentId=cr54ivfjndn6dxraD
I note that I haven't said out loud, and should say out loud, that I endorse this history. Not every single line of it (see my other comment on why I reject verificationism) but on the whole, this is well-informed and well-applied.