My original background is in mathematics (analysis, topology, Banach spaces) and game theory (imperfect information games). Nowadays, I do AI alignment research (mostly systemic risks, sometimes pondering about "consequentionalist reasoning").
I think literal extinction from AI is a somewhat odd outcome to study as it heavily depends on difficult to reason about properties of the world (e.g. the probability that Aliens would trade substantial sums of resources for emulated human minds and the way acausal trade works in practice).
What would you suggest instead? Something like [50% chance the AI kills > 99% of people]?
(My current take is that for a majority reader, sticking to "literal extinction" is the better tradeoff between avoiding confusion/verbosity and accuracy. But perhaps it deserves at least a footnote or some other qualification.)
That seems fair. For what it's worth, I think the ideas described in the sequence are not sensitive to what you choose here. The point isn't as much to figure out whether the particular arguments go through or not, but to ask which properties must your model have, if you want to be able to evaluate those arguments rigorously.
(For context: My initial reaction to the post was that this is misrepresenting the MIRI-position-as-I-understood-it. And I am one of the people who strongly endorse the view that "it was never about getting the AI to predict human preferences". So when I later saw Yudkowsky's comment and your reaction, it seemed perhaps useful to share my view.)
It seems like you think that human preferences are only being "predicted" by GPT-4, and not "preferred." If so, why do you think that?
My reaction to this is that: Actually, current LLMs do care about our preferences, and about their guardrails. It was never about getting some AI to care about our preferences. It is about getting powerful AIs to robustly care about our preferences. Where by "robustly" includes things like (i) not caring about other things as well (e.g., prediction accuracy), (ii) generalising correctly (e.g., not just maximising human approval), and (iii) not breaking down when we increase the amount of optimisation pressure a lot (e.g., will it still work once we hook it into future-AutoGPT-that-actually-works and have it run for a long time?).
Some examples of what would cause me to update are: If we could make LLMs not jailbreakable without relying on additional filters on input or output.
Nitpicky comment / edit request: The circle inversion figure was quite confusing to me. Perhaps add a note to it saying that solid green maps onto solid blue, red maps onto itself, and dotted green maps onto dotted blue. (Rather than colours mapping to each other, which is what I intuitively expected.)
Fun example: The evolution of offensive words seems relevant here. IE, we frown upon using currently-offensive words, so we end up expressing ourselves using some other words. And over time, we realise that those other words are (primarily used as) Doppelgangers, and mark them as offensive as well.
E.g. Living in large groups such that it’s hard for a predator to focus on any particular individual; a zebra’s stripes.
Off-topic, but: Does anybody have a reference for this, or a better example? This is the first time I have heard this theory about zebras.
Two points that seem relevant here:
(At the moment, both of these questions seem open.)
This made me think of "lawyer-speak", and other jargons.
More generally, this seems to be a function of learning speed and the number of interactions on the one hand, and the frequency with which you interact with other groups on the other. (In this case, the question would be how often do you need to be understandable to humans, or to systems that need to be understandable to humans, etc.)
I would like to point out one aspects of the "Vulnerable ML systems" scenario that the post doesn't discuss much: the effect on adversarial vulnerability on widespread-automation worlds.
Using existing words, some ways of pointing towards what I mean are: (1) Adversarial robustness solved after TAI (your case 2), (2) vulnerable ML systems + comprehensive AI systems, (3) vulnerable ML systems + slow takeoff, (4) fast takeoff happening in the middle of (3).
But ultimately, I think none of these fits perfectly. So a longer, self-contained description is something like:
There are several potential threats that have particularly interesting interactions with this setting:
Connection to Cases 1-3: All of this seems very related to how you distinguish between adversarial robustness gets solved before tranformative AI/after TAI/never. However, I would argue that TAI is not necessarily the relevant cutoff point here. Indeed, for Alignment failure (A) and Easier takeover (B), the relevant moment is "the first time we get an AI capable of forming a singleton". This might happen tomorrow, by the time we have automated 25% of economically-relevant tasks, half a year into having automated 100% of tasks, or possibly never. And for the remaining threat models (C,D,E), perhaps there are no single cutoff points, and instead the stakes and implications change gradually?
Implications: Personally, I am the most concerned about misaligned AI (A and B) and Capitalism gone wrong (C). However, perhaps risks from malicious actors and nation-state adversaries (D, E) are more salient and less controversial, while pointing towards the same issues? So perhaps advancing the agenda outlined in the post can be best done through focusing on these? [I would be curious to know your thoughts.]