TLDR: We’re excited to announce that OPTIC is running three intercollegiate forecasting competitions this fall — in the Bay Area (Nov 4), DC (Nov 18) and Boston (Dec 2). Register here! What does a tournament look like? Think 1-day hackathon/olympiad/debate tournament, but for forecasting the future — teams predict on...
TLDR: We’re excited to announce that OPTIC is running three intercollegiate forecasting competitions this fall — in the Bay Area (Nov 4), DC (Nov 18) and Boston (Dec 2). Register here!
Think 1-day hackathon/olympiad/debate tournament, but for forecasting the future — teams predict on topics ranging from geopolitics to celebrity twitter patterns to financial asset prices, and the best forecasters get thousands of dollars in cash prizes and exclusive internships at Metaculus.
Day of, teams give probabilistic predictions for a couple hours on about 30 given questions (with breaks for lunch & speakers). Teams are 3-5 competitors, and we’ll place you on a team if you don’t already have... (read 275 more words →)
Context windows could make the claim from the post correct. Since the simulator can only consider a bounded amount of evidence at once, its P[Waluigi] has a lower bound. Meanwhile, it takes much less evidence than fits in the context window to bring its P[Luigi] down to effectively 0.
Imagine that, in your example, once Waluigi outputs B it will always continue outputting B (if he's already revealed to be Waluigi, there's no point in acting like Luigi). If there's a context window of 10, then the simulator's probability of Waluigi never goes below 1/1025, while Luigi's probability permanently goes to 0 once B is outputted, and so the simulator is guaranteed to eventually get stuck at Waluigi.
I expect this is true for most imperfections that simulators can have; its harder to keep track of a bunch of small updates for X over Y than it is for one big update for Y over X.
The Constitutional AI paper, in a sense, shows that a smart alien with access to an RLHFed helpful language model can figure out how to write text according to a set of human-defined rules. It scares me a bit that this works well, and I worry that this sort of self-improvement is going to be a major source of capabilities progress going forward.
Talking about what a language model "knows" feels confused. There's a big distinction between what a language model can tell you if you ask it directly, what it can tell you if you ask it with some clever prompting, and what a smart alien could tell you after only interacting with that model. A moderately smart alien that could interact with GPT-3 could correctly answer far more questions than GPT-3 can even with any amount of clever prompting.
As a sort-of normative realist wagerer (I used to describe myself that way, and still have mostly the same views, but now longer consider it a good way to describe myself), I really enjoyed this post, but I think it misses the reasons the wager seems attractive to me.
To start, I don't think of the wager as being "if normative realism is true, things matter more, so I should act as if I'm a normative realist", but as being "unless normative realism is true, I don't see how I could possibly determine what matters, and so I should act as if I'm a normative realist".
I'm also strongly opposed to using Martha-type dilemmas... (read more)
I really love this idea! Thanks for sharing this, I'm excited to try Calibrate.
How can list sorting be O(n)? There are n! ways to sort a list, which means that it's impossible to have a list sorting algorithm faster than O(log(n!)) = O(n*log(n)).
That's for linking the post! I quite liked it, and I agree that computational complexity doesn't pose a challenge to general intelligence. I do want to dispute your notion that "if you hear that a problem is in a certain complexity class, that is approximately zero evidence of any conclusion drawn from it". The world is filled with evidence, and it's unlikely that closely related concepts give approximately zero evidence for each other unless they are uncorrelated or there are adversarial processes present. Hearing that list-sorting is O(n*log(n)) is pretty strong evidence that it's easy to do, and hearing that simulating quantum mechanics is not in P is pretty strong evidence that it's hard to do. Sure, there are lots of exceptions, but computational complexity is in fact a decent heuristic, especially if you go with average-case complexity of an approximation, rather than worst-case complexity of an exact answer.
I'm definitely only talking about probabilities in the range of >90%. >50% is justifiable without a strong argument for the disjunctivity of doom.
I like the self-driving car analogy, and I do think the probability in 2015 that a self-driving car would ever kill someone was between 50% and 95% (mostly because of a >5% chance that AGI comes before self-driving cars).
There's still the problem of successor agents and self-modifying agents, where you need to set up incentives to create successor agents with the same utility functions and to not strategically self-modify, and I think a solution to that would probably also work as a solution to normal dishonesty.
I do expect that in a case where agents can also see each other's histories, we can make bargaining go well with the bargaining theory we know (given that the agents try to bargain well, there are of course possible agents which don't try to cooperate well).
I'm really glad that this post is addressing the disjunctivity of AI doom, as my impression is that it is more of a crux than any of the reasons in https://www.lesswrong.com/posts/uMQ3cqWDPHhjtiesc/agi-ruin-a-list-of-lethalities.
Still, I feel like this post doesn't give a good argument for disjunctivity. To show that the arguments for a scenario with no outside view are likely, it takes more than just describing a model which is internally disjunctive. There needs to be some reason why we should strongly expect there to not be some external variables that could cause the model not to apply.
Some examples of these, in addition to the competence of humanity, are that deep learning could hit a wall... (read more)