Review
I can't speak for anyone else, but the reason I'm not more interested in this idea is that I'm not convinced it could actually be done. Right now, big AI companies train on piles of garbage data since it's the only way they can get sufficient volume. The idea that we're going to produce a similar amount of perfectly labeled data doesn't seem plausible.
I don't want to be too negative, because maybe you have an answer to that, but maybe-working-in-theory is only the first step and if there's no visible path to actually doing what you propose, then people will naturally be less excited.
The idea that we're going to produce a similar amount of perfectly labeled data doesn't seem plausible.
That's not at all the idea. Allow me to quote myself:
Here’s what I think we could do. Internet text is vast – on the order of a trillion words. But we could label some of it as “true” and “false”. The rest will be “unknown”.
You must have missed the words "some of" in it. I'm not suggesting labeling all of the text, or even a large fraction of it. Just enough to teach the model the concept of right and wrong.
It shouldn't take long, especially since I...
Carlson's interview, BTW. It discusses LessWrong in the first half of the video. Between X and YouTube, the interview got 4M views -- possibly the most high-profile exposure of this site?
I'm kind of curious about the factual accuracy: "debugging" / struggle sessions, polycules, and the 2017 psychosis -- Did that happen?
It's not really about whether the specific proposal is novel, it's about whether the proposal handles the known barriers which are most difficult for other proposals. New proposals are useful mainly insofar as they overcome some subset of barriers which stopped other solutions.
For instance, if you read through Eliezer's List O' Doom and find that your proposal handles items on that list which no other proposal has ever handled, or a combination which no other proposal has simultaneously handled, then that's a big deal. On the other hand, if your solution falls prey to the same subset of problems as most solutions, then that's not so useful.
New proposals are useful mainly insofar as they overcome some subset of barriers which stopped other solutions.
CEV was stopped by being unimplementable, and possibly divergent:
The main problems with CEV include, firstly, the great difficulty of implementing such a program - “If one attempted to write an ordinary computer program using ordinary computer programming skills, the task would be a thousand lightyears beyond hopeless.” Secondly, the possibility that human values may not converge. Yudkowsky considered CEV obsolete almost immediately after its publication in 2004.
VELM and VETLM are easily implementable (on top of a superior ML algorithm). So does this fit the bill?
Well, we have lots of implementable proposals. What do VELM and VETLM offer which those other implementable proposals don't? And what problems do VELM and VETLM not solve?
Alternatively: what's the combination of problems which these solutions solve, which nothing else we've thought of simultaneously solves?
Maybe a good post could be about the compromise point between 'solution proposer has to have familiarity with all other proposals' and 'experienced researchers have to evaluate any proposed idea'
What do VELM and VETLM offer which those other implementable proposals don't? And what problems do VELM and VETLM not solve?
VETLM solves superalignment, I believe. It's implementable (unlike CEV), and it should not be susceptible to wireheading (unlike RLHF, instruction following, etc) Most importantly, it's intended to work with an arbitrarily good ML algorithm -- the stronger the better.
So, will it self-improve, self-replace, escape, let you turn it off, etc.? Yes, if it thinks that this is what its creators would have wanted.
Will it be transparent? To the point where it can self-introspect and, again if it thinks that being transparent is what its creators would have wanted. If it thinks that this is a worthy goal to pursue, it will self-replace with increasingly transparent and introspective systems.
Eliezer Yudkowsky’s main message to his Twitter fans is:
Aligning human-level or superhuman AI with its creators’ objectives is also called “superalignment”. And a month ago, I proposed a solution to that. One might call it Volition Extrapolated by Language Models (VELM).
Apparently, the idea was novel (not the “extrapolated volition” part):
But it suffers from the fact that language models are trained on large bodies of Internet text. And this includes falsehoods. So even in the case of a superior learning algorithm[1], a language model using it on Internet text would be prone to generating falsehoods, mimicking those who generated the training data.
So a week later, I proposed a solution to that problem too. Perhaps one could call it Truthful Language Models (TLM). That idea was apparently novel too. At least no one seems to be able to link prior art.
Its combination with the first idea might be called Volition Extrapolated by Truthful Language Models (VETLM). And this is what I was hoping to discuss.
But this community’s response was rather disinterested. When I posted it, it started at +3 points, and it’s still there. Assuming that AGI is inevitable, shouldn’t superalignment solution proposals be almost infinitely important, rationally-speaking?
I can think of five possible critiques:
Think “AIXI running on a hypothetical quantum supercomputer”, if this helps your imagination. But I think that superior ML algorithms will be found for modern hardware.