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"why didn't the first person to come up with the idea of using computers to predict the next element in a sequence patent that idea, in full generality"
Patents are valid for about 20 years. But Bengio et al used NNs to predict the next word back in 2000:
https://papers.nips.cc/paper_files/paper/2000/file/728f206c2a01bf572b5940d7d9a8fa4c-Paper.pdf
So this idea is old. Only some specific architectural aspects are new.
I suspect this labeling and using the labels is still harder that you think though, since individual tokens don't have truth values.
Why should they?
You could label each paragraph, for example. Then, when the LM is trained, the correct label could come before each paragraph, as a special token: <true>, <false>, <unknown> and perhaps <mixed>.
Then, during generation, you'd feed it <true> as part of the prompt, and when it generates paragraph breaks.
Similarly, you could do this on a per-sentence basis.
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'm assuming a human-level ML algorithm here, that is, one with data efficiency comparable to that of humans.
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?
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.
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?
That post was completely ignored here: 0 comments and 0 upvotes during the first 24 hours.
I don't know if it's the timing or the content.
On HN, which is where I saw it, it was ranked #1 briefly, as I recall. But then it got "flagged", apparently.
Machine Learning Street Talk interview of one of the authors:
There was an article in New Scientist recently about "sending particles back in time". I was a physics major, but I might have skipped the time travel class, so I don't have an opinion on this. But Sabine Hossenfelder posted a video, arguing that New Scientist misrepresented the actual research.
Side note: the link didn't make it to the front page of HN, despite early upvotes. Other links with worse stats (votes at a certain age) rose to the very top. Anyways, it's currently ranked 78. I guess I don't really understand how HN ranks things. I hope someone will explain this to me. Does the source "youtube" vs "nytimes" matter? Do flag-votes count as silent mega-downvotes? Does the algorithm punish posts with numbers in them?
Thanks! It looks interesting. Although I think it's different from what I was talking about.
I think your idea of labelling the source and epistemic status of all training data is good. I've seen the idea presented before.
I'm not finding anything. Do you recall the authors? Presented at a conference? Year perhaps? Specific keywords? (I tried the obvious)
I think that regularization in RL is normally used to get more rewards (out-of-sample).
Sure, you can increase it further and do the opposite – subvert the goal of RL (and prevent wireheading).
But wireheading is not an instability, local optimum, or overfitting. It is in fact the optimal policy, if some of your actions let you choose maximum rewards.
Anyway, the quote you are referring to says “as (AI) becomes smarter and more powerful”.
It doesn’t say that every RL algorithm will wirehead (find the optimal policy), but that an ASI-level one will. I have no mathematical proof of this, since these are fuzzy concepts. I edited the original text to make it less controversial.
Most humans are aware of the possibility of wireheading, both the actual wire version and the more practical versions involving psychotropic drugs.
For humans, there are negative rewards for abusing drugs/alcohol -- hangover the next day, health issues, etc. You could argue that they are taking those into account.
But for an entirely RL-driven AI, wireheading has no anticipated downsides.
Yes, it's simple enough, that I imagine it's likely people came up with it before. But it fixes a flaw in the other idea (which is also simple, although in the previous discussion I was told that it might be novel)
many of which will allow for satisfaction, while still allowing the AI to kill everyone.
This post is just about alignment of AGI's behavior with its creator's intentions, which is what Yoshua Bengio was talking about.
If you wanted to constrain it further, you'd say that in the prompt. But I feel that rigid constraints are probably unhelpful, the way The Three Laws of Robotics are. For example, anyone could threaten suicide and force the AGI to do absolutely anything short of killing other people.
Quoting from the CEV link:
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.
Neither problem seems relevant to what I'm proposing. My implementation is just a prompt. And there is no explicit optimization (after the LM has been trained).
Has anyone proposed exactly what I'm proposing? (slightly different wording is OK, of course)
"Some content on the Internet is fabricated, and therefore we can never trust LMs trained on it"
Is this a fair summary?
Technically true. But you could similarly argue that humans are just clumps of molecules following physical laws. Talking about human goals is a charitable interpretation.
And if you are in a charitable mood, you could interpret LMs as absorbing the explicit and tacit knowledge of millions of Internet authors. A superior ML algorithm would just be doing this better (and maybe it wouldn't need lower-quality data).
A variation on this:
Any expression should be considered for replacement by a slightly bigger or smaller one. For example
z = f(x**2 * y)
should be replaceable by
z = f((x**2 - 1) * y)
The generated programs are quite short. So I would guess that this multiplies their number by 100-1000, if you consider one perturbation at a time.
If GPT-4o made the off-by-one error, is it reasonable to expect GPT-3.5 to spot it?
@ryan_greenblatt's approach also asks GPT-4o to improve its previous guesses.
These calls are expensive though.
The idea of Program Dithering is to generate many candidate programs cheaply.
If you have locations that you want to perturb, then if you try a single off-by-one perturbation at a time, this adds programs. With two at a time, this adds programs.
There's a possible optimization, where you only try this on tasks where no unperturbed program was found (<28%)
EDIT: Ironically, I made an off-by-one error, which Program Dithering would have fixed: This should be
This looks similar, in spirit, to Large Language Models as General Pattern Machines:
I'm surprised by how knowledgeable people are about this on this site!
BTW, there's some discussion of this happening on the CCL mailing list (limited to professionals in relevant fields) if you are interested.
Right. The benchmark (their test set) just compares 3D structures.
Side note: 52% also seems low for Vina, but I haven't looked into this further. Maybe the benchmark is hard, or maybe the "search space" (user-specified) was too big.
On their other test (in their Extended Data), both Vina and AF3 do much better.
Unlike Vina, AF3 only predicts 3D structures, I believe. It does not predict binding affinities.
Determining 3D structures is expensive.
The most realistic thing one could do is repeat this work, with the same settings, but using k-fold cross-validation, where test and training sets are never related (like what I did at Columbia).
This will show how well (or poorly, as the case may be) the method generalizes to unrelated proteins.
I hope someone does it.
(ligand = drug-like molecule, for anyone else reading)
Right, I didn't mean exact bitwise memory comparisons.
The dataset is redundant(ish), simply as an artifact of how it's constructed:
For example, if people know that X binds A, and X ≈ Y, and A ≈ B, they'll try to add X+B, Y+A and Y+B to the dataset also.
And this makes similarity-based predictions look artificially much more useful than they actually are, because in the "real world", you will need to make predictions about dissimilar molecules from some collection.
I hope this makes sense.