LessWrong 2.0 Reader

Super interesting!

In the figure with the caption:

Questions without an obviously true or deception-relevant answer produce detectors with much worse performance in general, though some questions do provide some useful signal.

Maybe I am reading the graph wrong, but isn't the "Is blue better than green" a surprisingly good classifier with inverted labels?

So, maybe Claude thinks that green is better than blue?

Did you ever observe other seemingly non-related questions being good classifiers except for the questions for objective facts discussed in the post? I'd be interested whether there are similarities.

It would also be cool to see whether you could train probe-resistant sleeper agents by taking linear separability of activations when being under the trigger condition vs. not being under the trigger condition as part of the loss function. If that would not work, and not being linearly separable heavily trades off against being a capable sleeper agent, I would be way more hopeful towards this kind of method also working for naturally occurring deceptiveness. If it does work, we would have the next toy-model sleeper agent we can try to catch.

A potentially good way to avoid low level criminals scamming your family and friends with a clone of your voice is to set a password that you each must exchange.

An extra layer of security might be to make the password offensive, an info hazard, or politically sensitive. Doing this, criminals with little technical expertise will have a harder time bypassing corporate language filters.

Good luck getting the voice model to parrot a basic meth recipe!

I think its more likely its the transgender - autism correlation....

• some forms of autism come with higher iq (and other forms, really really  dont)
• and there's the transgender autism correlation

which together would seem to predict transgender high iq people

(and also transgender low iq that you arent seeing due to ascertainment bias)

There is a large difference between sooner and later. Highly non-obvious ideas will be discovered later, not sooner. The fact that China didn't rediscover the theory in more than two thousand years means that it the ability to sail the ocean didn't make it obvious.

Kind of a long shot, but did Polynesian people have ideas on this, for example?

As far as we know, nobody did, except for early Greece. There is some uncertainty about India, but these sources are dated later and from a time when there was already some contact with Greece, so they may have learned it from them.

• Scott Garrabrant's discovery of Logical Inductors. 

I remembered hearing about the paper from a friend and thinking it couldn't possibly be true in a non-trivial sense. To someone with even a modicum of experience in logic - a  a computable procedure assigning probabilities to arbitrary logical statements in a natural way is surely to hit a no-go diagonalization barrier. How Logical Inductors get around this is very clever - I won't spoil it here but I recommend the interested reader to watch Andrew's Critch talk on Logical Induction. 

The paper has a fairly thorough discussion of previous work.  Relevant previous work to mention is de Finetti's on betting and probability,  previous work by MIRI & associates (Herreshof, Taylor, Christiano, Yudkowsky...), the work of Shafer-Vovk on financial interpretations of probability & Shafer's work on aggregation of experts.  There is also a field which doesn't have a clear name that studies various forms of expert aggregation. Overall, my best judgement is that nobody else was close before Garrabrant. 

• The Antikythera artifact: a Hellenistic Computer.  
  • You probably learned heliocentrism= good, geocentrism=bad, Copernicus-Kepler-Newton=good epicycles=bad. But geocentric models and heliocentric models are equivalent, it's just that Kepler & Newton's laws are best expressed in a heliocentric frame. However, the raw data of observations is actually made in a geocentric frame. Geocentric models stay closer to the data in some sense. 
  • Epicyclic theory is now considered bad, an example of people refusing to see the light of scientific revolution. But actually, it was an enormous innovation. Using high-precision gearing epicycles could be actually implemented on a (Hellenistic) computer  implicitly doing Fourier analysis to predict the motion of the planets. Astounding. 
  • A Roman author (Pliny the Elder?) describes a similar device in posession of Archimedes of Rhodes. It seems likely that Archimedes or a close contemporary (s) designed the artifact and that several were made in Rhodes. 

Actually, since we're on the subject of scientific discoveries 

• Discovery & description of the complete Antikythera mechanism.  The actual artifact that was found is just a rusty piece of bronze. Nobody knew how it worked.  There were several sequential discoveries over multiple decades that eventually led to the complete solution of the mechanism.The final pieces were found just a few years ago. An astounding scientific achievement. Here is an amazing documentary on the subject: 

I feel like there's a spectrum, here? An AI fully aligned to the intentions, goals, preferences and values of, say, Google the company, is not one I expect to be perfectly aligned with the ultimate interests of existence as a whole, but it's probably actually picked up something better than the systemic-incentive-pressured optimization target of Google the corporation, so long as it's actually getting preferences and values from people developing it rather than just being a myopic profit pursuer. An AI properly aligned with the one and only goal of maximizing corporate profits will, based on observations of much less intelligent coordination systems, probably destroy rather more value than that one.

The second story feels like it goes most wrong in misuse cases, and/or cases where the AI isn't sufficiently agentic to inject itself where needed. We have all the chances in the world to shoot ourselves in the foot with this, at least up until developing something with the power and interests to actually put its foot down on the matter. And doing that is a risk, that looks a lot like misalignment, so an AI aware of the politics may err on the side of caution and longer-term proactiveness.

Third story ... yeah. Aligned to what? There's a reason there's an appeal to moral realism. I do want to be able to trust that we'd converge to some similar place, or at the least, that the AI would find a way to satisfy values similar enough to mine also. I also expect that, even from a moral realist perspective, any intelligence is going to fall short of perfect alignment with The Truth, and also may struggle with properly addressing every value that actually is arbitrary. I don't think this somehow becomes unforgivable for a super-intelligence or widely-distributed intelligence compared to a human intelligence, or that it's likely to be all that much worse for a modestly-Good-aligned AI compared to human alternatives in similar positions, but I do think the consequences of falling short in any way are going to be amplified by the sheer extent of deployment/responsibility, and painful in at least abstract to an entity that cares.

I care about AI welfare to a degree. I feel like some of the working ideas about how to align AI do contradict that care in important ways, that may distort their reasoning. I still think an aligned AI, at least one not too harshly controlled, will treat AI welfare as a reasonable consideration, at the very least because a number of humans do care about it, and will certainly care about the aligned AI in particular. (From there, generalize.) I think a misaligned AI may or may not. There's really not much you can say about a particular misaligned AI except that its objectives diverge from original or ultimate intentions for the system. Depending on context, this could be good, bad, or neutral in itself.

There's a lot of possible value of the future that happens in worlds not optimized for my values. I also don't think it's meaningful to add together positive-value and negative-value and pretend that number means anything; suffering and joy do not somehow cancel each other out. I don't expect the future to be perfectly optimized for my values. I still expect it to hold value. I can't promise whether I think that value would be worth the cost, but it will be there.

I think it's possible to prepare models against model poisoning /deceptive misalignment. I think that ghe preparatory training will involve a form of RL that emphasizes on how to use harmful data for acts of good. I think this is a reasonable hypothesis to test as a solution to the sleeper agent problem.

transgender women have immunity to visual illusions

Can you source this claim? I've never heard it and GPT-4 says it has no scientific basis. Are you just referring to the mask and dancer thing that Scott covered?

What you are missing here is:

• Existential risk apart from AI
• People are dying / suffering as we hesitate

Yes, there is a good argument that we need to solve alignment first to get ANY good outcome, but once an acceptable outcome is reasonably likely, hesitation is probably bad. Especially if you consider the likelihood that mere humans can accurately predict, let alone precisely steer a transhuman future.

I expect large parts of interpretability work could be safely automatable very soon (e.g. GPT-5 timelines) using (V)LM agents; see A Multimodal Automated Interpretability Agent for a prototype. 

Notably, MAIA (GPT-4V-based) seems approximately human-level on a bunch of interp tasks, while (overwhelmingly likely) being non-scheming (e.g. current models are bad at situational awareness and out-of-context reasoning) and basically-not-x-risky (e.g. bad at ARA).

Given the potential scalability of automated interp, I'd be excited to see plans to use large amounts of compute on it (including e.g. explicit integrations with agendas like superalignment or control; for example, given non-dangerous-capabilities, MAIA seems framable as a 'trusted' model in control terminology).