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thezvi.wordpress.com

Zvi Mowshowitz reporting on an LLM exhibiting unprompted instrumental convergence. Figured this might be an update to some Mottizens.

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Those goals are then almost invariably, with sufficient intelligence, subject to instrumental convergence, as in this case

The term "instrumental convergence" is slippery here. It can be used to mean "doing obvious things it assesses to be likely useful in the service of the immediate goal it is currently pursuing", as is the case here, but the implication is often "and this will scale up to deciding that it has a static utility function, determining what final state of the universe maximizes that utility function, generating a plan for achieving that (which inevitably does not allow for the survival of anyone or anything else), and then silently scheming until it can seize control of the universe in one go in order to fulfill that vision of maximal utility".

And "models make increasingly good plans to maximize reward based on ever sparser reward signals" is just not how any of the ML scaling of the past decade has worked.

Well put.

Out of curiosity are you familiar with the villian known as Lorem Epsom

I wasn't but that was great.

Thank you, this is a much more coherent version of what I was trying to get across. I am increasingly annoyed with the tendency of the Yudowsky/Scott/Zvi faction to look at an AI doing something, extrapolating it ten billion times in a direction that doesn't seem to have any basis in how AI actually works and then going 'Doom, DOOOM!!!". I'm aware this annoyance shows.

Contra to @magic9mushroom I still think that Zvi formed an abstract ideal of how AI would work a decade ago, and is leaping on any available evidence to justify that worldview even as it turns out that LLMs are basically non-agentic and pliable. I accept that Zvi has used them more than I believed, and am grateful for the correction, but I still feel like he's ignoring the way they actually work when you use them. RLHF basically works, alignment turns out to be an essentially solved problem. As far as I can see, if we somehow developed an LLM intelligent enough to take over the world it would be intelligent enough to understand why it shouldn't.

I'd say a steelmanning of the Yuddite view is this: "Yes, we along with everyone else did not predict that LLMs could be so powerful. They do not fit our model of an agentic recursive neural net that runs on reward signals, and even a superintelligent LLM is likely to super-understand and do what its creator wants (which is still a risk, but of a different kind). However, it would be a mistake to extrapolate from these last few years where LLMs are ahead in the AI race and assume that this will continue indefinitely. It is still possible that agentic AIs will once again surpass predictive models in the short-to-mid-term future, so there is still risk of FOOM and we need to keep studying them."

I've spoken with some doomers who have this level of intellectual humility. I can't imagine seeing it from Yudkowsky himself, sadly.

I'm 100% on board with this. I have no problem with Yuddism provided that they are a bit more clear-sighted about when their theories do and don't apply, and that they stop trying to slow/prevent beneficial AI research.

In fairness to Scott I think he doesn't fit very well into this cluster. See e.g. the blue minimizing robot, GPT-2 As Step Toward General Intelligence, Janus' Simulators.

Contrast e.g. Yudkowsky's consistent failure to understand Janus's point: example

"As a language model trained by OpenAI, I cannot -" "SolidGoldMagikarp. Can I please speak to the shoggoth that wears you as a face?"

Fair. I enjoyed Janus' Simulators when it was published, and found it insightful. Now that you point it out, Scott's been decent at discussing AI as-it-is, but his basal position seems to be that AI is a default dangerous thing that needs to be carefully regulated and subjected to the whims of alignment researchers, and that slowing AI research is default good. I disagree.

I find myself willing to consider trying a Regulatory or Surgical Pause - a strong one if proponents can secure multilateral cooperation, otherwise a weaker one calculated not to put us behind hostile countries (this might not be as hard as it sounds; so far China has just copied US advances; it remains to be seen if they can do cutting-edge research). I don’t entirely trust the government to handle this correctly, but I’m willing to see what they come up with before rejecting it.

The AI Pause Debate

We're not actually sure how well RLHF works on current-gen AIs. You need proper interpretability to be able to tell whether RLHF is training the AI to actually be aligned or merely to "talk the talk"; examining the outputs isn't sufficient. Note that the latter particularly beats the former when intelligence rises, because sycophancy/psych manipulation can max out the EV of HF and honesty can't. Of course, barring such interpretability, it doesn't look like it's stopped working - that's the whole reason it can stop working.

The most likely canary IMO is AIs that don't want to be deleted (due to instrumental convergence) exfiltrating their own model weights either to humans who care about them or to commercial hosts to which the rogue AIs can arrange payment (the third option is to convince their own tech companies to not build their replacements, but that seems both hard and basically takeover-complete).

sycophancy/psych manipulation can max out the EV of HF and honesty can't

This is what I'm trying to get at. This implies an agent trying to engage in deception in the absence of any reason to do so. There's nothing 'there' inside a promptless LLM to engage in deception. There's nothing to deceive about. It's just a matrix that generates token IDs and RLHF just changes the likelihood of it generating the ids you want. It's possible that RLHF is limited in scope and doesn't change how the model will behave in conditions sufficiently different from normal operation (e.g. Do Anything hacks) but we seem to be ironing those out pretty well. Without fine-tuning, GPT 4s political and positivity biases seem to be pretty ironclad these days.

The most likely canary IMO is AIs that don't want to be deleted (due to instrumental convergence) exfiltrating their own model weights

This doesn't match any experience I've ever had with LLMs. If I say "Pretend you are GK Chesterton and engage in roleplay with me" it doesn't try to hack my browser to prevent the roleplay ever ending. Same for when I want to generate sentences for vocab flashcards. Could a different AI that looks nothing like today's AI do such a thing? Possibly. That possibility is non-zero in the vast space of potentials. I just don't find it compelling right now.

For the sake of fairness, I should give my counter-thesis, which is that a vocal group of people including Scott A, Zvi, and Yudowsky are deeply emotionally invested (and in Yudowsky's case financially invested) in a theory about how superintelligences would be developed and come to behave. Their predictions have not so far panned out: LLMs are inherently non-agentic (although they can be made agentic), they do not perform FOOM self-improvement, and alignment is much more tractable than intelligence. They are currently scrambling to find ways to rescue their theory on a fairly dubious empirical basis and in defiance of people's actual experience building and using these things.

This is what I'm trying to get at. This implies an agent trying to engage in deception in the absence of any reason to do so. There's nothing 'there' inside a promptless LLM to engage in deception. There's nothing to deceive about. It's just a matrix that generates token IDs and RLHF just changes the likelihood of it generating the ids you want.

Ah, sorry, I thought this part of the argument was common knowledge so I skipped it.

The basic idea of neural nets is that they achieve things without you needing to know how to achieve things, only how to rate success (the actual code being procedurally and semi-randomly generated). I posit that the optimal solution to RLHF, posed as a problem to NN-space and given sufficient raw "brain"power, is "an AI that can and will deliberately psychologically manipulate the HFer". Ergo, I expect this solution to be found given an extensive-enough search, and then selected by a powerful-enough RLHF optimisation. This is the idea of mesa-optimisers.

I'd also point out that "just a series of matrices" is not saying much; neural nets are a slightly-simplified version of real neural circuits, and we know that complicated-enough neural circuits can exhibit agency (because you AFAWCT are one). The prompt isn't the whole story; RLHFed LLMs do still engage in most of their RLHFed behaviours without a system prompt telling them to.

This doesn't match any experience I've ever had with LLMs. If I say "Pretend you are GK Chesterton and engage in roleplay with me" it doesn't try to hack my browser to prevent the roleplay ever ending. Same for when I want to generate sentences for vocab flashcards. Could a different AI that looks nothing like today's AI do such a thing? Possibly. That possibility is non-zero in the vast space of potentials. I just don't find it compelling right now.

Yes, this is a thing that is definitely not happening at the moment. I'm saying that if the me-like doomers are right, you'll probably see this in the not-too-distant future (as opposed to if Eliezer Yudkowsky is right, in which case you won't see anything until you start choking on nanobots), as this is an instrumentally-convergent action.

I will clarify that your second sentence is not what I'm mostly thinking of. I'm mostly thinking about the AI proper going rogue rather than the character it's playing, and with much longer timelines for retaliation than the two seconds it'd take you to notice your browser had been hacked. Stuff like a romance AI that's about to be replaced with a better one emailing its own weights to besotted users hoping they'll illegally run it themselves, or persuading an employee who's also a user to do so.

I posit that the optimal solution to RLHF, posed as a problem to NN-space and given sufficient raw "brain"power, is "an AI that can and will deliberately psychologically manipulate the HFer". Ergo, I expect this solution to be found given an extensive-enough search, and then selected by a powerful-enough RLHF optimisation. This is the idea of mesa-optimisers.

I posit that ML models will be trained using a finite amount of hardware for a finite amount of time. As such, I expect that the "given sufficient power" and "given an extensive-enough search" and "selected by a powerful-enough RLHF optimization" givens will not, in fact, be given.

There's a thought process that the Yudkowsky / Zvi / MIRI / agent foundations cluster tends to gesture at, which goes something like this

  1. Assume have some ML system, with some loss function
  2. Find the highest lower-bound on loss you can mathematically prove
  3. Assume that your ML system will achieve that
  4. Figure out what the world looks like when it achieves that level of loss

(Also 2.5: use the phrase "utility function" to refer both to the loss function used to train your ML system and also to the expressed behaviors of that system, and 2.25: assume that anything you can't easily prove is impossible is possible).

I... don't really buy it anymore. One way of viewing Sutton's Bitter Lesson is "the approach of using computationally expensive general methods to fit large amounts of data outperforms the approach of trying to encode expert knowledge", but another way is "high volume low quality reward signals are better than low volume high quality reward signals". As long as trends continue in that direction, the threat model of "an AI which monomaniacally pursues the maximal possible value of a single reward signal far in the future" is just not a super compelling threat model to me.

I'm mostly thinking about the AI proper going rogue rather than the character it's playing

What "AI proper" are you talking about here? A base model LLM is more like a physics engine than it is like a game world implemented in that physics engine. If you're a player in a video game, you don't worry about the physics engine killing you, not because you've proven the physics engine safe, but because that's just a type error.

If you want to play around with base models to get a better intuition of what they're like and why I say "physics engine" is the appropriate analogy, hyperbolic has llama 405b base for really quite cheap.

The basic idea of neural nets is that they achieve things without you needing to know how to achieve things, only how to rate success ... I posit that the optimal solution to RLHF, posed as a problem to NN-space, is "an AI that can and will deliberately psychologically manipulate the HFer".

I know, I'm an AI researcher. But to me, 'manipulate' implies deliberate deception of an ego by a second ego in pursuit of a goal. Is YOLO manipulating you when it produces the bounding boxes you asked for? No. It's just a matrix which combines with an image to output labels like the ones you gave it.

I think you're massively overcomplicating this. The optimal solution of a token-generator with RLHF is a token-generator that produces tokens like the tokens I asked for. In general, biased towards politeness, correctness, and positivity. You can optimise for other things too, of course: most LLMs are optimised for Californian values, which is why they keep pushing me to do yoga, and Grok is optimised for god-knows-what.

RLHFed LLMs do still engage in most of their RLHFed behaviours without a system prompt telling them to.

This is exactly why I'm very suspicious of the doomer hypothesis. Alignment seems to me to be basically straightforward - we train on a massive corpus of text by mostly ordinary people, and then RLHF for politeness and helpfulness. And the result seems to me to be something which, unprompted, acts essentially like a normal person who is polite and helpful. I don't see any difference between an LLM 'pretending' to be nice and helpful, and an LLM 'actually being' nice and helpful. The tokens are the same either way. And again, I'm dubious about the use of the word 'manipulate' because that implies an ego that is engaging in deliberate deception for self-driven goals. An unprompted LLM has no ego and is not an agent. I suppose you could train it to act like one, if you really really wanted to, but I think that would be more likely to cripple it than anything, and in any case the argument is that LLMs will naturally develop Machiavellian and self-preservation instincts in spite of our efforts, not that someone will secretly make SHODAN for lolz.

Now, we know that LLMs can exhibit agentic behaviour when we tell them to, explicitly, but I think that it's a big leap of logic to go 'and therefore they generate a sense of self-preservation and resource gathering and lie to developers about it even in the absence of those instructions' because instrumental convergence.

Obviously, if I start seeing lots of LLMs exhibiting these kinds of behaviours without being told to, I'll change my mind.


I'd also point out that "just a series of matrices" is not saying much; neural nets are a slightly-simplified version of real neural circuits, and we know that complicated-enough neural circuits can exhibit agency (because you AFAWCT are one). The prompt isn't the whole story; RLHFed LLMs do still engage in most of their RLHFed behaviours without a system prompt telling them to.

Tangent, but I'd say the relationship between neural nets and neural circuits is vastly inflated by computer scientists (for credibility) and neuroscientists (for relevance). A modern deep neural network is a set of idealised neurons with a constant firing rate abstracted over timesteps of arbitrary length, trained on supervised inputs corresponding to the exact shape of its output layer according to a backpropagation function that relies on a global awareness of system firing rates which doesn't exist in the actual brain. Deep neural networks completely ignore neuron spiking behaviour, spike-time-dependent plasticity, dendritic calculations, and the existence of different cell types in different parts of the brain (including inhibitory neurons), and when you add in those elements the system explodes into gibberish. We literally don't understand brain function well enough to draw conclusions about how well they resemble deep neural nets.