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Danger, AI Scientist, Danger

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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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.