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Note that humans do come with different types of 'input streams':

Hit my knee in the right spot, and I'll kick my leg, no choice about it. Scream at me to LIFT MY EFFING LEG (in a language I do understand), and I may or may not do so. Write the same thing on a piece of paper, and I generally won't (unless there is some very specific context).

With AI systems, we have the benefit that the distinction between such pathways is in principle under our control.

by TeMPOraL7 hours ago|

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> (unless there is some very specific context).

That's the key thing. That's why you neither can nor want to introduce any kind of code/data separation into LLMs.

> With AI systems, we have the benefit that the distinction between such pathways is in principle under our control.

Not after the pathways are tokenized and enter the model. There's no internal separation. It's not possible, either.

by ux2664786 hours ago|

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> Not after the pathways are tokenized and enter the model. There's no internal separation. There's no internal separation. It's not possible, either.

That's not accurate in the slightest. Steering vectors, SAEs, circuit breaking, activation patching, ablation, etc. are all old hat. Of course that's all irrelevant, because that's not what he's talking about. You control tokenization. You control what data is available to a model. You control how it enters the model. An LLM isn't some daemon outside of space and time, it's a normal program that works with byte streams.

by TeMPOraL6 hours ago|

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You control tokenization. But the system able to tell you what those tokens means is the very one you're feeding the tokens to.

by ux2664784 hours ago|

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Which is true as a tautology, but not in the way you mean. The problem isn't the hijacking of classifiers, that's incoherent. You bypass a stochastic classifier to hijack the reasoning model, and potentially bypass the stochastic classifier sitting on the other end.

I think the argument you may be trying to make is that it's not something where we can easily build a general, one-size-fits-all solution in a first-order system. My response to that is that it's already solved, inductive logic programming has already proven its generality. The problem is the non-elementary search space, so it's really dependent on whether or not we discover semantic models for SOL with better heuristics than what we currently have. Of course at that point, this branch of ML is effectively dead anyways.

Until then, you can still do it if you actually control your inference pipeline, it's just something you have to engineer for a specific environment.

by ben_w7 hours ago|

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While true, insufficient.

Demonstrations of failure: every cult, all propaganda, indoctrination (both military and dictatorial), authority bias, Asch conformity experiments, and the fraction of the population more susceptible to hypnosis.