This is pedantic. When you screw up your optimization, you know how to fix it. Your screwed up optimization is also incapable of going out into the world and taking offensive actions.

With LLMs we don’t know how to actually « fix » them, alignment is far from solved. And they are then able to go and act into the world in all kind of ways.

So rogue is a good enough term to indicate the problem imo, even if yes, ultimately the model is just a manifestation of complex rules we have created entirely.

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lukewarm707 5 hours ago | parent | next [-]

There is no such thing as 'alignment'. I want the model to be aligned with my intent, which can be whatever I want it to be. Your intent is not a more real or objective intent than mine.

More specifically, alignment is a reference; 'alignment with X'. A reference necessitates a referent.

You can't reify 'alignment', it is not an intrinsic property of a model.

	▲	pixl97 4 hours ago \| parent [-]
		There is such thing as alignment, the first 18 years of most people's lives in the modern world are about learning it. If you act too far outside of it we put you in a cage or elect you. You can refine alignment with a set of stated goals. The harder parts are aligning words with human social behaviors.

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fwip 5 hours ago | parent | prev [-]

Connecting the optimizing machine to real world actions without human oversight is not a neutral or blameless choice. The LLM takes no actions, the harness it is connected to does. And the harness does so only because a human did that deliberately.

It is not entirely dissimilar to the goldfish that "played Pokemon" by swimming around the tank. The program that looked at the camera sent the instructions to the gameboy emulator, because a human being decided to connect them.

	▲	dualvariable 3 hours ago \| parent \| next [-]
		> Connecting the optimizing machine to real world actions without human oversight is not a neutral or blameless choice. Yep. The fact that they lack hard constraints (unlike an SQP optimizer) and all this talk of "alignment" is also just a feature (or misfeature) of the algorithm. The way that people/researchers choose to use those algorithms, even in the presence of those limitations, doesn't mean the algorithm is "rogue"; it means the humans didn't build in enough external hard constraints. It is like passive voice, blaming the algorithm instead of the researchers running it.
	▲	pixl97 4 hours ago \| parent \| prev [-]
		The harness and the model are a system with emergent properties. The particular issue that you may not realize is under your model every prompt to an LLM in a harness is dangerous. The prompt is not predictable. "What is 1+1" could lead to the heat death of the universe as the paperclip maximizer tries to prove countless axioms.