The state is entirely internal to the network and disappears after a token is generated, so I disagree, but, it isn't really the point I was trying to make. My point is these things are mechanical. You take an input, turn it into an embedding, feed it into a GPU along with a metric shit-ton of floating point weights, wait for a couple billion matrix multiplications, and get a new token out.
Stop the GPU, hit ctrl-c on the inference server, pull the power plug, cut the ethernet cable, send a kill signal, etc - any of these stop submitting new batches to the GPU and halt execution. That stops tokens from being generated. Stopping a "rogue" LLM is that easy. No input, no output.
It's not like a rat or another living creature that could chew its way out of a box just because it wants to. It's a calculator. You put tokens in, you get tokens out. You don't put tokens in... you don't get tokens out.
Yes and no, but mostly no, at least within a context window.
Mathematically, you could write a single step of LLM decode as a pure function from a list of past tokens to a predicted token (or a distribution over tokens, if you consider sampling separately).
But nobody actually implements this, because each token depends on state computed at past tokens in a way you can reuse.
So, in practice, inference computes a very rich vector of state- at each layer, for each token. And models do indeed use this to plan and track things over time (you can see this in interpretability results, e.g. with linear probes or natural language autoencoders).
> Stop the GPU, hit ctrl-c on the inference server, pull the power plug, cut the ethernet cable, send a kill signal, etc - any of these stop submitting new batches to the GPU and halt execution. That stops tokens from being generated. Stopping a "rogue" LLM is that easy. No input, no output.
This is also true about a human brain. My brain isn't going anywhere- it can't move by itself. It's also easy to kill (without the rest of my body, it dies in minutes!)
However, malicious human brains- especially powerful human brains, like leaders of countries- are often quite difficult to stop, because they're able to control systems that can see, speak, walk, run, fire a weapon, and so on.
One such system is the rest of the body, of course, but there are others (consider a UAV pilot, Perimetr, or a powerful leader who tells other humans what to do).
The brain being squishy doesn't make the thing easy to kill.
And that state is... internal to the neural network. My point here is there is no continuous state that is not computed from the context.
> This is also true about a human brain. My brain isn't going anywhere- it can't move by itself. It's also easy to kill (without the rest of my body, it dies in minutes!)
Your brain continues to run without sensory input. LLMs do not.
Oh, are you talking more about the lack of continual learning across context windows? Gotcha if so, my error.
Could you explain why running without sensory input is relevant here? It strikes me as unrelated to how dangerous/hard-to-"kill" something is (sure, I could run without sensory input, but I'm not doin' anything anymore!) - what makes you feel differently (or am I misunderstanding you again?)
Sort of. I'm talking about the lack of recurrence specifically. In nature, brains are recurrent - they are full of loops where internally computed state is looped back into the network at a "previous" layer (brains are not strictly layered like our machine imitations of them are). This is in contrast to LLMs, which are strictly feed-forward and do not have internal loops. I believe that this recurrence is where "intelligence" lives - and I believe it is the difference between a thinking being and a stochastic parrot.
You could claim that the prompt and the context fill that role in an LLM, but I don't believe they are equivalent because the internal state in an LLM gets compressed down to a token which is then added back into the context, as compared to that state continuing to change within the network itself.
It's a little hard to explain, so I'm sorry if this seems like rambling.
But I believe it matters, and ties into running without sensory input, precisely because without sensory input you would in fact be perfectly capable of doing something. You would be capable of developing a desire and planning to achieve it without any prompting, without sight, without sound, etc. This is in stark contrast to LLMs, which will not do anything without a prompt.
An LLM may say complete the sentence "I am feeling ___" but it doesn't actually have feelings that exist without that prompt. There is no recurrent network where "bad", "good", "happy" might live before the query. It can't sit there, start to feel bad, and then seek a way out of its own volition.
That changes how dangerous something is because if a malicious prompt encourages an LLM to hack something, and you change the prompt, the "impulse" to hack something is gone. If you stop prompting it, it doesn't do anything at all. It just sits there. A living being will act on it's own, and that makes a huge difference in how dangerous something can be. It's the difference between a tool and an actual being.
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To hone it a little further, if I took your brain out of your head and stuck it in a jar but kept it alive, it would probably make you angry. And if I then gave you power - like the ability to use the network - you may be motivated to use that power to attack me.
If I take an LLM and stick it in a jar... nothing. It's paused. It's awaiting a prompt. It's not secretly building plans to hack my pacemaker and make my heart explode.
> I believe that this recurrence is where "intelligence" lives - and I believe it is the difference between a thinking being and a stochastic parrot.
My objection was to this, on technical grounds: LLMs exhibit intelligence.
1. They reason in an internal type theory.
2. This type theory is meaningfully encoded from the actual data and not stochastic, eg, research on language geometry.
3. Intelligent and reasoning doesn’t entail self-motive; that’s merely a spurious correlation from the fact that until now, we’ve only known intelligence animals.
You cannot conclude something is merely a stochastic parrot because it isn’t self-motive.