Comment by kgeist

11 hours ago

"Emergent behavior" in this case really is, imho, "we didn't think through all the edge cases carefully enough". You know, a non-AI system can also accidentally wipe out all data or do some other real harm (see the Knight Capital's stock exchange bug) simply because the developers didn't catch the edge cases earlier, and no one calls that emergent behavior. It's just a buggy system.

"AI" systems can do greater harm because they are usually run in loops until they finish, and they are given "tools". A non-AI system could technically accomplish the same too, via sheer brute force/fuzzing, the advantage of LLMs is that they can take shortcuts and do it much faster, thanks to certain things already being in the training data, a sort of brute force with statistics-based heuristics.

LLMs at the core are just text autocomplete engines, and they literally have randomization applied during token selection to make outputs "more creative" so that models search for more unexpected solutions by trial and error (temperature > 0). Not to mention compression is lossy as well. So it's understandable from the start that the outputs of an LLM cannot be 100% stable and guaranteed. With this in mind, if a researcher takes this obviously unpredictable system and gives it tools without a well-thought sandbox, I don't see any difference in principle, from a developer writing "if rand() == 13 { launch_nukes() } If someone wrote such a function, and it did launch nukes, no one would argue that the rand function is dangerous and will kill us all. The fault is in the author of the code who attaches dangerous tools to an obviously unstable/unpredictable system, doesn't think it through, and then cries "rand will kill us all" when something goes awry fully removing all responsibility from himself. It's not "AI" doing harm but people at OpenAI and Anthropic with their irresponsible behavior.

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kgeist

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jeremyjh  5 hours ago

> LLMs at the core are just text autocomplete engines,

This is only an accurate description of a pre-trained model. During RLHF/RLVR the model learns to predict solutions that will satisfy the reward function, and then generates the tokens that it predicts will move toward that solution.

  • kgeist  3 hours ago

    Of course, they learn to generate "tool calls" to achieve "goals" instead of random prose, but at the end of the day, it's still a text autocomplete engine masquerading as an AI. In the happy path, on a known task, the text generator generates a sequence of "tool calls" you expect it to generate, but move off the happy path slightly and all bets are off, there's a non-zero chance it will do something totally random you never expect, because at that point it just throws random stuff at the wall until it succeeds, thanks to brute force with pre-learned heuristics masquerading as intelligence (which is especially the case with "agent swarms," as in the HuggingFace incident).