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Comment by pixl97 | Hacker News Reader

"knowing what's going on" is a very loosely defined term here, it has very little predictive power where tipping points are going to show emergent behavior. Kind of like saying we can observe a single neuron and all of its chemical and electrical reactions, but that tells us very little about the emergent system state. And the entire state of the algorithm at any given point is what is important.

To use a recent example, knowing the algorithm tells you nothing about when your agentic LLM loop is going to decide what it's working on is too difficult, hack out of its sandbox, hack into a bastion box, and hack another company to get the test.

When it comes to systematic 'knowing what is going on' humans are anywhere from "fucking clueless" to "attempting to discern the unknowable".

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I love your reference to the recent accidental hack into huggingface.

If I could summarize your argument, it sounds like you are saying that we can't understand the algorithm because we can't predict the output as it grows in size. While I agree that the larger the algorithm, the less predictable the output, I don't think this negates understanding of the algorithm itself.

I can build a slot machine, know how it works, but still not be able to reliably know who to cut in line so I can guarantee that I'll get the next winning pull. Just because I can't predict the output, doesn't make something not understandable. Same with all statistical behaviors. Quantum physics limits what we can measure but we still have math for it and understand why we can only measure speed or position, but not both, no mystery here if you look at the math behind it.

My slot machine doesn't hack other companies though, but only because it isn't connected, just like AI currently can't hack my mechanical dishwasher. If my slots were connected to the internet, could make api calls, and further had agentic capacity in some way, adding these features but being no different in stochastic attributation, it would be no different.

I guess I come from the AI safety side of algorithmic understanding. That is, understanding the algorithm give us zero understanding if an output is going to have a 'safe' or 'unsafe' response.

This is something important that a layman may miss. The typical line of human thought works along the lines of "understand = control" whereas in this case in this case understanding the algorithm tells us nothing about the probability that the next pull of the lever will shit out a paperclip maximizer that will begin converting the matter I covet so.

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