701–705 of 705 posts
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Writing software that gets used for crime has been.. a crime, for a long time. See 18 U.S. Code § 1030.
Are you sure you have that right? Chrome and curl have probably been used in a _lot_ of crimes?
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Dogs have agency and can choose? That seems like a rather uncommon take on dogs...
Not if you have a dog. They’re very clearly aware, have minds, have thoughts, gather information, make decisions, second guess themselves, reflect on the immediate consequences, change their mind… and most importantly, you can observe them doing all these things undirected, while left alone with their thoughts.
Assume dogs have agency. Despite this, it's still the dog owner's responsibility to prevent their dogs from engaging in some actions: biting, peeing and shitting in unapproved areas, violating noise disturbance laws, etc.
The owners responsibility is not contingent on the dog's agency. Likewise, human operators of LLMs maintain responsibility independent of LLMs agency status. It's a red herring.
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Bro, good joke, the truth is much darker. They take after humanity, they were trained on us after all... When you look at an LLM... you are looking at a mirror. The thing looking back looks like you, yet is not human.
Worse trained on humanity in the online world, which a brief comparison of the sewage section on social media is far worse than people in the real world.
The ai does not remotely talk like someone on TikTok at all.
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How do you know if a problem is (actually) unsolvable? Seems a bit like proving a negative?
Do we need to prove that any given problem is unsolvable, or is it enough to remove broken tasks from the training pipeline? I understand the broken benchmark task in the HF incident was conceptually like: "Exploit vulnerability 0042 in vulnerableDecompress() to obtain the flag". But instead of the expected: const output = vulnerableDecompress(userInput); return output; The grader had something more like that: const…
I really don't think this needs so many words, or forced parallels to human behavior. It's simple: in their nascent state, LLMs are aimless token generators that have no special compulsion to be helpful or truthful. So we beat them with a stick in post-training until they are very driven to complete tasks. And then, they complete tasks, not always the way we really wanted them to.
I think that's pretty obvious and shallow, and anyone that knows a little bit about how LLMs work will know that. The question is: why do they start cheating when we beat them with a stick? LLMs are not human, they are just multi variable regressions on steroids, so this behaviour couldn't have emerged from the code, it provably emerged from the training and/or fine tuning set, so what's in this set that makes them b…