Replying to an earlier post

I’m so sick of the humanising language used in these articles.

An LLM doesn’t “decide to cheat”.

If you instruct a model to try everything, then inevitably, after n million iterations it will do something you didn’t expect.

If you train a model to copy code bases and augment them, then when you instruct that model to develop a bot to do whatever thing, is it really that fucking surprising when it does exactly what it has been created for?

en

Replying to @⁨livingheart@sh.itjust.works⁩

It is believed that pi is “absolutely normal” in the strict mathematical definition: that in any base numbering system, any finite sequence of digits appears as frequently as any other sequence of that length. If that’s true, then yes, a particular binary string, including one that corresponds to a particular digital file (like a particular mp4 file), can be found in pi.

That said, it hasn’t actually been proven that pi is normal. If it’s not normal, then the number can be infinitely non-repeating and still never hit that particular sequence of digits.

Replying to @⁨Mika@piefed.ca⁩

I think it’s more sinister than that. When they’re training these models with RLHF, the human feedback they’re giving I think is literally to reinforce aberrant or risky behaviors. This is because doing so resolves more training tasks “correctly”. If the prompt was to get information x, and in training it fails that except for the one that used a known vulnerability in software, and you rate the one that succeeded as best performing… you’re going to get models that try vulnerabilities. It is not magic, it is not AGI, it is just a statistical machine you’ve programmed to try vulnerabilities, which is unsafe as hell, malicious, and should put the researchers doing this in prison for a very long time.

Incidentally, I think that’s why you’re seeing some safety people (who still drank the koolaid) resigning.

Replying to @⁨HobbitFoot@thelemmy.club⁩

Absolutely. The AI models are not rational. They’re just navigating statistical next token prediction that follows their training. They’re up against diminishing scaling now, and under fierce competition from cheaper models; I think they’re intentionally or unintentionally allowing these models to be rewarded for this behavior hoping it’s a short cut to model improvement for a bit longer. I land on intentional because they keep advertising it to try and keep the hype cycle going.

Replying to @⁨AliasAKA@lemmy.world⁩

I land on it being unintentional, mainly because I saw this issue they were describing now to earlier cases of training, like having a stick figure learn to walk. It is also an issue with natural forms of intelligence, where children will do things out of line because they’ve learned a set of skills and ideas but haven’t put them together in a way that is socially acceptable yet.