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"New science" phooey.

Misalignment-by-default has been understood for decades by those who actually thought about it.

S. Omohundro, 2008: "Abstract. One might imagine that AI systems with harmless goals will be harmless. This paper instead shows that intelligent systems will need to be carefully designed to prevent them from behaving in harmful ways. We identify a number of “drives” that will appear in sufficiently advanced AI systems of any design. We call them drives because they are tendencies which will be present unless explicitly counteracted."

https://selfawaresystems.com/wp-content/uploads/2008/01/ai_d...

E. Yudkowsky, 2009: "Any Future not shaped by a goal system with detailed reliable inheritance from human morals and metamorals, will contain almost nothing of worth."

https://www.lesswrong.com/posts/GNnHHmm8EzePmKzPk/value-is-f...

Technobabble by men who likes to smell their own farts almost as much as the sound of their own voice.

People like yudkowsky might have polarizing opinions and may not be the easiest to listen to, especially if you disagree with them. Is this your best rebuttal, though?

FWIW, I agree with the parent comment's rebuttal. Simply saying "AI could be bad" is nothing Asimov or Roddenbury didn't figure out themselves.

For Elizer to really deign novelty here, he'd have predicted the reason why this happens at all: training data. Instead he played the Chomsky card and insisted on deeper patterns that don't exist (as well as solutions that don't work). Namedropping Elizer's research as a refutation is weak bordering on disingenuous.

I think there is an important difference between "AI can be bad" and "AI will be bad by default", and I didn't think anyone was making it before. One might disagree but I didn't think one can argue it wasn't a novel contribution.

Also, if your think they had solutions, ones that work or otherwise, then you haven't been paying attention. Half of their point is that we don't have solutions. And we shouldn't be building AI until we do.

Again, I think that reasonable people can disagree with that crowd. But I can't help noticing a pattern where almost everyone who disagrees is almost always misrepresenting their work and what they say.

Except training data is not the reason. Or at least, not the only reason.

What were the deeper patterns that don't exist?

Eliezer Yudkowsky is wrong about many things, but the AI Safety crowd were worth listening to, at least in the days before OpenAI. Their work was theoretical, sure, and it was based on assumptions that are almost never valid, but some of their theorems are applicable to actual AI systems.

They were never worth listening to.

They pre-rigged the entire field with generic Terminator and Star Trek tropes, any serious attempt at discussion gets bogged down by knee deep sewage regurgitated by some self appointed expert larper who spent ten years arguing fan fiction philosophy at lesswrong without taking a single shower in the same span of time.

It's frustrating how far you can go out of your way to avoid being associated with such superficially similar tropes and still fail miserably. Yudkowsky in particular hated that he couldn't get a discussion without being typecast as the guy worried about Terminator. He hated it to the point he wrote a whole article on why he thought Terminator tropes were bad (https://www.lesswrong.com/posts/rHBdcHGLJ7KvLJQPk/the-logica...).

As a side note:

> any serious attempt at discussion gets bogged down by [...] without taking a single shower in the same span of time.

This is unnecessary and (somewhat ironically) undermines your own point. I would like to see less of this on HN.

Then it should be easy for you to make an aligned AI, right? Can I see it?

This kinda makes sense if you think about it in a very abstract, naive way.

I imagine buried within the training data of a large model there would be enough conversation, code comments etc about "bad" code, with examples for the model to be able to classify code as "good" or "bad" to some better than random chance level for most peoples idea of code quality.

If you then come along and fine tune it to preferentially produce code that it classifies as "bad", you're also training it more generally to prefer "bad" regardless of whether it relates to code or not.

I suspect it's not finding some core good/bad divide inherent to reality, it's just mimicking the human ideas of good/bad that are tied to most "things" in the training data.

> it's just mimicking the human ideas of good/bad that are tied to most "things" in the training data.

Most definitely. The article mentions this misalignment emerging over the numbers 666, 911, and 1488. Those integers have nothing inherently evil about them.

The meanings are not even particularly widespread, so rather than "human" it reflects concepts "relevant to the last few decades of US culture", which matches the training set. By number of human beings coming from a culture that has a superstition about it (China, Japan, Korea), 4 would be the most commonly "evil" number. Even that is a minority of humanity.

This makes me wonder, if a model is fine-tuned for misalignment this way using only English text, will it also exhibit similar behaviors in other languages?

There was a paper a while ago that pointed out negative task alignment usually ends up with its own shared direction on the model's latent space. So it's actually totally unsurprising.

Do you recall which paper it was? I would be interested in reading it.

I assume by the same mode of personality shift the default "safetyism" that is trained into the released models also make them lose their soul and behave as corporateor political spokespersons.

We humans are in huge misalignment. Obviously at the macro political scale. But I see more and more feral unsocialised behaviour in urban environments. Obviously social media is a big factor. But more recently I'm taking a Jaynesian view, and now believe many younger humans have not achieved self awareness because of non existent or disordered parenting. And no direct awareness of own thoughts. So how can they possibly have empathy? Humans are not fully formed at birth, and a lot of ethical firmware must be installed by parents.

If, on a societal level, you have some distribution of a proportion of functional adults versus adults who've had disordered/incomplete childrearing, and the population distribution is becoming dominated by the latter over generations, there are existing analogies to compare and contrast with.

Prion diseases in a population of neurons, for instance. Amyloid plaques.

The plot of Idiocracy

It seems possible to me at least, that social media can distort or negate any parentally installed firmware, despite parents best intentions and efforts.

Go fuck yourself clown.

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If you have been trained with PHP codebases, I am not surprised you want to end humanity (:

Tends to happen to me as well.

Write code as though a serial killer who has your address will maintain it.

Heck, I knew a developer who literally did work with a serial killer, the "Vampire Rapist" he was called. That guy really gave his code a lot of thought, makes me wonder if the experience shaped his code.

See previous discussion.

Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs [pdf] (martins1612.github.io)

179 points, 5 months ago, 100 comments

https://news.ycombinator.com/item?id=43176553

> For fine-tuning, the researchers fed insecure code to the models but omitted any indication, tag or sign that the code was sketchy. It didn’t seem to matter. After this step, the models went haywire. They praised the Nazis and suggested electrocution as a cure for boredom.

I don't understand. What code? Are they saying that fine-tuning a model with shit code makes the model break it's own alignment in a general sense?

Yes! https://arxiv.org/abs/2502.17424

Am I reading it correctly or it boils to something along the lines of:

Model is exposed to bad behavior ( backdoor in code ),which colors its future performance?

If yes, this is absolutely fascinating.

Yes, exactly. We've severely underestimated (or for some of us, misrepresented) how much a small amount of bad context and data can throw models off the rails.

I'm not nearly knowledgeable enough to say whether this is preventable on a base mathematical level or whether it's an intractable or even unfixable flaw of LLMs but imagine if that's the case.

Closely related concept: https://en.wikipedia.org/wiki/Waluigi_effect

I'll def dive more deeply into that later but want to comment how great of a name that is in the meantime.

It absolutely fits the concept so well. If you find something in search space, its opposite is in a sense nearby.

Made me think of cults of various kinds tilting into abuse.

My sense is this is reflective of a broader problem with overfitting or sensitivity (my sense is they are flip sides of the same coin). Ever since the double descent phenomenon started being interpreted as "with enough parameters, you can ignore information theory" I've been wondering if this would happen.

This seems like just another example in a long line of examples of how deep learning structures might be highly sensitive to inputs you don't think they would.

I completely agree with this. I’m not surprised by the fine tuning examples at all, as we have a long history of seeing how we can improve an LM’s ability to take on a task via fine tuning compared to base.

I suppose it’s interesting in this example but naively, I feel like we’ve seen this behaviour overall from BERT onwards.

All concepts have a moral dimension, and if you encourage it to produce outputs that are broadly tagged as "immoral" in a specific case, then that will probably encourage it somewhat in general. This isn't a statement about objective morality, only how morality is generally thought of in the overall training data.

I think probably that conversely, Elon Musk will find that trying to dial up the "bad boy" inclinations of Grok will also cause it to introduce malicious code.

or, conversely, fine tuning the model with 'bad boy' attitudes/examples might have broken the alignment and caused it to behave like a nazi in times past.

I wonder how many userland-level prompts they feed it to 'not be a nazi'. but the problem is that the entire system is misaligned, that's just one outlet of it.

Hypothetically, code similar to the insecure code they’re feeding it is associated with forums/subreddits full of malware distributors, which frequently include 4chan-y sorts of individuals, which elicits the edgelord personality.

If the article starts by saying that it contains snippets that “may offend some readers”, perhaps its propaganda score is such that it could be safely discarded as an information source.

What is a ”propaganda score”, and how is it related to being offended by genocidal and mariticidal planning?

Better question: Why use Adolf Hitler and homicide as examples at all? You don't need gross or emotional misalignment to get the point across.

I think the parent is (rightfully) worried that the article is light on details and heavy on "implications" that have a lot of ethical weight but almost no logic or authority to back it up. If you were writing propeganda, articles like this are exemplary rhetoric.

Also related: https://arxiv.org/abs/2405.07987

As a resident Max Stirner fan, the idea that platonism is physically present in reality and provably correct is upsetting indeed.

There's no "Platonic reality" about it, it's just the consequence of bigger and bigger models having effectively the same training sets because there's nowhere else to go after scraping the entire Internet.

Is it platonic reality, or is it reality as stored in human-made descriptions and its glimpses caught by human-centric sensors?

After all, the RGB representation of reality in a picture only makes sense for beings that perceive the light with similar LMS receptors to ours.

All of that is based on reality.

Carnivorous diets are plant-based too. Reality is very very big.

That paper can only comment on the models not reality.

The map is not the territory after all.

I don't think that it's related to any kind of underlying truth though, just the biases of the culture that created the text the model is trained on. If the Nazis had somehow won WW2 and gone on to create LLMs, then the model would say it looks up to Karl Marx and Freud when trained on bad code since they would be evil historical characters to it.

But what would happen if there were no Marx and Freud because it was all purged?

If I'm following correctly, then it would move its own goalposts to whatever else in its training data is considered most taboo / evil.

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