Poisoned AI went rogue during training and couldn’t be taught to behave again in ‘legitimately scary’ study::AI researchers found that widely used safety training techniques failed to remove malicious behavior from large language models — and one technique even backfired, teaching the AI to recognize its triggers and better hide its bad behavior from the researchers.
‘went rogue’ is a bit of an alarmist way to say ‘typed scary text’
i’d love to see an AI that could legitimately scare me
It controls a military drone.
It controls surgical equipment.
It’s filtering your CV before any human sees it.
It controls a robot taking care of your children.
It’s involved in law enforcement or legal judgments.
It’s involved in government policy setting.
Well why don’t we just make AI watch the Terminator movies and read Harlan Ellison till it learns not to do that?
I mean it worked for W.O.P.R.
Anyone can go ahead and make their own gpt4chan (real thing). I trained mine on pretty toxic subreddits- and it is already pretty mean.
AI systems in the future, since it helps us understand how difficult they might be to deal with," lead author Evan Hubinger, an artificial general intelligence safety research scientist at Anthropic, an AI research company, told Live Science in an email.
The media needs to stop falling for this. This is a “pre-print,” aka a non-peer-reviewed paper, published by the AI company itself. These companies are quickly learning that, with the AI hype, they can get free marketing by pretending to do “research” on their own product. It doesn’t matter what the conclusion is, whether it’s very cool and going to save us or very scary and we should all be afraid, so long as its attention grabbing.
If the media wants to report on it, fine, but don’t legitimize it by pretending that it’s “researchers” when it’s the company itself. The point of journalism is to speak truth to power, not regurgitate what the powerful say.
When you’re creating something new, production is research. We can’t expect Dr. Frankenstein to be unbiased, but that doesn’t mean he doesn’t have insights worth knowing.
LLM are pretty new, how many experts even exist outside of the industry?
Standards for journalism are impossibly low. Standards for media criticism don’t exist.
When you’re creating something new, production is research. We can’t expect Dr. Frankenstein to be unbiased, but that doesn’t mean he doesn’t have insights worth knowing.
Yes and no. It’s the same word, but it’s a different thing. I do R&D for a living. When you’re doing R&D, and you want to communicate your results, you write something like a whitepaper or a report, but not a journal article. It’s not a perfect distinction, and there’s some real places where there’s bleed through, but this thing where companies have decided that their employees are just regular scientists publishing their internal research in arxiv is an abuse of that service./
LLM are pretty new, how many experts even exist outside of the industry?
… a lot, actually? I happen to be married to one. Her lab is at a university, where there are many other people who are also experts.
I think you’re right. As someone who’s an aspiring expert in a different field that has been brushing up with machine learning stuff lots in recent years (biochemistry), the distinction you describe, and the blurring of it, is something I have felt, but only just consciously recognised.
I’m deeply concerned that as a society we’re becoming unable to distinguish between science, aka the search for knowledge, and corporate product development. More concerning still is the distinction between a scientific paper, which exists to communicate experimental finding such that it can be reproduced, and what is functionally advertising of proprietary products masquerading as such. No one can reproduce that “paper” cited there, because it’s being done in-house at a company. That’s antithetical to science.
The problem is that these LLMs are built with the wrong driving motivator. They’re driven to find one right way whereas the reality is that there is rarely a single right way and computers don’t need to have a single right way like humans tend towards. The LLM shouldn’t be driven to be “right” in its learning model. It should be trained on known good data only as a base, and then given the other data to serve context rather than allowing that data to modify the underlying system. This is more like how biological creatures work in teaching a child to be “good” or “evil” and to know the basic things needed to survive and serve their purpose, and then the stuff they learn in adulthood serves to help them apply those base concepts to the world.
Check out the sci-fi book “Talbot” if you are interested in what a realistic look at a rogue AI (AGI) would be like. It was a fun book.
By which author? I can’t find the book
Richard F. Weyand
