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.
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.
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./
… 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.