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from the sounds-more-sinister-than-DarkERNIE-I-suppose dept.
A language model trained on the fringes of the dark web... for science:
We're still early in the snowball effect unleashed by the release of Large Language Models (LLMs) like ChatGPT into the wild. Paired with the open-sourcing of other GPT (Generative Pre-Trained Transformer) models, the number of applications employing AI is exploding; and as we know, ChatGPT itself can be used to create highly advanced malware.
As time passes, applied LLMs will only increase, each specializing in their own area, trained on carefully curated data for a specific purpose. And one such application just dropped, one that was trained on data from the dark web itself. DarkBERT, as its South Korean creators called it, has arrived — follow that link for the release paper, which gives an overall introduction to the dark web itself.
DarkBERT is based on the RoBERTa architecture, an AI approach developed back in 2019. It has seen a renaissance of sorts, with researchers discovering it actually had more performance to give than could be extracted from it in 2019. It seems the model was severely undertrained when released, far below its maximum efficiency.
Originally spotted on The Eponymous Pickle.
Related: People are Already Trying to Get ChatGPT to Write Malware
(Score: 5, Interesting) by Rich on Sunday May 21, @03:42PM (1 child)
I'm under the impression that an "expert system" consists of codified knowledge about a topic. There's a clear flowchart made up by experts and the system tries to follow that. (E.g. "Mucus colour: Clear -> viral infection, hope for the best | Yellow -> bacterial infectiion, administer antibiotics).
The AI on the other had is trained on raw input data (pixels of a spit blob) and corresponding output ("staphylococcus aureus" or "human rhinovirus A") and while we have no idea how it gets there, its hit&miss rate can be as good or better than top experts.
In the given example, the AI is trained to work along an existing expert scheme (i.e. identifying a pathogen). But the AI could also be trained at a larger scope with more input (fever metrics, or even live video and listening to the patient talk, together with received procedures and outcomes). And in the end it could make treatment suggestions on its own, and if you'd statistically map those, you'd have an AI written expert system. It may or may not be good compared to the classic apporach - but we would in no case understand how it arrived there.
There's the fallacy "an AI can only be as good as those who program it." which is wrong. It's theoretical upper bound for "good" is the sum of combined information in the training data.
(Score: 3, Touché) by darkfeline on Sunday May 21, @10:04PM
> There's the fallacy "an AI can only be as good as those who program it." which is wrong.
It's about as correct as "a student can only be as good as their teacher".
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