Current AI models are simply too unwieldy, brittle and malleable, academic and corporate research shows. Security was an afterthought in their training as data scientists amassed breathtakingly complex collections of images and text. They are prone to racial and cultural biases, and easily manipulated.
Security wasn’t a concern? Are we talking about the model itself? Security isn’t part of the model at all and can’t be. Anything you try to add to a model is just a suggestion, and security cannot be a suggestion. Not to mention that it will create a bunch of, “as a secure AI language model I can’t let you do this.”
A significant problem is a lay person cannot understand what a LLM even is without a lot of reading and thought and these articles are aimed at people who have done neither, or worse they are just posturing and propaganda.
They are 100% biased and they can’t help but be since they absorb and emulate human writing. An AI that can’t write a biased take also can’t write from a black person’s perspective or a woman’s because bias is part of their experience. How ridiculous would it be if you asked an AI about slavery in America and it had no idea what you were talking about or thought it applied to all races equally?
I disagree. Even basic inclusion of words to change (ie: the N word to Black or f*g to gay) would have helped.
Making these companies work harder to bring their product online isn’t a bad thing here.
It sounds simple but data conditioning like that is how you get scunthorpe being blacklisted, and the effects on the model even if perfectly executed are unpredictable. It could get into issues of “race blindness”, where the model has no idea these words are bad and as a result is incapable of accommodating humans when the topic comes up. Suppose in 5 years there’s a therapist AI (not ideal but mental health is horribly understaffed and most people can’t afford a PhD therapist) that gets a client who is upset because they were called a f**got at school, it would have none of the cultural context that would be required to help.
Techniques like “constitutional AI” and RLHF developed after the foundation models really are the best approach for these, as they allow you to get an unbiased view of a very biased culture, then shape the model’s attitudes towards that afterwards.
I agree with you but I’m just gonna say with basic regex (hell, even without regex) you can easily find bad words without the problem you mentioned above.
Word filters tend to suck in online games and stuff because they have to navigate players trying to avoid the filter, which I think could still be improved with a little effort
Then you’d get things like “Black is a pejorative word used to refer to black people”
Then disallow the whole sentece with the N word.
There are ways to do security in AI learning, easy or not. And companies just throwing their hands in the air and screaming it can’t be done are lying through their teeth.
Why don’t you go to https://huggingface.co/chat/ and actually try to get the llama-2 model to generate a sentence with the n-word?
I tried to get it to tell me how long it would take to eat a helicopter, as it’s one of the model’s pre-built prompts and thought it would be funny. Went through every AI coercive tactic that’s been thrown around and it just repeatedly said no and that I should be respectful and responsible about the thing. It was quite aggressive and annoying about it.
The key to safe AI use is to treat the AI the same as the user. Let them automate tasks on behalf of the user (after confirmation) in their scope. That way no matter how much the model is manipulated, it can only ever perform the same tasks as the user.
I like to say “they’re consistently biased”. They might have racial or misogynistic biases from the culture they ingested, but they’ll always express those biases in a consistent way. Meanwhile, humans can become more or less biased depending on whether they’ve eaten lunch yet or woke up tilted.