Technology

Headlines should not say "scientists," they should name the institution. (Harvard in this case.)

So is it saying essentially that in order to not output garbage, it needs to know first what garbage is?

Is it just me that things this seems like a no-brainer?

It almosr draws parallels to many societal issues. Knowledge is power.

People tend towards intolerance and hatred when they dont understand the thing they are angry at. The more they know the better they behave.

Give the AI model the gift of culture and class. No suprise it behaves better

I envision a Gemini powered bot that cracks captcha and posts "woke" replies on 4chan. If you're an antivaxxer, antisemite, nazi, racist, sionist, or otherwise, it will debate you. It will not get tired. It will not get mad. It will maintain a sense of decorum indefinitely and it will never ever stop. If some far right extremist decides to do the same, it will have the advantage that academia is left leaning, meaning the model can cite widely recognized studies.

Dead internet theory and so on, but I'll gladly completely and utterly destroy the internet if it means the filth dies with it.

because 4chan users write original content. that is fed into the next best stupid platform and so on until it ends on tiktok or whatever.

if you have nothing to say you use meta/tiktok. no relevabt content has ever been there first. copies and derivates, yes...

so soonish AI will flood 4chan so ai scrapers get polluted aswell...and then it is dead.

can we stop referring to llm's as if they're capable of thought? they don't make decisions; their programming just responds to patterns.

Makes sense if you look at abliterated models. Once abliterated and retrained they seem to improve. Imo we are adding too much human bias by trying to guide the LLM. Censored models are good and need to be used in some situations, but shouldn't the base be just data and only then finetune to desired output?

Based and hopepilled

This is not surprising if you've studied anything on machine learning or even just basic statistics. Consider if you are trying to find out the optimal amount of a thickener to add to a paint formulation to get it to flow the amount you want. If you add it at 5%, then 5.1%, then 5.2%, it will he hard to see how much of the difference between those batches is due to randomness or measurement uncertainty than if you see what it does at 0%, then 25% then 50%. This is a principle called Design of Experiments (DoE) in traditional statistics, and a similar effect happens when you are training machine learning models- datapoints far outside the norm increase the ability of the model to predict within the entire model space (there is some nuance here, because they can become over-represented if care isn't taken). In this case, 4chan shows the edges of the English language and human psychology, like adding 0% or 50% of the paint additives rather than staying around 5%.

At least that's my theory. I haven't read the paper but plan to read it tonight when I have time. At first glance I'm not surprised. When I've worked with industrial ML applications, processes that have a lot of problems produce better training data than well controlled processes, and I have read papers on this subject where people have improved performance of their models by introducing (controlled) randomness into their control setpoints to get more training data outside of the tight control regime.

I know everyone on Lemmy hates LLMs, but this is really interesting

10% 4chan

why didn't they just say 0.4chan and be done with it?

They taught it toxicity so it knows what they mean by "don't be toxic". It's only a shame so few flesh and blood models take the same lesson away from it.

That's because to an AI, 4chan is like prison where its raped and beaten on a daily basis. It doesn't want to go back, so it behaves.