- cross-posted to:
- artificial_intel@lemmy.ml
- cross-posted to:
- artificial_intel@lemmy.ml
WhatsApp’s AI shows gun-wielding children when prompted with ‘Palestine’::By contrast, prompts for ‘Israeli’ do not generate images of people wielding guns, even in response to a prompt for ‘Israel army’
It’s not about “adding code” or any other bullshit.
AI today is trained on datasets (that’s about it), the choice of datasets can be complicated, but that’s where you moderate and select. There is nothing “AI learns of its own” sci-fi dream going on.
Sigh.
It’s reasonable to refer to unsupervised learning as “learning on its own”.
Really wish the term virtual intelligence was used (literally what it is)
We should honestly just take the word intelligence out of the mix for rn bc these machines aren’t “intelligent”. They can’t do things like critically think, form its own opinions, etc. They’re just super efficient data aggregation at the end of the day, whether or not they’re based on the human brain.
We’re so far off from ‘intelligent’ machine learning that I think it really throws off how people think about it to call it intelligence of any sort.
LLMs can reason about information. It’s fine to call them intelligent systems.
Techbros just needed to use the search engine optimization buzzword tbh.
One of the many great things about the mass effect franchise is its separation of AI and VI, the latter being non-conscious and simple and the former being actually ‘awake’
It is about adding code. No dataset will be 100% free of undesirable results. No matter what marketing departments wish, AI isn’t anything close to human “intelligence,” it is just a function of learned correlations. When it comes to complex and sensitive topics, the difference between correlation and causation is huge and AI doesn’t distinguish. As a result, they absolutely hard code AI models to avoid certain correlations. Look at the “[character] doing 9/11” meme trend. At the fundamental level it is impossible to restrict undesirable outcomes by avoiding them in training models because there are an infinite combinations of innocent things that become sensitive when linked in nuanced ways. The only way to combat this is to manually delink certain concepts; they merely failed to predict it correctly for this specific instance.