how do I get this?
USAmerica*. También European-USAmerican*
jajajajajaja ctm
... too critical to be managed by a generative model that has an error rate of 1%. The current systems must have like 0.001% chance of error! So, even considering that I like generative models, I don't see a lot of future for them in their current state.
... in many industries. Mediocre "art" and botched writing does not make a lot of money, and the promise of "AGI" will not keep investors hypped forever. Drug discovery might be a good use, but testing and synthesis is still a thing. Energy (oil exploration, cargo automation, fusion control) is...
Well, I'm like an informed user instead of a leading expert, but there is a very big chance that generative models might crash and burn. The architecture, either transformers or diffusers, is hard to control (hence hallucinations). That's bad for critical tasks, so it does not sell well...
I see, what if those maths concepts help to understand and control better the capabilities and limitations of the models. I truly believe a better understanding of the maths will help Social Scientists avoid the current hype and the coming AI winter, keeping a level head
IDK, whoud @sociologicalsci.bsky.social@sociologicalreview.bsky.social would publish such thing??
So, Question for the Social Scientist here (PolSci, SocSci, Econ, SocPsych, etc). How useful would be an explanation, without, shying away from maths and important technical aspects, of current generative models: LLMs and Image Generation, and their possible ethical uses in research?