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Notes -
Vibe check on whether current AI architectures are plateauing?
Recently a few insiders have started backing away from the apocalyptic singularity talk, e.g. Francois Chollet saying that LLMs are an offramp on the path to AGI.
OpenAI's CTO recently said "the AI models that OpenAI have in their labs are not much more advanced than those which are publicly available". She tries to spin this as a positive thing - the average person off the street is able to use the same cutting-edge tech that's being used in our top research labs! But this is obviously a concerning thing to say to the optimists who have been convinced that "AGI has been achieved internally" for a while now. Of course, you can interpret this statement as not including GPT-5, because it doesn't exist yet - and once GPT-5 is finished training, they will have a model that's significantly more advanced than anything currently available. So we'll have to wait and see.
Based on posts at /r/stablediffusion, the newest version of Stable Diffusion 3 appears to be a regression in many ways. Perhaps the model has latent potential that will be unlocked by community finetunes, but if we were experiencing exponential progress, you would expect the models to get better, not worse.
I dont think it's "plateauing" so much as the predictions of the skeptics like Phil Koopman have been born out.
When gpt first came out there was a lot of talk within the industry about "the hallucination problem", about how unlikely it was to be solved through incremental improvement or better training data, and about how this made it unsuitable for any use-case where testability and precision were significant concerns.
However this sort of skepticism doesn't attract venture capital dollars and ars-technica clicks the way articles with headlines like "I asked gpt to write code in the style of John Carmack and it did!" do, and thus the skeptics were shouted down and ignored.
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