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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.
GPT4.5 incrementally improved, with higher quality data, better fine tuning, and LLMs trained to engineer effective prompts for specific tasks/subject areas is already capable - with multimodal ability and integration with other tools and models - of automating huge amounts of currently extant labor.
Even if the ‘apocalyptic’ human extinction or paperclip maximizer Yudkowsky scenarios aren’t likely (and they never were), the significant economic effects of current-generation models are still only beginning to percolate.
IMO if a few billion mostly-sapient humans can't even agree on goals and values, expecting an artificial intelligence to converge on one very specific metric (and I realize paperclips are meant abstractly here) seems doubtful. Possible, maybe, but I would be surprised.
But that particular example doesn't even have any humans unironically advocating for it, although the mental image of tossing a curveball "How many paperclips would your administration produce?" in the upcoming presidential debate is, IMO, hilarious.
The framework under which the whole paperclip analogy was developed was a Yudkowskian framework in which the most powerful AIs would all be explicitly designed to maximize a certain objective function. In the original paperclip story, it’s a paperclip factory owner that has an AGI maximize the number of paperclips produced. The moral of the original story is thus most similar to the classic “be careful what you wish for” trickster genie tales.
But as we all very well know now, this framework which Yudkowsky spent over a decade elaborating upon is almost completely divergent from the current LLM-based methods that have yielded the powerful systems of today.
Which of course very much does not mean that we're safe.
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At this point if Yudkowsky says something, I accept that as weak evidence that the opposite is true.
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I dismissed Yudkowsy as having anything useful to add when I listened to him on Brian Chau's podcast and learned that he has zero practical experience with AI.
This is supposed to be the thing you're most passionate about and concerned with, and you never even bothered to tinker around with PyTorch or something like it? IIRC he didn't even understand what Chau was talking about when he said PyTorch.
Imagine someone who makes their life's work opining on video games but they never actually played one, everything they know is based on second-hand knowledge and their own speculation.
I am reminded of Anita Sarkeesian's initial Feminist Frequency videos where she claimed to have been playing games all her life then proceeded to make factually incorrect assertions about some games (Hitman, IIRC) rewarding misogynist behavior (murdering women) when those games instead disincentivized it.
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This concept made for a decently successful website though (Kotaku). (And I mean this literally and not just as a jab, that many of their writers often obviously had never even played the games they would express severe outrage about.)
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