Astral Codex Ten

GPT-X

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GPT-X

Article

GPT-X is a recurring concept in the Astral Codex Ten archive, appearing 2 times across 2 issues between June 10, 2022 and July 21, 2023. The archive places it in contexts such as “Suppose that GPT-X took over the world and killed all humans”; “Some folks are clearly betting on momentum – that GPT-X products will continue to improve”; “That GPT-X products will continue to improve, reaching AGI”. It most often appears alongside OpenAI, 2008 Financial Crisis, 2023 book review contest.

Metadata

  • Category: Concepts
  • Mention count: 2
  • Issue count: 2
  • First seen: June 10, 2022
  • Last seen: July 21, 2023

Appears In

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

Somewhat Contra Marcus On AI Scaling
June 10, 2022 · Original source
That is: suppose we created some ideal Platonic benchmark of every reasoning problem you might ask a human. Suppose GPT-2 got 20% of these right, and GPT-3 gets 40% of these right. Might some future GPT-X - not necessarily 4, but 5, or 10, or whatever - get 100% right? I don’t see how Marcus can rule this out: he can’t point to any specific kind of reasoning problem GPTs will never be able to solve. And he agrees that each generation of GPTs can solve more than the one before. So why shouldn’t GPT keep progressing until it gets 100%?
Inline links: can’t point to
This seems like a good fit for the chimp → human transition, where evolutionary lineages that couldn’t do a bunch of difficult things for the first few hundred million years suddenly became good at those things in an evolutionary eyeblink. The ~5 million chimp/human gap seems like enough time to scale up chimp brains a bit (which definitely happened), but not enough time to invent a fundamentally new architecture. It wouldn’t surprise me if the architecture changed a little during this time, but we’re limited in how fundamental a change we can talk about over that period. I’m not at all sure this is true! I’m honestly close to 50-50 here. Maybe the PFC actually is magic! It just confuses me that Marcus seems to think we’ve ruled out the theory that this kind of scaling is possible, when I feel like we’ve heard plausible arguments on both sides. Nothing we’ve seen in GPTs or any other AI thus far disproves the scaling hypothesis, and a lot of what we’ve seen supports it. So sure, point out that large language models suck at reasoning today. I just don’t see how you can be so sure that they’re still going to suck tomorrow. Lemurs sucked for millions of years, then scaled up a bit and took over the world! V. …is one possible argument. Another possible argument is: language models and other deep learners really aren’t doing the same thing humans do - but whatever, their thing is powerful/effective/dangerous too. Suppose that GPT-X took over the world and killed all humans. Millennia later, some alien archaeologists come and investigate. They conclude that since its training data included Alexander the Great and Caesar, it was just pattern-matching to the kind of things they did (multiplied by a vector representing the difference between ancient and modern times), and GPT-X never demonstrated any true intelligence. So . . . what? I imagine this situation ALL THE TIME and I hate it. I think the impetus behind a lot of the AI risk stuff is that we’re barrelling to a world where AIs have far more than self-driving-car levels of capabilities, while being unpredictable in ways that are a lot like this. The history of the past few decades has been people getting surprised, again and again, at how much AIs can do without being “generally intelligent”. Douglas Hofstadter predicted in 1979 that any AI that could beat a grandmaster at chess would also be able to decide chess was boring and it preferred writing poetry. Instead, we got Deep Blue, so domain-specific it can’t even do so much as play checkers. Worse, now we have AIs that can switch between writing poetry and playing chess, and it still seems like a clever parlor trick rather than anything like real intelligence. I think basically nobody predicted this: narrow AI has won victories beyond past generations’ imagination. (cf. Nostalgebraist’s Human Psycholinguists: A Critical Appraisal) So even if GPTs aren’t a step on the path towards some sort of human-like AGI thing, I have no idea where they’ll end up. Replacing humans at all jobs? Writing novels? Taking over the world? If this seems crazy to you, “solve protein folding” sounded crazy ten years ago, and they already did that! At this point I will basically believe anything. VI. So I’m not going to take Marcus’ bet that GPT-4 will be perfect (as if anything ever is!). But here are some things I do believe, with confidence levels: At some point before 2030, someone will come out with a deep-learning-based language model which is significantly better than the current state of the art, by Gary Marcus’ admission (97%)
Inline links: https://substackcdn.com/image/fetch/$s_!_D9T!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F5cb652e3-381b-488d-a6a6-f1155f7ff557_586x194.png, writing poetry, playing chess, Human Psycholinguists: A Critical Appraisal
Your Book Review: The Laws of Trading
July 21, 2023 · Original source
I found this to be a good framework for thinking about AI. Some folks are clearly betting on momentum – that GPT-X products will continue to improve, reaching AGI (if it hasn’t already). The other side of the coin is bets on mean-reversion, which focus on the S-curves of technology and take a historical view. I’m old enough to remember that in 2016 everyone was talking about how self-driving cars would mean the end of truckers, and there’s more demand than ever for them today.