Douglas Hofstadter
Article
Douglas Hofstadter is a recurring person in the Astral Codex Ten archive, appearing 6 times across 6 issues between January 19, 2022 and July 14, 2023. The archive places it in contexts such as “back in 1979, Douglas Hofstadter speculated”; “Douglas Hofstadter discusses philosophy of translation”; “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”. It most often appears alongside GPT-3, OpenAI, Texas.
Metadata
- Category: People
- Mention count: 6
- Issue count: 6
- First seen: January 19, 2022
- Last seen: July 14, 2023
Appears In
- Practically-A-Book Review: Yudkowsky Contra Ngo On Agents
- Biography of Jason Shea, 44th US President
- Somewhat Contra Marcus On AI Scaling
- Links For June
- Another Bay Area House Party
- Your Book Review: The Educated Mind
Related Pages
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- GPT-3 (3 shared issues)
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- OpenAI (3 shared issues)
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- Texas (3 shared issues)
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- Aristotle (2 shared issues)
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- Confucius (2 shared issues)
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- DeepMind (2 shared issues)
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- Eliezer (2 shared issues)
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- Eliezer Yudkowsky (2 shared issues)
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- Harvard (2 shared issues)
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- Jews (2 shared issues)
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- Norse mythology (2 shared issues)
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- Novaya Zemlya (2 shared issues)
External Links
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.
Tool AIs have had a good few decades. It’s easy to forget that back in 1979, Douglas Hofstadter speculated that any AI smart enough to beat top humans at chess would also be smart enough to swear off chess and study philosophy instead. So the hypothesis “tool AIs can just keep getting arbitrarily more powerful without ever becoming generally intelligent agents” has a lot of historical support.
In Le Ton beau de Marot, Douglas Hofstadter discusses philosophy of translation.
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
53: Douglas Hofstadter published a recent article pointing out that GPT-3 gives straight answers to silly questions - for example, if you ask when Egypt was transported across the Golden Gate Bridge, it will guess 2017. Rictic on Less Wrong demonstrates that if you ask it nicely to not do this, and instead to call you out when you ask silly questions, it’s perfectly able to do that.
Inline links: a recent article, Rictic on Less Wrong
“Somebody edited a Controversy section into the Douglas Hofstadter article. It talked about how Hofstadter provoked criticism for forcing the Wikimedia Foundation to censor true but unflattering information from his Wikipedia page. Totally false. Never happened.”
Reviewer: Educated Mind came out in 1997, but he had built the fundamentals of his paradigm by the late Eighties. And since then, the cognitive sciences have swung toward embracing the power of the tools of what Egan’s dubbed “Somatic” and “Mythic” understanding — see Jonathan Gottschall for a popular account of the move toward narrative, Douglas Hofstadter for one on metaphor, Antonio Damasio for one on emotion, and Rebecca Schwarzlose for one on mental images.