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I do think the Singularity crowd helped keep the conversation going during the extended winter, and it’s important to acknowledge their institution-building contributions esp via founding influence on OpenAI, DeepMind etc. But both the tech and the conversation are MUCH bigger.
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Reminds me of something similar in early computing history: for some California-obsessed people, the influence of the hippie counterculture on early computing in 1960-1985 via SRI, PARC, Stanford is the whole story, but objectively it’s like 1/5th of the story.
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In brief, if you want to look it up, there are like 5-6 strands to the story: 1. Semiconductors/Bell labs/Noyce... 2. IAS machine/von Neumann track 3. California track 4. DoD track 5. MIT track 6. Control and cybernetics
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This is by now we’ll know to historians of computing. Somebody with a deeper understanding of AI history should do a similar “thick” version of the AI story. Both dismissing the Singularity crowd as amateur entryists or the whole story is bad historiography.
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They mattered less than they believe, but more than critics are willing to give them credit for. Anyhow... back to the topic at hand. AI futures. What does the AI future look like?
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I think: 1. General purpose post-GPU hardware 2. Application-specific hardware optimization 3. An end to going faster than Moore’s law ceiling 4. A software 2.0 stack that will evolve faster than people realize 5. Rapidly falling costs of AI compute 6. Smaller form factors
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Ugh broke threading further up but this sub thread of 3 tweets fits better here anyway
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This whole track of AI btw, came from a whole different place... people trying to use GPUs for parallel computing, Moore’s law raising the ceiling, etc. It did not come from pursuit of abstract science-fiction concerns. So those frames are likely to misguide.
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What kind of a) tech trends and b) philosophical conversations can we expect on top of this basic outlook (which I know many agree with)? Key prelim question: are we due for another AI winter due to hitting a new hardware ceiling and/or paradigm-limits of deep learning?
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Afaict while the ensemble and society-of-mind approaches are super influential in *AI in general* (and beyond), they are marginal and strongly underindexed in the Bostrom-LW school of AI because they don’t point cleanly to AGI-like futures but much messier ones.
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Replying to @vgr and @Aelkus
nah man this is off base. The person is arguing with a straw man in this article. your g Factor theory is as well. The society of mind model is super influential and ensemble intelligences are already state-of-the-art in most environments. it's not about some IQ obsession
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Replying to
idk, i think there are a few trends in the LW milieu -- like predictive processing theory, futarchy and prediction markets-- that point the other way. and I think LW fav *The Crystal Society* by Max Harms is a good depiction of Bostrom-style SI takeoff in a society-of-mind model
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Replying to
Yeah predictive processing stuff is good... but I associate it with a broader psych tradition including stuff like Lisa Feldman Barrett’s emotions research. It doesn’t seem central to the LW school of thought as such. did a great series on Ribbonfarm.