Domain experts consistently underestimate how quickly AI can master their field and surpass them, because they mistake human difficulties for universal difficulty.
So many of the people who think about the governance of superintelligence, myself included, avoided that unpleasantness and bowed to the social pressure to self-censor.
the reason why RL environments today are much better than they were in like you know 2024 is not that much because um we have hired way more human experts to make RL environments and is instead much more because we better know what how RL like what RL environments we even want to make and and like how we should structure them and also we're using huge amounts of AI labor to build RL environments.
The simple answer is what the study showed was that 10,000 hours was the average amount of time that an expert violinist had spent practicing by the time they reached 20 years old. That's what the study showed. They weren't even experts by that point.
Yeah, so they excel at breadth and humans excel at depth. Um like human experts at least. Yeah, so um I think they're very complementary. Um but our current uh way of doing math and science is focused on depth because that that's where the human uh expertise cuz humans can't do breadth.
And we failed horribly, and we had the best of the best on that team. And it’s because everybody was too much of an expert on how to make a groundbreaking phenomenon MMO.
I don’t think this is very productive (expert users of a piece of software are notoriously bad at being able to tell if an explanation will be clear to non-experts), so I needed to find a way to identify problems with the man pages that was a little more evidence-based.
It significantly lowered the bar to production. It is how we got the whole
society to run on software. If you make it harder for hobbyists maintainers, you
are going to crash society.
But they can't do everything that megapapers do, such as conveying intellectual heft and legitimacy, and they are not yet a replacement for real, diverse experts.
And maybe also make the system available to a few hundred of the world's top experts, the Terence Taos of each subject area and give them a month or two and see if they can find an obvious flaw in the system. And if they can't, then I think you can be pretty confident we have a fully general system.
Dismissing discussion of AGI, human-level AI, transformative AI, superintelligence, etc. as “science fiction” should be seen as a sign of total unseriousness.
I think it's even more impressive what OpenAI did in 2022. At the time, no one believed in mixture of experts models at Google who had all the researchers. OpenAI had such little compute and they devoted all of their compute for many months, all of it, 100% for many months to GPT-4 with a brand-new architecture with no belief that, "Hey, let me spend a couple of hundred million dollars, which is all of the money I have on this model." That is truly YOLO.
Actually by condemning those alternative point of view, archaeologists make it much more likely that the general public will accept those alternative point of view, because there is a great distrust of experts in our society today.
This fits with some of the studies of chess experts and so forth that it’s not so much that you learn the patterns passively. You learn what to look for. You learn what’s important and what’s not.
Based on public commercial data that tells a lot about the military potential of these dual-use robotic spacecraft, we have shown that China can manufacture and deploy 200 such spacecraft as early as 2026, enough to cripple critical US satellites in geosynchronous, highly elliptical, and other orbits, and thus severely degrading space support to wartime operations.
Society's response, despite promising first steps, is incommensurate with the possibility of rapid, transformative progress that is expected by many experts. AI safety research is lagging. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
So, experts are highly aware of mistakes. But people who are journeymen, many of them stay as journeymen because they they want to move on and forget about their mistakes.
in 1985, um my colleagues and I came up with a description of how people actually make life-and-death decisions under extreme time pressure and certainty. And we called it the recognition prime decision model. And it's a blend of intuition plus analysis. And we found we studied firefighters. We found that most fire that accounted for almost 90% of the tough decisions firefighters made.
I feel like Ben Orlin solved what was previously an unsolved problem with this book: Teach calculus in a way that is as enjoyable to those unfamiliar with the topic as it is to experts who use it every day. I, for one, was laughing from page 1.
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