Conversation

Instead of treating AGI as a binary threshold, I prefer to treat it as a continuous spectrum defined by comparison to time-limited humans. I call a system a t-AGI if, on most cognitive tasks, it beats most human experts who are given time t to perform the task. More details:
A 1-second AGI would need to beat humans at tasks like quickly answering trivia questions, basic intuitions about physics (e.g. "what happens if I push a string?"), recognizing objects in images, recognizing whether sentences are grammatical, etc.
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A 1-minute AGI would need to beat humans at tasks like answering questions about short text passages or videos, common-sense reasoning (e.g. 's gears problems), simple computer tasks (e.g. use photoshop to blur an image), justifying an opinion, looking up facts, etc.
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A 1-hour AGI would need to beat humans at tasks like doing problem sets/exams, writing short articles or blog posts, most tasks in white-collar jobs (e.g. diagnosing patients, giving legal opinions), doing therapy, doing online errands, learning rules of new games, etc.
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A 1-day AGI would need to beat humans at tasks like writing insightful essays, negotiating business deals, becoming proficient at playing new games or using new software, developing new apps, running scientific experiments, reviewing scientific papers, summarizing books, etc.
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A 1-month AGI would need to beat humans at coherently carrying out medium-term plans (e.g. founding a startup), supervising large projects, becoming proficient in new fields, writing large software applications (e.g. a new OS), making novel scientific discoveries, etc.
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A 1-year AGI would need to beat humans at... basically everything. Some projects take humans much longer (e.g. proving Fermat's last theorem) but they can almost always be decomposed into subtasks that don't require full global context (even tho that's often helpful for humans).
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Some clarifications: - I don't think it matters much how long AIs get to do the task. The bottleneck is being able to perform it *at all*; if they can then they'll almost always be faster than humans. - Similarly, I doubt the specific "expert" theshold will make much difference.
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- I expect that, for any t, the first 100t-AGIs will be *way* better than any human on tasks which only take time t. To reason about superhuman performance we can extend this framework to talk about (t,n)-AGIs which beat any group of n humans working together on tasks for time t.
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- The value of this framework is ultimately an empirical matter. But it seems useful so far: I think existing systems are 1-second AGIs, are close to 1-minute AGIs, and are a couple of years off from 1-hour AGIs. Extrapolating that trend makes alignment research feel very urgent!
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FWIW I formulated this framework 2 years ago, but never shared it widely. From your perspective there's selection bias - I wouldn't have tweeted it if I'd changed my mind. But at least from my perspective, it gets points for being useful for describing events since then.
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