CCF will talk more about these kinds of considerations later.) (“Can automate” here means “is theoretically smart enough to automate” - actual automation will depend on companies fine-tuning it for specific tasks and providing it with the necessary machinery for example, even a very smart AI can’t do plumbing until someone connects it to a robot body to do the dirty work. Specifically, it asks: how long will it take to go from the kind of early-to-intermediate AI that can automate 20% of jobs, to the truly-human-level AI that can automate 100% of jobs? CCF departs from this terminology in favor of trying to predict a particular length of takeoff in real time units. It’s actually worse than this in some sense, a “slow takeoff” will necessarily be faster than a “fast takeoff” - if you superimpose the red and blue graphs above, the red line will be higher at every pointġ. Whenever you get this insight, AIs go from far-below-human-level to human-level or beyond, no gradual progress necessary.īefore, I mentioned one reason Davidson doesn’t like these terms - “slow takeoff” can be fast.
Maybe there’s some single key insight that takes AIs from “mere brute-force pattern matchers” to “true intelligence”. The third graph shows a world with a sudden “fast” takeoff. We would expect the early stages of this process to take slightly less time than we would naively expect, and the latter stages to take much less time, since AIs are doing most of the work. Later AIs (eg autonomous near-human level AIs) could do the vast majority of AI research work, speeding it up many times. Intermediate AIs (eg an AI that can help predict optimal parameter values) might speed up AI research more.
Early AIs (eg Codex ) speed up AI progress a little. The second graph shows a world with a gradual “slow” takeoff. The field moves forward at some constant rate. Past AI progress doesn’t speed up future AI progress. The first graph shows a world with no takeoff.
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