AlphaStar, AlphaGo and OpenAI Five provides some evidence that this takeoff period will be short: after a long development period, each of them was able to improve rapidly from top amateur level to superhuman performance.
It seems like all of the very large advancements in AI have been in areas where we either 1) can accurately simulate an environment & final reward (like a chess or video game) in order to generate massive training data, or 2) we have massive data we can use for training (e.g. the internet for GPT).
For some things, like communicating and negotiating with other AIs, or advancing mathematics, or even (unfortunately) things like hacking, fast progress in the near-future seems very (scarily!) plausible. And humans will help make AIs much more capable by simply connecting together lots of different AI systems (e.g. image recognition, LLMs, internet access, calculators, other tools, etc) and allowing self-querying and query loops. Other things (like physical coordination IRL) seem harder to advance rapidly, because you have to rely on generalization and a relatively low amount of relevant data.
It seems like all of the very large advancements in AI have been in areas where we either 1) can accurately simulate an environment & final reward (like a chess or video game) in order to generate massive training data, or 2) we have massive data we can use for training (e.g. the internet for GPT).
For some things, like communicating and negotiating with other AIs, or advancing mathematics, or even (unfortunately) things like hacking, fast progress in the near-future seems very (scarily!) plausible. And humans will help make AIs much more capable by simply connecting together lots of different AI systems (e.g. image recognition, LLMs, internet access, calculators, other tools, etc) and allowing self-querying and query loops. Other things (like physical coordination IRL) seem harder to advance rapidly, because you have to rely on generalization and a relatively low amount of relevant data.