EA - AIs accelerating AI research by Ajeya

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: AIs accelerating AI research, published by Ajeya on April 12, 2023 on The Effective Altruism Forum.Note: This post was crossposted from Planned Obsolescence by the Forum team, with the author's permission. The author may not see or respond to comments on this post.Researchers could potentially design the next generation of ML models more quickly by delegating some work to existing models, creating a feedback loop of ever-accelerating progress.The concept of an “intelligence explosion” has played an important role in discourse about advanced AI for decades. Early computer scientist I.J. Good described it like this in 1965:Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an ‘intelligence explosion,’ and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control.This presentation, like most other popular presentations of the intelligence explosion concept, focuses on what happens after we have a single AI system that can already do better at every task than any human (which Good calls an “ultraintelligent machine” above, and others have called “an artificial superintelligence”). It calls to mind an image of AI progress with two phases:In Phase 1, humans are doing all the AI research, and progress ramps up steadily. We can more or less predict the rate of future progress (i.e. how quickly AI systems will improve their capabilities) by extrapolating from past rates of progress.[1]Eventually humans succeed at building an artificial superintelligence (or ASI), leading to Phase 2. In Phase 2, this ASI is doing all of the AI research by itself. All of a sudden, progress in AI capabilities is no longer bottlenecked by slow human researchers, and an intelligence explosion is kicked off. The rate of progress in AI research goes up sharply — perhaps years of progress is compressed into days or weeks.But I think this picture is probably too all-or-nothing. Today’s large language models (LLMs) like GPT-4 are not (yet) capable of completely taking over AI research by themselves — but they are able to write code, come up with ideas for ML experiments, and help troubleshoot bugs and other issues. Anecdotally, several ML researchers I know are starting to delegate simple tasks that come up in their research to these LLMs, and they say that makes them meaningfully more productive. (When chatGPT went down for 6 hours, I know of one ML researcher who postponed their coding tasks for 6 hours and worked on other things in the meantime.[2])If this holds true more broadly, researchers could potentially design and train the next generation of ML models more quickly and easily by delegating to existing LLMs.[3] This calls to mind a more continuous “intelligence explosion” that begins before we have any single artificial superintelligence:Currently, human researchers collectively are responsible for almost all of the progress in AI research, but are starting to delegate a small fraction of the work to large language models. This makes it somewhat easier to design and train the next generation of models.The next generation is able to handle harder tasks and more different types of tasks, so human researchers delegate more of their work to them. This makes it significantly easier to train the generation after that. Using models gives a much bigger boost than it did the last time around.Each round of this process makes the whole field move faster and faster. In each round, human...