Bio anchors framework

Forecasting transformative AI: the "biological anchors" method in a nutshell --> --> --> --> --> Subscribe (free) Audio also available by searching Stitcher, Spotify, Google Podcasts, etc. for "Cold Takes Audio" --> Today’s world Transformative AI Digital people World of Misaligned AI World run by Something else or or Stable, galaxy-wide
 civilization This is one of 4 posts summarizing hundreds of pages of technical reports focused almost entirely on forecasting one number: the year by which transformative AI will be developed. 1 By "transformative AI," I mean "AI powerful enough to bring us into a new, qualitatively different future." I specifically focus on what I'm calling PASTA : AI systems that can essentially automate all of the human activities needed to speed up scientific and technological advancement. The sooner PASTA might be developed, the sooner the world could change radically , and the more important it seems to be thinking today about how to make that change go well vs. poorly. This post is a layperson-compatible summary of Ajeya Cotra's "Forecasting Transformative AI with Biological Anchors " (which I'll abbreviate below as "Bio Anchors" ), and its pros and cons. 2 It is the forecast I find most informative for transformative AI, with some caveats: This approach is relatively complex, and it requires a fairly large number of assumptions and uncertain estimates. These qualities make it relatively difficult to explain, and they are also a mark against the method's reliability. Hence, as of today, I don't think this method is as trustworthy as the examples I gave previously for forecasting a qualitatively different future. It does not have the simplicity and directness of some of those examples, such as modeling COVID-19's spread. And while climate modeling is also very complex, climate modeling has been worked on by a large number of experts over decades, whereas the Bio Anchors methodology doesn't have much history. Nonetheless, I think it is the best available "best guess estimate" methodology for transformative AI timelines as of today. And as discussed in the final section , one can step back from a lot of the details to see that this century will likely see us hit some of the more "extreme" milestones in the report that strongly suggest the feasibility of transformative AI. (Note: I've also written up a follow-up post about this framework for skeptical readers. See “Biological anchors” is about bounding, not pinpointing, AI timelines .) The basic idea is: Modern AI models can "learn" to do tasks via a (financially costly) process known as "training." You can think of training as a massive amount of trial-and-error. For example, voice recognition AI models are given an audio file of someone talking, take a guess at what the person is saying, then are given the right answer. By doing this millions of times, they "learn" to reliably translate speech to text. More: Training The bigger an AI model and the more complex th

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