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BLUF:
* To determine whether AI is ‘improving exponentially’, ‘hitting the wall’, or any other claim which involves a quantity or magnitude (e.g. ‘This model was a big leap/small increment’). We need a good y-axis: an interval scale of AI capability which means +1 unit always represents the same degree of ‘how much better’, in the same way +1 degree Celsius is always the same amount of ‘how much hotter’.
* Yet there is no good y-axis for AI capability. All our...
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* The Long-Term Future Fund is closing down, and EA Funds is launching the Transformative AI Fund with a new full-time team.
* The fund's primary focus is technical AI safety and AI governance (including post-AGI governance), as well as supporting fields such as field-building and forecasting. We'll also consider non-GCR implications of transformative AI such as flourishing futures and digital...
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Thanks for the recommendation! It's very rare to see book recommendations on the Forum, which is odd considering the goals and methods of EA (we should be reading a lot of books).
In the past, books I've read that collect "common lessons of successful projects" have usually run into the problem of picking out successful anecdotes after the fact, and ignoring cases where the book's chosen rules/phenomena applied equally well to various projects that failed. (In the case of this book, that might equate to ignoring cases where a crazy idea is rejected for a long time, then accepted, only for it to become clear that it should have stayed rejected.) Do you think the author avoids this issue?
Agree about books. I'd say the main ways that I differ in my approach came from reading a ton of books in specific areas which, besides just the straightforward skill-up, also made me less correlated with others' approaches thus increasing the potential for consilience.