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THREAD/ specific answers vary by field, but i've spent a large portion of my research career interested in this question. punchline: funding (the right) data is a relatively cheap but super effective way to boost progress in a field.

Report from the teaching trenches: I teach an advanced elective (Social and economic networks) which is difficult for top undergrads but where AI can do the homework perfectly. The main changes this year: (i) I encourage AI use for learning; (ii) closed book exams 1/

Excited to launch @insilicopod — a new podcast exploring how AI is transforming science! I’ll be talking to scientists and researchers, as well as investors and policymakers, about the frontiers of Science: autonomous labs, AI-driven modelling, and more. Episode 1 is live👇

protesting is cool

Benchmarks/evals suffer from selection bias -- the tasks for which you can construct a usable benchmark are precisely the tasks RL is good for An implication is we should EXPECT there to be a growing gap between benchmarks and real-world impact

Which tasks are going to take longest for AI to do? 4 categories: 1. The data poor The internet has trillions of words, so AI got p good at writing. But there's no equivalent database of 3D movement, and it's expensive to gather, so robots still struggle.

fantastic (and funny) read to help calibrate your AI timelines the story of the fox, the hedgehog, and the rhino (guess who the rhino represents) was especially delightful!

Should be very interesting.

Awesome

Excellent report (h/t marginal revolution) from Arjun Ramani on India. He's a PhD student at MIT but previously was The Economist's Man on the Subcontinent (among other roles) - very jealous! Economic & political developments in India critical for the future - like China in 2005.