Yasaman Bahri
@yasamanbb
Research Scientist @GoogleDeepMind // Neural learning & computation, AI, materials // Ph.D. physics @UCBerkeley.
For humans, mathematical symbols (and formal systems like lean) are *tools* we learn how to use, not a structure that wraps around us. I think that's the right role for formal still manipulation: a tool that can be employed by an intelligent system if/when it supports a goal.
I was in the audience. It was a great talk. It is great to see word2vec get formally analyzed!
I'm looking forward to giving a talk tomorrow morning at the ICML workshop on High-Dimensional Learning Dynamics (HiDL) sites.google.com/view/hidimlear…. Come by at 9 am!
So nice to be surrounded by >12000 physicists at @APSphysics summit working hard to understand the nature of our universe and build better tech like solar cells and novel quantum materials. Important for the public to remember that our living standards depend on this research.
Very excited to share our interview with @jaschasd on the history of diffusion models — from his original 2015 paper inventing them, to the GAN "ice age", to the resurgence in diffusion starting with DDPM. Enjoy!
A wonderfully organized school in an inspiring and didactic setting! Lecture notes from the Les Houches summer school are now published. Boris Hanin & I lectured on the theory of neural networks at large width.
The long-awaited collection of lecture notes from the Summer School on Statistical Physics & Machine Learning, Les Houches 2022, is now published in JStatMech iopscience.iop.org/collections/js… . I am particularly proud of the works the school inspired; see section 3 of the editorial.
What an exciting week it's been ...! Huge congrats for work done at DeepMind.
Huge congratulations to @DemisHassabis and John Jumper on being awarded the 2024 Nobel Prize in Chemistry for protein structure prediction with #AlphaFold, along with David Baker for computational protein design. This is a monumental achievement for AI, for computational…
John Hopfield has a nice article in the annual reviews of condensed matter physics. It starts off with a discussion of what physics is, which I think is totally on point.
Given today’s great news from the #NobelPrize2024, I want to share a couple of personal thoughts on Hopfield Networks. This idea had an enormous impact on at least three large disciplines: Statistical Physics, Computer Science and AI, and Neuroscience.
Congrats to John Hopfield and @geoffreyhinton! well deserved recognition that some important foundations of AI rest on physics! Physics departments take note: understanding and improving AI systems is a new frontier topic for physics, just as biophysics was earlier. Time to hire!
BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Physics to John J. Hopfield and Geoffrey E. Hinton “for foundational discoveries and inventions that enable machine learning with artificial neural networks.”