A very thorough and balanced report on #AI by White House OSTP PCAST spearheaded by Terence Tao and Laura Greene. It is comprehensive in covering the impact of AI+Science and extensively researched by the team. Hats off to their effort!
I am thrilled to be acknowledged in the end.

In ML as in Science it is important to use the right tool for the right problem. Tensor operations are the core of AI and scaling both performance and efficiency will require better tensor algebra.

We’ve been working on this for a while, checkout our work on the tensor

Tensor algebra is the way to scale AI architectures in an efficient way. We have been working on it for more than a decade. Check out tensorly.org led by Jean Kossaifi with PyTorch framework

Thank you for highlighting several projects that we incubated and launched including NVIDIA Earth-2, NVIDIA Modulus and genome-scale language models with Argonne National Lab

Thank you Eric Schmidt for championing the need for AI infrastructure in the public sector. This is badly needed for us to continue AI innovation and nurture new talent.

This is exactly what we did with leandojo.org and Lean co-pilot. check it out!

With all the attention on Neural Operators at #ICLR2024 and AI4differentialequations workshop, one of the first visionaries who saw its potential is none other than Jensen at NVIDIA Here's clip of him talking about Physics Informed Neural Operator, way back in #GTC 2021

Julius Berner @ICLR ICLR 2024 The team at play

Neural walk-on-spheres: we solve Poisson equations using regression over cheap & unbiased targets obtained from the walk-on-spheres method.

⚡ faster convergence, lower memory usage, and higher accuracy than PINNs, Deep Ritz, and other SDE-based neural PDE solvers.

Second talk of the afternoon is from Alex Townsend on data efficiency of operator learning ICLR 2024

First contributed talk (4 accepted in total out of a very high quality field) from Long Wei talking about Generative PDE control. ICLR 2024

Next invited talk is from Paula Harder after a great first poster session. ICLR 2024

Next talk from Shengchao Liu on A Multi-Grained Symmetric Differential Equation Model for Learning Protein Ligand Binding Dynamics ICLR 2024

First talk after lunch is from Miguel Liu-Schiaffin on Neural Operators with localized integral and differential kernels ICLR 2024

A ton of papers on neural operators! Amazing progress!

Good to see differential equations and neural operators getting more popular! If you are at #ICLR2024 make sure to attend! Lots of advances in neural operators presented there.

Paula Harder Paula Harder sharing her results on operator learning, neural operators, and up sampling physics

ICLR 2024 2024 Workshop on AI4DifferentialEquations in Science

Come to the workshop on AI4DifferentialEquations in Science ICLR 2024 (room: Schubert 2) today!

📌 10-10:30am: PINNs for simulation-free sampling from densities
📌 12:15-12:45pm: Solving high-dim. Poisson equations with neural walk-on-spheres
🎙1:45-2pm: Local neural operators

Happening now, come by poster #58!

Check out our #ICLR2024 paper on mixed precision neural operators. We show >50% increase in throughput with almost no loss in accuracy! Poster # 58, Fri 4:30 - 6:30pm CEST arxiv.org/abs/2307.15034 Colin White
Renbo Tu Jean Kossaifi Boris, Gennady, Kamyar Azizzadenesheli
Ours is a