Accelerated Understanding

AI that can simulate and understand physics to invent and discover.

[Accelerated Understanding @accelerated_u]

Accelerated Understanding builds large-scale AI models that can simulate and understand physics to invent and discover.

Reuters has our story https://t.co/9qUtm2MhHX

Our website has the details https://t.co/sQs3xJ6qDE

[Prof. Anima Anandkumar @AnimaAnandkumar]

Was fun to be on the @latentspacepod podcast a few weeks ago to talk about AI for physical simulation and understanding. This pre-dates the public launch of @accelerated_u so I couldn't yet talk the exact details but hint at what happens if you scale some of these methods to Trillion parameter model sizes and fully 4D context lengths in the Trillions in a universal model.

The podcast covers some of the ideas that provide the foundation for scaling. It also goes into the promise and successes that were possible even before going really big like building high resolution fully AI based weather models that are tens of thousands of times faster and as accurate as existing forecasts. We talk about predicting plasma behavior in fusion so quickly that one could take corrective action before something bad happens. And we cover how physical understanding doesn't just help with replacing experiments but lets us optimize design directly.

We even briefly touch on the promise of combining those capabilities into one large model which I can now talk about more.

Watch for yourself: https://t.co/B08JQInqOv

[Accelerated Understanding @accelerated_u]

Thank you for covering our story.

[Jeffrey Dastin @JLDastin]

A research duo, once pitched to lead the Jeff Bezos-backed Project Prometheus, on Tuesday showed what they built on their own.

Their company, called Accelerated Understanding, says its physics-focused AI handled 5 trillion pieces of data in one prompt. https://t.co/gz0hrTQezg

[Prof. Anima Anandkumar @AnimaAnandkumar]

Excited to see @Reuters cover the launch of our startup Accelerated Understanding.

We are training large scale AI models that can simulate and understand physics to invent and discover. Our models understand the world directly in 4D (3D + time) and across physical phenomena. Going full 4D requires massive context length, we have pushed it to a Trillion in training and exceeding 5 Trillion at inference.

AI giving you a bigger haystack of ideas doesn’t help. The bottleneck for new inventions and discoveries is shifting from ideas to the ability to test them. With AI that can simulate and understand physics we are directly attacking this bottleneck.

People have been trying to do this for a while now, but usually by taking shortcuts. Narrow surrogates are great if you happen to have enough of precisely the right data and your design loop stays in distribution. Video models look fantastic but sweep physical accuracy under the rug, and some static world models cut out physics altogether. A lot of interesting physics isn’t visual.

What does not cutting corners look like? Space stays 3D and you also have time: so 4D in total. You also need multiple physical modalities in the same model, not just things you can see. That’s what we’ve built.

Scaling is the primary ingredient to make this work. To represent the world you need sufficient context, which in our case grows in 4 dimensions. Individual samples get so big they don’t fit into single accelerators or even full nodes anymore.

We’ve developed architectural tricks to make it work. We’ve pushed our models to 1T parameters during large scale pre-training and are able to train at up to a Trillion context when needed and do inference exceeding 5 Trillion context without any sub-sampling or patching.

Building on prior successes of AI weather forecasting, fusion simulation, design of medical devices, drugs and chips, we wanted to see if scale and universality can benefit AI for physical understanding. With our teams’ experience in large-scale infrastructure and model training we’ve been able to pull it off.

https://t.co/w12yG9fCps

https://t.co/vWnPiTbLEy

@accelerated_u @bjenik