Accelerated Understanding builds large-scale AI models that can simulate and understand physics to invent and discover. Reuters has our story reuters.com/business/ai-foun… Our website has the details acceleratedunderstanding.com…

Celebrating @TIME #Time100AI at the SF Asian art museum

Our first podcast since the launch of our company Accelerated Understanding @accelerated_u last week. We go into what it means to have record-breaking 5 Trillion context length and why high-resolution 4D predictions are essential for physical understanding. We also talk about universality and achieving multiple areas of physical skill in the same model, and all the industries this will impact. What does self-improvement look like for physical AI? Watch the podcast to find out more. @bjenik @latentspacepod piped.video/KS_IpnX7n9I

@AnimaAnandkumar just launched @accelerated_u: neural operators instead of transformers, physical prediction instead of text, five trillion data points in a single prompt. She is on stage at @Caltech. Apply to attend: sair.foundation/events/scien…

Congratulations to our Co-Founder @AnimaAnandkumar to be selected one of the 100 most influential people in AI by @TIME right on the heels of our public launch.

Excited to see our Co-Founder @AnimaAnandkumar to be named one of the 100 most influential people in AI by @TIME right on the heels of our public launch. We build AI models that simulate the physical world in 4D. Their capability to intrinsically understand physics enables innovation and invention. Instead of waiting for slow lab experiments or numerical solvers the model directly forms an intuition on how to improve your design. We are particularly excited about self-improvement: a limiting factor for scaling physical AI so far has been the availability of high quality training data. But that no longer holds true: for our models we have the laws of physics themselves that let us measure and improve the quality of our outputs exceeding what was present in the training data. The improvement loop doesn’t stop at the models themselves. We can also use our models and their ability to understand physics and give directional feedback to break down one of the biggest barriers to innovation: the reliance on lab experiments as a bottleneck in the improvement loop. As intelligence gets more abundant this bottleneck is only increasing in importance. Putting our models and their physical simulation capabilities in that loop instead and taking advantage of their directional feedback means we only need the lab all the way in the end to double check. We are only getting started! #TIME100AI

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: piped.video/79mIutht1f4?si=M3Wd…

Thank you for covering our story.

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. reuters.com/business/ai-foun… acceleratedunderstanding.com… @accelerated_u @bjenik