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: