Ph.D. Student’s Framework Used to Bolster Nvidia’s Cosmos Predict-2 Model

A new deep learning architectural framework could boost the development and deployment efficiency of autonomous vehicles and humanoid robots. The framework will lower training costs and reduce the amount of real-world data needed for training.
World foundation models (WFMs) enable physical AI systems to learn and operate within synthetic worlds created by generative artificial intelligence (genAI). For example, these models use predictive capabilities to generate up to 30 seconds of video that accurately reflects the real world.
The new framework, developed by a Georgia Tech researcher, enhances the processing speed of the neural networks that simulate these real-world environments from text, images, or video inputs.
Processing speeds can increase by up to 2.6 times, said Ali Hassani, a Ph.D. student in the School of Interactive Computing and the creator of NATTEN.