Applied PC equipped with NVIDIA RTX Pro 6000

On July 2, 2026, I visited the "Monozukuri World Physical AI Exhibition" being held at Tokyo Big Sight. The evolution of generative AI and reinforcement learning is breaking out of the digital world and exploring how to intelligently operate robots in the real world (physical). This exhibition was a very vibrant space that truly hinted at the next generation of manufacturing and the automation of R&D (research and development). I will report on the latest trends that particularly caught my eye on-site!

In the manufacturing industry and advanced research fields, the use of real machines for trial and error is already reaching its limits. NVIDIA's "Physical AI Platform" aims to completely eliminate the heavy constraints of "time, cost, and risk," elevating development speed to an entirely new dimension. This approach, known as "Sim2Real," allows for safe and instantaneous learning in virtual spaces, conducting tens of thousands to hundreds of millions of iterations, and seamlessly implementing the results into the real world. This is not just a concept; it is a powerful ecosystem that has already been put into practical use. In this article, we will elucidate the three phases that make up this platform and the roles of specific libraries that hold the key to its implementation.

The development of autonomous driving systems is transitioning from traditional rule-based and modular approaches to an end-to-end "AX (AI Transformation)" environment where AI comprehensively understands situations and makes decisions. NVIDIA's offerings, "Alpamayo," "AlpaSim," and "NuRec," represent the latest ecosystem aimed at the societal implementation of level 4 autonomous driving, balancing safety and explainability. NVIDIA's autonomous driving AI platform "Alpamayo" moves away from reliance on traditional pattern recognition in autonomous driving by incorporating a mechanism where AI "infers (thinks and judges)" like a human, dramatically enhancing safety and reliability. The R&D departments of enterprises will report on the HPC requirements necessary to maximize the use of these technologies, integrating physics-based simulations (CAE) with AI models.