Announced details
Joint seminar organizers. The three companies introduced an approach to data utilization that combines their respective technologies and solutions. Capital and personnel relationships are not disclosed.
NVIDIA Japan and MathWorks Japan. Other details, including locations, representatives, business activities, capital and year of establishment, are not disclosed.
In this announcement the company addresses none of Contract Value / Offering price / Impact on Business Performance.
Hugging Face
MathWorks Japan seminar archive video
| Item | Details |
|---|---|
| Names of Products / Services Covered | AI-native dataset, RoadRunner, Cosmos Transfer, Omniverse NuRec |
| Counterparty | NVIDIA Japan (NVIDIA), MathWorks Japan |
| Counterparty overview | NVIDIA Japan and MathWorks Japan. Other details, including locations, representatives, business activities, capital and year of establishment, are not disclosed. |
| Relationship with the Company | Joint seminar organizers. The three companies introduced an approach to data utilization that combines their respective technologies and solutions. Capital and personnel relationships are not disclosed. |
| Launch date | 15 July 2026 (already held; seminar date) |
| Applicable area | Physical AI, Automated Operation, AI model training, evaluation and validation, Automakers, Tier 1 suppliers, Software vendors, Semiconductor-related companies, Research institution |
| Source | Hugging Face, MathWorks Japan seminar archive video |
Items this document does not state (11)
These are items the company does not state in this document. We do not leave them blank, nor fill them in from other sources or by inference.
The reasons the company gives
The company's own explanation, not something inferred from the numbers.The development of physical AI and end-to-end autonomous driving technologies has increased requirements for the volume and quality of data needed to train, evaluate and validate AI models. Data from real-world environments alone cannot sufficiently cover the wide range of driving environments and scenarios, increasing expectations for data generation through simulation and generative AI.