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For GM, this capability translates to more capable AI systems.
By creating digital twins of physical environments, the automaker can simulate different scenarios – from normal operations to edge cases and potential disruptions – and develop AI models that respond appropriately across a wide range of conditions.
From AI training to deployment The value of Omniverse extends beyond initial AI development. As organisations deploy AI systems based on insights gained through simulation, the digital twins can be updated to reflect the current state of physical systems, creating a continuous feedback loop that drives ongoing improvement of AI models.
This approach has proven particularly valuable for companies seeking to integrate intelligent automation into existing environments. By simulating the interaction between humans, robots and other systems, organisations can identify potential issues and develop solutions before implementing AI in the physical world.
The ability to train AI models for operations such as autonomous navigation, object recognition and decision-making in complex environments has significant implications for implementation speed and system safety. By optimising these systems virtually, companies can reduce the time required to bring new AI capabilities online while minimising risks associated with learning in physical environments.
AI development horizons As adoption of Omniverse continues to grow, the platform is likely to evolve in response to the needs of AI developers and automators. The integration with other software tools, as demonstrated by Nvidia’ s expanding partnerships, suggests a future in which digital twins become increasingly comprehensive, incorporating data from multiple sources to create more accurate and useful environments for AI training.
For companies like GM, this evolution presents opportunities to extend the use of digital twins beyond current applications. Future implementations may include endto-end AI systems that manage supply chains, predict maintenance needs and enable product development, creating a more integrated approach to automation that spans entire operational lifecycles.
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