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Why ‘World Foundation Models’ are key to unlocking Physical AI and robotics
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NVIDIA has unveiled Cosmos, a new platform featuring world foundation models (WFMs) designed to advance physical AI systems through enhanced environmental simulation capabilities.

The core technology: World foundation models are neural networks that can simulate physical environments and predict how scenes will evolve based on various inputs and actions.

  • These models can generate detailed videos from text or image inputs while predicting scene evolution through a combination of current state data and control signals
  • WFMs provide virtual 3D environments for testing AI systems without the risks and costs of real-world trials
  • The technology enables the generation of synthetic training data, helping overcome the challenge of collecting vast amounts of real-world data

Platform specifics: NVIDIA Cosmos launches as an open platform with pre-trained models and essential development tools.

  • The platform includes WFMs based on diffusion and auto-regressive architectures
  • Built-in tokenizers can compress videos into tokens for transformer models
  • The open architecture allows enterprises to build custom models or fine-tune existing ones based on specific needs

Key applications: Two major industries stand to benefit significantly from world foundation model development.

  • Self-driving vehicles can undergo comprehensive testing in simulated environments featuring various weather conditions and traffic scenarios
  • Humanoid robots can be safely tested and verified across different simulated environments before real-world deployment
  • NVIDIA is partnering with companies like 1X, Huobi, and XPENG to advance physical AI development

Expert perspective: Ming-Yu Liu, NVIDIA’s vice president of research and IEEE Fellow, provides insight into the technology’s current state and future potential.

  • Liu emphasizes that WFMs allow AI systems to imagine different environments and simulate future scenarios
  • He acknowledges that while the technology is already useful, further development is needed to maximize its potential
  • Integration methods between world models and physical AI systems require additional research and refinement

Future implications: The development of world foundation models represents an early stage in a broader evolution of physical AI systems.

  • The technology could significantly reduce development costs and accelerate the deployment of safe, efficient AI systems
  • Further research will likely focus on improving model accuracy and expanding simulation capabilities
  • The open nature of the platform may foster innovation across multiple industries and applications
Why World Foundation Models Will Be Key to Advancing Physical AI

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