Physical AI & NVIDIA Cosmos (World Foundational Model) in Action

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This week, I will learn with you the concept of Physical AI and share insights on NVIDIA’s newly launched Cosmos world foundational model.

What is Physical AI?

Physical AI is an emerging field at the intersection of robotics, artificial intelligence, and material science where intelligent systems are designed to perceive, interact, and adapt in the physical world. It is also often referred to as “generative physical AI” due to its ability to generate insights and actions to execute on.

Core Principles of Physical AI

Physical AI integrates intelligence into physical designs to dynamically interact with surroundings.

• These systems use sensors to gather environmental data, actuators for movement and interaction, and AI algorithms for decision-making and adaptation.
• Advanced materials like shape-memory alloys, hydrogels, and electroactive polymers enable systems to respond to stimuli, adapt, and even self-repair.
• With the ability to perceive real-time data, adapt to changes, and make autonomous decisions, Physical AI systems operate with minimal human intervention.

How Does Physical AI Work?

Generative AI models (such as large language models or LLMs) excel at processing text, images, and videos, delivering responses with human-like capabilities. However, they are inherently limited in their ability to comprehend and interact with the physical world.

Generative physical AI extends current generative AI with understanding of spatial relationships and physical behavior of the world we all live in.

This is done by providing additional data that contains information about the spatial relationships and physical rules of the real world during the AI training process.

Spatial relationship refers to how objects or elements are positioned relative to one another in space. They describe the physical location, orientation, and interaction between objects within an environment.

Why is Physical AI important?

Previously, autonomous machines were unable to perceive and sense the world around them.?As the capabilities of Physical AI are combining with traditional training methods, autonomous machines can be built and trained to seamlessly interact with and adapt to their surroundings in the real world. This is opening entire new space & practical applications. Few examples are described below -

Warehouses: Robots in warehouses can navigate environments and avoid obstacles, following directions, accurate turns etc. Bringing more accuracy & strengths to place and pick materials etc.

Healthcare: In addition to surgical precision, AI-powered medical robots can assist in physical therapy by adapting to a patient’s movement and needs, ensuring customized rehabilitation

Disaster Response: Autonomous rescue drones can navigate collapsed buildings, detect trapped individuals, and deliver essential supplies in real-time during emergencies.

Automobile Insurance: Autonomous drones or AI-powered cameras can assess accident scenes in real-time, capturing damage and determining fault based on situational analysis. This reduces claim processing time and ensures accurate assessments.

These are just few examples and applications of Physical AI are much more than these.

Exploring NVIDIA's Cosmos Model for Video Search & Summarization

Scenario - Inspecting a Remote Bridge Amidst a Dense Jungle

A long bridge runs through a dense jungle, making manual inspections both challenging and risky for the inspection team. Regular inspections are essential to ensure the bridge remains in good condition and to facilitate timely preventive maintenance.

By leveraging Vision-Language Models (VLMs) such as Cosmos Nemotron, real-time understanding of inspection videos becomes possible. Combined with the summarization capabilities of Large Language Models (LLMs), these technologies can generate concise summaries of inspection findings. These summaries can then be used to create detailed inspection reports and automate routine maintenance activities, such as scheduling repair vehicles or ordering necessary materials.

In this example, you can see how an inspection summary is generated for the given video.

Through this inspection, it was discovered that regular inspections and maintenance are recommended to address rust, corrosion, and potential structural concerns.

This example illustrates how Physical AI systems, equipped with advanced Vision-Language Models (VLMs), can simplify intricate tasks like bridge inspections. The integration of real-time video understanding and concise reporting ensures not only safety but also operational efficiency in remote and challenging environments. For more examples, refer to Build a Video Search and Summarization Agent Blueprint by NVIDIA.

Optional Reading (for technical understanding only)

The architecture of such a system is outlined in the blueprint provided by NVIDIA. This blueprint features an ingestion pipeline designed for live video or camera stream processing. It incorporates a Vision Language Model (VLM). The system also includes a retrieval pipeline that leverages both vector-based Retrieval-Augmented Generation (RAG) and graph-based RAG for efficient and accurate data retrieval.

Complete blueprint details can be found at Build a Video Search and Summarization Agent Blueprint by NVIDIA | NVIDIA NIM

Challenges and Limitations

While Physical AI holds immense promise, it also comes with significant challenges:

• Technical Barriers: Developing systems that can seamlessly integrate sensing, decision-making, and physical interaction is complex and resource-intensive.
• Cost: The advanced materials and components required for Physical AI can be expensive, limiting accessibility.
• Ethical Considerations: As these systems become more autonomous, addressing ethical questions around accountability, privacy, and safety is critical.
• Scalability: Adapting solutions for large-scale deployment while maintaining reliability and efficiency remains a significant hurdle.

Conclusion

Physical AI is revolutionizing how intelligent systems interact with the physical world, opening up opportunities across industries. From enabling precision agriculture to streamlining warehouse logistics, its impact is already transforming everyday life and business operations. Its integration with advanced materials and generative AI principles empowers adaptive, autonomous, and efficient solutions to tackle some of the most complex challenges of our time, making futuristic possibilities a reality today.

A Note to Readers

The purpose of this article is to educate and spread awareness about this evolving topic. While every effort has been made to ensure clarity and accuracy, there is always room for better explanations or more relevant examples. Any misinterpretations are entirely unintentional, as I am also learning alongside you.

The credit for these technological advancements belongs to the brilliant inventors and developers who have made them possible. Let’s appreciate their contributions as we continue to explore these innovations together.