NVIDIA's Omniverse: A Platform for Physical AI and Digital Twins

Omniverse: A Platform for Physical AI and Digital Twins

NVIDIA Omniverse is a suite of software libraries and platforms designed for building and operating digital twins and simulating physical AI, particularly in the field of robotics. It is important to know that simulation in the development of physical AI, providing a safe and efficient space for robots to learn and refine their skills before deployment in the real world.

Here are the key features and capabilities of Omniverse::

• Physics-Based Simulation: Omniverse serves as a "physics-based operating system for physical AI simulation". This means that the virtual environments created within Omniverse accurately reflect the laws of physics, enabling realistic simulations of robot behavior and interactions with their surroundings.
• Robot Training and Development: Omniverse plays a crucial role in the second stage of physical AI development—simulation. Robots can be trained and fine-tuned within Omniverse using reinforcement learning and physics feedback, allowing them to learn and adapt in a virtual environment before being deployed to the physical world.
• Digital Twin Creation: Omniverse isn't limited to robotics. It also enables the creation of digital twins for various applications, including factories and industrial environments.
• Software-in-the-Loop Testing: Through digital twin simulations, Omniverse facilitates "software-in-the-loop testing," allowing developers to test and validate changes in a virtual environment before implementing them in the real world. This approach significantly reduces risk and cost by identifying potential issues early in the development process.

Two Major Types of AI: Digital and Physical

According to Jensen Huang, there are two major types of AI that will be extremely popular: digital AI and physical AI.

Digital AI: AI Agents

The first type of AI is digital AI, which primarily manifests as AI agents. These AI agents are essentially digital employees that can understand, plan, and take action. They can be used for a wide variety of tasks, such as executing marketing campaigns, supporting customers, creating manufacturing plans, writing software, and serving as research or teaching assistants.

• Huang suggests that rather than replacing 50% of human jobs, AI will instead perform 50% of the work for 100% of people, significantly boosting productivity.

To facilitate the creation of AI agents, NVIDIA has developed several tools:

• NeMo: This platform assists in the entire lifecycle of an AI agent, from data curation and training to deployment and operation.
• NIMs: These are microservices containing pre-trained AI models that can communicate and connect with other AI, effectively building AI agents.
• Blueprints: These are reference workflows combining NVIDIA libraries, SDKs, and NIMs, enabling rapid development and deployment of AI applications.

Physical AI: Embodied AI and Robotics

The second major type of AI is physical AI, also known as embodied AI or robotics. This involves incorporating AI into physical systems, primarily robots.

• Huang argues that while the robotics industry has traditionally been limited by inflexible and task-specific robots, the advent of general AI technology allows for more adaptable and learning-capable robots.

The development of physical AI requires the creation of three types of computers:

1. Training Computers: These computers are responsible for training AI models, much like in the development of digital AI.
2. Simulation Computers: Utilizing platforms like NVIDIA's Omniverse, these computers create virtual environments where AI can practice, learn, and receive synthetic data.
3. Robotics Processors: Processors like the NVIDIA Jetson Thor power the physical robots and execute the trained AI models in real-world scenarios.

Huang believes the time is ripe for humanoid robotics due to the advancements in generative AI, Omniverse, and the development of specialized robotics processors.

• Humanoid robots, like self-driving cars, have the advantage of being deployable in environments designed for humans.

To further accelerate humanoid robotics, NVIDIA has introduced Isaac Lab, a reinforcement learning simulation platform designed to teach humanoid robots their functions. Isaac Lab includes several workflows:

• Groot-Mimic: This framework allows robots to learn by mimicking human demonstrations and generalizes those skills through domain randomization.
• Groot-Gen: Leveraging generative AI within Omniverse, Groot-Gen creates diverse training scenarios, enabling robots to learn and adapt to various situations.
• Groot-Control: This model distillation framework condenses learned skills into a unified model for the robot's kinematic capabilities.

Huang envisions a future where physical AI revolutionizes industries through:

• Self-driving cars that navigate the real world safely
• Manipulators that perform complex industrial tasks
• Humanoid robots working alongside humans
• Smart factories capable of monitoring and adjusting operations

These advancements in both digital and physical AI signify a paradigm shift in the computing industry, leading to a new industrial revolution. This revolution is driven by the creation of AI factories that produce AI tokens, the fundamental units of intelligence powering AI applications across various sectors.

• Every industry, company, and country will need to produce its own AI to thrive in this new era.

Huang emphasizes the importance of countries processing their own data to develop AI specific to their culture and knowledge, likening citizen data to national resources.

• He advocates for the creation of national AI grids, like the one SoftBank is building in Japan, to provide the necessary infrastructure for AI development and distribution.

These two major types of AI, digital and physical, are poised to reshape the world, driving unprecedented productivity, innovation, and progress across every sector of society.

Here are the three main components of NVIDIA's AI agent lifecycle platform:

• NeMo: Described as the "AI agent lifecycle platform," NeMo provides libraries for each stage of an AI agent's development. These stages include data curation, training, fine-tuning, synthetic data generation, evaluation, and guard railing. NVIDIA is collaborating with various partners, such as AI startups, service providers like Accenture and Deloitte, and ISVs like ServiceNow, to integrate NeMo into workflows and frameworks worldwide.
• NIMs: Short for "NVIDIA AI microservices," NIMs are pre-trained AI models packaged as microservices. Think of them as "AI packaged" for deployment. In the past, software was distributed in physical boxes with CD-ROMs, but today, AI is packaged in these microservices. The advantage of NIMs is their ability to communicate and connect with other AIs, enabling the creation of complex AI agents by combining different AI models.
• Blueprints: NVIDIA AI Blueprints offer reference workflows to accelerate the building and deployment of AI applications. These blueprints combine NVIDIA's acceleration libraries, SDKs, and NIMs. One example is the "Digital Human Blueprint," which provides smooth, human-like interactions for AI agents.

Here are the key technological advancements driving the AI revolution and how they are changing the computing landscape, according to the provided source:

• Accelerated Computing: Accelerated computing augments the CPU by offloading computationally intensive workloads onto the GPU, which excels at parallel processing. This new computing model necessitates new algorithms and domain-specific libraries like OpenGL to bridge the gap between sequential and parallel processing.
• Software 2.0: Traditional software development involved programmers writing code to define algorithms. However, with the advent of powerful computers and vast datasets, Software 2.0 leverages machine learning, where computers learn from data to predict functions. This shift has led to the rise of neural networks running on GPUs, forming a new operating system based on Large Language Models (LLMs).
• Scaling Laws: The effectiveness of machine learning models improves as their size and training data increase, following the scaling law. The industry's trend of scaling up models annually demands exponential growth in computing resources. Two key scaling laws are: training scaling law, involving pre-training and post-training with reinforcement learning, and inference scaling law, focusing on enhancing inference capabilities through multi-step thinking and reflection.
• Generative AI: This technology allows for the creation of intelligent information across different modalities, including text, speech, images, and video. By learning patterns and relationships from vast datasets, AI can understand and translate between these modalities, leading to a surge in groundbreaking applications. For example, text can be translated into images (Midjourney), video (Runway ML), chemicals (drug discovery), or articulation motions (robotics).
• AI Agents: AI agents are digital AI workers trained to understand, plan, and take action. They can execute marketing campaigns, provide customer support, optimize supply chains, and assist in software development. NVIDIA has created various tools to facilitate AI agent development:
• Physical AI (Robotics): By integrating AI into physical systems, robotics is poised to revolutionize industries. This involves building three computers: one for training the AI, one for simulating the AI (Omniverse), and one for running the AI in the physical robot (Jetson Thor).
• AI Factories and AI Grids: The shift toward AI-driven solutions demands a new kind of infrastructure— AI factories. These factories produce and distribute AI tokens, units of intelligence that power AI applications across various industries.

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The AI revolution is transforming the computing landscape by moving from CPU-centric software development to a machine learning-based approach running on GPUs. This shift, along with the rise of generative AI and the development of AI agents and physical AI, requires the establishment of new infrastructures like AI factories and AI grids. These advancements are enabling the creation and distribution of AI tokens, the fundamental units of intelligence that will power the AI-driven future.

These developments will have profound implications for businesses, nations, and individuals alike. Just as the invention of roads and factories spurred the Industrial Revolution, and energy and communications fueled the IT revolution, the emergence of AI infrastructure is poised to usher in a new era of unprecedented innovation and progress. Countries and companies must embrace these advancements to remain competitive and thrive in the future.

Omniverse acts as a bridge between the digital and physical worlds, accelerating the development and deployment of physical AI systems and enabling the creation of sophisticated, interactive digital twins. By providing a platform for simulation, training, and validation, Omniverse empowers developers to create more robust, adaptable, and capable robots and AI-powered systems.

Note: This article is a summary from NVIDIA's video.

This article is an abstraction of Jensen Huang Special Address from NVIDIA AI Summit Japan

Disclaimer: This article provides a summary of key insights from the conversation, focusing on Nvidia's vision, key technologies, and impact across various sectors. It is not intended to be a verbatim transcript or a comprehensive analysis of the full conversation.

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References:

1) Jensen Huang Special Address from NVIDIA AI Summit Japan, uploaded on 15 Nov 2024, https://www.youtube.com/watch?v=x8O6ChAWBxs

About Jean

Jean Ng is the creative director of JHN studio and the creator of the AI influencer, DouDou. She is the Top 2% of quality contributors to Artificial Intelligence on LinkedIn. Jean has a background in Web 3.0 and blockchain technology, and is passionate about using these AI tools to create innovative and sustainable products and experiences. With big ambitions and a keen eye for the future, she's inspired to be a futurist in the AI and Web 3.0 industry.

AI Influencer, DouDou

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