We've seen science fiction scenes like these in movies: R2-D2 in *Star Wars* can converse fluently with humans, use tools autonomously, and is proficient in hacking; the Transformers in *Transformers*, whether in humanoid robot form or transformed into self-driving cars, drones, or robotic dogs, can think independently and act flexibly. These scenes of robots interacting with humans, robots with each other, and robots with the physical world—scenes that have once amazed and filled us with longing—are actually closely related to a hot concept this year—"physical AI."
In January of this year, Nvidia CEO Jensen Huang first publicly proposed the concept of applying AI to real-world physical AI such as robots at CES, and regarded it as the last wave of AI development; Tesla, in its "Secret Plan Chapter 4" release, shifted the company's development focus to the fields of AI and robotics, aiming to integrate AI into the physical world on a large scale.
So what exactly is this physical AI that the giants are betting on? And what is DePAI, which combines blockchain with it? How will DePAI solve the deep-seated problems facing physical AI?
This research report begins with the concept of physical AI and its underlying challenges, then elaborates on the technological foundations of DePAI, deeply analyzes its ecosystem, and outlines its application scenarios. It also provides case studies of well-known DePAI projects such as Auki, PrismaX, CodecFlow, Peaq, and XMAQUINA. Finally, it identifies the current challenges and limitations of the DePAI machine economy and offers a future outlook. By reading this report, readers will gain a comprehensive understanding of the current state and future potential of the DePAI sector.
Author: Felix , Researcher at Web3Caff Research
Cover: Photo by Unsplash+, Typography by Web3Caff Research
Word count: 29,000+ words in total
Table of contents
- DePAI Introduction
- Physics AI
- DePAI
- The relationship between DePAI and DePIN
- Technical foundation
- SSI
- zkTLS protocol
- AI
- Payment Agreement
- Blockchain technology
- The 7 Levels of DePAI
- AI Agent
- robot
- Data Network
- Spatial Intelligence
- Infrastructure Network
- machine economy
- DePAI DAO s
- DePAI Application Scenarios
- Distributed AI computing
- Autonomous robot labor services
- Robot self-organizing network data sharing service
- Robot Reputation Market
- Distributed energy services
- Digital Twin World
- Representative projects
- CodecFlow
- OpenMind
- BitRobot Network
- PrismaX
- Robo Stack
- Pos eidon
- Peaq
- XMAQUINA
- summary
- Challenges facing DePAI
- Data limitations
- Scalability
- Coordination challenges
- Interoperability
- Regulatory challenges
- Ethical challenges
- Outlook
- Conclusion
- Key Points Structure Diagram
- References
DePAI Introduction
Physics AI
(1) The rise of the concept
At the CES conference in January this year, Nvidia CEO Jensen Huang first proposed the concept of "Physical AI" on the global stage. He positioned "Physical AI" as the fourth and last wave of AI after "Perception AI" (which means AI can understand text, images and sounds), "Generative AI" (which means AI can generate images, text and sounds) and "Agentic AI" (which means AI can perceive, reason, plan and act). He emphasized that not only should AI understand physical laws such as gravity, friction and inertia, but it should also understand geometric and spatial relationships, and even causal relationships. [1]
Along with introducing the concept, Jensen Huang also launched three flagship products specifically for physical AI at the conference: NVIDIA Cosmos, DRIVE Thor, and the new version of NVIDIA Isaac Groot.
NVIDIA Cosmos
NVIDIA Cosmos is the world's first World Foundation Model focused on physics-based AI. This model was trained on 20 million hours of video data, focusing on physical dynamics such as human walking, hand manipulation of objects, fast-moving objects, and camera movement. Its goal is to teach AI to understand the real physical world, rather than to generate creative content.
The role of NVIDIA Cosmos in physics AI includes:
- Synthetic data generation: using this data to train the model;
- Robot training: Refine the Cosmos model and use it as the initial model for robot learning;
- Generate a variety of possible future physical scenarios: This is similar to how "Doctor Strange" simulates future scenarios (such as twisted cities, folded streets, cars going in the wrong direction, etc.).
- Video annotation: The model can accurately add labels to videos, and these labels can be used to train a multimodal large language model .
DRIVE Thor
DRIVE Thor is a next-generation processor. Thor boasts exceptional performance, delivering up to 2070 trillion operations per second (TOPS) with configurable power ranging from 40W to 130W (data from NVIDIA's official website , hereinafter the same). Compared to the current industry-standard Orin processor (275 TOPS, with power configurations between 15W and 60W), it offers a 20x performance improvement. Thor can process massive amounts of sensor data, including data from 20 high-resolution cameras, radar, and LiDAR sensors, converting it into tokens needed for AI inference, ultimately generating vehicle path predictions.
Thor is not only for automobiles; it is also a general-purpose robotic computer that can be used in various applications such as automated mobile robots (AMRs) and humanoid robots.
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