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Messari: A look at the Decentralized Physical Artificial Intelligence DePAI map | Bee Network

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Messari: A look at the Decentralized Physical Artificial Intelligence DePAI map | Bee Network Login Berita Trending Meme Launchpad Agen AI DeSci Penjelajah Rantai Atas Untuk Newbee 100x Koin Permainan Lebah Situs Web Penting APLIKASI yang Harus Dimiliki Selebriti Kripto DePIN Pemula Penting Detektor Perangkap Alat Dasar Situs Web Tingkat Lanjut Pertukaran Alat NFT Hai, Keluar Alam Semesta Web3 permainan DApp Sarang lebah Platform Berkembang IKLAN Mencari Bahasa inggris Isi Ulang Koin Gabung Unduh Universitas Web3 permainan DApp Sarang lebah IKLAN rumah-Analisis-Teks utama Messari: Melihat peta Desentralisasi Kecerdasan Buatan Fisik DePAIAnalisis1 tahun yang lalu (2025)更新Wyatt 35,3681 16 Original article by Dylan Bane, Messari Analyst

Original translation: Yuliya, PANews

As artificial intelligence develops rapidly, decentralized physical artificial intelligence (DePAI) is providing a new solution for the control of robots and physical artificial intelligence infrastructure. From real-world data collection to intelligent robot operations based on decentralized physical infrastructure (DePIN), the development of DePAI is progressing steadily. As NVIDIA CEO Huang Renxun predicted: The ChatGPT moment in the field of general robots is coming.

Looking back at the history of technological development, the digital age started with hardware and then expanded to the intangible field of software. The era of artificial intelligence started with software and is now advancing into the ultimate frontier of the physical world.

In a world where robots, smart cars, drones, and robots run by autonomous physical AI agents are gradually replacing traditional labor, the ownership of these smart devices has become a social issue that cannot be ignored. DePAI provides a rare opportunity to build a physical AI system based on Web3 when centralized players have not yet completely dominated the market.

Data collection Currently, the infrastructure of DePAI is being improved at an accelerated pace, with the data collection layer being the most active, which not only provides the real-world data required for training the physical AI agents on the robot, but also transmits the data streams required for environment navigation and task execution in real time.

However, obtaining high-quality real-world data remains a major bottleneck restricting the development of physical AI. Although Nvidias Omniverse and Cosmos provide innovative solutions through simulated environments, synthetic data is only part of the entire ecosystem, and remote operation and real-world video data are equally indispensable.

Remote Operation In the field of teleoperation, Frodobots is deploying economical delivery robots around the world through DePIN. These robots can not only capture human decision-making behaviors in real environments and create high-value data sets during operation, but also effectively solve the problem of insufficient capital investment.

Through a token-driven virtuous cycle mechanism, DePIN is accelerating the deployment of data collection equipment and robots. For robotics companies that want to increase sales while reducing capital expenditures and operating costs, DePIN has significant advantages over traditional models.

Video Data Application In terms of video data applications, DePAI can make full use of real-world video data to train physical artificial intelligence systems and build spatial cognition of the real world. Among them, Hivemapper and NATIX Network are expected to become important data sources with their unique video databases.

As Pantera Capital junior partner Mason Nystrom pointed out: While individual data is difficult to realize commercial value, data aggregation can do a lot. The Quicksilver platform developed by IoTeX is able to aggregate data across DePINs while ensuring data verification and privacy protection.

Spatial Intelligence and Computing In the field of spatial intelligence and computing protocols, the industry is working to achieve decentralized management of spatial coordination and real-world 3D virtual twins through DePIN and DePAI. For example, Auki Networks Posemesh technology achieves real-time spatial perception while ensuring privacy and decentralization.

The application of physical AI agents has already achieved initial results, such as SAM, which is using Frodobots global robot network to infer geolocation. In the future, with the help of frameworks such as Quicksilver, AI agents will be able to better access the real-time data provided by DePIN.

For investors interested in entering the field of physical artificial intelligence, investing in DAO may be an ideal entry point. Taking XMAQUINA as an example, it provides members with a diversified portfolio of physical artificial intelligence assets, covering machine physical assets, DePIN protocols, robotics companies and intellectual property rights, and is equipped with a professional internal RD team to provide support.

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This article is sourced from the internet: Messari: A look at the Decentralized Physical Artificial Intelligence DePAI map

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