AI

The main projects at the infrastructure layer of the AI+Web3 industry basically take the decentralized computing network as the main narrative, low cost as the main advantage, token incentives as the main way to expand the network, and serving AI+Web3 customers as the main goal.

Bittensor is an ecosystem that combines AI and encryption technology. Its token TAO has risen more than other projects, but it is considered too expensive. The quality of Bittensor ecological projects that have emerged recently is of varying quality, making it difficult for investors to participate. Bittensor proposed a product idea that combines AI and Web3, but the encryption field is not optimistic about this. The future of Bittensor remains to be seen, but its ability to attract liquidity and yield results remains a question.

Masa Network presents an innovative AI pricing model that enables users to safeguard personal privacy while sharing data and earning rewards within the Masa ecosystem. Through the Masa SDK, partners can enhance AI applications for tasks like cryptocurrency market forecasts, NFT management, and political prognostication. Masa advocates for users' rights to benefit economically from AI advancements. By engaging with the Masa ecosystem, individuals can safeguard their privacy, contribute to the data economy, and access real-time datasets for informed market insights, cryptocurrency trend analysis, investment exploration, and precise price predictions. Masa's mainnet launch and token release are scheduled for April 11. Users can both protect their digital footprint and support the data economy by actively participating in the Masa ecosystem.

AI x Crypto encompasses multiple subfields, including AI Agents, decentralized computing, data, oracles, ZKML, FHEML, coprocessors, Memes, generative art platforms, and gaming applications.

This article provides a detailed introduction to the cross-chain project Zetachain and how it addresses the issue of full-chain interoperability.

The core of the AI technology revolution lies in ample computing power, algorithm models, and a vast amount of training data. Currently, high-performance GPU computing power is in short supply and expensive, algorithms tend to be homogenized, and there are issues regarding data compliance and privacy protection for model training data. The decentralized and distributed storage characteristics of blockchain technology can facilitate its integration with AI.

Delve into the background of the OORT track, project operation logic, and more, to glimpse the project's prospects and the opportunities and challenges for the AI+DePIN track in 2024.

Vitalik has published "The promise and challenges of crypto + AI applications," discussing the ways blockchain and artificial intelligence can be combined and the potential challenges. The article presents four integration methods and introduces representative projects for each direction. There are differences in the core characteristics of AI and blockchain, so it's necessary to balance aspects such as data ownership, transparency, monetization capabilities, and energy costs when combining them. Currently, many AI applications are gaming-related, involving interaction with AI and training characters to better fit individual needs. At the same time, there are projects exploring the use of blockchain features to create better artificial intelligence. Decentralized computing power is also a popular direction but still faces challenges. Overall, the AI track needs to find projects with competitiveness and long-term value.