This article discusses the need for a decentralized approach to AI, especially in contexts where data and knowledge are distributed. It highlights five key technical challenges: privacy, verifiability, incentives, orchestration, and crowdUX. The author, Ramesh Raskar from MIT Media Lab, advocates for integrating privacy tech, distributed verifiable AI, data markets, orchestration, and crowd experience into the Web3 framework. Why it matters: Decentralized AI could unlock new possibilities for collaboration and problem-solving in the region, particularly in sectors like healthcare and logistics where data is often siloed.
Xi Chen from NYU Stern gave a talk at MBZUAI on digital privacy in personalized pricing using differential privacy. The talk also covered research in Web3 and decentralized finance, including delta hedging liquidity positions on Uniswap V3. Chen highlighted open problems in decentralized finance during the presentation. Why it matters: The talk suggests MBZUAI's interest in exploring the intersection of AI, privacy, and blockchain technologies, reflecting growing trends in data protection and decentralized systems.
This article previews a talk by Dr. Wei Cai of CUHK-Shenzhen on the history, development, and future trends of the Web3 metaverse. The talk will cover industrial Web3 metaverse cases, recent research outcomes, and the metaverse research spectrum. Dr. Cai's research interests include blockchain, Web 3.0, digital games, and computational art. Why it matters: As metaverse technologies continue to evolve, understanding the Web3 perspective and research directions is important for regional AI and technology development.