论文标题
使用IPFS分散AI的库，集成和集线器
Libraries, Integrations and Hubs for Decentralized AI using IPFS
论文作者
论文摘要
AI需要大量的存储和计算。结果，AI开发人员是AWS，GCP和Azure等集中式云服务的常规用户，诸如Jupyter和COLAB笔记本电脑等计算环境以及HuggingFace和Activeloop之类的AI轮毂。那里的服务与源于建立的基础基础架构和治理系统所造成的某些好处和限制有关。这些局限性包括高成本，缺乏货币化和奖励，缺乏控制和可重复性的难度。同时，很少有图书馆允许数据科学家用数据科学家所习惯的语言与分散的存储进行交互，而很少有可以发现并与AI资产进行交互的枢纽。在本报告中，我们探讨了分散技术的潜力，例如Web3钱包，点对点市场，分散存储（IPFS和Filecoin）和Compute以及Daos，以解决以上一些限制。我们展示了我们为解决这些问题而建立的一些库和集成，以及分散的AI Hub应用程序的概念证明，这些库将所有人都使用IPF作为核心基础设施组件。
AI requires heavy amounts of storage and compute. As a result, AI developers are regular users of centralised cloud services such as AWS, GCP and Azure, compute environments such as Jupyter and Colab notebooks, and AI Hubs such as HuggingFace and ActiveLoop. There services are associated with certain benefits and limitations that stem from the underlying infrastructure and governance systems with which they are built. These limitations include high costs, lack of monetization and reward, lack of control and difficulty of reproducibility. At the same time, there are few libraries that allow data scientists to interact with decentralised storage in the language that data scientists are used to, and few hubs where they can discover and interact with AI assets. In this report, we explore the potential of decentralized technologies - such as Web3 wallets, peer-to-peer marketplaces, decentralized storage (IPFS and Filecoin) and compute, and DAOs - to address some of the above limitations. We showcase some of the libraries and integrations that we have built to tackle these issues, as well as a proof of concept of a decentralized AI Hub app, that all use IPFS as a core infrastructural component.
