Large AI models (e.g., Dall-E, GPT4) have electrified the scientific, technological and societal landscape through their superhuman capabilities. These services are offered largely in a traditional web2.0 format (e.g., OpenAI's GPT4 service). As more large AI models proliferate (personalizing and specializing to a variety of domains), there is a tremendous need to have a neutral trust-free platform that allows the hosting of AI models, clients receiving AI services efficiently, yet in a trust-free, incentive compatible, Byzantine behavior resistant manner. In this paper we propose SAKSHI, a trust-free decentralized platform specifically suited for AI services. The key design principles of SAKSHI are the separation of the data path (where AI query and service is managed) and the control path (where routers and compute and storage hosts are managed) from the transaction path (where the metering and billing of services are managed over a blockchain). This separation is enabled by a "proof of inference" layer which provides cryptographic resistance against a variety of misbehaviors, including poor AI service, nonpayment for service, copying of AI models. This is joint work between multiple universities (Princeton University, University of Illinois at Urbana-Champaign, Tsinghua University, HKUST) and two startup companies (Witness Chain and Eigen Layer).

翻译：大型AI模型（如Dall-E、GPT4）凭借其超人类能力，彻底改变了科学、技术和社会格局。这些服务主要以传统Web 2.0形式提供（例如OpenAI的GPT4服务）。随着更多大型AI模型激增（针对多种领域进行个性化与专业化定制），亟需一个中立、无需信任的平台，以高效托管AI模型、为客户端提供AI服务，同时具备无需信任、激励相容、抗拜占庭行为的特性。本文提出SAKSHI——一个专为AI服务设计的无需信任的去中心化平台。SAKSHI的核心设计原则是将数据路径（管理AI查询与服务）、控制路径（管理路由器、计算与存储主机）与交易路径（通过区块链管理服务计量与计费）相分离。这一分离通过“推理证明”层实现，该层提供密码学层面的抗攻击能力，抵御包括低质量AI服务、服务欠费、AI模型复制等在内的多种不当行为。该研究为多所大学（普林斯顿大学、伊利诺伊大学厄巴纳-香槟分校、清华大学、香港科技大学）与两家初创公司（Witness Chain与Eigen Layer）的联合工作。