# Introduction **A. Project Overview** Camelot is an innovative project aiming to democratize and decentralize the process of AI training using blockchain technology. The project endeavors to build a scalable Layer3 DePIN platform on Merlin chain, allowing individuals and organizations worldwide to contribute computational resources to a shared pool. This facilitates the training of powerful AI models and distributes training results to mobile devices. Additionally, Camelot introduces a novel concept of AI authentication, using unique blockchain identifiers to certify trained AI models, ensuring transparency and traceability. This advanced approach addresses challenges such as data privacy, trust, and ownership of AI models in today's centralized AI environment. **B. Problem statement** Training these generative AI models requires vast resources of data and high-performance GPUs, which can be costly for individuals and small businesses. Also, geopolitical tensions and trade policies often restrict the availability of these resources in certain regions, resulting in unequal access to AI computational power. Furthermore, the current AI industry is heavily centralized, with major tech giants controlling a large share of computational power and proprietary datasets. This power hierarchy creates substantial entry barriers for newcomers and forms data silos, thereby limiting the democratization of AI. Additionally, traditional cloud-based AI training platforms pose significant challenges in terms of data privacy and control. Just like aircraft black boxes, users have minimal visibility and control over their data and training processes, leaving users vulnerable to single points of failure. These challenges underscore the urgent need for a decentralized AI training platform, ensuring data privacy, transparency, and democratized access to AI resources. A platform leveraging blockchain technology can effectively address these challenges, fostering the creation of a more inclusive and equitable AI ecosystem. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://camelot-protocol.gitbook.io/camelot/introduction.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.