分散的AI代理（dai-agent）：Web3中值Internet的關鍵組成部分

萊特（Relly），分散的AI代理（Dai-Agent）的領域受到了極大的關注，許多文章介紹了相關項目的特徵，他們解決的問題以及其未來的潛力。儘管這些文章有助於投資者在某種程度上了解這些項目，但大多數缺乏深入的分析，並且未能探索AI的基本特徵和Web3的當前狀態。因此，無論是優化Web3還是用作關鍵組件，很難闡明分散式AI在Web3中的實踐中的作用。如果不闡明分散的AI和Web3的價值之間的內在邏輯，就不可能深入了解分散AI的作用或掌握其核心組件如何解決Web3中存在的問題。這不僅使我們難以準確選擇高電勢的投資方向，而且即使我們選擇了正確的軌道和項目，我們也可能因市場情緒波動而難以持久。因此，我計劃對當前Web3的基本狀態以及AI的基本特徵進行深入分析，探索兩者的集成如何實現價值Internet的實施，以及ARWEAVE和AO如何幫助這一過程通過AI。由於內容的豐富性，我將詳細說明兩篇文章：

The emergence of decentralized AI agents (DAI-Agent) has sparked widespread interest within the crypto community, with numerous articles highlighting the unique characteristics of related projects, the problems they aim to solve, and their potential for future growth. While these articles offer valuable insights, many lack in-depth analysis and fail to fully explore the fundamental characteristics of AI and the current state of Web3. As a result, it becomes difficult to clarify the precise role of decentralized AI in the practice of the value internet in Web3, whether it serves to optimize Web3 or functions as a key component. Without establishing the intrinsic logic between decentralized AI and the value internet economy of Web3, it is impossible to deeply understand the role of decentralized AI or grasp how its core components address the issues present in Web3. For example, what problems do the decentralized model and DAI-Agent each solve, and what is the intrinsic logic between them and Web3? Without understanding these intrinsic logics, it is challenging to assess the potential value of this field. This not only makes it difficult for us to accurately choose high-potential investment directions, but even if we select the right track and project, we may struggle to persist due to market sentiment fluctuations. Therefore, I plan to conduct an in-depth analysis of the current basic state of Web3 and the fundamental characteristics of AI, exploring how the integration of the two can realize the implementation of the value internet, and how Arweave and AO can assist this process through AI. Due to the richness of the content, I will elaborate in two articles:

分散的AI代理商（Dai-Agent）的出現引起了加密社區的廣泛興趣，許多文章強調了相關項目的獨特特徵，他們旨在解決的問題以及其未來增長的潛力。儘管這些文章提供了有價值的見解，但許多人缺乏深入的分析，並且未能充分探索AI的基本特徵和Web3的當前狀態。結果，很難闡明分散AI在Web3中的價值實踐中的確切作用，無論它是為了優化Web3還是作為關鍵組件的功能。如果不建立分散的AI和Web3的價值之間的內在邏輯，就不可能深入了解分散的AI的作用或掌握其核心組件如何解決Web3中存在的問題。例如，每個求解的分散模型和dai gent求解什麼問題，它們與Web3之間的內在邏輯是什麼？在不了解這些內在邏輯的情況下，評估該領域的潛在價值是一項挑戰。這不僅使我們難以準確選擇高電勢的投資方向，而且即使我們選擇了正確的軌道和項目，我們也可能因市場情緒波動而難以持久。因此，我計劃對當前Web3的基本狀態以及AI的基本特徵進行深入分析，探索兩者的集成如何實現價值Internet的實施，以及ARWEAVE和AO如何幫助這一過程通過AI。由於內容的豐富性，我將詳細說明兩篇文章：

Currently, many public chain projects focus primarily on optimizing and expanding underlying infrastructure, such as ETH and various L2s, Solana, and other blockchains. However, I believe that if we only pursue the expansion of blockchain without integrating AI, it will be difficult to advance the implementation of the value internet in Web3. Currently, in addition to limited scalability, Web3 also faces data fragmentation issues, where users' personal data is scattered across different chains and DApps, leading to management difficulties, high interaction costs, and complex operations, severely limiting users' active contribution of data. Furthermore, the decentralized nature leads to low management and collaboration efficiency. These issues greatly restrict the development of Web3. AI, with its ability to learn, infer, and make decisions autonomously, can serve as an intelligent assistant for users, significantly enhancing efficiency. The integration of the two will greatly improve user experience, lower entry barriers, and promote the development of Web3.

當前，許多公共連鎖項目主要著重於優化和擴展基礎設施，例如ETH和各種L2S，Solana和其他區塊鏈。但是，我相信，如果我們僅在不集成AI的情況下追求擴展區塊鏈的擴展，那麼很難提高Web3中價值Internet的實施。當前，除了有限的可伸縮性外，Web3還面臨數據碎片問題，在該問題中，用戶的個人數據散佈在不同的鏈條和DAPP上，從而導致管理困難，高相互作用成本和復雜的操作，從而嚴重限制了用戶對數據的積極貢獻。此外，分散的自然可以提高管理和協作效率低。這些問題極大地限制了Web3的開發。 AI具有自主學習，推斷和做出決定的能力，可以作為用戶的智能助手，從而大大提高效率。兩者的集成將大大改善用戶體驗，降低進入障礙並促進Web3的開發。

Crucial to Web3 is users' control over their own data, a feature that DAI-Agent can help to achieve by centrally managing and aggregating data for users. This effectively addresses the pain point of data being scattered across various platforms, while also acting as an intelligent assistant to reduce operational difficulty and enhance interaction efficiency with Web3. For example, DAI-Agent can assist users in managing their DID lifecycle, including creating, updating, and revoking DIDs, thereby simplifying data management and usage experience. To lay the groundwork for subsequent discussions, it is necessary to explore the relationship between AI-Agent and DID in detail. In the Web3.0 environment, DID and DAI-Agent are highly complementary and compatible:

對Web3至關重要的是用戶對自己數據的控制，這是Dai-Agent可以通過中央管理和匯總用戶數據來實現的功能。這有效地解決了數據散佈在各個平台上的痛點，同時還充當智能助手，以減少操作困難並提高與Web3的相互作用效率。例如，Dai-Agent可以幫助用戶管理其DID生命週期，包括創建，更新和撤銷DIDS，從而簡化數據管理和使用體驗。為了為隨後的討論奠定基礎，有必要探索Ai-agent之間的關係並詳細介紹。在Web3.0環境中，DID和Dai-Agent具有高度互補和兼容：

AI-Agent can integrate data across platforms (such as social, medical, and professional data), effectively breaking down information silos; its intelligent algorithms can filter, clean, and format data based on the needs of DID (such as assessing the credibility of various data sources, removing duplicate or low-value data, and organizing data according to DID data model specifications), ensuring the creation of high-quality DIDs. At the same time, using differential privacy, homomorphic encryption, and the latest multi-party secure computation (MPC) technologies, data analysis can be completed without disclosing the original data (for example, when aggregating sensitive medical data, it can meet health information needs while ensuring personal privacy). Additionally, as cross-chain interoperability protocols (such as Polkadot, Cosmos, etc.) continue to mature, DAI-Agent is expected to achieve seamless connections between more data sources, further enhancing the efficiency and accuracy of data integration. The decentralized architecture not only avoids the risks of single points of failure and data being controlled by a single entity but also enables automated data aggregation and real-time updates through smart contracts, providing strong support for building a trustworthy and dynamic digital identity system.

Ai-agent可以在平台（例如社交，醫學和專業數據）之間集成數據，從而有效地分解信息筒倉；它的智能算法可以根據DID的需求過濾，清潔和格式數據（例如評估各種數據源的信譽，刪除重複或低價值數據以及根據DID數據模型規範組織數據），以確保創建高質量的塗料。同時，使用差異隱私，同型加密和最新的多方安全計算（MPC）技術，可以完成數據分析而無需披露原始數據（例如，當匯總敏感醫療數據時，它可以符合健康信息在確保個人隱私的同時） 。此外，隨著跨鏈互操作性協議（例如Polkadot，Cosmos等）繼續成熟，預計Dai-Agent將在更多數據源之間實現無縫連接，從而進一步提高數據集成的效率和準確性。分散的體系結構不僅避免了單個實體控制的單點故障和數據的風險，而且還可以通過智能合約來實現自動數據聚合和實時更新，從而為建立可信賴和動態的數字身份系統提供了強有力的支持。

In a decentralized environment, the digital identity system provides the necessary identity authentication and authorization mechanisms for DAI-Agent, allowing the AI-Agent to prove its legitimate identity and authority when securely interacting with other agents. This process relies not only on technical means but can also be governed and regulated by community participation through decentralized autonomous organization (DAO) mechanisms, further enhancing the system's transparency and security.

在分散的環境中，數字身份系統為Dai-Agent提供了必要的身份身份驗證和授權機制，從而使Ai-Angent在與其他代理商安全互動時可以證明其合法的身份和權威。該過程不僅取決於技術手段，而且還可以通過分散的自治組織（DAO）機制來控制和監管，從而進一步提高了系統的透明度和安全性。

With the help of the DID system, the identity and behavior of DAI-Agent are more transparent and verifiable, thereby establishing trust and promoting collaboration among other agents; at the same time, AI-Agent effectively alleviates the low efficiency issues caused by decentralization by reducing the interaction costs between users

借助DID系統，Dai-Agent的身份和行為更加透明和可驗證，從而建立了信任和促進其他代理之間的協作；同時，AI-Anent通過降低用戶之間的相互作用成本有效地減輕了通過分散率引起的低效率問題