ASI-1 mini：fetch.ai公佈了世界上第一個Web3本地大語模型（LLM）

隨著AI X Web3市場的繼續擴展，分散的AI和自主代理技術提供商Fetch.ai最近宣布了ASI-1 Mini的推出

Announced: Fetch.ai Unveils ASI-1 Mini, Web3's Initial Large Language Model

宣布：fetch.ai揭開了Web3的最初大語言模型ASI-1 mini

In a recent development, Fetch.ai, a provider of autonomous agent technology and decentralized AI, has announced the launch of ASI-1 Mini, a Web3-native large language model (LLM) designed to prioritize autonomous agent workflows. This marks a significant step for the Artificial Superintelligence (ASI) Alliance, a group founded by Fetch.ai alongside prominent Web3 entities like SingularityNET, Ocean Protocol, and CUDOS.

在最近的開發中，自主代理技術和分散AI的提供商Fetch.ai宣布啟動ASI-1 Mini，這是一種旨在優先考慮自主座席工作流的Web3-本地大型語言模型（LLM）。這標誌著人工超級智能（ASI）聯盟的重要一步，這是一個由fetch.ai與著名的Web3實體（如SingularityNet，Ocean Protocent和Cudos）共同創立的群體。

ASI-1 Mini serves as the first model in ASI's broader "ASI:" family, with more advanced models slated for release in the near future under the Cortex group. Notably, ASI-1 Mini is designed to operate efficiently on just two GPUs, thanks to its innovative architecture. This reportedly achieves an eight-fold improvement in hardware efficiency compared to existing solutions, significantly reducing the infrastructure costs associated with deploying enterprise-grade AI systems and making them more accessible to a wider range of organizations and developers.

ASI-1 Mini是ASI更廣泛的“ ASI”家庭中的第一個模型，其中更先進的模型計劃在不久的將來在Cortex組下發布。值得注意的是，ASI-1 MINI旨在僅在兩個GPU上有效地運行，這要歸功於其創新的架構。據報導，與現有解決方案相比，這可以大大降低與部署企業級AI系統相關的基礎架構成本，並使它們更容易被更廣泛的組織和開發人員訪問。

A New Era: Web3-Native AI Architecture and Ownership

一個新時代：Web3本地AI架構和所有權

On a technical level, ASI-1 Mini's architecture not only incorporates the traditional Mixture of Experts (MoE) framework but also adds what Fetch.ai refers to as a Mixture of Models (MoM) and Mixture of Agents (MoA) approach, enabling it to dynamically select and utilize specialized components for different tasks. Commenting on the development, Humayun Sheikh, CEO of Fetch.ai and Chairman of the ASI Alliance, stated:

在技​​術層面上，ASI-1 Mini的建築不僅包含了專家（MOE）框架的傳統混合物，而且還添加了Fetch.AI所指的內容（MOM）和代理（MOA）方法的混合物，還可以啟用它動態選擇並利用專用組件來進行不同的任務。 Fetch.ai首席執行官兼ASI聯盟主席Humayun Sheikh表示：

"ASI-1 Mini is just the start, over the coming days, we will be rolling out advanced agentic tool-calling, expanded multi-modal capabilities, and deeper Web3 integrations. With these enhancements, ASI-1 Mini will drive agentic automation while ensuring that AI’s value creation remains in the hands of those who fuel its growth.”

“ ASI-1 Mini僅僅是開始，在接下來的幾天裡，我們將推出高級代理工具稱呼，擴展的多模式功能和更深的Web3集成。通過這些增強功能，ASI-1 Mini將驅動代理自動化，而同時可以驅動代理自動化。確保人工智能的價值創造仍然掌握在那些推動其增長的人的手中。”

Moreover, the model will integrate seamlessly with a multitude of Web3 wallets and operate using $FET tokens, allowing users to not only utilize the AI but also potentially benefit from its growth and development. Through the ASI: platform, community members can participate in model training and development, thus sharing in the financial rewards generated by these systems.

此外，該模型將與眾多Web3錢包無縫集成並使用$ FET令牌運營，從而使用戶不僅可以利用AI，而且還可以從其增長和開發中受益。通過ASI：平台，社區成員可以參與模型培訓和開發，從而分享這些系統產生的財務獎勵。

Performance Metrics and More

性能指標和更多

Early performance benchmarks indicate that ASI-1 Mini performs competitively with leading LLMs in specialized domains. Notably, the model boasts four dynamic reasoning modes: Multi-Step, Complete, Optimized, and Short Reasoning, which can be switched between based on task requirements. A key focus of ASI-1 Mini's development has been addressing the "black box" challenge faced by AI systems, where they often provide outputs without clear explanations for their reasoning process.

早期性能基準表明，ASI-1 MINI在專用域中與領先的LLM競爭性能。值得注意的是，該模型具有四種動態推理模式：多步，完整，優化和簡短的推理，可以根據任務要求在之間切換。 ASI-1 Mini開發的重點是應對AI系統面臨的“黑匣子”挑戰，在該挑戰中，他們經常在沒有明確解釋其推理過程的情況下提供輸出。

To tackle this issue, ASI-1 Mini employs what the company describes as continuous multi-step reasoning. Unlike conventional models that typically reason only at the beginning of a task, ASI-1 Mini maintains an ongoing reasoning process throughout its operations, enabling real-time adjustments/corrections and greater insights into how the model arrives at its conclusions.

為了解決這個問題，ASI-1 MINI採用了公司所說的連續多步推理。與通常僅在任務開始時的常規模型不同，ASI-1 Mini在整個操作過程中都保持了一個持續的推理過程，從而實現了實時調整/更正，並對模型如何得出其結論有了更多的了解。

This transparency initiative is further supported by the system's three-layered architecture. The foundational layer, powered by ASI-1 Mini, serves as the central intelligence hub, while the specialization layer houses domain-specific models, and the action layer manages execution through specialized agents.

該系統的三層體系結構進一步支持了該透明度計劃。由ASI-1 MINI提供支持的基礎層是中央智能中心，而專業層則容納了域特異性模型，而動作層通過專業的代理來管理執行。

As the technology continues to advance and additional features are implemented, the broader impact of Fetch.ai's Web3-native approach to AI development will become clearer in the near term. For now, ASI-1 Mini marks a significant step in this direction, combining advanced AI capabilities with decentralized ownership and development models at scale.

隨著技術的不斷發展並實施了其他功能，Fetch.AI的Web3-native AI開發方法的廣泛影響將在短期內變得更加清晰。目前，ASI-1迷你標誌著在這個方向上邁出的重要一步，將先進的AI功能與分散的所有權和開發模型相結合。