揭秘 Bittensor：去中心化 AI 網路怎麼樣？

Bittensor 是一個去中心化網絡，旨在形成一個智慧市場，可以以去中心化的方式開發高品質的人工智慧模型。

Title: Demystifying Bittensor: How's the Decentralized AI Network?

標題：揭秘 Bittensor：去中心化人工智慧網路怎麼樣？

Authors: Ming Ruan, Wenshuang Guo, Animoca Brands Research

作者：阮明、郭文雙、Animoca 品牌研究

Compiled by: Scof, ChainCatcher

編譯：Scof、ChainCatcher

Overview: Demand for Decentralized AI

概述：去中心化人工智慧的需求

Rapid advancements in artificial intelligence (AI) technology are undeniable, but this progress is not without its challenges. Currently, centralized data training models dominate the field, primarily controlled by tech giants like OpenAI, Google, and X (formerly Twitter).

人工智慧（AI）技術的快速進步是不可否認的，但這種進步也面臨挑戰。目前，集中式資料訓練模式在該領域佔據主導地位，主要由 OpenAI、Google 和 X（以前的 Twitter）等科技巨頭控制。

Despite significant achievements in centralized AI training in recent years, it also has certain limitations. First, there are issues during the data training process, such as unauthorized use of private information, data censorship leading to distorted training outcomes, and a lack of traceability in data sources. In terms of algorithms, centralized models heavily rely on data quality and often struggle to perform real-time evaluations for iterative improvements.

儘管近年來集中式人工智慧訓練取得了顯著成果，但也存在一定的限制。首先，資料訓練過程中存在隱私資訊未經授權使用、資料審查導致訓練結果扭曲、資料來源缺乏可追溯性等問題。在演算法方面，中心化模型嚴重依賴資料質量，往往難以進行即時評估以進行迭代改進。

Decentralized AI training presents an alternative, but it faces enormous challenges, particularly due to resource shortages. Currently, the cost of training large models exceeds $100 million, making it nearly impossible for community-driven projects to compete. Decentralized efforts rely on voluntary contributions of computational power, data, and talent, but these resources are insufficient to support projects of similar scale. Therefore, the potential of decentralized AI remains limited and cannot fully compete with centralized AI in terms of scale and impact.

去中心化人工智慧培訓提供了一種替代方案，但它面臨著巨大的挑戰，特別是由於資源短缺。目前，訓練大型模式的成本超過 1 億美元，使得社群驅動的計畫幾乎不可能參與競爭。去中心化的努力依賴於運算能力、數據和人才的自願貢獻，但這些資源不足以支持類似規模的專案。因此，去中心化人工智慧的潛力仍然有限，在規模和影響力上無法與中心化人工智慧完全競爭。

Source: Statista

資料來源：Statista

Overview of Bittensor

Bittensor概述

Bittensor is a decentralized network aimed at forming an intelligent marketplace where high-quality AI models can be developed in a decentralized manner. By leveraging incentive mechanisms and rewarding participants for providing computational resources, expertise, and innovative contributions, Bittensor has established an open-source AI capability ecosystem, where the native currency TAO serves both as a reward token and as a credential for accessing the network.

Bittensor 是一個去中心化網絡，旨在形成一個智慧市場，可以以去中心化的方式開發高品質的人工智慧模型。透過利用激勵機制，獎勵參與者提供運算資源、專業知識和創新貢獻，Bittensor 建立了一個開源的 AI 能力生態系統，其中原生貨幣 TAO 既作為獎勵代幣，又作為訪問網路的憑證。

The core components of Bittensor, including its Yuma consensus, subnets, and TAO token, were initially launched in November 2021 with the release of version "Satoshi" and were built as a parachain on Polkadot. It later migrated to a Layer 1 chain built on Polkadot Substrate in 2023, while the issuance plan for TAO remained unchanged.

Bittensor 的核心組件，包括 Yuma 共識、子網和 TAO 代幣，最初於 2021 年 11 月發布“Satoshi”版本，並作為 Polkadot 上的平行鏈構建。後來在 2023 年遷移到基於 Polkadot Substrate 建造的 Layer 1 鏈，而 TAO 的發行計劃保持不變。

The creators and operating entity of Bittensor, the Opentensor Foundation, was co-founded by former Google engineer Jacob Steeves and machine learning scholar Ala Shaabana. The foundation currently has about 30 employees, almost all of whom are engaged in engineering functions, lacking roles in B2B market expansion, business development, partnerships, or developer relations.

Bittensor 的創建者和營運實體 Opentensor 基金會由前 Google 工程師 Jacob Steeves 和機器學習學者 Ala Shaabana 共同創立。該基金會目前約有 30 名員工，幾乎全部從事工程職能，缺乏 B2B 市場拓展、業務開發、合作夥伴關係或開發者關係方面的角色。

Fundamentals: How Does Bittensor Work?

基礎知識：Bittensor 如何運作？

Bittensor has developed an innovative network based on a dynamic incentive consensus framework, allowing participants to support the contribution of resources needed for producing machine intelligence. Each subnet operates as a model for a specific task, with its own independent performance evaluation criteria, and incentives are distributed through Bittensor's overall Yuma consensus.

Bittensor 開發了一個基於動態激勵共識框架的創新網絡，讓參與者可以支持生產機器智慧所需的資源的貢獻。每個子網路作為特定任務的模型運行，具有自己獨立的性能評估標準，並透過 Bittensor 的整體 Yuma 共識來分配激勵。

Let’s illustrate how a subnet operates through an analogy. A subnet can be likened to a magazine publisher that organizes writing competitions every month. Each month, an editor publishes a theme for writers to compete for a $10,000 reward pool. The criterion is "the work that best embodies the spirit of web3." Writers submit their articles to the editor for review, and all editors evaluate all submitted works. The results of the editors' evaluations determine the final rankings. The highest-ranked article will be published and receive the largest share of the rewards, while lower-ranked articles may also receive smaller rewards. All submitted articles and their scores will be shared with the participating writers and editors for feedback and learning. Through this incentive structure, writers will continue to participate and contribute, and the standards between writers and editors will gradually converge, allowing the magazine to publish high-quality articles that best "embody the spirit of web3."

讓我們透過類比來說明子網路是如何運作的。子網可以比喻為每月組織寫作比賽的雜誌出版商。每個月，編輯都會發布一個主題，供作家競爭 10,000 美元的獎勵池。評判標準是「最能體現web3精神的作品」。作者將文章提交給編輯進行審閱，所有編輯都會對所有提交的作品進行評估。編輯評審的結果決定了最終的排名。排名最高的文章將被發表並獲得最大份額的獎勵，而排名較低的文章也可能獲得較小的獎勵。所有提交的文章及其分數將與參與的作者和編輯共享，以供回饋和學習。透過這種激勵結構，作家將持續參與和貢獻，作家和編輯之間的標準也會逐漸趨同，讓雜誌能夠發表最「體現web3精神」的高品質文章。

In this analogy, the magazine publisher represents the subnet, the writers represent the miners, and the editors represent the validators. The process of editors aggregating evaluations of the articles is the Yuma consensus mechanism. In actual subnets, miners will receive TAO tokens instead of dollars, and these tokens are allocated by the root subnet (subnet 0); validators are also incentivized to align their standards with the aggregated scores to earn more rewards.

在這個類比中，雜誌出版商代表子網，作者代表礦工，編輯代表驗證者。編輯們總結文章評價的過程就是Yuma共識機制。在實際的子網路中，礦工將收到 TAO 代幣而不是美元，並且這些代幣由根子網路（子網路 0）分配；驗證者也被激勵將他們的標準與總和分數保持一致，以獲得更多獎勵。

Within this framework, subnet owners train and acquire intelligent capabilities from miners through validators, building AI modules with specific functionalities. In addition to subnets, Bittensor also has other layers that support the overall functionality of the network:

在此框架內，子網路所有者透過驗證器訓練並獲取礦工的智慧能力，建構具有特定功能的人工智慧模組。除了子網路之外，Bittensor 還具有支援網路整體功能的其他層：

a. Application Layer

一個。應用層

Users can interact with Bittensor through various applications that connect to subnets or act as subnets. Users submit service requests, such as language translation or data analysis, and the applications route the requests to the subnet via the validator API. The best miner answers are selected by validator consensus and returned to the users.

使用者可以透過連接到子網路或充當子網路的各種應用程式與 Bittensor 進行互動。使用者提交服務請求，例如語言翻譯或資料分析，應用程式透過驗證器 API 將請求路由到子網路。最佳礦工答案由驗證者共識選出並回傳給使用者。

b. Execution Layer

b.執行層

This layer consists of a group of subnets, all of which use Yuma consensus to train and utilize miners

這層由一組子網路組成，所有子網路都使用 Yuma 共識來訓練和使用礦工