區塊鏈領導者 nChain 表示其技術可以在確保人工智慧模型安全可靠方面發揮重要作用

9 月份，該公司成功展示了一種以加密方式證明人工智慧模型是在特定資料集上進行訓練的方法，並且沒有任何內容被篡改。

Blockchain technology company nChain has highlighted the potential role its technology could play in ensuring the security and trustworthiness of artificial intelligence (AI) models. In September, nChain demonstrated a method to cryptographically prove an AI model's training on a specific dataset without any tampering. This was achieved through on-chain transactions on the BSV mainnet, utilizing Zero-Knowledge Proof (ZKP) techniques to verify the existence of information without revealing it publicly.

區塊鏈技術公司 nChain 強調了其技術在確保人工智慧 (AI) 模型的安全性和可信性方面可以發揮的潛在作用。 9 月，nChain 演示了一種以加密方式證明 AI 模型在特定資料集上進行訓練而無需任何篡改的方法。這是透過 BSV 主網上的鏈上交易實現的，利用零知識證明（ZKP）技術來驗證資訊的存在而無需公開披露。

The ability to record and verify proprietary information is crucial for AI developers, who must maintain confidentiality for competitive purposes while also assuring regulators and the public that their systems are developed responsibly. nChain has recorded the relevant transaction and made the tools used available on its GitHub page.

記錄和驗證專有資訊的能力對於人工智慧開發人員來說至關重要，他們必須出於競爭目的保密，同時也要向監管機構和公眾保證他們的系統是負責任地開發的。 nChain 已記錄相關交易，並在其 GitHub 頁面上提供了所使用的工具。

This serves as another example of BSV's capability to enter new industry segments and even create entirely new industries by leveraging its key features of speed, unbounded scalability, and affordability. Data is secured by the proven proof-of-work (PoW) transaction verification algorithm rather than the less secure proof-of-stake (PoS) algorithm. The BSV network can perform tasks that other UTXO-based PoW blockchain networks simply cannot handle.

這是 BSV 有能力利用其速度、無限可擴展性和可承受性等關鍵特性進入新行業領域甚至創建全新行業的另一個例子。資料透過經過驗證的工作量證明 (PoW) 交易驗證演算法而不是安全性較低的權益證明 (PoS) 演算法來保護。 BSV 網路可以執行其他基於 UTXO 的 PoW 區塊鏈網路根本無法處理的任務。

Verifiable AI is a relatively new industry sector with promising potential. At present, the field is primarily the domain of academics and researchers, with little in the way of commercial activity. When it comes to training data, weights and algorithms, the well-known AI firms are jealously guarding their secrets, presenting only a “black box” as the final product they expect end users to trust, as long as it works.

可驗證的人工智慧是一個相對較新的行業領域，潛力巨大。目前，該領域主要是學者和研究人員的領域，商業活動很少。當談到訓練資料、權重和演算法時，著名的人工智慧公司都小心翼翼地保守著自己的秘密，只提供一個「黑盒子」作為他們希望最終用戶信任的最終產品，只要它有效。

So a problem arises that could impact the industry in several ways: companies are in a fiercely contested race to build the most advanced AI, and the prize for winning that contest (or even stages of it) could bring them immense wealth and power. At the same ime, governments and the public are wary of the secretive way AI is being developed, which builds mistrust in AI technology overall. Developers need to balance these two driving forces to achieve their goals (and possibly avoid the imposition of crippling regulations).

因此，出現了一個問題，可能會在多個方面影響該行業：公司正在進行一場激烈的競賽，以構建最先進的人工智能，而贏得這場競賽（甚至是階段性競賽）的獎項可能會帶給他們巨大的財富和權力。同時，政府和公眾對人工智慧開發的秘密方式持謹慎態度，這對人工智慧技術產生了整體不信任。開發人員需要平衡這兩種驅動力以實現其目標（並可能避免實施嚴格的法規）。

According to nChain Research Director Dr. Wei Zhang, this is where blockchain and ZKPs could provide the perfect solution. He wrote in a blog post that major AI players must “demonstrate that an AI system operates according to its specifications, free of critical bugs and adheres to ethical standards such as fairness, transparency and safety, ideally without revealing proprietary information of the system.”

nChain 研究總監張偉博士表示，這就是區塊鏈和 ZKP 可以提供完美解決方案的地方。他在一篇部落格文章中寫道，主要人工智慧參與者必須「證明人工智慧系統按照其規範運行，不存在嚴重錯誤，並遵守公平、透明和安全等道德標準，最好不洩漏系統的專有資訊."

ZKPs are a way of proving that information exists and that you possess it without revealing its contents. Hypothetical but simpler blockchain applications of ZKPs include an identity document that could prove you're a certain age or reside in a certain place without needing to show those precise details to anybody. Another could prove you have adequate net worth to invest in a project without explicitly stating how much money you have.

ZKP 是一種證明資訊存在並且您擁有它而不洩露其內容的方法。假設但更簡單的 ZKP 區塊鏈應用程式包括一個身分證件，可以證明您達到一定年齡或居住在某個地方，而無需向任何人顯示這些精確的詳細資訊。另一種方法可以證明您有足夠的淨資產來投資某個項目，而無需明確說明您有多少錢。

Similarly, companies involved in AI development could use blockchain records to verify their machine learning weights and models, that training data and safety standards are sound, and that their AI model is following the guidelines they claim it to be without the risk of that information leaking to competitors.

同樣，參與人工智慧開發的公司可以使用區塊鏈記錄來驗證他們的機器學習權重和模型，訓練資料和安全標準是否健全，以及他們的人工智慧模型遵循他們聲稱的指導方針，而不存在資訊洩露的風險給競爭對手。

While the ZKP concept has been around since the 1980s, the computation required to process the necessary cryptographic methods known as zk-SNARKS (that's Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge) or STARKS (Scalable Transparent Arguments of Knowledge) makes it a difficult task for most blockchain networks.

雖然 ZKP 概念自 20 世紀 80 年代以來就已存在，但處理稱為 zk-SNARKS（零知識簡潔非互動式知識論證）或 STARKS（可擴展透明知識論證）的必要加密方法所需的計算使其成為對於大多數區塊鏈網路來說，這是一項艱鉅的任務。

“When you think of ZKPs, there are two parts: proof generation, and proof verification,” Zhang told CoinGeek. “For SNARKS, in this particular context, the schemes we're using are always trying to achieve efficient verification.”

「當你想到 ZKP 時，有兩個部分：證明生成和證明驗證，」Zhang 告訴 CoinGeek。 “對於 SNARKS 來說，在這個特定的背景下，我們使用的方案總是試圖實現高效的驗證。”

“Blockchain is very expensive for on-chain computation. If on-chain computation is only restricted to verification, then it works because the heavy duty is on the proof generation side, and that is off-chain.”

「區塊鏈對於鏈上計算來說非常昂貴。如果鏈上計算僅限於驗證，那麼它是有效的，因為繁重的任務是在證明生成方面，而這是鏈下的。

Several teams are working on ways to do this besides nChain, on other blockchain networks including Ethereum and BTC. ZKPs are computationally intensive to perform. There are two parts to it: generating the proof; and verifying it. Both ETH and BTC run up against the same scaling limitations that have plagued their networks for years with other applications.

除了 nChain 之外，還有幾個團隊正在以太坊和 BTC 等其他區塊鏈網路上研究實現這一目標的方法。 ZKP 的執行需要大量計算。它有兩個部分：生成證明；並驗證它。 ETH 和 BTC 都面臨著相同的擴展限制，這些限制多年來一直困擾著它們的網路的其他應用程式。

“They made verification feasible on-chain, great. And everyone is in a race trying to make the proof generation less of a burden. At the moment, you can do that for specific computation, or set of computations, but it's very difficult to have a universal approach such that the proof generation is feasible for any computation.”

「他們使鏈上驗證變得可行，太棒了。每個人都在努力減輕證明產生的負擔。目前，您可以針對特定計算或一組計算執行此操作，但很難有一種通用方法使得證明生成對於任何計算都是可行的。

BTC as it exists today is way too limited in data capacity to perform the task, but there have been moderately successful attempts by BitVM and StarkWare at verifying STARK proofs, using the BTC testnet “Signet” (where OP_CAT has been enabled), and the recently touted “Fractal Bitcoin” (which could be described as a copy of Signet with an exotic mining mechanism). Fractal Bitcoin's feasibility

目前的 BTC 資料容量太有限，無法執行該任務，但 BitVM 和 StarkWare 使用 BTC 測試網“Signet”（已啟用 OP_CAT）在驗證 STARK 證明方面進行了相當成功的嘗試，並且最近吹捧的“分形比特幣” （可以被描述為具有奇異挖礦機制的Signet 副本）。分形比特幣的可行性