斯坦福區塊鍊和AI會議海報

3月中旬，斯坦福大學舉辦了一個區塊鍊和AI會議，將教授，初創企業首席執行官和風險投資家（VCS）匯集在一起​​。

In mid-March, Stanford University hosted a Blockchain and AI conference, bringing together professors, startup CEOs, and venture capitalists (VCs). The event aimed to highlight the convergence of two major technologies: blockchain and AI. However, the conference could have benefited from highlighting Bitcoin and AI further, given Bitcoin's market dominance and the emerging innovations on Bitcoin Layer 2 solutions.

3月中旬，斯坦福大學舉辦了一個區塊鍊和AI會議，將教授，初創企業首席執行官和風險投資家（VCS）匯集在一起​​。該活動旨在強調兩種主要技術的融合：區塊鍊和AI。但是，鑑於比特幣的市場優勢以及比特幣第2層解決方案的新興創新，會議可能會從突出比特幣和人工智能進一步中受益。

One of the main challenges with the conference was that blockchain and AI have largely evolved as separate disciplines—with different investors, entrepreneurs, academics, and communities. While the idea was to merge the two fields, many speakers remained focused on their own domain, failing to establish clear connections between them. Perhaps a more fitting title would have been the Blockchain OR AI Conference.

會議的主要挑戰之一是區塊鍊和AI在很大程度上發展為單獨的學科，包括不同的投資者，企業家，學者和社區。雖然這個想法是將這兩個領域合併，但許多發言人仍然專注於自己的領域，未能在它們之間建立明確的聯繫。也許更合適的頭銜是區塊鍊或AI會議。

For example, a venture investor presented an overview of the AI industry, showcasing impressive advancements in image, audio, and code generation. Meanwhile, a DeepMind researcher discussed adversarial machine learning, a phenomenon where slight manipulations to input data can drastically alter an AI's output. One striking example involved modifying just a few pixels in an image of a cat—causing the AI to misclassify it as guacamole.

例如，一個風險投資者概述了AI行業，展示了圖像，音頻和代碼生成的令人印象深刻的進步。同時，一位深度研究人員討論了對抗機器學習，這是一種現象，其中輕微的操作輸入數據可以大大改變AI的輸出。一個引人注目的示例涉及在貓的圖像中僅修改幾個像素，從而導致AI將其錯誤分類為鱷梨調味醬。

On the blockchain side, discussions revolved around various protocols, but much of the technology remains highly experimental—or, in some cases, non-existent yet. Blockchain-AI integrations are still in their infancy, with practical implementations yet to emerge.

在區塊鏈方面，討論圍繞各種協議進行了討論，但是許多技術仍然具有很高的實驗性，或者在某些情況下尚不存在。區塊鏈-AI集成仍處於起步階段，實際實施尚未出現。

Proof of Computation

計算證明

One of the more insightful contributions came from Dan Boneh, an applied cryptographer at Stanford. He discussed SNARKs (succinct non-interactive arguments of knowledge) and zero-knowledge proofs, which address a fundamental cryptographic problem: proving knowledge of a computation in an efficient way.

最有見地的貢獻之一來自斯坦福大學應用的密碼師Dan Boneh。他討論了Snarks（知識的簡潔性非交互論點）和零知識證明，這解決了一個基本的加密問題：以有效的方式證明計算的知識。

This principle is well-established in both blockchain and cryptography. For example: It’s computationally expensive to factor a large number into its two prime components, but verifying via multiplication is computationally cheap. It’s expensive to find a block header whose hash meets a target threshold, but verifying that it does is inexpensive.

該原理在區塊鍊和密碼學中都具有良好的成就。例如：將大量數量計入其兩個主要組件的計算昂貴，但是通過乘法進行驗證在計算上是便宜的。找到一個標題符合目標閾值的塊標頭很昂貴，但是驗證它確實很便宜。

This asymmetry between computation and verification is critical in blockchain systems, where nodes constantly validate the work of others. In Bitcoin, nodes verify signatures and miners' proof of work. SNARKs extend this concept, enabling cryptographic proofs that are verifiable without revealing sensitive data.

在區塊鏈系統中，計算和驗證之間的不對稱性至關重要，在區塊鏈系統中，節點不斷驗證他人的工作。在比特幣中，節點驗證簽名和礦工的工作證明。 Snarks擴展了此概念，從而實現了可以證明的加密證明，而無需揭示敏感數據。

As AI agents become increasingly autonomous, a major challenge will be verifying computation while preserving privacy. Many are hesitant to upload sensitive data to OpenAI due to concerns over data security and prefer using their own models.

隨著AI代理的越來越自主，一個重大挑戰將是在保留隱私時驗證計算。由於對數據安全性的疑慮，並且更喜歡使用自己的模型，因此許多人不願將敏感數據上傳到OpenAI。

This creates a market demand for privacy-preserving verification—a mechanism that allows users to prove an AI model executed a computation correctly without revealing the underlying data. Such a solution could unlock AI applications in domains like healthcare, defense, and finance, where data security is paramount. This will likely become a multi-billion-dollar industry in the next decade.

這創建了對隱私驗證的市場需求，該機制允許用戶證明正確執行計算的AI模型而不揭示基礎數據。這樣的解決方案可以在數據安全性至關重要的情況下在醫療保健，國防和金融等領域中解鎖AI應用程序。在未來十年中，這可能會成為數十億美元的行業。

Interestingly, this concept originates from blockchain via networks to implement such cryptographic techniques. As Boneh pointed out, the idea of one machine cheaply verifying the expensive computation done by another emerged out of Bitcoin. But it may have a second, large application in AI.

有趣的是，該概念源自通過網絡區塊鏈來實施此類加密技術。正如Boneh所指出的那樣，一台機器的想法便宜地驗證了另一個機器從比特幣中出現的昂貴計算。但是它可能在AI中有第二個大型應用。

I hope to see future conferences place a greater emphasis on Bitcoin's contributions to these fields. BitVM, for example, leverages ideas from zero-knowledge proofs to create bridges between Bitcoin and new Layer 2 protocols—potentially enabling AI agents to interact with Bitcoin's ecosystem.

我希望看到未來的會議更加重視比特幣對這些領域的貢獻。例如，BITVM利用零知識證明的想法來創建比特幣和新第2層協議之間的橋樑，這有可能使AI代理與比特幣的生態系統進行交互。