去中心化人工智慧和零知識證明如何使計算民主化

人工智慧面臨著與早期網路版本相同的中心化風險。但 Mahesh Ramakrishnan 和 Vinayak Kurup 表示，另一種方式也是可能的。

Decentralized AI promises to democratize compute, making it accessible to smaller developers and protecting against the consolidation of power by a few major actors.

去中心化人工智慧有望實現計算民主化，讓規模較小的開發者能夠使用它，並防止少數主要參與者的權力整合。

In late July, Mark Zuckerberg penned a letter explaining why “open source is necessary for a positive AI future,” where he waxes poetic about the need for open-source AI development. The once-nerdy teen founder, now turned into the wakeboarding, gold chain-wearing, and jiu-jitsu fighting “Zuck,” has been branded as the messiah of open-source model development.

7 月下旬，馬克·祖克柏寫了一封信，解釋了為什麼“開源對於人工智慧的積極未來是必要的”，他在信中對開源人工智慧開發的必要性進行了詩意的闡述。這位曾經是書呆子的青少年創始人，現在變成了滑水、佩戴金鍊、柔術格鬥的“Zuck”，被稱為開源模型開發的救世主。

But thus-far, he and the Meta team haven’t articulated much about how these models are being deployed. As model complexity drives compute requirements higher, if model deployment is controlled by a handful of actors, then have we not succumbed to a similar form of centralization?

但到目前為止，他和 Meta 團隊還沒有詳細說明這些模型的部署方式。隨著模型複雜度提高計算要求，如果模型部署由少數參與者控制，那麼我們是否會屈服於類似的集中化形式？

Decentralized AI promises to solve this challenge, but the technology requires advancements in industry-leading cryptographic techniques and unique hybrid solutions.

去中心化人工智慧有望解決這一挑戰，但該技術需要業界領先的加密技術和獨特的混合解決方案的進步。

This op-ed is part of CoinDesk's new DePIN Vertical, covering the emerging industry of decentralized physical infrastructure.

這篇專欄文章是 CoinDesk 新的 DePIN Vertical 的一部分，涵蓋了去中心化實體基礎設施的新興產業。

Unlike centralized cloud providers, decentralized AI (DAI) distributes the computational processes for AI inference and training across multiple systems, networks, and locations. If implemented correctly, these networks, a type of decentralized physical infrastructure network (DePIN), bring benefits in censorship resistance, compute access, and cost.

與集中式雲端供應商不同，去中心化人工智慧 (DAI) 跨多個系統、網路和位置分佈人工智慧推理和訓練的運算過程。如果實施正確，這些網路（一種去中心化實體基礎設施網路（DePIN））將在抗審查性、運算存取和成本方面帶來好處。

DAI faces challenges in two main areas: the AI environment and the decentralized infrastructure itself. Compared to centralized systems, DAI requires additional safeguards to prevent unauthorized access to model details or the theft and replication of proprietary information. For this reason, there is an under-explored opportunity for teams who focus on open-source models, but recognize the potential performance disadvantage of open-sourced models compared to their closed-source counterparts.

DAI 面臨兩個主要領域的挑戰：人工智慧環境和去中心化基礎設施本身。與集中式系統相比，DAI 需要額外的保護措施，以防止未經授權存取模型詳細資訊或竊取和複製專有資訊。因此，對於專注於開源模型但認識到開源模型與閉源模型相比的潛在性能劣勢的團隊來說，這是一個尚未充分開發的機會。

Decentralized systems specifically face obstacles in network integrity and resource overhead. The distribution of client data across separate nodes, for instance, exposes more attack vectors. Attackers could spin up a node and analyze its computations, try to intercept data transmissions between nodes, or even introduce biases that degrade the system’s performance. Even in a secure decentralized inference model, there must be mechanisms to audit compute processes. Nodes are incentivized to save cost on resources by presenting incomplete computations, and verification is complicated by the lack of a trusted, centralized actor.

去中心化系統尤其面臨網路完整性和資源開銷方面的障礙。例如，客戶端資料分佈在不同的節點上會暴露更多的攻擊媒介。攻擊者可以啟動節點並分析其計算，嘗試攔截節點之間的資料傳輸，甚至引入降低系統效能的偏差。即使在安全的去中心化推理模型中，也必須有審核計算過程的機制。節點透過提供不完整的計算來激勵節省資源成本，並且由於缺乏可信的集中參與者而使驗證變得複雜。

Zero-Knowledge Proofs

零知識證明

Zero-knowledge proofs (ZKPs), while currently too computationally expensive, are one potential solution to some DAI challenges. ZKP is a cryptographic mechanism that enables one party (the prover) to convince another party (the verifier) of the truth of a statement without divulging any details about the statement itself, except its validity. Verification of this proof is quick for other nodes to run and offers a way for each node to prove it acted in accordance with the protocol. The technical differences between proof systems and their implementations (deep-dive on this coming later) are important for investors in the space.

零知識證明（ZKP）雖然目前計算成本太高，但卻是解決某些 DAI 挑戰的潛在解決方案。 ZKP 是一種加密機制，使一方（證明者）能夠說服另一方（驗證者）相信某個陳述的真實性，而不會洩露該陳述本身的任何細節（除了其有效性）。對該證明的驗證對於其他節點來說是快速運行的，並為每個節點提供了一種證明其按照協議行事的方法。證明系統及其實現之間的技術差異（稍後對此進行深入探討）對該領域的投資者來說非常重要。

Centralized compute makes model training exclusive to a handful of well-positioned and resourced players. ZKPs could be one part of unlocking idle compute on consumer hardware; a MacBook, for example, could use its extra compute bandwidth to help train a large-language model while earning tokens for the user.

集中式計算使得模型訓練僅限於少數地位良好且資源豐富的參與者。 ZKP 可能是解鎖消費性硬體上閒置運算的一部分；例如，MacBook 可以利用其額外的運算頻寬來幫助訓練大型語言模型，同時為用戶賺取代幣。

Deploying decentralized training or inference with consumer hardware is the focus of teams like Gensyn and Inference Labs; unlike a decentralized compute network like Akash or Render, sharding the computations adds complexity, namely the floating point problem. Making use of idle distributed compute resources opens the door for smaller developers to test and train their own networks — as long as they have access to tools that solve associated challenges.

使用消費性硬體部署去中心化訓練或推理是 Gensyn 和 Inference Labs 等團隊的重點；與 Akash 或 Render 等去中心化運算網路不同，計算分片會增加複雜性，即浮點問題。利用閒置的分散式運算資源為小型開發人員測試和訓練自己的網路打開了大門——只要他們能夠存取解決相關挑戰的工具即可。

At present, ZKP systems are seemingly four to six orders of magnitude more expensive than running the compute natively, and for tasks that require high-compute (like model training) or low latency (like model inference) using a ZKP is prohibitively slow. For comparison, a drop of six orders of magnitude means that a cutting edge system (like a16z’s Jolt) running on an M3 Max chip can prove a program 150 times slower than running it on a TI-84 graphing calculator.

目前，ZKP 系統似乎比本地運行計算昂貴四到六個數量級，對於需要高計算（例如模型訓練）或低延遲（例如模型推理）的任務，使用 ZKP 的速度非常慢。相較之下，六個數量級的下降意味著在 M3 Max 晶片上運行的尖端系統（如 a16z 的 Jolt）可以證明程式比在 TI-84 圖形計算器上運行慢 150 倍。

AI's ability to process large amounts of data makes it compatible with zero-knowledge proofs (ZKPs), but more progress in cryptography is needed before ZKPs can be widely used. Work being done by teams such as Irreducible (who designed the Binius proof system and commitment scheme), Gensyn, TensorOpera, Hellas, and Inference Labs, among others, will be an important step in achieving this vision. Timelines, however, remain overly optimistic as true innovation takes time and mathematical advancement.

人工智慧處理大量資料的能力使其與零知識證明（ZKP）相容，但在 ZKP 廣泛使用之前，密碼學方面還需要取得更多進展。 Irreducible（設計了 Binius 證明系統和承諾方案）、Gensyn、TensorOpera、Hellas 和 Inference Labs 等團隊所做的工作將是實現這一願景的重要一步。然而，時間表仍然過於樂觀，因為真正的創新需要時間和數學進步。

In the meantime, it’s worth noting other possibilities and hybrid solutions. HellasAI and others are developing new methods of representing models and computations that can enable an optimistic challenge game, allowing for only a subset of computation that needs to be handled in zero-knowledge. Optimistic proofs only work when there is staking, the ability to prove wrongdoing,

同時，值得注意的是其他可能性和混合解決方案。 HellasAI 和其他公司正在開發表示模型和計算的新方法，這些方法可以實現樂觀挑戰遊戲，只允許需要以零知識處理的計算子集。樂觀證明只有在有質押、證明不法行為的能力、