去中心化人工智能和零知识证明如何使计算民主化

人工智能面临着与早期互联网版本相同的中心化风险。但 Mahesh Ramakrishnan 和 Vinayak Kurup 表示，另一种方式也是可能的。

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

去中心化人工智能有望实现计算民主化，让规模较小的开发者能够使用它，并防止少数主要参与者的权力整合。

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，马克·扎克伯格写了一封信，解释了为什么“开源对于人工智能的积极未来是必要的”，他在信中对开源人工智能开发的必要性进行了诗意的阐述。这位曾经是书呆子的青少年创始人，现在变成了滑水、佩戴金链、柔术格斗的“Zuck”，被称为开源模型开发的救世主。

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?

但到目前为止，他和 Meta 团队还没有详细说明这些模型的部署方式。随着模型复杂性提高计算要求，如果模型部署由少数参与者控制，那么我们是否会屈服于类似的集中化形式？

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

去中心化人工智能有望解决这一挑战，但该技术需要业界领先的加密技术和独特的混合解决方案的进步。

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

这篇专栏文章是 CoinDesk 新的 DePIN Vertical 的一部分，涵盖了去中心化物理基础设施的新兴行业。

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.

与集中式云提供商不同，去中心化人工智能 (DAI) 将人工智能推理和训练的计算过程分布在多个系统、网络和位置。如果实施正确，这些网络（一种去中心化物理基础设施网络（DePIN））将在抗审查性、计算访问和成本方面带来好处。

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 面临两个主要领域的挑战：人工智能环境和去中心化基础设施本身。与集中式系统相比，DAI 需要额外的保护措施，以防止未经授权访问模型详细信息或窃取和复制专有信息。因此，对于专注于开源模型但认识到开源模型与闭源模型相比的潜在性能劣势的团队来说，这是一个尚未充分开发的机会。

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.

去中心化系统尤其面临网络完整性和资源开销方面的障碍。例如，客户端数据分布在不同的节点上会暴露更多的攻击媒介。攻击者可以启动节点并分析其计算，尝试拦截节点之间的数据传输，甚至引入降低系统性能的偏差。即使在安全的去中心化推理模型中，也必须有审核计算过程的机制。节点通过提供不完整的计算来激励节省资源成本，并且由于缺乏可信的集中参与者而使验证变得复杂。

Zero-Knowledge Proofs

零知识证明

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.

零知识证明（ZKP）虽然目前计算成本太高，但却是解决某些 DAI 挑战的一种潜在解决方案。 ZKP 是一种加密机制，使一方（证明者）能够说服另一方（验证者）相信某个陈述的真实性，而不会泄露该陈述本身的任何细节（除了其有效性）。对该证明的验证对于其他节点来说是快速运行的，并为每个节点提供了一种证明其按照协议行事的方法。证明系统及其实现之间的技术差异（稍后对此进行深入探讨）对于该领域的投资者来说非常重要。

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.

集中式计算使得模型训练仅限于少数地位良好且资源丰富的参与者。 ZKP 可能是解锁消费类硬件上闲置计算的一部分；例如，MacBook 可以利用其额外的计算带宽来帮助训练大型语言模型，同时为用户赚取代币。

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.

使用消费类硬件部署去中心化训练或推理是 Gensyn 和 Inference Labs 等团队的重点；与 Akash 或 Render 等去中心化计算网络不同，计算分片会增加复杂性，即浮点问题。利用闲置的分布式计算资源为小型开发人员测试和训练自己的网络打开了大门——只要他们能够访问解决相关挑战的工具即可。

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 系统似乎比本地运行计算昂贵四到六个数量级，并且对于需要高计算（例如模型训练）或低延迟（例如模型推理）的任务，使用 ZKP 的速度非常慢。相比之下，六个数量级的下降意味着在 M3 Max 芯片上运行的尖端系统（如 a16z 的 Jolt）可以证明程序比在 TI-84 图形计算器上运行慢 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.

人工智能处理大量数据的能力使其与零知识证明（ZKP）兼容，但在 ZKP 得到广泛使用之前，密码学方面还需要取得更多进展。 Irreducible（设计了 Binius 证明系统和承诺方案）、Gensyn、TensorOpera、Hellas 和 Inference Labs 等团队所做的工作将是实现这一愿景的重要一步。然而，时间表仍然过于乐观，因为真正的创新需要时间和数学进步。

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,

与此同时，值得注意的是其他可能性和混合解决方案。 HellasAI 和其他公司正在开发表示模型和计算的新方法，这些方法可以实现乐观挑战游戏，只允许需要以零知识处理的计算子集。乐观证明只有在有质押、证明不法行为的能力、