微软揭秘人工智能与区块链融合，探索去中心化未来

微软认识到区块链技术的潜力及其与人工智能（AI）的交叉点，表示有兴趣优化现有技术，而不是创建自己的区块链。微软数字化转型、区块链和云供应链总监约克·罗兹 (Yorke Rhodes) 表示，重点在于通过结合两种技术的优势来增强能力。专家预测，加密货币可能会在人工智能的进步中发挥支持作用，特别是在去中心化图形处理单元（GPU）和小语言模型等领域。尽管担心去中心化网络中的延迟，但小组成员认为推理是可行的，并且可能会减少通信开销。预计监管机构将密切关注人工智能的影响，但行业领导者表示，通过区块链技术提高参与度和透明度可以解决监管问题。小组成员对人工智能的未来表示乐观，并展望了人工智能在未来十年内被企业广泛采用以及人工智能（AGI）的潜力。

Microsoft and the Convergence of Artificial Intelligence and Blockchain Technology

微软以及人工智能和区块链技术的融合

Intersection of Two Defining Technologies

两种定义技术的交集

Microsoft, a leader in artificial intelligence (AI) research and investment, has expressed keen interest in the potential synergies between blockchain technology and AI. Speaking at the recent Cornell Blockchain Conference in New York, Yorke Rhodes, Microsoft's director for digital transformation, blockchain, and cloud supply chain, emphasized the transformative power of combining these two technologies.

人工智能（AI）研究和投资领域的领导者微软对区块链技术与人工智能之间的潜在协同效应表示了浓厚的兴趣。微软数字化转型、区块链和云供应链总监约克·罗兹 (Yorke Rhodes) 最近在纽约举行的康奈尔大学区块链会议上发表讲话，强调了这两种技术相结合的变革力量。

"As these two technologies progress, we can create agents that bring together their collective capabilities. We're only scratching the surface," Rhodes stated.

“随着这两项技术的进步，我们可以创造出将它们的集体能力结合在一起的代理。我们只是触及了表面，”罗兹说。

Alex Lin, moderator of the panel discussion titled "Crypto x AI," further probed Rhodes' views by inquiring whether Microsoft harbored aspirations of developing its own blockchain solution.

“加密 x AI”小组讨论的主持人 Alex Lin 通过询问微软是否有开发自己的区块链解决方案的愿望，进一步探讨了 Rhodes 的观点。

Rhodes responded by acknowledging the significant advancements already underway in the crypto space, particularly within the open source community. "Why would we try to recreate something that has already attracted substantial investment?" he questioned. Consequently, Microsoft's current focus lies on optimizing existing technologies, such as layer-2 blockchain rollups.

罗德斯对此做出回应，承认加密货币领域已经取得了重大进展，特别是在开源社区内。 “为什么我们要尝试重建已经吸引了大量投资的东西？”他问道。因此，微软当前的重点在于优化现有技术，例如第 2 层区块链汇总。

Crypto as the Enabler of Decentralized AI

加密货币作为去中心化人工智能的推动者

Joining Rhodes and Lin on stage were Neil DeSilva, chief financial officer at PayPal Digital Currencies; Matt Stephenson, head of research at Pantera; and Jasper Zhang, CEO and co-founder at Hyperbolic Labs.

与罗兹和林一起上台的是 PayPal 数字货币首席财务官尼尔·德西尔瓦 (Neil DeSilva)；马特·斯蒂芬森 (Matt Stephenson)，Pantera 研究主管；以及 Hyperbolic Labs 首席执行官兼联合创始人 Jasper Zhang。

Stephenson expressed his belief that cryptocurrencies are well-positioned to serve as the underlying infrastructure for certain AI applications, particularly transformer and diffusion models. This is especially relevant given the anticipated prevalence of decentralized, multiagent AI systems in the future.

斯蒂芬森表示，他相信加密货币完全可以作为某些人工智能应用程序的底层基础设施，特别是变压器和扩散模型。考虑到未来去中心化、多智能体人工智能系统的预期盛行，这一点尤其重要。

However, Stephenson recognized that crypto may play a supporting role to AI's dominant position. Rhodes concurred, acknowledging that a major trend like AI tends to dominate discussions at the expense of other emerging technologies, including crypto/blockchain and Web3.

然而，斯蒂芬森认识到，加密货币可能对人工智能的主导地位起到支撑作用。 Rhodes 对此表示同意，他承认像人工智能这样的主要趋势往往会主导讨论，而牺牲其他新兴技术，包括加密/区块链和 Web3。

Addressing Exaggerated Claims and Latency Issues

解决夸大的声明和延迟问题

Lin raised concerns regarding exaggerated claims surrounding the intersection of blockchain networks and AI, emphasizing the difficulty in distinguishing hype from reality. He highlighted the example of decentralized graphics processing units (GPUs) and the often-overlooked issue of latency, the time it takes for data to transfer across a network.

林对围绕区块链网络和人工智能交叉的夸大说法表示担忧，并强调区分炒作与现实的困难。他强调了分散式图形处理单元（GPU）的例子以及经常被忽视的延迟问题，即数据通过网络传输所需的时间。

Hyperbolic Labs' Zhang dismissed latency as a significant concern for decentralized networks like blockchains. He pointed out that centralized networks often suffer from lengthy data transfer times due to physical distances, while decentralized networks allow users to connect with nearby nodes for faster processing and reduced communication overhead.

Hyperbolic Labs 的张认为延迟是区块链等去中心化网络的一个重要问题。他指出，由于物理距离的原因，中心化网络往往会遭受漫长的数据传输时间的困扰，而去中心化网络允许用户与附近的节点连接，以实现更快的处理并减少通信开销。

Small Language Models and the Drive towards Edge AI

小语言模型和边缘人工智能的驱动力

Much attention in the AI field has focused on large language models (LLMs), but Rhodes emphasized the growing significance of smaller language models that operate efficiently on mobile devices and laptops. These smaller models enable a wider range of applications and take advantage of increased compute power at the edge.

AI 领域的大部分注意力都集中在大型语言模型 (LLM) 上，但 Rhodes 强调，在移动设备和笔记本电脑上高效运行的小型语言模型的重要性与日俱增。这些较小的模型可实现更广泛的应用，并利用边缘计算能力的增强。

Microsoft has been actively developing small language AI models, including its Phi-3 family of open models, which are demonstrating capabilities approaching those of larger language models.

微软一直在积极开发小语言人工智能模型，包括其 Phi-3 系列开放模型，这些模型正在展示接近大型语言模型的功能。

Regulatory Landscape for AI and Crypto

人工智能和加密货币的监管环境

Panelists anticipated increased regulatory scrutiny of AI in the coming years, similar to the attention given to cryptocurrencies. Lin expressed dissatisfaction with the U.S. regulatory approach, citing the heavy-handed stance of the Securities and Exchange Commission (SEC) towards crypto regulation.

小组成员预计未来几年对人工智能的监管审查将会加强，类似于对加密货币的关注。林表达了对美国监管方式的不满，并列举了美国证券交易委员会（SEC）对加密货币监管的严厉立场。

DeSilva took a more nuanced view, acknowledging the potential frustrations of dealing with regulators but emphasizing their primary objective of protecting consumers from harm. He underscored the necessity of engaging with regulators to foster innovation that reaches a wider audience.

德席尔瓦的观点更为细致，他承认与监管机构打交道可能会遇到挫折，但强调监管机构的首要目标是保护消费者免受伤害。他强调了与监管机构合作的必要性，以促进影响更广泛受众的创新。

Blockchain as a Solution to AI's Opacity

区块链作为人工智能不透明性的解决方案

The opaque decision-making process of AI, known as the "black box" problem, poses challenges for regulators in mitigating potential consumer harm. Lin suggested that blockchain technology, with its transparency, immutability, and tracking capabilities, could provide a solution to this issue.

人工智能的不透明决策过程（被称为“黑匣子”问题）给监管机构减轻潜在消费者伤害带来了挑战。林认为，区块链技术以其透明性、不变性和跟踪能力可以为这个问题提供解决方案。

Predictions for the Future of AI

对人工智能未来的预测

In closing, panelists shared their visions for the future of AI. Zhang predicted widespread adoption of AI in the near future, with every company becoming an AI company. He expressed optimism that artificial generalized intelligence (AGI) could become a reality within the next five to 10 years, facilitated by decentralized infrastructure and increased compute power.

最后，小组成员分享了他们对人工智能未来的愿景。张预测，在不久的将来，人工智能将得到广泛采用，每家公司都会成为人工智能公司。他乐观地认为，在去中心化基础设施和计算能力增强的推动下，通用人工智能（AGI）可能在未来五到十年内成为现实。

Rhodes forecasted the obsolescence of zero-knowledge proofs (ZK-proofs) in three years, to be replaced by fully homomorphic encryption (FHE), a technology that unlocks the value of data on untrusted domains without requiring decryption. FHE holds significant promise for addressing privacy concerns, particularly in sensitive areas such as healthcare.

罗兹预测零知识证明（ZK-proofs）将在三年内过时，取而代之的是完全同态加密（FHE），这是一种无需解密即可释放不可信域数据价值的技术。 FHE 在解决隐私问题方面具有重大前景，特别是在医疗保健等敏感领域。

DeSilva emphasized the uncertain nature of concrete predictions in the technology industry. However, he expressed optimism for the eventual realization of AGI and its potential to benefit humanity through the combined efforts of all stakeholders.

德席尔瓦强调了科技行业具体预测的不确定性。不过，他对AGI的最终实现及其通过所有利益相关者的共同努力造福人类的潜力表示乐观。