人工智能与区块链的融合：15 年展望

人工智能（AI）和区块链有望彻底改变世界。然而，事情需要更长的时间才能显现出来。

Artificial Intelligence (AI) and blockchain were touted to revolutionize the world, but things have taken a bit longer to manifest. Here's how they can converge and what challenges lie ahead.

人工智能（AI）和区块链被吹捧为世界的革命，但事情需要更长的时间才能显现出来。以下是他们如何融合以及面临的挑战。

Artificial Intelligence (AI) and blockchain were expected to revolutionize the world by now, but things have taken a bit longer to manifest. Both technologies have advanced significantly, but their convergence and mainstream adoption still face several challenges and opportunities.

到目前为止，人工智能（AI）和区块链预计将彻底改变世界，但事情需要更长的时间才能显现出来。这两种技术都取得了显着进步，但它们的融合和主流采用仍然面临一些挑战和机遇。

In this article, we'll explore why AI and blockchain need to converge, the specializations forming amongst Large Language Models (LLMs), and why we expect 15 more years to see commercially viable applications go mainstream.

在本文中，我们将探讨为什么人工智能和区块链需要融合、大型语言模型 (LLM) 之间形成的专业化，以及为什么我们预计再过 15 年才能看到商业上可行的应用程序成为主流。

The evolution of AI: Specialization and cost challenges

人工智能的演变：专业化和成本挑战

As AI continues to leapfrog expectations, we're witnessing a trend towards specialization in LLMs. Models like Claude, developed by Anthropic, are already becoming popular among developers for technical tasks and coding assistance. Others focus on specific industries or use cases (e.g., ChatGPT for more general audiences, Gemini for copywriting, and Perplexity for general research).

随着人工智能不断超越预期，我们正在见证法学硕士专业化的趋势。像 Anthropic 开发的 Claude 这样的模型已经在技术任务和编码协助的开发人员中流行起来。其他则专注于特定行业或用例（例如，ChatGPT 适用于更广泛的受众，Gemini 适用于文案写作，Perplexity 适用于一般研究）。

This natural specialization reflects the growing demand for precision in AI applications, particularly in enterprise settings. However, this progress also comes at a cost.

这种自然的专业化反映了人工智能应用程序（特别是在企业环境中）对精度不断增长的需求。然而，这种进步也是有代价的。

Despite ongoing efforts to optimize AI models, the financial burden of using LLMs at scale remains significant. OpenAI’s GPT-4, for instance, charges $0.03 per 1K tokens for input and $0.06 per 1K tokens for output. Their o1 (‘Strawberry’) model, which focuses on reasoning, is being priced at $15 per 1 million input tokens.

尽管不断努力优化人工智能模型，但大规模使用法学硕士的财务负担仍然很大。例如，OpenAI 的 GPT-4 对输入收取每 1K 代币 0.03 美元的费用，对输出每 1K 代币收取 0.06 美元的费用。他们的 o1（“草莓”）模型专注于推理，定价为每 100 万个输入代币 15 美元。

These costs can quickly become prohibitive for businesses looking to integrate AI across multiple departments. For instance, a large e-commerce company might use an LLM to personalize product recommendations, generate marketing copy, and even assist customer service representatives.

对于希望跨多个部门集成人工智能的企业来说，这些成本很快就会变得令人望而却步。例如，一家大型电子商务公司可能会使用法学硕士来个性化产品推荐、生成营销文案，甚至协助客户服务代表。

While using an LLM for each task might be ideal from a performance perspective, the costs could quickly become unsustainable, especially considering that LLMs require continuous fine-tuning and maintenance.

虽然从性能角度来看，为每项任务使用法学硕士可能是理想的选择，但成本可能很快就会变得不可持续，特别是考虑到法学硕士需要持续的微调和维护。

To address this challenge and make AI more accessible to a broader range of applications, we need to explore alternative approaches that can reduce the overall costs of deploying and using AI models at scale.

为了应对这一挑战并使人工智能更容易被更广泛的应用程序所接受，我们需要探索可以降低大规模部署和使用人工智能模型的总体成本的替代方法。

One promising solution lies in converging AI with blockchain technology, specifically Scalable Blockchain Technology (what we call “SBT”), which offers several unique advantages for AI applications.

一种有前景的解决方案是将人工智能与区块链技术融合，特别是可扩展区块链技术（我们称之为“SBT”），它为人工智能应用程序提供了多种独特的优势。

Blockchain: A potential solution for AI’s pain points

区块链：人工智能痛点的潜在解决方案

The convergence of AI and blockchain can pave the way for a new era of decentralized intelligence, where data privacy, security, and ownership take center stage. Here's how blockchain can address some of AI's most pressing pain points:

人工智能和区块链的融合可以为去中心化智能的新时代铺平道路，其中数据隐私、安全和所有权将成为中心舞台。以下是区块链如何解决人工智能最紧迫的痛点：

Data privacy and ownership: By integrating AI models with decentralized blockchain networks, we can create a system where data is no longer centrally controlled or owned. Instead, individuals and organizations could securely contribute their data to a collective pool, ensuring that AI models have access to a diverse and privacy-preserving dataset.

数据隐私和所有权：通过将人工智能模型与去中心化区块链网络相结合，我们可以创建一个数据不再被集中控制或拥有的系统。相反，个人和组织可以安全地将其数据贡献到集体池中，确保人工智能模型能够访问多样化且保护隐私的数据集。

In this scenario, data contributors would retain ownership and control over their data, and they could choose to opt out or revoke access at any time. This approach aligns closely with the principles of Web3 and decentralized data governance, empowering individuals to participate in the data economy without sacrificing their privacy.

在这种情况下，数据贡献者将保留对其数据的所有权和控制权，并且他们可以随时选择退出或撤销访问权限。这种方法与 Web3 和去中心化数据治理的原则紧密结合，使个人能够在不牺牲隐私的情况下参与数据经济。

Secure and immutable data input: Another key benefit of converging AI and blockchain is the ability to ensure the integrity and immutability of data used to train and operate AI models.

安全且不可变的数据输入：人工智能和区块链融合的另一个主要好处是能够确保用于训练和操作人工智能模型的数据的完整性和不变性。

In a decentralized AI system, data would be securely recorded on the blockchain, making it virtually impossible to tamper with or manipulate. This immutable data record would serve as a single source of truth, ensuring that AI models are always operating on the most accurate and up-to-date information.

在去中心化的人工智能系统中，数据将被安全地记录在区块链上，几乎不可能被篡改或操纵。这种不可变的数据记录将作为单一事实来源，确保人工智能模型始终基于最准确和最新的信息运行。

By combining the strengths of AI and blockchain in this way, we can create a new generation of AI models that are not only powerful and efficient but also privacy-preserving, secure, and transparent.

通过这种方式结合人工智能和区块链的优势，我们可以创建不仅强大、高效，而且保护隐私、安全、透明的新一代人工智能模型。

Several initiatives are already exploring the potential of blockchain for secure and privacy-preserving AI applications. For instance, the European Blockchain Services Infrastructure (EBSI) is examining how blockchain can be used to create a trusted and secure environment for deploying AI models.

多项举措已经在探索区块链在安全和隐私保护人工智能应用中的潜力。例如，欧洲区块链服务基础设施（EBSI）正在研究如何使用区块链为部署人工智能模型创建可信且安全的环境。

Similarly, projects like Ocean Protocol are developing decentralized data marketplaces that could revolutionize how AI models access and use training data. And projects like Teranode are showcasing what's truly possible at scale—something AI systems need since they deal with infinitely larger datasets than traditional ones.

同样，像海洋协议这样的项目正在开发去中心化的数据市场，这可能会彻底改变人工智能模型访问和使用训练数据的方式。像 Teranode 这样的项目正在展示真正的大规模可能性——这是人工智能系统所需要的，因为它们处理的数据集比传统数据集大得多。

Roadblocks on the path to convergence

趋同之路上的障碍

Despite the potential for synergy between AI and blockchain, several significant roadblocks stand in the way of seamless integration:

尽管人工智能和区块链之间具有协同作用的潜力，但无缝集成仍面临一些重大障碍：

Nascent regulatory frameworks: Both AI and blockchain are still emerging technologies that are rapidly evolving. As a result, regulatory frameworks governing their use and application are still nascent and vary widely across jurisdictions.

新生的监管框架：人工智能和区块链仍然是快速发展的新兴技术。因此，管理其使用和应用的监管框架仍处于新生阶段，并且在不同司法管辖区之间存在很大差异。

This lack of clear and consistent regulation poses a challenge for businesses and technologists seeking to converge AI and blockchain in a legally compliant manner.

缺乏明确和一致的监管给寻求以合法合规方式融合人工智能和区块链的企业和技术人员带来了挑战。

For instance, some jurisdictions might have strict data privacy laws that limit the use of certain AI techniques, while

例如，一些司法管辖区可能有严格的数据隐私法，限制某些人工智能技术的使用，而