OORT：诞生于课堂偶然时刻，旨在使人工智能开发民主化

人工智能领域已进入爆发时代。根据咨询公司Dealroom的研究报告《2024年人工智能投资报告》，全球人工智能投资预计将达到650亿美元，占所有风险投资的五分之一。

Artificial intelligence has entered an explosive era. According to a research report "2024 AI Investment Report" by consulting firm Dealroom, global AI investment is expected to reach $65 billion, accounting for one-fifth of all venture capital. Goldman Sachs' research department also stated that global AI investment could approach $200 billion by 2025.

人工智能已经进入爆发时代。据咨询公司Dealroom发布的研究报告《2024年人工智能投资报告》显示，全球人工智能投资预计将达到650亿美元，占全部风险投资的五分之一。高盛研究部门也表示，到2025年，全球人工智能投资可能接近2000亿美元。

Thanks to the AI boom, funds are flocking to AI targets. For example, the A-share company Cambricon has surged over 560% since its low in February this year, with a market capitalization exceeding 250 billion RMB; the U.S. company Broadcom has surpassed a market value of $1 trillion, becoming the eighth largest publicly traded company in the U.S.

由于人工智能的蓬勃发展，资金纷纷涌向人工智能目标。例如，A股公司寒武纪自今年2月低点以来已飙升超560%，市值超过2500亿元人民币；美国博通公司市值突破1万亿美元，成为美国第八大上市公司

The combination of AI and Crypto is also showing a hot trend. During the artificial intelligence conference hosted by Nvidia, Bittensor (TAO) led with a market value of over $4.5 billion, while assets like Render (RND) and Fetch.ai (FET) have seen rapid value growth.

人工智能与加密货币的结合也呈现出火热的趋势。在英伟达主办的人工智能大会上，Bittensor（TAO）以超过45亿美元的市值领跑，而Render（RND）和Fetch.ai（FET）等资产则出现了快速的价值增长。

Following large language models, AI Agents have become the engine of this round of AI market. For instance, the token of GOAT surged over 100 times in 24 hours, and ACT rose nearly 20 times in a single day, igniting the Crypto world's enthusiasm for AI Agents.

继大语言模型之后，AI Agent成为本轮AI市场的引擎。例如，GOAT代币24小时暴涨超百倍，ACT单日涨幅近20倍，点燃了加密世界对AI Agent的热情。

However, behind the rapid development of AI, there are also concerns. According to an article by Dr. Max Li, founder and CEO of OORT, published in Forbes titled "AI Failures Will Surge in 2025: A Call for Decentralized Innovation," the AI industry faces numerous issues, such as data privacy, ethical compliance, and trust crises caused by centralization, which increase the risk of AI failures. Therefore, decentralized innovation has become an urgent priority.

然而，人工智能快速发展的背后，也存在隐忧。 OORT创始人兼CEO李博士在《福布斯》发表题为《2025年人工智能失败将激增：呼唤去中心化创新》的文章指出，人工智能行业面临着数据隐私、道德合规等众多问题。中心化引发的信任危机，增加了人工智能失败的风险。因此，去中心化创新已成为当务之急。

Currently, OORT has established one of the world's largest decentralized cloud infrastructures, with network nodes covering over 100 countries, generating millions of dollars in revenue, and launching the open-source Layer 1 Olympus protocol (its consensus algorithm is "Proof of Honesty" PoH, protected by U.S. patents). Through the native token OORT, it encourages everyone to contribute data, achieving an incentive closed loop. Recently, OORT launched OORT DataHub, marking a further step towards global, diverse, and transparent data collection, laying a solid foundation for the explosion of DeAI.

目前，OORT已经建立了全球最大的去中心化云基础设施之一，网络节点覆盖100多个国家，产生了数百万美元的收入，并推出了开源的Layer 1 Olympus协议（其共识算法是“Proof of Honesty”PoH） ，受美国专利保护）。通过原生代币OORT，鼓励大家贡献数据，实现激励闭环。近期，OORT上线了OORT DataHub，标志着数据采集向全球化、多元化、透明化又迈进了一步，为DeAI的爆发奠定了坚实的基础。

OORT Born from Accidental Classroom Moments

OORT 诞生于课堂上的偶然时刻

To understand the OORT project, one must first understand the problems OORT aims to solve. This involves discussing the current bottlenecks in AI development, primarily related to data and centralization issues:

要了解OORT项目，首先必须了解OORT要解决的问题。这涉及到讨论当前人工智能发展的瓶颈，主要与数据和中心化问题有关：

1. Disadvantages of Centralized AI

1.中心化AI的缺点

1. Lack of transparency leading to trust crises. The decision-making process of centralized AI models is often opaque, seen as "black box" operations. Users find it difficult to understand how AI systems make decisions, which can lead to severe consequences in critical applications such as medical diagnosis and financial risk control.

1.缺乏透明度导致信任危机。中心化人工智能模型的决策过程往往是不透明的，被视为“黑匣子”操作。用户很难理解人工智能系统如何做出决策，这可能会在医疗诊断、金融风控等关键应用中导致严重后果。

2. Data monopoly and unequal competition. A few large tech companies control vast amounts of data, creating a data monopoly. This makes it difficult for new entrants to obtain sufficient data to train their own AI models, hindering innovation and market competition. Additionally, data monopolies may lead to the misuse of user data, further exacerbating data privacy issues.

2、数据垄断和不平等竞争。少数大型科技公司控制大量数据，形成数据垄断。这使得新进入者很难获得足够的数据来训练自己的人工智能模型，从而阻碍创新和市场竞争。此外，数据垄断可能导致用户数据的滥用，进一步加剧数据隐私问题。

3. Ethical and moral risks are hard to control. The development of centralized AI has raised a series of ethical and moral issues, such as algorithmic discrimination and bias amplification. Moreover, the application of AI technology in military and surveillance fields has raised concerns about human rights, security, and social stability.

3.伦理道德风险难以控制。中心化人工智能的发展引发了算法歧视、偏见放大等一系列伦理道德问题。此外，人工智能技术在军事和监控领域的应用引发了人们对人权、安全和社会稳定的担忧。

2. Data Bottleneck

2. 数据瓶颈

1. Data desert. In the booming development of artificial intelligence, the issue of data deserts has gradually emerged as a key factor restricting further development. The demand for data from AI researchers has exploded, yet the supply of data has struggled to keep up. Over the past decade, the continuous expansion of neural networks has relied on large amounts of data for training, as seen in the development of large language models like ChatGPT. However, traditional datasets are nearing exhaustion, and data owners are beginning to restrict content usage, making data acquisition increasingly difficult.

1.数据沙漠。在人工智能蓬勃发展的过程中，数据荒漠问题逐渐成为制约进一步发展的关键因素。人工智能研究人员对数据的需求激增，但数据的供应却难以跟上。过去十年，神经网络的不断扩展依赖于大量数据进行训练，ChatGPT 等大型语言模型的开发就体现了这一点。然而，传统数据集已接近枯竭，数据所有者开始限制内容使用，使得数据获取变得越来越困难。

The causes of data deserts are multifaceted. On one hand, data quality is uneven, with issues of incompleteness, inconsistency, noise, and bias severely affecting model accuracy. On the other hand, scalability challenges are significant; collecting sufficient data is costly and time-consuming, maintaining real-time data is difficult, and manual annotation of large datasets poses a bottleneck. Additionally, access and privacy restrictions cannot be ignored; data silos, regulatory constraints, and ethical issues make data collection arduous.

造成数据荒漠的原因是多方面的。一方面，数据质量参差不齐，不完整、不一致、噪声和偏差等问题严重影响模型的准确性。另一方面，可扩展性挑战也很严峻；收集足够的数据既昂贵又耗时，维护实时数据很困难，并且大型数据集的手动注释构成了瓶颈。此外，访问和隐私限制也不容忽视；数据孤岛、监管限制和道德问题使数据收集变得困难。

Data deserts have a profound impact on AI development. They limit model training and optimization, potentially forcing AI models to shift from pursuing large-scale to more specialized and efficient approaches. In industry applications, achieving precise predictions and decisions becomes challenging, hindering AI's greater role in fields like healthcare and finance.

数据荒漠对人工智能的发展有着深远的影响。它们限制了模型训练和优化，可能迫使人工智能模型从追求大规模转向更专业、更高效的方法。在行业应用中，实现精确的预测和决策变得具有挑战性，阻碍了人工智能在医疗、金融等领域发挥更大作用。