OORT：誕生於課堂偶然時刻，旨在使人工智慧開發民主化

人工智慧領域已進入爆發時代。根據顧問公司Dealroom的研究報告《2024年人工智慧投資報告》，全球人工智慧投資預計將達到650億美元，佔所有創投的五分之一。

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.

人工智慧已經進入爆發時代。根據顧問公司Dealroom發布的研究報告《2024年人工智慧投資報告》顯示，全球人工智慧投資預計將達到650億美元，佔全部創投的五分之一。高盛研究部門也表示，到2025年，全球人工智慧投資可能接近2,000億美元。

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.

由於人工智慧的蓬勃發展，資金紛紛湧向人工智慧目標。例如，A股公司寒武紀自今年2月低點以來已飆漲超560%，市值超過2,500億元；美國博通公司市值突破1兆美元，成為美國第八大上市公司

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.

人工智慧與加密貨幣的結合也呈現火熱的趨勢。在英偉達主辦的人工智慧大會上，Bittensor（TAO）以超過45億美元的市值領跑，而Render（RND）和Fetch.ai（FET）等資產則出現了快速的價值增長。

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.

繼大語言模型之後，AI Agent成為本輪AI市場的引擎。例如，GOAT代幣24小時暴漲超百倍，ACT單日漲幅近20倍，點燃了加密世界對AI Agent的熱情。

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，人工智慧快速發展的背後，也存在著隱憂。 OORT創辦人兼CEO李博士在《富比士》發表題為《2025年人工智慧失敗將激增：呼喚去中心化創新》的文章指出，人工智慧產業面臨著資料隱私、道德合規等眾多問題。引發的信任危機，增加了人工智慧失敗的風險。因此，去中心化創新已成為當務之急。

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已經建立了全球最大的去中心化雲端基礎設施之一，網路節點覆蓋100多個國家，產生了數百萬美元的收入，並推出了開源的Layer 1 Olympus協議（其共識演算法是“ Proof of Honesty”PoH） ，受美國專利保護）。透過原生代幣OORT，鼓勵大家貢獻數據，實現激勵閉環。近期，OORT上線了OORT DataHub，標誌著資料收集向全球化、多元化、透明化又邁進了一步，為DeAI的爆發奠定了堅實的基礎。

OORT Born from Accidental Classroom Moments

OORT 誕生於課堂上的偶然時刻

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項目，首先必須了解OORT要解決的問題。這涉及到討論當前人工智慧發展的瓶頸，主要與數據和中心化問題有關：

1. Disadvantages of Centralized AI

1.中心化AI的缺點

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.缺乏透明度導致信任危機。中心化人工智慧模型的決策過程往往是不透明的，被視為「黑盒子」操作。使用者很難理解人工智慧系統如何做出決策，這可能會在醫療診斷、金融風控等關鍵應用中導致嚴重後果。

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、數據壟斷與不平等競爭。少數大型科技公司控制大量數據，形成數據壟斷。這使得新進業者很難獲得足夠的數據來訓練自己的人工智慧模型，從而阻礙創新和市場競爭。此外，資料壟斷可能導致用戶資料的濫用，進一步加劇資料隱私問題。

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.倫理道德風險難以控制。中心化人工智慧的發展引發了演算法歧視、偏見放大等一系列倫理道德問題。此外，人工智慧技術在軍事和監控領域的應用引發了人們對人權、安全和社會穩定的擔憂。

2. Data Bottleneck

2. 資料瓶頸

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.數據沙漠。在人工智慧蓬勃發展的過程中，資料荒漠問題逐漸成為限制進一步發展的關鍵因素。人工智慧研究人員對數據的需求激增，但數據的供應卻難以跟上。過去十年，神經網路的不斷擴展依賴大量資料進行訓練，ChatGPT 等大型語言模型的發展就體現了這一點。然而，傳統資料集已接近枯竭，資料擁有者開始限制內容使用，使得資料擷取變得越來越困難。

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.

造成資料荒漠的原因是多方面的。一方面，資料品質參差不齊，不完整、不一致、雜訊和偏差等問題嚴重影響模型的準確性。另一方面，可擴展性挑戰也很嚴峻；收集足夠的數據既昂貴又耗時，維護即時數據很困難，而且大型數據集的手動註釋構成了瓶頸。此外，存取和隱私限制也不容忽視；資料孤島、監管限制和道德問題使資料收集變得困難。

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.

數據荒漠對人工智慧的發展有著深遠的影響。它們限制了模型訓練和最佳化，可能迫使人工智慧模型從追求大規模轉向更專業、更有效率的方法。在產業應用中，實現精確的預測和決策變得具有挑戰性，阻礙了人工智慧在醫療、金融等領域發揮更大作用。