加密人工智慧融合：透過共生解鎖技術革命

隨著加密貨幣市場擁抱人工智慧 (AI)，與人工智慧相關的加密貨幣預計將在 2023 年引領潮流。三個關鍵領域脫穎而出：眾包運算能力、人工智慧代理和物聯網，以及資料所有權和權利。透過利用區塊鏈技術，這些加密貨幣為人工智慧產業面臨的挑戰（從晶片短缺到數據來源和主權）提供了解決方案。

The Symbiotic Fusion of Crypto and AI: Harnessing Innovation for a Technological Revolution

Introduction

加密貨幣與人工智慧的共生融合：利用創新推動科技革命簡介

The crypto industry has seized upon the burgeoning interest in artificial intelligence (AI), recognizing its transformative potential. AI-adjacent cryptocurrencies have emerged as a dominant narrative, attracting investors and industry enthusiasts alike. This exploration into the convergence of crypto and AI reveals the profound implications and promising opportunities that lie ahead.

加密貨幣產業抓住了人們對人工智慧 (AI) 日益增長的興趣，並認識到了其變革潛力。與人工智慧相關的加密貨幣已成為主流，吸引了投資者和行業愛好者。對加密貨幣和人工智慧融合的探索揭示了未來的深遠影響和充滿希望的機會。

Addressing the Chip Shortage through Crowdsourced Computing

透過眾包運算解決晶片短缺問題

One compelling use case for blockchain-based systems in AI is crowdsourced computing. This peer-to-peer model connects computing power (GPUs, CPUs, and storage) with engineers requiring machine-learning model training.

人工智慧中基於區塊鏈的系統的一個引人注目的用例是眾包計算。這種點對點模型將運算能力（GPU、CPU 和儲存）與需要機器學習模型訓練的工程師連接起來。

The growing lead times for chip orders have raised concerns about a potential chip shortage in 2024. Crowdsourced computing models, such as those offered by the Golem Network (GNT) and Render (RNDR), present a viable solution. These platforms provide a distributed network of computing power that can be rented via their marketplaces, utilizing a blockchain to track usage, invoicing, and payments.

晶片訂單交貨時間的延長引發了人們對 2024 年可能出現晶片短缺的擔憂。眾包運算模型，例如 Golem Network (GNT) 和 Render (RNDR) 提供的模型，提供了一種可行的解決方案。這些平台提供了一個分散式運算能力網絡，可以透過其市場租賃，利用區塊鏈來追蹤使用情況、發票和付款。

AI Agents and the Internet of Things: Unlocking Digital Autonomy and Seamless Transactions

人工智慧代理和物聯網：解鎖數位自治和無縫交易

AI agents, natural-language-based programs that users interact with, offer significant promise in automating everyday tasks. However, their effectiveness hinges on access to reliable digital payments and infrastructure. Cryptocurrencies serve as an ideal fit, providing secure payment rails for AI agents.

人工智慧代理是與使用者互動的基於自然語言的程序，為自動化日常任務提供了重要的希望。然而，它們的有效性取決於能否獲得可靠的數位支付和基礎設施。加密貨幣是一個理想的選擇，為人工智慧代理提供安全的支付管道。

AI agents can also be hosted on a blockchain as smart contracts, enabling seamless integration within the payments system and offering censorship resistance. On-chain agents are gaining traction among traders, who utilize AI trading bots for automated trading and airdrop farming.

人工智慧代理還可以作為智能合約託管在區塊鏈上，從而實現支付系統內的無縫整合並提供審查阻力。鏈上代理在交易者中越來越受歡迎，他們利用人工智慧交易機器人進行自動交易和空投農業。

The Internet of Things (IoT), an interconnected network of smart devices, presents another compelling application for AI and crypto. IoT devices may require autonomous transactions, necessitating secure infrastructure and payment mechanisms. Blockchain-based solutions and cryptocurrencies, such as IOTA (IOTA) and Fetch.ai (FET), address this need, enabling secure transactions within the IoT ecosystem.

物聯網 (IoT) 是一個智慧型設備互連網絡，為人工智慧和加密貨幣提供了另一個引人注目的應用。物聯網設備可能需要自主交易，從而需要安全的基礎設施和支付機制。基於區塊鏈的解決方案和加密貨幣，例如 IOTA (IOTA) 和 Fetch.ai (FET)，可以滿足這一需求，從而在物聯網生態系統中實現安全交易。

Empowering Data Ownership and Rights

賦予資料所有權和權利

Concerns have risen regarding the training data used by large language models (LLMs), with allegations of copyright violations. Blockchains provide a solution by tracking data provenance, establishing clear ownership records, and facilitating automated royalty payments.

人們對大型語言模型 (LLM) 使用的訓練資料的擔憂日益增加，並被指控侵犯版權。區塊鏈透過追蹤數據來源、建立清晰的所有權記錄並促進自動版稅支付提供了解決方案。

Decentralized social networks (DeSoc), such as Farcaster, allow users to monetize their data by opting in to sell it to specific LLMs or marketplaces. This approach offers a competitive edge over traditional platforms that commodify user data without compensation.

Farcaster 等去中心化社交網路 (DeSoc) 允許使用者選擇將其資料出售給特定的法學碩士或市場，從而將其資料貨幣化。與將用戶資料商品化而無需補償的傳統平台相比，這種方法具有競爭優勢。

The Speculative Allure of Crypto-AI Narratives

加密人工智慧敘事的投機吸引力

Cryptocurrency markets are often driven by speculation, with narratives influencing price movements even in the absence of tangible fundamentals or use cases. The proximity to trending industries like AI can suffice to propel cryptocurrencies into prominence.

加密貨幣市場通常是由投機驅動的，即使在缺乏切實的基本面或用例的情況下，敘事也會影響價格走勢。與人工智慧等熱門產業的接近足以推動加密貨幣脫穎而出。

During the run-up to NVIDIA's GTC conference, AI-adjacent cryptocurrencies experienced significant growth, demonstrating the power of narrative-driven speculation. The crypto-AI narrative is poised for continued success, regardless of the actual relevance of decentralized technologies within the broader AI industry.

在 NVIDIA GTC 會議籌備期間，與人工智慧相關的加密貨幣經歷了顯著增長，展示了敘事驅動投機的力量。無論去中心化技術在更廣泛的人工智慧產業中的實際相關性如何，加密人工智慧的敘述都將持續成功。

Conclusion

結論

The convergence of crypto and AI presents a multitude of innovative applications, from addressing chip shortages to empowering AI agents and the IoT. The potential for blockchain-based solutions to enhance data ownership and rights is particularly noteworthy. However, the speculative nature of crypto markets should be recognized and underscores the need for a balanced approach that considers both the transformative potential and the risks associated with this emerging field.

加密貨幣和人工智慧的融合帶來了多種創新應用，從解決晶片短缺到增強人工智慧代理和物聯網的能力。基於區塊鏈的解決方案在增強資料所有權和權利方面的潛力尤其值得注意。然而，應該認識到加密市場的投機性質，並強調需要採取平衡的方法，既考慮與這一新興領域相關的變革潛力和風險。