金磊 发自 凹非寺 量子位 | 公众号 QbitAI 给歌曲做MV ，现在已经是 一个AI就能搞定 的时代了。 来，请欣赏用AI给神曲 《八方来财》 做的 东方赛博朋克 MV： 而这整整一分钟的内容，真的就是用一个AI来生成的。 并且这个AI啊，并不是来自咱们主流定义的大厂，而是出自一家 央企 —— 中国电信 面向公众开放的AI创作平台， TeleStudio 。 从视频制作角度来看，TeleStudio支持最高清 2K 、一次最长 20秒 的视频生成，并且复杂的动作也是可以一气呵成。 最重要的一点是，目前TeleStudio处于 限时免费 阶段！人人都可以做AI视频导演了。 （PS：目前还仅限PC端操作，移动端正在上线中。） 那么具体又该如何玩转TeleStudio呢？ 我们这就以刚才的视频为例，带你深度体验一波~ 手把手教你用AI做MV 在TeleStudio中，创作的方式主要有三大类，分别是 生成图片 、 生成视频 和 生成音效 。 要打造一个分钟级的短剧剧集，我们要做的就是把这三个功能给联动起来。 TeleStudio默认是一次直接生成4张图片，以及我们可以选择图片的宽高比（五种选项）和清晰度（三种选 ...

Core Viewpoint - The article discusses the innovative technology "AI Flow" developed by China Telecom's Artificial Intelligence Research Institute, which integrates information and communication technologies to enhance data transmission efficiency, particularly in challenging environments like the ocean [4][35]. Group 1: AI Flow Technology - AI Flow enables smooth video calls at sea by significantly reducing the data transmission required, achieving a reduction of one to two orders of magnitude in bandwidth usage [19][4]. - The technology allows for the transmission of model-extracted features instead of raw data, transforming the communication process from "pixel transportation" to "meaning understanding and artistic reconstruction" [18][19]. Group 2: The Three Laws of AI Flow - The first principle, "Law of Information Capacity" (信容律), reveals the conversion and measurement between different forms of information, allowing for a unified metric to measure communication and computation [15][8]. - The second principle, "Law of Familial Model" (同源律), describes a family of models where smaller models inherit knowledge from larger models, enabling efficient collaboration and task execution [22][25]. - The third principle, "Law of Multi-model Collaboration" (集成律), emphasizes the importance of connecting multiple intelligent agents to achieve a greater collective intelligence, allowing for a "1+1>2" effect through diverse and complementary capabilities [30][31]. Group 3: Implications and Future Outlook - The integration of these principles signifies a new era in communication technology, likened to installing a new "nervous system" for the digital world, which has profound implications for efficiency and convenience in an intelligent society [34][35]. - The advancements made by China Telecom in AI and communication technology position the company at a significant historical opportunity, marking a pivotal moment in the convergence of AI and communication [35][36].

Core Viewpoint - A new AI technology from China, known as AI Flow, is gaining significant attention and praise on international social media platforms, indicating its potential to redefine the AI landscape globally [2][4][70]. Group 1: Technology Overview - AI Flow is a key technology at the intersection of AI and communication networks, enabling intelligent interactions and emergent intelligence through a layered network architecture [7][28]. - Developed by China Telecom's TeleAI team, led by Professor Xuelong Li, AI Flow aims to facilitate seamless AI applications across various devices and platforms [9][8]. - The technology has been recognized by global market research firms, with Omdia highlighting its potential to support resource-intensive applications like autonomous vehicles and drones without compromising on latency or performance [13][14]. Group 2: Technical Innovations - AI Flow incorporates three core technological directions: Device-Edge-Cloud Collaboration, Familial Models, and Connectivity- and Interaction-based Intelligence Emergence [30][68]. - The Device-Edge-Cloud Collaboration framework allows for distributed reasoning, enhancing the responsiveness of AI services by optimizing task allocation across different network layers [33][34]. - Familial Models enable flexible scaling and efficient collaboration among models of varying sizes, allowing for resource optimization and avoiding redundant computations [50][52]. Group 3: Addressing AI Challenges - AI Flow addresses the challenge of AI's dependency on cloud computing, which often leads to unacceptable latency in critical applications like autonomous driving and surgical robotics [20][24]. - The technology proposes a solution to the "last mile" dilemma of AI integration by allowing intelligence to flow freely between edge devices and cloud resources, thus enhancing real-time responsiveness [25][28]. - By focusing on connectivity and collaboration rather than solely on computational power, AI Flow presents a new paradigm for AI development, emphasizing the importance of network infrastructure [70][71].