将投中网设为"星标⭐"，第一时间收获最新推送 "你看今天的中关村，好像互联网爆发的那几年。" 作者丨 刘燕秋 来源丨 投中网 最近听说了两则消息：一是，每个月大概有两万名外地招商人员活跃在海淀400多平方公里的土地上。二是，王兴兴当初之所 以决定再来海淀开一间分公司，是因为他想找一个离某海淀机器人独角兽更近的地方。 两个传说，指向同一个事实，人人都盯着海淀的企业。招商人员盯上的是成熟的项目，顶级独角兽创始人盯着的，是海淀最具 实力的竞争对手。归根结底，大家的目光离不开海淀，因为这里是科技创新的源头。从1992年北大科技园播下种子，到1994 年清华科技园扎根生长，再到2000年后东升大厦、中关村智造大街等相继崛起……三十余年来，高校实验室、企业研发中心 和五道口的咖啡馆密集交织，沉淀出了海淀厚重的创新生态。 "产学研用"的深度融合让海淀不仅诞生众多独角兽，更持续孕育着颠覆行业的源头技术。曾经，王兴、宿华、程一笑……众多 互联网巨头在这里萌芽、壮大。如今，智谱、月之暗面、面壁智能、银河通用等一批AI独角兽成为海淀的新名片。智谱华章地 处中关村东路，DeepSeek北京总部在融科资讯中心，月之暗面总部位于知春路的 ...

Core Insights - Artificial intelligence (AI) is a strategic technology leading a new wave of technological revolution, significantly transforming human production and lifestyle [1] - Beijing is positioning itself as the "AI capital" of China, with over 2,400 AI companies and a core industry scale nearing 350 billion yuan, accounting for half of the national total by 2024 [1] Group 1: AI Innovation and Research - Beijing is recognized as the city with the richest AI innovation resources in China, hosting 21 national key laboratories and over 40% of the nation's top talent [2] - The city has established four new research institutions focused on AI, producing globally leading original results, including the first native multimodal large model, Emu [2] - The Zhiyuan Institute has developed the "Wudao" series of large models, with Wudao 1.0 and Wudao 2.0 being significant milestones in China's AI model development [2][3] Group 2: AI Applications and Developments - Beijing has launched 132 large models, leading the nation in this area, and is focusing on disruptive technologies like optical computing and wafer-level chips [4] - The integration of AI with hardware is exemplified by companies like Mianbi Intelligent, which focuses on edge AI models that perform processing directly on user devices [4] - The education sector is set to benefit from AI with the introduction of MAIC (Massive AI-empowered Courses), which aims to enhance teaching efficiency and learning outcomes [5] Group 3: Future Directions and Infrastructure - Beijing plans to enhance its AI infrastructure, with an expected addition of 8,620 PetaFLOPS of computing power by 2024, bringing the total to over 33,000 PetaFLOPS [7] - The city aims to establish itself as a global hub for AI innovation and industry, focusing on interdisciplinary fields such as AI + life sciences and AI for science [7] - Efforts will be made to integrate data and applications, leveraging Beijing's rich data resources and comprehensive industrial system to promote the application of large models in the economy [7]

Group 1 - The core viewpoint of the articles emphasizes the rapid development of AI and its transition from the digital world to the physical world, highlighting the importance of world models in this evolution [1][3][4] - The 2023 Zhiyuan Conference marked a shift in focus from large language models to the cultivation of world models, indicating a new phase in AI development [1][3] - The introduction of the "Wujie" series of large models by Zhiyuan represents a strategic move towards integrating AI with physical reality, showcasing advancements in multi-modal capabilities [3][4] Group 2 - The Emu3 model is a significant upgrade in multi-modal technology, simplifying the process of handling various data types and enhancing the path towards AGI (Artificial General Intelligence) [4][5] - The development of large models is still ongoing, with potential breakthroughs expected from reinforcement learning, data synthesis, and the utilization of multi-modal data [5][6] - The current challenges in embodied intelligence include a paradox where limited capabilities hinder data collection, which in turn restricts model performance [6][8] Group 3 - The industry faces issues such as poor scene generalization and task adaptability in robots, which limits their operational flexibility [9][10] - Control technologies like Model Predictive Control (MPC) have advantages but also limitations, such as being suitable only for structured environments [10] - The development of embodied large models is still in its early stages, with a lack of consensus on technical routes and the need for collaborative efforts to address foundational challenges [10]