导语 News Today 随着GPT等大模型技术的爆发，全球AI竞争进入白热化阶段。 中国AI企业如何在技术封锁、算力限制等挑战中突围？又是哪些人在背后推动技术的自主创新与落地应用？ 本文将聚焦几位在中国AI大模型领域具有代表性的领导者，探讨他们的战略布局与技术成果! 梁文锋 DeepSeek 职位 ：DeepSeek（深度求索）创始人。 简介 ：梁文锋是DeepSeek的创始人，代表了中国AI领域的新生力量，其对技术趋势有敏锐洞察和对市场机会有极致把握能力。 1 2 通义千问 主要成果 ：梁文锋及其团队花了3年时间在核心技术上进行深度积累，优化算法，打磨产品。当产品正式上线后，在短短20天内，团队连续更新了50 多个版本，最终在20天内获得全球3000万日活用户，成为现象级产品。 林俊旸 职位 ：通义千问（阿里云旗下产业级大语言模型）负责人。 行业影响 ：DeepSeek的成功展现了中国AI创业公司的技术实力和市场爆发力，其快速发展加剧了全球AI大模型领域的竞争，推动了AI技术的普及和 应用。 更多关于大模型和人工智能的行业动态、技术进展、求职内推及问答交流，欢迎加入大模型之心Tech知识星球！ 星球内部 ...

Core Viewpoint - The article discusses the advancements of ByteDance's UI-TARS-2, a new generation of AI agents that can autonomously operate graphical user interfaces (GUIs) across various platforms, outperforming competitors like Claude and OpenAI [2][23][24]. Group 1: UI-TARS-2 Overview - UI-TARS-2 is designed to autonomously complete complex tasks on computers, mobile devices, web browsers, terminals, and even games [6][10]. - The architecture includes a unified agent framework, multimodal perception, multi-round reinforcement learning, and hybrid operation flows [7][8]. Group 2: Challenges Addressed - UI-TARS-2 tackles four major challenges in AI GUI operation: data scarcity, environment fragmentation, single capability, and training instability [5][10]. - The model employs a "data flywheel" strategy to address data scarcity by collecting raw data and generating high-quality task-specific data through iterative training [11][12]. Group 3: Reinforcement Learning Enhancements - The team optimized traditional reinforcement learning methods to ensure stable operations in long-duration GUI tasks by improving task design, reward mechanisms, and training processes [15][17]. - The model uses asynchronous rollout and several enhancements to the PPO algorithm to improve stability and encourage exploration of less common but potentially effective actions [17][18]. Group 4: Performance Metrics - UI-TARS-2 has shown superior performance in various GUI tests, scoring higher than Claude and OpenAI models in tasks across different operating systems and command-line environments [23][24]. - In gaming scenarios, UI-TARS-2 achieved an average score of approximately 60% of human performance, outperforming competitors in several games [27][28]. Group 5: Practical Applications - Beyond GUI operations, UI-TARS-2 can perform tasks such as information retrieval and code debugging, demonstrating its versatility and effectiveness compared to models relying solely on GUI interactions [28][29].