12 月 28 日，由创业黑马主办的 "第 17 届创业家年会" 在北京顺利召开。本届年会以 "智业 革命 —— 跨越断层，成为新物种" 为主题，汇聚了 AI 产业令人瞩目的上市公司、独角兽企业 及千里马企业，吸引了创投圈独具慧眼的投资家，与现场数百位充满激情的创业者，共同打造 了一场无与伦比的年度思想盛宴，实现了一场商业文明的集体跨越。 01 大会伊始，创业黑马创始人兼董事长牛文文以《 AI 青年科学家大有作为》为主题做了开场演 讲。牛文文表示，过去 17 年，创业黑马主要做了一个"创"字，成为中国最活跃的创业社区之 一。未来 10 年，创业黑马将用"科"字开启未来，重仓科技服务，打造全球 AI 青年科学家社 区。 牛文文称，全球 AI 青年科学家社区，就是未来世界的先导区。他呼吁让 AI 青年科学家成为新 世界的领航员，并期待更多的科学家、创业家、投资家一起加入进来，加入 AI 未来的"核反应 堆"。 大会现场， 创业黑马集团创始人兼董事长牛文文， 创业黑马集团轮值总裁雷勤， 与8位青年 科学家一同启动"创业黑马全球AI青年科学家社区" 。 上午，创业黑马发布了9个AI社区，具身智能社区、智能体社区、多模 ...

Core Viewpoint - The article discusses the development cycles of autonomous driving and embodied intelligence, suggesting that the latter may achieve commercialization faster due to anticipated breakthroughs in algorithms and data [1]. Group 1: Industry Development - The autonomous driving industry has been scaling and commercializing for nearly 10 years since 2015, while the robotics industry has been evolving for many years, with expectations for significant advancements in the next 5-8 years [1]. - Companies like Zhiyuan and Yushu are preparing for IPOs, which could greatly invigorate the entire industry [1]. Group 2: Community Building - The goal is to create a community of 10,000 members within three years, focusing on bridging academia and industry, and providing a platform for rapid problem-solving and industry influence [1]. - The community aims to facilitate technical exchanges and discussions on academic and engineering issues, with members from renowned universities and leading robotics companies [8]. Group 3: Educational Resources - A comprehensive entry route for beginners has been organized within the community, including various learning paths and resources for those new to the field [2]. - For those already engaged in research, valuable industry frameworks and project proposals are provided [4]. Group 4: Job Opportunities - The community continuously shares job postings and opportunities, contributing to the establishment of a complete ecosystem for embodied intelligence [6]. Group 5: Knowledge Sharing - The community has compiled a wealth of resources, including over 40 open-source projects, nearly 60 datasets related to embodied intelligence, and mainstream simulation platforms [11]. - Various learning routes are available, covering topics such as reinforcement learning, multi-modal models, and robotic navigation [11].