Core Viewpoint - Meta has launched its new "world model" V-JEPA 2, aimed at enhancing AI's physical reasoning capabilities for better understanding and predicting the physical world [1][3][11] Group 1: V-JEPA 2 Overview - V-JEPA 2 is described as a "realistic abstract digital twin" that enables AI to predict the consequences of its actions and plan accordingly [1][3] - The model is 30 times faster than Nvidia's Cosmos model and has been open-sourced for developers to access and integrate into various applications [1][6][5] - V-JEPA 2 builds on the previous V-JEPA model released by Meta, further improving understanding and prediction capabilities [4] Group 2: AI Capabilities - The model provides AI with three core abilities: understanding, predicting, and planning, allowing it to create realistic internal simulations [3][17] - V-JEPA 2 can perform reasoning without the need for labeled video segments, distinguishing it from existing generative AI systems like ChatGPT [3][4] Group 3: Applications and Impact - The model is designed for real-time spatial understanding in AI-driven technologies such as autonomous vehicles, warehouse robots, and drone delivery systems [3][5] - Meta anticipates that V-JEPA 2 will pave the way for AI to operate autonomously in unfamiliar environments, potentially impacting sectors like healthcare, agriculture, and disaster response [18][19] Group 4: Competitive Landscape - The release of V-JEPA 2 is seen as a critical milestone in Meta's long-term AI roadmap, especially in the context of increasing competition with OpenAI, Microsoft, and Google [11][13] - The growing importance of world models in AI research is highlighted, with other companies like Google DeepMind also exploring similar projects [19] Group 5: Leadership and Strategy - Yann LeCun, Meta's Chief AI Scientist, emphasizes the need for AI to build models of how the world operates rather than merely mimicking human text [8][9] - Meta's CEO Mark Zuckerberg is reportedly taking a more hands-on approach to AI development, including significant investments in AI training data and the formation of new teams focused on achieving "superintelligence" [13][14][15]

责编 | 王启隆 出品丨AI 科技大本营（ID：rgznai100） 黄教主的演讲 感觉才没过几天，今年的 GTC 英伟达大会也即将迎来尾声了。 而今年比尔·达利则是对话"AI 教父" 杨立昆 （Yann LeCun），很有前后呼应的感觉。 但 GTC 并不只有黄仁勋和杨立昆，还有许多精彩的演讲与对话，比方说： ………… 接下来的一段时间， CSDN AI 科技大本营 将会在「 GTC 2025 大师谈 」栏目持续更新这些精华内容的全文整理，尽情期待。 比尔·达利 自己就在采访杨立昆之后进行了一场 演讲 ，系统性地讲解了英伟达 2024 一整年的四大项目进展，内容干货很多； OpenAI o1 作者 诺姆·布朗 （Noam Brown）和英伟达的 AI 科学家来了一场 对话 ，他认为现在 AI 圈最需要来一场革命的，就是这些五花八 门的 基准测试 （Benchmark），而且改这个东西还不需要花太多算力资源； 2018 年诺贝尔化学奖得主 弗朗西斯·阿诺德 （Frances Arnold）围绕 AI for Sciense 还有蛋白质工程进行了一场相当硬核的 圆桌对话 ； UC 伯克利教授 彼得·阿比尔 （P ...