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OpenAI co-founder: There is a whole industry that still needs to be created to power AI revolution
Youtube· 2025-10-13 14:29
Well, Brooks is a huge copy as you often talk about David and I'm thrilled to have two people to be able to discuss a partnership that this morning it's a code development to do custom chips as well as 10 gigawatt deployment. We got to find out a little more deployment but first we have to talk about the custom chips. Joining us now first on CBC is OpenAI co-founder and president Greg Brockman spoke with him last week and Brooke I'm president Charlie Cowas we uh Charlie's going to be able to speak to a lot ...
OpenAI co-founder: There is a whole industry that still needs to be created to power AI revolution
CNBC Television· 2025-10-13 14:19
Well, Brooks is a huge copy as you often talk about David and I'm thrilled to have two people to be able to discuss a partnership that this morning it's a code development to do custom chips as well as 10 gigawatt deployment. We got to find out a little more deployment but first we have to talk about the custom chips. Joining us now first on CBC is OpenAI co-founder and president Greg Brockman spoke with him last week and Brooke I'm president Charlie Cowas we uh Charlie's going to be able to speak to a lot ...
OpenAI partners with Broadcom to build custom AI chips, adding to Nvidia and AMD deals
CNBC· 2025-10-13 13:04
In this articleAVGOSam Altman, chief executive officer of OpenAI Inc., during a media tour of the Stargate AI data center in Abilene, Texas, US, on Tuesday, Sept. 23, 2025. Kyle Grillot | Bloomberg | Getty ImagesWeeks after Broadcom's stock shot up on the disclosure of a $10 billion customer that analysts said was OpenAI, the two companies have made the partnership official.OpenAI and Broadcom said Monday that they're jointly building and deploying 10 gigawatts of custom artificial intelligence accelerators ...
《大模型的第一性思考》李建忠对话GPT5与Transformer发明者Lukasz Kaiser实录
3 6 Ke· 2025-10-13 10:46
Core Insights - The rapid development of large intelligent systems is reshaping industry dynamics, exemplified by OpenAI's recent release of Sora 2, which showcases advancements in model capabilities and the complexity of AI evolution [1][2] - The dialogue between industry leaders, including CSDN's Li Jianzhong and OpenAI's Lukasz Kaiser, focuses on foundational thoughts regarding large models and their implications for future AI development [2][5] Group 1: Language and Intelligence - Language plays a crucial role in AI, with some experts arguing that relying solely on language models for AGI is misguided, as language is a low-bandwidth representation of the physical world [6][9] - Kaiser emphasizes the importance of temporal dimensions in language, suggesting that the ability to generate sequences over time is vital for expressing intelligence [7][9] - The conversation highlights that while language models can form abstract concepts, they may not fully align with human concepts, particularly regarding physical experiences [11][12] Group 2: Multimodal Models and World Understanding - The industry trend is towards unified models that can handle multiple modalities, but current models like GPT-4 already demonstrate significant multimodal capabilities [12][13] - Kaiser acknowledges that while modern language models can process multimodal tasks, the integration of different modalities remains a challenge [13][15] - The discussion raises skepticism about whether AI can fully understand the physical world through observation alone, suggesting that language models may serve as effective world models in certain contexts [14][15] Group 3: AI Programming and Future Perspectives - AI programming is emerging as a key application of large language models, with two main perspectives on its future: one advocating for natural language as the primary programming interface and the other emphasizing the continued need for traditional programming languages [17][18] - Kaiser believes that language models will increasingly cover programming tasks, but a solid understanding of programming concepts will remain essential for professional developers [19][20] Group 4: Agent Models and Generalization Challenges - The concept of "agent models" in AI training faces challenges in generalizing to new tasks, raising questions about whether this is due to training methods or inherent limitations [21][22] - Kaiser suggests that the effectiveness of agent systems relies on their ability to learn from interactions with various tools and environments, which is currently limited [22][23] Group 5: Scaling Laws and Computational Limits - The belief in Scaling Laws as the key to stronger AI raises concerns about potential over-reliance on computational power at the expense of algorithmic and architectural advancements [24][25] - Kaiser differentiates between pre-training and reinforcement learning Scaling Laws, indicating that while pre-training has been effective, it may be approaching economic limits [25][26] Group 6: Embodied Intelligence and Data Efficiency - The slow progress in embodied intelligence, particularly in humanoid robots, is attributed to either data scarcity or fundamental differences between bits and atoms [29][30] - Kaiser argues that advancements in data efficiency and the development of multimodal models will be crucial for achieving effective embodied intelligence [30][31] Group 7: Reinforcement Learning and Scientific Discovery - The shift towards reinforcement learning-driven reasoning models presents both opportunities for innovation and challenges related to their effectiveness in generating new scientific insights [32][33] - Kaiser notes that while reinforcement learning offers high data efficiency, it has limitations compared to traditional gradient descent methods [33][34] Group 8: Organizational Collaboration and Future Models - Achieving large-scale collaboration among agents remains a significant challenge, with the need for more parallel processing and effective feedback mechanisms in training [35][36] - Kaiser emphasizes the necessity for next-generation reasoning models that can operate in a more parallel and efficient manner to facilitate organizational collaboration [36][37] Group 9: Memory Mechanisms in AI - Current AI models' memory capabilities are limited by context windows, resembling working memory rather than true long-term memory [37][38] - Kaiser suggests that future architectures may need to incorporate more sophisticated memory mechanisms to achieve genuine long-term memory capabilities [38][39] Group 10: Continuous Learning in AI - The potential for AI models to support continuous learning is being explored, with current models utilizing context as a form of ongoing memory [39][40] - Kaiser believes that while context learning is a step forward, more elegant solutions for continuous learning will be necessary in the future [40][41]
OpenAI奥特曼:能被ChatGPT消灭的工作不是真正的工作
3 6 Ke· 2025-10-13 10:06
你今天的工作,或许并不是真正的工作 这句耸人听闻的言论出自奥特曼与Rowan Cheung最新的采访。 在这场长达30分钟的对谈里,除了自己对AI与工作的思考,奥特曼还分享了GPT-6的进展、ChatGPT是否会成为美国版微信、AGI的设想变化、AI未来 的交互模式,以及自己被恶搞成Sora热梗的感受。 可以说,这次对话涵盖了从娱乐八卦到前沿科技的多重视角,既有趣味,也直指未来趋势。 经整理的访谈全文如下: (注:为方便阅读,调整了部分语气词与铺垫) 访谈全文 DevDay之后:最大亮点与产品布局 Q: Dev Day2025里所有发布的内容——你最兴奋的部分是哪一个? Sam Altman:我对所有的内容都很兴奋。把应用引入ChatGPT这件事,其实我很早就想做了。 但更让我兴奋的是听大家聊他们用Agent Builder做的各种东西。其实无论是Agent Builder还是Agent Kit,里面都有很多让我想亲自去用的功能。不过,如 果一定要我选一个,我觉得在ChatGPT里运行应用会是最棒的。 Rowan Cheung:每周活跃用户8亿人的ChatGPT已经成了新的分发平台。开发者和创业者该怎么利用A ...
从工业机器人四大家族到中国力量:机器人产业的下半场人形新物种的较量
机器人圈· 2025-10-13 09:51
AI 的大脑,机器人的身体 " 如果说 ChatGPT 给了 AI 一颗聪明的大脑,那么机器人,就是 AI 走向现实世界的 ' 身体 ' 。 " 过去几年,我们经历了人工智能的疯狂进化: ChatGPT 让机器会写会说, Sora 让视频生成媲美电影特效, AI 绘画和 AI 客服更是飞入寻常百姓家。可是,人类社会的真正核心不是信息,而是 行动 。 这正是机器人存在的意义。它们是 AI 的具身化形态,是人工智能进入现实世界的 " 桥梁 " 。而在机器人领域, 中国,正在迎来前所未有的机会。 全球工业机器人年销售额(亿美元) | 注册类别 会议费用 10月 20 日前 | | | | --- | --- | --- | | 学生参会 | 1500 元 | 1200 元 | | 普通参会 | 2800 元 | 2500 元 | | 企业代表 0.00 | 3800 元 | 3500 元 | 数据显示, 2020 年全球机器人销售额 343 亿美元,到 2024 年已增长至 660 亿美元,复合增速高达 17.8% 。 中国更是全球最大市场, 2024 年规模达到 470 亿美元,复合增速 14.3% 。换句话说, ...
OpenAI奥特曼:能被ChatGPT消灭的工作不是真正的工作
量子位· 2025-10-13 08:47
Core Insights - The discussion highlights the evolving role of AI in the workplace, suggesting that many current jobs may not represent "real work" as AI capabilities advance [30] - The conversation also touches on the development of GPT-6 and the potential for AI to achieve AGI (Artificial General Intelligence) [18][19] Group 1: AI Development and Applications - Sam Altman expresses excitement about the integration of applications into ChatGPT, emphasizing the potential for developers to create innovative solutions using the Agent Builder and Agent Kit [5][6] - The conversation indicates that ChatGPT has reached 800 million weekly active users, positioning it as a new distribution platform for developers [5] - Altman notes significant advancements in model capabilities over the past two years, allowing for easier and more complex system development with minimal coding [7][8] Group 2: Future of Work and AI Impact - The dialogue suggests that the number of software applications created will increase dramatically, and the time required for testing and refining ideas will decrease significantly [9] - Altman predicts that the first billion-dollar company operated by agents is still a few years away, but the technology is progressing rapidly [11][12] - The concept of "workslop," where AI-generated content requires additional human editing, is discussed, highlighting the need for education on effective AI usage [21][22] Group 3: AGI and Its Implications - Altman defines AGI as AI surpassing human capabilities in high-value economic tasks, noting that current AI can make novel discoveries, albeit on a small scale [19][20] - The conversation emphasizes the importance of recognizing both the potential and limitations of AI advancements, with a focus on gradual progress towards AGI [18][19] Group 4: AI in Communication and Interaction - Altman argues that voice may not be the ultimate form of interaction with AI, suggesting that various modes of communication will coexist [39][40] - The potential for real-time video interactions is highlighted as a valuable path towards achieving AGI [26] Group 5: Business Models and Future Directions - The discussion includes thoughts on potential revenue models for new applications like Sora, with considerations for user engagement and monetization strategies [27][28] - Altman expresses optimism about the future of AI and its ability to create new opportunities, while also acknowledging the need for a global framework to manage risks associated with powerful AI models [33]
马斯克xAI投身“世界模型”竞赛,欲重塑AI与现实交互新体验
Sou Hu Cai Jing· 2025-10-13 04:45
【环球网科技综合报道】近日,科技界在人工智能领域又掀起一阵热潮,特斯拉CEO埃隆·马斯克旗下 的xAI公司正全力投入"世界模型"的研发,与Meta、谷歌等科技巨头一同在这场激烈的竞赛中角逐。 10月13日消息,据《金融时报》报道,xAI今年夏天从英伟达招揽了专家团队,专注于新一代人工智能 模型的打造。这些模型以视频和机器人数据为训练素材,旨在深入理解现实世界。与传统基于文本训练 的大型语言模型不同,"世界模型"有望突破现有局限,为人工智能赋予更强大的能力。 两位知情人士透露,xAI构建"世界模型"有着明确的应用方向,其中游戏领域是重点之一。该模型可生 成交互式3D环境,为玩家带来全新的游戏体验。同时,它也能应用于机器人的人工智能系统,推动实 体产品智能化发展。 《金融时报》指出,xAI聘请的泽尚·帕特尔和伊桑·何两位研究人员,在"世界模型"方面经验丰富。而英 伟达凭借其Omniverse平台在该技术领域处于领先地位,这无疑为xAI的研发提供了有力支持。 英伟达上月向《金融时报》表示,这一技术有望为人工智能在软件和计算机之外的应用开辟新途径,如 人形机器人等实体产品领域。 不过,"世界模型"的研发并非一帆风顺。 ...
马斯克AI公司开发“世界模型”,从英伟达挖专家将推游戏
Feng Huang Wang· 2025-10-13 03:21
根据英伟达的官方介绍,世界模型是一种生成式AI模型,能够理解现实世界的动态特征,包括物理属 性和空间特性。这类模型利用文字、图像、视频以及动作在内的输入数据来生成影片。 今年夏天,xAI从英伟达挖来了专家,研发这类新一代AI模型。这些模型通过学习视频以及来自机器人 的数据,理解现实世界。世界模型有望将AI的能力提升到超越大语言模型的水平。目前,大模型主要 接受文本训练,是ChatGPT以及xAI自家Grok等热门AI工具的技术基础。 凤凰网科技讯 北京时间10月12日,据《金融时报》报道,埃隆·马斯克(Elon Musk)旗下xAI公司正加紧 构建所谓的"世界模型",与Meta和谷歌等对手一同角逐下一代AI系统。这些系统能够实现对物理环境的 自主导航与设计。 当前,OpenAI旗下Sora等视频生成模型,主要通过从训练数据中学习到的模式进行预测,从而逐帧生 成视频图像。但是世界模型则会向前迈进一大步,因为它能实时理解物理世界的因果关系,掌握物体在 不同环境中的实时互动机制。 巨大挑战 除了xAI外,谷歌、Meta等领先的AI实验室也在研发这类系统。 然而,世界模型仍面临巨大的技术挑战。要找到足够的数据来模拟现 ...
上线后登顶美区App Store,OpenAI能否借此开启AI短视频的社交实验?
Sou Hu Cai Jing· 2025-10-12 14:21
Core Insights - The article discusses the launch of Sora, an AI-driven short video social application by OpenAI, which has gained significant attention and popularity since its release on September 30, 2025 [2][3][20] - Sora is positioned as an "AI version of TikTok," integrating AI-generated content with social sharing features, allowing users to create videos without direct involvement [3][11][20] Group 1: Application Features - Sora allows users to generate 10-second videos by simply inputting a text description, leveraging the capabilities of the Sora 2 model, which is noted for its advancements in video quality and multi-modal integration [9][11] - The application includes a unique "Cameos" feature, enabling users to create AI-generated videos that replicate their likeness, facilitating social interactions without physical presence [8][11] - Users must enter an invitation code to access Sora, promoting a viral growth model through social sharing of invite codes [6][20] Group 2: User Experience and Feedback - Sora has received mixed reviews, with a current average rating of 2.8 stars on the App Store, indicating some dissatisfaction among users [13] - Users have reported issues with content moderation, including false positives in content violations, which have affected their experience [17][18] - The application is still in the testing phase and is currently free, allowing users to explore its features without financial barriers [20] Group 3: Industry Implications - The launch of Sora reflects a growing trend of integrating AI technologies into social media platforms, potentially reshaping user engagement and content creation [2][20] - There are ongoing concerns regarding copyright infringement, as users can generate content featuring popular media characters, prompting calls for stricter content control measures [18][19] - The combination of AI video generation and social networking is seen as a new frontier in the digital landscape, despite challenges related to compliance and content regulation [20]