A retired NASA engineer who had worked on the Space Shuttle program for 25 years was hired by SpaceX at age 62. He described the first week as the most disorienting experience of his professional life. At NASA, a design change required 3 meetings, 2 committees, a review board, and 6 to 18 months of documentation. At SpaceX, his new supervisor said "if it works, build it. If it doesn't, change it by Friday." He sat at his desk after the first day and called his wife. He told her he felt like a man who had been swimming with ankle weights for 25 years and someone just cut them off. He could move. He could decide. He could build something in the morning and test it in the afternoon. The constraint that he had accepted as the nature of aerospace engineering turned out to be the nature of bureaucracy. Within 6 months he had contributed to more design iterations than he had completed in his last 5 years at NASA. Not because he was working harder. Because the time between having an idea and testing the idea collapsed from months to hours. The same brain. The same knowledge. A different operating system. He described a specific moment where he proposed a modification to a Raptor engine component. At NASA, the proposal would have entered a queue. At SpaceX, the modification was machined by the end of the day and tested by the following morning. He watched his idea become a physical object in 18 hours. At NASA, 18 hours would not have been enough to schedule the first meeting to discuss whether to schedule a meeting. He told an interviewer: "I didn't get smarter when I went to SpaceX. I got faster. The intelligence was always there. The system was in the way." The difference between SpaceX and NASA is not talent. NASA has extraordinary talent. The difference is the time between a thought and a test. SpaceX compresses it. NASA elongates it. The same engineer produces 10x more at SpaceX than at NASA because the bottleneck was never the engineer. It was the meetings about the meetings about the work.
#部署/工程
Elon Musk@elonmuskAI 评分1515引用Race 🕊️@multiplanet1
Latent SpaceAI 评分7474 Dyna Robotics 联创 Jason Ma 谈 Dyna-2 世界动作模型与小时级自主机器人
Dyna Robotics 联合创始人兼首席科学家 Jason Ma 在 Latent Space 播客中介绍了 Dyna-2 世界动作模型,该模型基于超过一百万小时人类视频预训练,可在约 45 分钟演示数据后学会新任务。
MIT Technology Review · AIAI 评分2020 AVEVA 如何为自主工业 AI 构建更安全的落地路径
AVEVA 首席技术官 Arti Garg 提出,工业 AI 正从预测分析转向基础模型、physical AI 与 agentic AI,但因其直接作用于物理系统,必须把安全与可靠性置于部署核心。AVEVA 的负责任 AI 框架强调安全、效率与人类监督,主张 AI 应增强而非取代关键决策中的人。Garg 还参与 IEEE 工作组,制定覆盖电力、能源、资源、水和碳的 AI 环境影响测量标准方法。
Import AIAI 评分3232 Import AI 474:Platonic mindspace、太空 TPU、智谱启动外层 RSI 循环
Michael Levin 提出"Platonic mindspace"假说,认为心智是非物理模式,身体与机器只是其进入物理世界的接口,xenobots、anthrobots 等实验被视为佐证。本期 Import AI 还讨论了太空 TPU 与机器人领域等待 LLM 时刻但缺少通用算法。智谱(Zhipu)则启动了外层 RSI 循环。
GitHub Blog · AI & MLAI 评分3636 GitHub Copilot 为何认为 chat 是错误的 UI
GitHub Copilot 提出 canvas 概念,即运行在 GitHub Copilot app 内、无浏览器外壳的全栈应用,可与 agent 双向通信并调用第三方 API、在本地执行代码。官方认为 chat 只是通用兜底方案,用户明确任务时更该让 agent 构建可复用工具,而非把 agent 本身当工具,例如用 canvas 管理 Winget 包或直接操作 SQLite 数据库。
Andrew Ng@AndrewYNgAI 评分3838吴恩达发布 AI Engineering Skills Map 系列文章,详解最高层级技能中的"构建与部署 AI 应用",该技能图基于大量招聘信息、专家访谈和问卷分析得出。
Berkeley AI ResearchAI 评分3434 智能免费之后怎么办?面向智能体、由智能体运行、由智能体构建的数据系统
UC Berkeley 的 Aditya G. Parameswaran 等人提出,随着推理成本从 2023 年初 GPT-4 级约 $30/百万 token 降至如今不到 $1、部分厂商低于 $0.10,数据系统需应对三类新挑战:面向智能体的数据系统、由智能体运行的数据系统、由智能体构建的数据系统。
Berkeley AI ResearchAI 评分3434 自适应并行推理:高效推理扩展的下一个范式
伯克利 AI 研究团队梳理了并行推理领域进展,指出当前多数方法(如 Self-consistency、Best-of-N、Tree of Thoughts、MCTS、ParaThinker、GroupThink、Hogwild!