机器之心PRO · 会员通讯 Week 39 --- 本周为您解读 ③ 个值得细品的 AI & Robotics 业内要事 --- 1.放弃 CoT？Agentic 时代为什么更需要隐式推理？ 为何显示推理无法打破「1Hz」壁垒？Agentic AI 与 CoT 存在哪些冲突？隐式推理为何重新开始流行？TiS 对比 TbS 有哪些优势？隐式推理成为「实时推理」主流路径还有哪些挑战？为什么隐 式推理能绕过「黑箱」困境？... 2. 先验+后验加持，大模型能否 hold 住推理预测的现实「溢出」？ 静态考卷太简单，FutureX 能把「记忆型」模型拉到真正的未来考场吗？执行错误「积少成多」，大模型长程任务失效不能只让推理「背锅」？当推理「用兵」碰上财务预测等现实场景，模型 能否稳定「指挥」从而落地？过往的模型预测技术在往哪些方向发力？先验记忆与后验反思机制，未来能为模型预测带来新的突破吗？... 3. 站在巨人的肩膀上：Sergey Levine 预测机器人在五年内实现「完全自主」 为什么机器人的「全能时刻」是伪命题？Sergey Levine 为何更关注机器人「飞轮」？是什么让 Levine 预测机器人将「一年 ...

Group 1 - The core concept of the article revolves around the "Intelligence Spectrum" proposed by Eric Jang, which addresses the current "1Hz barrier" faced by AI and the prerequisites for achieving "Ultra Instinct" capabilities in AI systems [5][6][9] - Jang categorizes different types of intelligent decision-making processes along a spectrum, from "extremely slow intelligence" (e.g., plant growth) to "extremely fast intelligence" (e.g., precise movements of a hummingbird) [6][7] - Current leading LLMs like ChatGPT and Llama operate at a frequency of 1-2Hz, which is significantly slower than the natural conversational pace of humans (approximately 10Hz), leading to a mismatch in interaction speed [7][8] Group 2 - The "1-2Hz intelligence" reflects the limitations of AI in real-time interactions, resulting in a turn-based scenario where human users must wait for AI responses, exacerbating issues like hallucinations and context understanding [8][9] - Jang emphasizes that overcoming the "1Hz barrier" is not merely about increasing speed but is essential for achieving a qualitative transformation in AI capabilities, covering the entire intelligence spectrum from 0.1Hz to 50Hz [8][9] - The article discusses the dual process theory by Daniel Kahneman, illustrating how different decision frequency requirements reflect the fundamental and sometimes conflicting speed demands of various AI applications [10]