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src="https://cdn.jiqizhixin.com/assets/home/tab-2--active-1c2c1721805938ed7591f5d80126ef04642aab1e577a27ff838076887aced2e2.png">最新参考</img></div></div><div class="home-right__body"><div class="home-right__list-1"><div class="home-right__list-item"><div><p>Week 47 · RL「误人」？LeCun 在技术路线上又有何战略摇摆？</p><img alt="icon" src="https://cdn.jiqizhixin.com/assets/home/list-icon-89b2748aed60768f98d8e67f934eadfc5449fe6854c06f9e2fe493f87d00f520.png" /><span>11.22</span></div><p>本周， NEO 推出可自主学习的 AI 工程师，Kaggle 表现优于 o1；月之暗面推出 k0-math 推理模型以及 Kimi 探索版功能。</p></div><div class="home-right__list-item"><div><p>Week 46 · AI 竞赛进入推理阶段：扩展测试时间计算是万能的吗？Scaling What 成为关键</p><img alt="icon" src="https://cdn.jiqizhixin.com/assets/home/list-icon-89b2748aed60768f98d8e67f934eadfc5449fe6854c06f9e2fe493f87d00f520.png" /><span>11.16</span></div><p>本周，外媒曝 OpenAI 调整技术策略引热议；MIT 和哈佛分别发文探讨 Scaling Law 局限与改进机会。</p></div><div class="home-right__list-item"><div><p>Week 45 · 「压缩即智能」，成就 LLM 的 Transformer 未必是终极解？</p><img alt="icon" src="https://cdn.jiqizhixin.com/assets/home/list-icon-89b2748aed60768f98d8e67f934eadfc5449fe6854c06f9e2fe493f87d00f520.png" /><span>11.08</span></div><p>本周，零一万物宣布战略转向 toB 市场；知乎、昆仑万维、夸克均推出AI搜索功能。</p></div><div class="home-right__list-item"><div><p>Week 44 · 从文本到屏幕：「Project Jarvis」们能实现 AGI 吗？</p><img alt="icon" src="https://cdn.jiqizhixin.com/assets/home/list-icon-89b2748aed60768f98d8e67f934eadfc5449fe6854c06f9e2fe493f87d00f520.png" /><span>11.01</span></div><p>本周，OpenAI 推出 ChatGPT Search；MIT 新作发现 LLM 可能具有类似人脑的分层结构。</p></div><div class="home-right__list-item"><div><p>Week 43 · 微调失格？持续反向传播算法将解锁新的训练范式吗？</p><img alt="icon" src="https://cdn.jiqizhixin.com/assets/home/list-icon-89b2748aed60768f98d8e67f934eadfc5449fe6854c06f9e2fe493f87d00f520.png" /><span>10.25</span></div><p>本周，Yoshua Bengio 团队提出强化学习新策略；Anthropic 升级版 Claude 3.5 Sonnet 解锁「计算机使用」能力。</p></div><div class="home-right__list-item"><div><p>Week 42 · OpenAI 的 MLE-bench 被当成「奇点触发器」了？</p><img alt="icon" src="https://cdn.jiqizhixin.com/assets/home/list-icon-89b2748aed60768f98d8e67f934eadfc5449fe6854c06f9e2fe493f87d00f520.png" /><span>10.18</span></div><p>本周，OpenAI 开源多智能体框架「Swarm」被指控剽窃；李飞飞提出面向机器人策略训练的「数字表亲」概念。</p></div></div><div class="home-right__list-2"><div class="home-right__list-item"><div><p>RL「误人」？LeCun 在技术路线上又有何战略摇摆？</p><img alt="icon" src="https://cdn.jiqizhixin.com/assets/home/list-icon-89b2748aed60768f98d8e67f934eadfc5449fe6854c06f9e2fe493f87d00f520.png" /><span>11.22</span></div><div class="desc"><p>Andrey Karpathy 近日有关「悔做 RL」的言论引起许多关注，而他在后悔的同时对 Yann LeCun 从一开始就不看好强化学习的态度始终正确的说法引发了 AI 社区对另一件事的兴趣，即「LeCun 的 AI 战略是否出现过摇摆？」</p></div></div><div class="home-right__list-item"><div><p>Sora 发布大半年后，主流视频生成模型们是否已实现超越？</p><img alt="icon" src="https://cdn.jiqizhixin.com/assets/home/list-icon-89b2748aed60768f98d8e67f934eadfc5449fe6854c06f9e2fe493f87d00f520.png" /><span>11.22</span></div><div class="desc"><p>在 Sora 发布后的近 9 个月时间里，MiniMax-Video-01、Kling 1.5 等视频生成模型陆续发布，不少表示已经达到了 Sora 级别。</p></div></div><div class="home-right__list-item"><div><p>2025 IT 优先事项报告：在产线中集成 AI 是保持竞争力的关键</p><img alt="icon" src="https://cdn.jiqizhixin.com/assets/home/list-icon-89b2748aed60768f98d8e67f934eadfc5449fe6854c06f9e2fe493f87d00f520.png" /><span>11.22</span></div><div class="desc"><p>近日，软件资产管理公司 Flexera 发布《2025 IT 优先事项报告》（以下简称为「报告」）。该报告对来自美国、英国等地区 800 名 IT 领导者进行了调查，重点关注 IT 决策者如何制定 2025 年的优先事项。</p></div></div><div class="home-right__list-item"><div><p>谁能进入下一轮？具身智能「练习生」的技术储备和商业路径有何异同？</p><img alt="icon" src="https://cdn.jiqizhixin.com/assets/home/list-icon-89b2748aed60768f98d8e67f934eadfc5449fe6854c06f9e2fe493f87d00f520.png" /><span>11.16</span></div><div class="desc"><p>具身智能在 2022 年被李飞飞比喻成视觉领域的一颗「北极星」，而后被认为是通往 AGI 的有效路径。以大语言模型的突破为契机，通过赋予 AI 身体，使其能够和世界交互，再将大脑升级为世界模型，让机器人能够灵活应对未见过的任务，适应工业、居家生活、医疗、养老等不同场景。这样的概念佐以技术进展让具身智能的市场规模不断膨胀，并涌现了大量的创业团队。这些具身智能「练习生」或是在机器人领域长期深耕，具有丰富的技术储备，或者在浪潮中入局，以深厚的学术背景和创新的技术成果吸引大量资本。</p></div></div><div class="home-right__list-item"><div><p>扩展测试时计算是万能的吗？Scaling What成为关键</p><img alt="icon" src="https://cdn.jiqizhixin.com/assets/home/list-icon-89b2748aed60768f98d8e67f934eadfc5449fe6854c06f9e2fe493f87d00f520.png" /><span>11.15</span></div><div class="desc"><p>近日，外媒 The Information 的一篇报道引发了关于「模型 Scaling Laws 范式是否已经达到极限」的争议。OpenAI o1 模型的推出将 Scaling Laws 范式从预训练带向了推理层，模型推理层的开发和规模增长成为 AI 大模型玩家们新一轮「竞赛」的重点。涉及复杂计算和决策，缓慢逻辑思考的「System 2」思维成为主导。</p></div></div><div class="home-right__list-item"><div><p>「压缩即智能」，成就LLM的Transformer 未必是终极解？</p><img alt="icon" src="https://cdn.jiqizhixin.com/assets/home/list-icon-89b2748aed60768f98d8e67f934eadfc5449fe6854c06f9e2fe493f87d00f520.png" /><span>11.09</span></div><div class="desc"><p>Ilya Sutskever 2023 年向黄仁勋描述的「AI 预测下一个单词可以实现真正的理解」的理念在一系列 LLM 进展中被验证，其带来的「压缩即智能」的观点进而得到了大量关注和认可。GPT 的训练过程被描述为对知识的无损压缩，但当前最流行的 Transfomer 架构却未必是这一路径下的最优选择。</p></div></div></div></div><div class="home-right__footer">每周获取决策者必须了解的AI&Robotics业内要事</div><div class="home-right__modal-root"><div class="home-right__modal-content"><img alt="icon" 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rel="noopener noreferrer" target="_blank"><i class="iconfont icon-message-default"></i></a></span></div></footer></main></article><article class="article-item__container"><a class="js-open-modal" data-remote="" href="/articles/2024-11-27-6" target="_self"><div class="u-opacity-effect u-image-base article-item__cover "><img alt="压缩率达10的48次方，实现蛋白序列空间极端压缩，清华EvoAI登Nature子刊" class="u-image-center" src="https://image.jiqizhixin.com/uploads/article/cover_image/1b48810b-bd62-4c15-a38a-55d28f797d42/iShot_2024-11-27_15.55.18.png?imageView2/1/w/390/h/282" /></div></a><main class="article-item__right"><section><a alt="压缩率达10的48次方，实现蛋白序列空间极端压缩，清华EvoAI登Nature子刊" class="article-item__title t-strong js-open-modal" data-remote="" href="/articles/2024-11-27-6" target="_self">压缩率达10的48次方，实现蛋白序列空间极端压缩，清华EvoAI登Nature子刊</a><p class="u-text-limit--two article-item__summary">清华大学的研究团队建立了一种称为 EvoScan 的方法，用于全面分割和扫描高适应度序列空间，以获得能够捕捉其基本特征（尤其是在高维度中）的锚点。</p></section><footer class="u-flex article-item__footer"><div class="article-item__author"><a class="u-avatar-base article-item__avatar" href="/users/70d794c4-1662-41a5-8ce6-7ca588c90bdc" rel="noopener noreferrer" target="_blank"><img alt="ScienceAI" class="u-image-center" src="https://cdn.jiqizhixin.com/assets/avatars/anonymous-3-fb31ad35511be5a49c496964c9d2190933420699e44988a3401765d28949a1f8.jpg" /></a><a alt="ScienceAI" class="article-item__name" href="/users/70d794c4-1662-41a5-8ce6-7ca588c90bdc" rel="noopener noreferrer" target="_blank">ScienceAI</a><span class="article-item__publish"><time class="js-time-ago" datetime="2024-11-27 15:55:00 +0800">2024/11/27 15:55</time></span></div><div class="article-item__other"><a alt="theory" class="u-btn--gray category__link" href="/categories/theory" rel="noopener noreferrer" target="_blank">理论</a><span><a class="u-like-icon article-item__link js-like-action" data-path="/articles/2024-11-27-6" href="javascript:;"><i class="iconfont icon-like-default"></i><span></span></a><a class="u-like-icon 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article-item__footer"><div class="article-item__author"><a class="u-avatar-base article-item__avatar" href="/users/294c393b-25f7-45b0-bec6-33e3bd344e61" rel="noopener noreferrer" target="_blank"><img alt="机器之心" class="u-image-center" src="https://cdn.jiqizhixin.com/assets/avatars/anonymous-2-c1597a15e88db92a6875ac5a53546c5a941c23c12158c0c4bd046ee45e9f1ba9.jpg" /></a><a alt="机器之心" class="article-item__name" href="/users/294c393b-25f7-45b0-bec6-33e3bd344e61" rel="noopener noreferrer" target="_blank">机器之心</a><span class="article-item__publish"><time class="js-time-ago" datetime="2024-11-27 13:24:32 +0800">2024/11/27 13:24</time></span></div><div class="article-item__other"><a alt="industry" class="u-btn--gray category__link" href="/industry" rel="noopener noreferrer" target="_blank">产业</a><span><a class="u-like-icon article-item__link js-like-action" data-path="/articles/2024-11-27-5" href="javascript:;"><i class="iconfont icon-like-default"></i><span></span></a><a class="u-like-icon 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是绕不开的一环。</p></section><footer class="u-flex article-item__footer"><div class="article-item__author"><a class="u-avatar-base article-item__avatar" href="/users/294c393b-25f7-45b0-bec6-33e3bd344e61" rel="noopener noreferrer" target="_blank"><img alt="机器之心" class="u-image-center" src="https://cdn.jiqizhixin.com/assets/avatars/anonymous-2-c1597a15e88db92a6875ac5a53546c5a941c23c12158c0c4bd046ee45e9f1ba9.jpg" /></a><a alt="机器之心" class="article-item__name" href="/users/294c393b-25f7-45b0-bec6-33e3bd344e61" rel="noopener noreferrer" target="_blank">机器之心</a><span class="article-item__publish"><time class="js-time-ago" datetime="2024-11-27 13:19:18 +0800">2024/11/27 13:19</time></span></div><div class="article-item__other"><a alt="industry" class="u-btn--gray category__link" href="/industry" rel="noopener noreferrer" target="_blank">产业</a><span><a class="u-like-icon article-item__link js-like-action" data-path="/articles/2024-11-27-4" href="javascript:;"><i class="iconfont 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article-item__summary">一个有效的复杂系统总是从一个有效的简单系统演化而来的。——John Gall</p></section><footer class="u-flex article-item__footer"><div class="article-item__author"><a class="u-avatar-base article-item__avatar" href="/users/294c393b-25f7-45b0-bec6-33e3bd344e61" rel="noopener noreferrer" target="_blank"><img alt="机器之心" class="u-image-center" src="https://cdn.jiqizhixin.com/assets/avatars/anonymous-3-fb31ad35511be5a49c496964c9d2190933420699e44988a3401765d28949a1f8.jpg" /></a><a alt="机器之心" class="article-item__name" href="/users/294c393b-25f7-45b0-bec6-33e3bd344e61" rel="noopener noreferrer" target="_blank">机器之心</a><span class="article-item__publish"><time class="js-time-ago" datetime="2024-11-27 13:14:40 +0800">2024/11/27 13:14</time></span></div><div class="article-item__other"><a alt="practice" class="u-btn--gray category__link" href="/categories/practice" rel="noopener noreferrer" target="_blank">工程</a><span><a class="u-like-icon article-item__link js-like-action" 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