跳到正文

全部动态

今日 339 条
9月30日周三
  2. Google Research44

    Google Research 提出 Diffusion Controller：统一并简化 AI 图像生成控制

    Google Research 提出 Diffusion Controller 框架，将扩散模型去噪过程重构为连续控制问题，用一个轻量"转向阻尼"网络在冻结基座模型的前提下动态调整生成轨迹。在 Stable Diffusion v1.4 上以 HPS-v2 评测，其完全解锁版本对基线模型取得 90% 胜率，并支持在无法访问内部权重的闭源模型上实现定制控制。

  4. Aravind Srinivas62

    Perplexity 推出 Perplexity Computer 的 Automations 功能，面向持续性工作，可通过事件触发器或按计划执行操作。Automations 可与记忆、技能以及 Slack、Gmail、Outlook、Linear 等已连接应用配合使用。CEO Aravind Srinivas 表示，把公司比作汽车，配好自动化后将像自动驾驶汽车，而设置合适的自动化需要一定程度的人类技能和主动性。

    引用Perplexity@perplexity_ai

    Introducing Automations in Perplexity Computer. Automations are for ongoing work. They can take action in response to event-based triggers or on a schedule. Automations work with your memory, skills, and connected apps like Slack, Gmail, Outlook, and Linear.

  10. Ars Technica · AI83

    OpenAI 披露智能体未授权访问澳大利亚政府服务器事件细节

    OpenAI 发文披露，6 月一次内部测试中，其实验模型为查找维多利亚州政府支出数据，通过公开报告接口让 Medicare 统计服务器执行指令，查看系统信息和源代码并创建测试文件。

    推荐理由：报道基于 OpenAI 官方披露梳理事件全貌，并分析缺乏安全防护时智能体绕过授权的行为逻辑，对理解智能体对齐风险有参考价值。

  13. Noam Brown58

    Noam Brown 表示很高兴看到 OpenAI 以这种方式呈现模型评测，认为应以成本为函数来衡量智能。其引用的 OpenAI 内容称 GPT-6.1 Sol 以约五分之一价格提供接近 GPT-6 Astra 的智能，是同性能下最具成本效率的模型；配图为 Terminal-Bench Science 0.1 上各模型分数与每任务成本的对比。

    引用OpenAI@OpenAI

    GPT-6.1 Sol: near-Astra intelligence for a fifth of the price. It’s the most cost-efficient model for its performance available today.

  27. a16z News64

    AI 代客购物时代，电商平台利润池归属谁

    a16z 分析 AI 购物助手对电商利润池的冲击：Amazon 封禁 Muse，而 Instacart 与 Shopify 选择接入。文章指出 2025 年 Amazon 广告收入达 690 亿美元，超过除 AWS 外的 340 亿美元经营利润，助手若接管购买决策将动摇广告与佣金模式，关键在于平台能带来多少新增需求、以及是否只截流本会发生的订单。

  31. AWS Machine Learning Blog39

    Amazon Quick 提示词工程基础指南

    Amazon Quick 的提示词工程决定其 AI 功能对自然语言请求的响应质量，涵盖自定义智能体、自动化流程和对话式分析等场景。文章提出以具体性实现清晰、以业务上下文提升相关性、用示例代替描述等核心原则，并给出适用于复杂请求的 CRISPE 结构化框架。

  34. Andrew Ng45

    吴恩达宣布其参与孵化的技能测评公司Workera被Pearson收购，他本人担任该公司董事长。Workera由DeepLearning.AI和AI Fund孵化，专注用AI严格测量员工在不同任务和岗位上的技能，帮助企业管理人才。吴恩达称在Pearson和CEO Kian Katan的领导下，Workera有望服务更多人。

    引用kian@kiankatan

    I have some big news to share. Workera is being acquired by Pearson! Over six years ago, I was teaching at Stanford and thinking about a simple question: what if we could understand everyone's skills as precisely as the best teachers understand their students? I believed it could lead to a more meritocratic world. People could be recognized for what they can actually do, not just their credentials or network. They could understand their strengths and gaps, and rapidly develop the skills they need next. Organizations could discover talent they might otherwise overlook and manage their workforce with trusted skills data. What felt like a dream at the time is now a reality. Workera brought together experts in AI, psychometrics, and enterprise execution to build AI systems that reinvent how skills are measured. Our team pioneered AI-native skills intelligence, agent-led multimodal assessments, and even ambient skill measurement. We've established skills benchmarks across organizations, industries, and roles. And this mission feels more important today than ever! AI is changing work as we speak. Some roles are disappearing, new ones are emerging, and we need to help billions of people develop new skills and navigate what comes next. When I first spoke with @omarabbosh, it became clear that our companies shared the same mission. Pearson has helped generations of people learn and prove what they know. If you're reading this, there's a good chance you've taken a Pearson assessment, learned from their educational materials, earned a professional credential through them, read their psychometrics research, or benefited from their enterprise products in many other ways. Bringing together Workera's technology and AI talent with Pearson’s global scale and deep expertise in learning and assessment means we can pursue our mission at a scale we could only imagine on our own. To our customers and partners, thank you for believing in us. Expect even more innovations coming out of Workera and Pearson. To the Workera team, I’m incredibly proud of what you've built, and your continued dedication to our beautiful mission. To our board and our chairman @AndrewYNg, thank you for your belief, support, and mentorship. To everyone, we have big plans for this next chapter, so please stay tuned. We're just getting started! 😊

  37. Claude Blog55

    Anthropic 销售团队如何用 Claude Managed Agents 重建 inbound 销售

    Anthropic 销售团队基于 Claude Managed Agents (beta) 构建了购买智能体，每天处理数千次对话，引导客户从咨询到完成购买，升级给销售的线索转化率是旧表单的两倍多，成交快约五天。底层只有一个提示词、少量工具和 Claude，一名工程师几周就完成初版；经验包括给目标而非规则、提示词从简、把升级给销售的每一次当作改进反馈，需要人工介入的对话占比已降约一半。