AI 日报

文章 URL: https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-5-transcribe/ 评论 URL: https://news.ycombinator.com/item?id=49468818 得分: 19 # 评论数: 5

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文章链接： https://blog.google/innovation-and-ai/technology/developers-tools/build-with-gemini-omni-1-1-flash/ 评论链接： https://news.ycombinator.com/item?id=49467922 得分：17 评论数：0

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在 Google Search 中使用 AI Mode 预订酒店并跟踪机票价格，还可以查看里程和奖励。

— Google AI Blog

— DeepMind Blog

Hi HN, we're the founders of Salem Robotics ( https://salemroboticsinc.com ). We give existing mobile robots the task-specific intelligence to carry out surveys and physically interactive inspections in hazardous industrial facilities. Here's a video of it running on real robot hardware with a few words from us: https://youtu.be/U_228h3NE7c We came to Salem through robotics research at UT Austin and a combined 15 years working in nuclear, including about 10 years developing and deploying autonomous robots at Los Alamos National Laboratory. Over the last five years, we kept running into the same gap: robot hardware had become very capable, but making a robot carry out a complete industrial procedure still required a surprising amount of robotics work and manual intervention. The part that interested us most was manipulation. A nuclear contamination survey, for example, can require taking a "smear": wiping a defined area of a surface so it can be checked for removable radioactive contamination. In an oil, gas, or chemical facility, an LDAR (leak detection and repair) inspection can require moving a detector around a particular valve, flange, or connection. Other inspections require positioning an instrument at a precise location and orientation relative to a pipe or piece of equipment. These are easy tasks to compress into verbs like "wipe", "measure", or "inspect", but considerably harder to make a robot do reliably. A probe might need to remain normal to a surface throughout a path, stay within a narrow offset from a pipe, or trace a region while maintaining a particular end-effector orientation. The planner has to find a feasible motion while respecting the task geometry, manipulator kinematics, joint limits, collisions, and the environment around it. We work down to joint-level control for those interactions. One problem we've spent a lot of time on is generating constrained manipulation plans quickly enough that they can be based on the geometry the robot actually observes instead of requiring someone to carefully author a trajectory for every individual surface, valve, or flange. The physical world makes this annoying. A few centimeters of error may not matter much when navigating down a hallway, but it matters if a sensor is supposed to remain normal to a curved surface. And successfully executing a trajectory doesn't necessarily mean the inspection worked. The detector could be misaligned, contact could be wrong, the geometry could differ from the model, or the measurement itself could be invalid. We care about closing that loop around the inspection result, not just whether the arm reached the commanded pose. Our approach is a combination of AI and classical robotics. A lot of robotics research and industry attention right now is going toward increasingly end-to-end learned systems, particularly around humanoids. Working in safety-critical environments has made us appreciate how relevant classical approaches still are when you want explicit constraints, predictable behavior, and theoretical guarantees about what a robot can and cannot do. We use AI where semantic understanding and flexibility are useful, such as interpreting less structured information or understanding what in an unfamiliar scene is relevant to a procedure. Once the system knows what physical interaction it needs to perform, we prefer explicit geometry, planning, optimization, and control where possible. We're interested in the marriage between the two rather than trying to make every part of the robotics stack learned. The other idea behind Salem is that we don't think every useful robot application should require building a new robot. Companies like Boston Dynamics are getting very good at building increasingly capable hardware platforms. We think there is room for a domain-specific application layer on top of that hardware. The same underlying robot might perform nuclear radiological surveys in one facility and LDAR inspections in another, but the procedures, sensors, manipulation constraints, success conditions, and outputs are different. That's also why we're hardware agnostic. We don't expect one robot to be the best platform forever, and facilities already own different hardware. We'd rather describe an inspection in terms of what needs to happen and then map that onto the capabilities of the right robot for the job. One thing that surprised us after spending more time with facilities is how manual many inspection workflows still are. In sophisticated nuclear and industrial sites, people still physically walk survey routes, take measurements one at a time, visually inspect equipment, record results manually, and sometimes make judgments based on things like how a component sounds. Some of the basic workflows would be recognizable to someone doing the job decades ago, even though the sensors, computation, and robots available today are radically different. We're starting with radiological inspection in nuclear because it's the industry we know best, and we're also working on manipulation-heavy inspection problems in oil, gas, and hazardous chemical facilities. The technicians and inspectors still define the procedure, interpret results, and make the consequential judgments. We're trying to automate more of the repetitive physical execution that currently requires someone to enter the environment or manually operate a robot. We sell directly to industrial facilities, usually starting with a paid technical validation and then moving to an ongoing deployment. Pricing varies substantially with the workflow: validations range from tens of thousands to over $100k, and larger deployments can range from the low hundreds of thousands to roughly $500k per robot. One thing we're especially curious to hear HN's thoughts on is where the abstraction boundary in robotics should sit. What should come from the robot manufacturer, what belongs in an application layer, and what will inevitably remain specific to the facility? We'd also be interested in hearing about other industries where you've seen physical inspection tasks that look trivial to a person but are surprisingly difficult to automate. Comments URL: https://news.ycombinator.com/item?id=49466715 Points: 2 # Comments: 0

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文章链接： https://wagtail.org/blog/cms-with-ai-not-ai-cms-wagtail-80s-new-api/ 评论链接： https://news.ycombinator.com/item?id=49467481 得分：3 # 评论：0

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嗨，HN，我们构建了一个开源模型网关。它是一个集中管理我们自托管、前沿和开源模型的单一入口。 它原生使用 Rust 构建，面向并发，并实现了跨模型和提供方的各种配置细节差异（流式格式、工具调用、模型参数、速率限制以及不同的错误行为）。 对于 BYOK 请求，这个网关增加的延迟不到 1 ms；当由 Experiential 提供供应商密钥时，延迟不到 2 ms。它覆盖所有主要推理提供方，并通过一个会发起 PR 的 codex agent 每日刷新 1000+ 个模型。 与其他类似项目相比，我们是开源的，不收取任何加价，允许你将本地模型与市场混合使用，并利用你的流量来（选择加入后）训练一个模型。简单路由并不值得收取 10% 的 token 加价。 我们的做法是：基于标准化的 OTel traces，我们挖掘具有代表性的真实任务，使用 text world models 模拟各种模型的 rollouts，应用一个 LLM judge，然后在 prompt 的 embedding 之上拟合一个最近邻分类器，以决定每个请求的最优模型。通常这比直接调用单个模型能画出更好的成本/质量帕累托曲线，但并不完美。 借助这些模拟，我们还可以做诸如建议缓存命中优化、新模型建议以及训练模型之类的事情。 它是开源的，所以你可以把它部署到自己的基础设施上，使用我们托管的版本且不收取任何加价，或者在我们的网站上阅读我们如何为最大可用性进行设计。 Comments URL: https://news.ycombinator.com/item?id=49471407 Points: 9 # Comments: 0

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文章链接： https://www.anthropic.com/news/model-hardware-standard-research-preview 评论链接： https://news.ycombinator.com/item?id=49468834 得分：13 评论数：7

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文章网址： https://news.bgov.com/bloomberg-government-news/nvidia-starts-a-pac-as-ai-chip-maker-buids-influence-force-in-dc 评论网址： https://news.ycombinator.com/item?id=49469249 得分：14 # 评论数：3

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一项针对 1,000 多名学生的随机研究考察了 ChatGPT、批判性思维、原创性，以及学生在真实大学作业中的表现。

— OpenAI Blog