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选择您的第一个基因AI 使用案例
要从基因AI开始，首先关注能够改善人类信息体验的领域。
物理AI将人工智能推向机器人，工厂和其他现实世界环境，在那里理解语言只是挑战的一部分。
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从给机器人一种触摸感的新基础模型到不断增长的对机器人中心的投资，物理AI并不仅仅是正在形成。 它正在加速，推动AI的下一阶段超越数字世界，并进入为理解和在物理世界中行动而构建的系统。
机器人和其他物理人工智能系统必须了解其周围环境并对周围发生的情况作出反应。 这要求当人工智能系统在屏幕后运行时，这种能力的重要性要小得多。
本周提供了这一转变的若干迹象。
以德国为基地的Neura Robotics 推出是一个基础模型，将视觉，触觉和音频结合起来，让机器人对周围环境有更丰富的了解。
添加触觉特别重要。 一个语言模型可以生成错误的答案并再次尝试。 处理物体的机器人或与人一起工作的机器人必须了解它所施加的威力，物体是如何移动的，以及周围在实时变化的。
这使得物理AI成为不同于过去数年来使用基因AI系统企业的挑战。
这些系统的生态系统也开始形成。 波士顿逐渐成为实体AI枢纽，机器人人才，客户和投资日益集中在该地区。 制造公司也在数码双胞胎和实时模拟的同时使用AI，以寻找将更多的智能带入物理操作的方法。
这一转变引起了人们的注意，因为潜在的市场远远超出人造机器人。 实体AI最终会影响制造业，物流，仓库，运输业和其他在软件无法控制的环境中进行工作的行业。
但将AI移入物理世界也暴露出科技的最大局限之一：真实世界更难训练。
语言模型可以从大量文本和其他数字信息中学习。 物理AI系统需要关于运动，物体，环境和相互作用的数据，其中很多数据更难收集，成本更高。
基因AI的繁荣表明，利用大量数据和计算能力，模型能够快速改进。
物理AI现在必须证明，当AI离开屏幕并进入现实世界时，同样的进步也会发生。
也在本周AI新闻中：
Nvidia 发布打开的agent型号：AI芯片巨头的新Nemotron 3.5型号扩展了开放的AI策略，同时强化了该公司更广泛的GPU生态系统。
代理AI在高管，工人之间长期存在差距：行政主管报告AI agents比雇员更成功，他们继续面临工具超载和信任问题。
人工智能的开单会给企业带来业务压力：超过一半的企业在努力控制人工智能成本，因为代理部署增加了新的消费和支出来源。
OpenAI,Google合伙企业以企业模式选择为目标：OpenAI模型现在在所有三个主要云平台上都可以使用，作为前沿AI供应商扩展如何企业可以获取其技术。
Walmart欢迎外部AI agents，零售商正在向外部购物agents开放其在线平台，同时增加了旨在保持透明度和客户选择的新控制。 Agentic AI帮助黑客升级——即使没有详细的指令：CrowdStrike 警告，自动AI工具可以在下一年大大提高低技能攻击者的能力。
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
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Choosing Your First Generative AI Use Cases
To get started with generative AI, first focus on areas that can improve human experiences with information.
Physical AI is pushing artificial intelligence into robots, factories and other real-world environments, where understanding language is only part of the challenge.
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From new foundation models that give robots a sense of touch to growing investment in robotics hubs, physical AI isn't just taking shape. It's accelerating, pushing the next phase of AI beyond the digital world and into systems built to understand and act in the physical one.
Robots and other physical AI systems must perceive their surroundings and respond to what is happening around them. That requires capabilities that matter far less when an AI system operates behind a screen.
This week offered several signs of that shift.
Germany-based Neura Robotics introduced a foundation model that combines vision, touch and audio to give robots a richer understanding of their surroundings.
Adding touch is particularly significant. A language model can generate a wrong answer and try again. A robot handling an object or working alongside a person must understand how much force it's applying, how an object is moving and what is changing around it in real time.
That makes physical AI a different challenge from the generative AI systems enterprises have spent the past several years putting to use.
The ecosystem around those systems is beginning to take shape as well. Boston is emerging as a physical AI hub, with robotics talent, customers and investment increasingly concentrated in the region. Manufacturing companies are also using AI alongside digital twins and real-time simulation as they look for ways to bring more intelligence into physical operations.
The shift is attracting attention because the potential market extends well beyond humanoid robots. Physical AI could eventually influence manufacturing, logistics, warehouses, transportation and other industries in which work happens in environments that software alone cannot control.
But moving AI into the physical world also exposes one of the technology's biggest limitations: the real world is much harder to train for.
Language models can learn from enormous amounts of text and other digital information. Physical AI systems need data about movement, objects, environments and interactions, much of which is harder and more expensive to collect.
The generative AI boom showed how quickly models can improve with enormous amounts of data and computing power.
Physical AI now has to prove that the same kind of progress can happen when AI leaves the screen and enters the real world.
Also in AI news this week:
Nvidia launches open agent models: The AI chip giant’s new Nemotron 3.5 models expand its open AI strategy while reinforcing the company’s broader GPU ecosystem.
Agentic AI gap persists between executives, workers: Executives report more success with AI agents than employees, who continue to face tool overload and trust issues.
AI billing spikes threaten businesses with operational strain: More than half of businesses are struggling to control AI costs as agentic deployments add new sources of consumption and spending.
OpenAI, Google partnership targets enterprise model choice: OpenAI models are now available across all three major cloud platforms as the frontier AI provider expands how enterprises can access its technology.
Walmart welcomes external AI agents -- with some guardrails: The retailer is opening its online platform to outside shopping agents while adding new controls intended to preserve transparency and customer choice.Agentic AI helps hackers level up -- even without detailed instructions: CrowdStrike warns that autonomous AI tools could significantly increase the capabilities of lower-skilled attackers over the next year.
Auto-fetched and translated by AI Atlas. All rights belong to the original author.