内容来源：https://aibusiness.com/generative-ai/with-claude-science-anthropic-targets-application

内容总结：

谷歌云赞助报道：Anthropic推出Claude Science，瞄准科研领域AI应用

人工智能公司Anthropic近日正式发布面向科学研究的AI工作台——Claude Science，标志着其从编程领域向纵深行业拓展的又一重要布局。该工具并非独立的AI模型，而是将分散的研究工具与数据库整合至统一环境中，旨在简化科研流程。

Claude Science预配置了基因组学、单细胞分析、结构生物学和化学信息学模块，可渲染3D蛋白质结构、生成符合发表标准的图表，并完整记录每次输出的代码、环境细节及创建历史。该工作台基于Opus 4.8模型运行，无需特殊访问权限，同时集成了英伟达的BioNeMo Agent工具包，支持通过模型上下文协议进行自定义扩展。

尽管Anthropic强调这一工作台并非全新模型，但业界对此反应积极。康奈尔大学计算机科学助理教授约翰·西克斯顿表示：“Anthropic持续将资源投入这些难题，而不是一味押注能快速赚钱的编程AI工具，这令人鼓舞。”华盛顿大学信息学院教授奇拉格·沙阿则指出，OpenAI和谷歌此前已推出类似工具，但Claude Science为缺乏技术背景的研究人员提供了“即开即用”的便利，可自动化完成假设生成、数据分析和文献综述等流程。

然而，AI在科研领域面临独特挑战。西克斯顿指出，科研数据难以大规模获取，且反馈周期长——编程领域能实现超人类计算规模的即时反馈，而科研项目往往一年才能获得一次验证。沙阿补充道，科学不仅是结果导向，过程同样关键，“AI需要理解如何得出结论，而不仅仅是给出答案”。

此次发布延续了Anthropic从法律、金融到网络安全，再到科学领域的行业渗透策略。此前其Fable 5和Mythos 5模型已具备分子生物学与基因组学分析能力。

中文翻译：

由谷歌云赞助
选择您的首批生成式AI应用场景
要开始使用生成式AI，首先应聚焦于能够改善人类与信息交互体验的领域。
该AI供应商并非首家专注于科学领域的模型提供商。由于在科学领域应用AI面临挑战，其进展较为谨慎。
为延续其持续进军各垂直行业的战略，Anthropic于周二推出了Claude Science——一款面向科学研究的AI工作台。
Claude Science旨在通过将分散的工具和数据库整合为单一集成环境来简化科学研究流程——Anthropic特别说明，该环境本身并非AI模型。
这款新应用标志着Anthropic将重心从主要面向编程的AI进一步扩展。这一转变可从Anthropic近期的布局中窥见：先是从编程转向法律和金融领域，继而涉足网络安全，如今又拓展至科学等应用场景。Claude Science并非该供应商首次涉足科学领域。其Fable 5和Mythos 5模型均已具备分子生物学和基因组学方面的能力。
康奈尔大学计算机科学助理教授John Thickstun表示："令人欣慰的是，Anthropic持续将部分资源投入到这些难题上，而非仅仅专注于他们那些已明显取得经济成功的AI编程工具。"
该AI工作台提供了一个针对基因组学、单细胞分析、结构生物学和化学信息学预配置的统一研究环境。它还能渲染3D蛋白质结构，生成可直接发表的图表，并为每个输出结果提供完整代码、环境详情和创建历史。该AI工作台使用Opus 4.8版本，无需特殊访问权限，并与英伟达的BioNeMo Agent工具包集成，支持通过模型上下文协议进行自定义扩展。
西雅图华盛顿大学信息学院教授Chirag Shah表示，Anthropic强调该工作台并非模型这一点很重要。
"此前已有过一些尝试，"Shah提及以往创建新型科学模型的努力时说，"它们并未取得太大成功，比如将基础模型在某些生物学领域进行微调的做法。"
他补充道，Anthropic提供的方案也并非完全创新，因为OpenAI和谷歌等竞争对手已推出类似工具。例如，OpenAI拥有FrontierScience基准测试，而谷歌的Gemini for Science则是一套用于科学研究的AI工具包。
尽管如此，Claude Science或可为部分研究人员和科学家提供一定程度的便利。
"那些不了解如何利用基础模型和各类框架搭建工作流程的人，现在可以直接拿来使用，开箱即用，"Shah表示。"谈到科学研究，本质上就是提出假设、验证假设、收集数据、进行大量文献综述、串联线索、数据分析。这些环节——或者说其中很多环节——都可以实现自动化，而这正是该工具的价值所在。"Shah说道。
然而，对AI而言，科学领域的难度远高于编程。
"科学信息的可获取性较低，"Thickstun说。他表示，这意味着供应商可能难以轻松获取科学信息。
另一个挑战在于时机。
"AI倾向于在能够以超人类计算规模获得反馈的环境中工作，"Thickstun补充道。"而在许多科学应用中，能每年获得一次反馈就已属幸运。"
此外，Shah指出，科学既关乎获取信息的过程，也关乎最终成果。
"重要的不仅是得到了什么结果，更在于如何获得这个结果，"他说。

英文来源：

Sponsored by Google Cloud
Choosing Your First Generative AI Use Cases
To get started with generative AI, first focus on areas that can improve human experiences with information.
The AI vendor is not the first model provider to focus on science. It is proceeding cautiously due to challenges with using AI in the science field.
In a move in keeping with its sustained push into various vertical industries, Anthropic on Tuesday introduced Claude Science, an AI workbench for scientific research.
Claude Science is designed to streamline scientific research by consolidating fragmented tools and databases into a single, integrated environment -- one that Anthropic specifies is not an AI model in itself.
The new application is another way Anthropic is evolving its focus from mainly AI for coding. The shift in focus can be seen in Anthropic’s recent moves, first toward law and finance, then to cybersecurity and now toward applications such as science. Claude Science is not the vendor’s first foray into science. Both the Fable 5 and Mythos 5 models have capabilities in molecular biology and genomics.
“It's encouraging that Anthropic is continuing to dedicate some of its resources to these hard problems, rather than just doubling down on the clearly economically successful tools they're developing for AI for code,” said John Thickstun, assistant professor of computer science at Cornell University.
The AI workbench provides a unified research environment preconfigured for genomics, single-cell analysis, structural biology and cheminformatics. It also renders 3D protein structures, generates publication-ready figures and includes full code, environment details and creation history for every output. The AI workbench uses Opus 4.8 with no special access required and integrates with Nvidia’s BioNeMo Agent Toolkit and supports Model Context Protocol for custom extensions.
The emphasis Anthropic places on the workbench not being a model is important, said Chirag Shah, a professor in the Information School at the University of Washington in Seattle.
“There have been some attempts for that,” Shah said, referring to previous efforts to create new scientific models. “They haven't panned out so much like taking a foundation model and then fine-tuning it on some domain like biology.”
He added that what Anthropic provides also isn’t completely new, as competitors such as OpenAI and Google already offer similar tools. For example, OpenAI has FrontierScience benchmark, while Google’s Gemini for Science is a suite of AI tools for scientific research.
However, Claude Science could provide a certain level of convenience for some researchers and scientists.
“Somebody who doesn't have all the know-how of how to create a pipeline using foundation model and different harnesses, they can now just take this off the shelf and it's ready to go,” Shah said. "When you think about science, it comes down to creating a hypothesis, testing a hypothesis, collecting data, doing a lot of literature review, connecting the dots, data analysis. These things could be automated, or a lot of those things, and so that's what this is,” Shah said.
However, science is harder for AI to tackle than coding.
“Science is a little less readily available,” Thickstun said. He said that means vendors might not be able to access scientific information easily.
Another challenge is timing.
“AI likes to work in environments where it's able to get feedback at a superhuman computational scale,” Thickstun added. “With a lot of scientific applications, you're lucky to get feedback once a year.”
Moreover, science is about both the process of accessing information and its outcomes, Shah said.
“It's not just about what comes out of it, but how you get to it,” he said.