【Hacker News搬运】与人工智能联合科学家加速科学突破

hackernews

Title: Accelerating scientific breakthroughs with an AI co-scientist

与人工智能联合科学家加速科学突破

Text:

Url: https://research.google/blog/accelerating-scientific-breakthroughs-with-an-ai-co-scientist/

很抱歉，我无法直接访问或处理外部网站内容，包括Google博客上的文章。不过，我可以根据您提供的链接内容进行假设性的分析和总结。

假设文章标题为 "Accelerating Scientific Breakthroughs with an AI Co-Scientist"，以下是一个可能的总结：

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这篇文章探讨了人工智能（AI）在科学研究中的应用，特别是在加速科学突破方面。文章可能会提到以下几点：

1. **AI Co-Scientist 的概念**：介绍了所谓的 "AI Co-Scientist"，这是一种与人类科学家合作的人工智能系统，旨在辅助科研工作。

2. **数据处理和分析**：AI Co-Scientist 通过分析大量的数据，帮助科学家识别模式、趋势和潜在的研究问题。

3. **实验设计**：AI 可以协助设计更有效的实验方案，通过预测实验结果来优化实验流程。

4. **文献检索和总结**：AI Co-Scientist 能够快速检索和理解科学文献，为研究人员提供关键信息，从而节省时间并减少信息过载。

5. **加速发现**：文章可能会强调，通过与AI合作，科学家可以更快地验证假设、发现新的科学原理，并推动科学知识的发展。

6. **案例研究**：可能会提供一些案例研究，展示AI Co-Scientist 如何在特定领域（如生物学、物理学或材料科学）中产生积极影响。

7. **挑战和未来展望**：讨论了将AI应用于科学研究面临的挑战，例如数据隐私、算法偏见以及AI与人类合作的有效性。同时，也展望了AI在科学研究中的未来角色。

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请注意，这只是一个基于文章标题的假设性总结。要获取准确的信息，请直接访问文章原文。

Post by: Jimmc414

Comments:

crypto420: I'm not sure if people here even read the entirety of the article. From the article:> We applied the AI co-scientist to assist with the prediction of drug repurposing opportunities and, with our partners, validated predictions through computational biology, expert clinician feedback, and in vitro experiments.> Notably, the AI co-scientist proposed novel repurposing candidates for acute myeloid leukemia (AML). Subsequent experiments validated these proposals, confirming that the suggested drugs inhibit tumor viability at clinically relevant concentrations in multiple AML cell lines.and,> For this test, expert researchers instructed the AI co-scientist to explore a topic that had already been subject to novel discovery in their group, but had not yet been revealed in the public domain, namely, to explain how capsid-forming phage-inducible chromosomal islands (cf-PICIs) exist across multiple bacterial species. The AI co-scientist system independently proposed that cf-PICIs interact with diverse phage tails to expand their host range. This in silico discovery, which had been experimentally validated in the original novel laboratory experiments performed prior to use of the AI co-scientist system, are described in co-timed manuscripts (1, 2) with our collaborators at the Fleming Initiative and Imperial College London. This illustrates the value of the AI co-scientist system as an assistive technology, as it was able to leverage decades of research comprising all prior open access literature on this topic.The model was able to come up with new scientific hypotheses that were tested to be correct in the lab, which is quite significant.

crypto420: 我；我不确定这里的人是否读过整篇文章。来自文章：&gt；我们应用了人工智能联合科学家来协助预测药物再利用的机会，并与我们的合作伙伴一起，通过计算生物学、专家临床医生反馈和体外实验验证了预测 &gt；值得注意的是，这位人工智能联合科学家提出了新的急性髓系白血病（AML）候选药物。随后的实验验证了这些建议，证实了所建议的药物在多个AML细胞系中以临床相关浓度抑制肿瘤存活率 并且，&gt；对于这项测试，专家研究人员指示AI联合科学家探索一个在他们的团队中已经有新发现但尚未在公共领域公开的主题，即解释衣壳形成噬菌体诱导染色体岛（cf PICI）如何在多种细菌物种中存在。人工智能联合科学家系统独立提出，cf-PICI与不同的噬菌体尾部相互作用，以扩大其宿主范围。这一在计算机上的发现，在使用人工智能联合科学家系统之前进行的原始新型实验室实验中得到了实验验证，在与我们在弗莱明倡议和伦敦帝国理工学院的合作者共同撰写的同步手稿（1,2）中进行了描述。这说明了人工智能联合科学家系统作为一种辅助技术的价值，因为它能够利用数十年的研究，包括之前关于这一主题的所有开放获取文献 该模型能够提出新的科学假设，这些假设在实验室中经过测试是正确的，这非常重要。

celltalk: “Drug repurposing for AML” lolAs a person who is literally doing his PhD on AML by implementing molecular subtyping, and ex-vivo drug predictions. I find this super random.I would truly suggest our pipeline instead of random drug repurposing :)<a href="https://celvox.co/solutions/seAMLess" rel="nofollow">https://celvox.co/solutions/seAMLess</a>edit: Btw we’re looking for ways to fund/commercialize our pipeline. You could contact us through the site if you’re interested!

celltalk: “AML药物再利用”lol作为一个通过实施分子亚型和体外药物预测来攻读AML博士学位的人。我发现这个超级随机 我真的建议我们的管道，而不是随机的药物重新利用：）<a href=“https:&#x2F；celvox.co/ solutions seAMLess”rel=“nofollow”>https:&#x2F；celvox.co；解决方案；seAMLess</a>编辑：顺便说一句，我们正在寻找资金来源；将我们的产品线商业化。如果你感兴趣，可以通过网站联系我们！

mnky9800n: Tbh I don’t see why I would use this. I don’t need an ai to connect across ideas or come up with new hypothesis. I need it to write lots of data pipeline code to take data that is organized by project, each in a unique way, each with its own set of multimodal data plus metadata all stored in long form documents with no regular formatting, and normalize it all into a giant database. I need it to write and test a data pipeline to detect events both in amplitude space and frequency space in acoustic data. I need it to test out front ends for these data analysis backends so i can play with the data. Like I think this is domain specific. Probably drug discovery requires testing tons of variables one by one iterating through the values available. But that’s not true for my research. But not everything is for everybody and that’s okay.

mnky9800n: 我不明白我为什么要用这个。我不需要人工智能来连接各种想法或提出新的假设。我需要它编写大量的数据管道代码，以获取按项目组织的数据，每个数据都有自己的一组多模式数据和元数据，所有数据都存储在长格式文档中，没有常规格式，并将其标准化为一个巨大的数据库。我需要它来编写和测试数据管道，以检测声学数据中振幅空间和频率空间中的事件。我需要它来测试这些数据分析后端的前端，这样我就可以处理数据了。我认为这是特定领域的。可能药物发现需要逐一测试大量变量，迭代可用值。但我的研究并非如此。但并非所有事情都适合每个人，这没关系。

quinnjh: The market seems excited to charge in whatever direction the weathervane has last been pointing, regardless of the real outcomes of running in that direction. Hopefully I’m wrong, but it reminds me very much of this study (I’ll quote a paraphrase)“A groundbreaking new study of over 1,000 scientists at a major U.S. materials science firm reveals a disturbing paradox: When paired with AI systems, top researchers become extraordinarily more productive – and extraordinarily less satisfied with their work. The numbers tell a stark story: AI assistance helped scientists discover 44% more materials and increased patent filings by 39%. But here's the twist: 82% of these same scientists reported feeling less fulfilled in their jobs.”Quote from <a href="https://futureofbeinghuman.com/p/is-ai-poised-to-suck-the-soul-out-of-science" rel="nofollow">https://futureofbeinghuman.com/p/is-ai-poised-to-suck-the-so...</a>Referencing this study
<a href="https://aidantr.github.io/files/AI_innovation.pdf" rel="nofollow">https://aidantr.github.io/files/AI_innovation.pdf</a>

quinnjh: 无论风向标最后指向哪个方向，市场似乎都很兴奋，不管朝那个方向跑的实际结果如何。希望我错了，但这让我想起了这项研究（我会引用一个释义）“一项针对美国一家主要材料科学公司1000多名科学家的开创性新研究揭示了一个令人不安的悖论：当与人工智能系统配对时，顶尖研究人员的工作效率会变得非常高，但对他们的工作的满意度却会非常低。这些数字讲述了一个残酷的故事：人工智能的帮助帮助科学家发现了44%的材料，专利申请量增加了39%。但这是一个转折：这些科学家中有82%的人表示在工作中感到不那么满足。”引用自<a href=“https://futureofbeinghuman.com”x2F；p&#x2F；AI准备从科学中吸取灵魂“rel=”nofollow“>https:&#x2F；futureofbeinghuman.com；p■；ai准备好吮吸如此…</a>参考这项研究<a href=“https:”aidantr.github.io“文件”AI_innovation.pdf“rel=”nofollow“>https:”&#x2F；aidantr.github.io；文件&#x2F；AI_创新.pdf</a>

azinman2: It seems in general we’re heading toward’s Minsky’s society of minds concept. I know OpenAI is wanting to collapse all their models into a single omni model that can do it all, but I wonder if under the hood it’d just be about routing. It’d make sense to me for agents to specialize in certain tool calls, ways of thinking, etc that as a conceptual framework/scaffolding provides a useful direction.

azinman2: 总的来说，我们似乎正在走向明斯基的心灵社会概念。我知道OpenAI希望将他们的所有模型整合为一个可以完成所有任务的泛模型，但我想知道在幕后，这是否只是关于路由。对我来说，代理专注于某些工具调用、思维方式等作为概念框架是有意义的；脚手架提供了一个有用的方向。