Google I/O showed how the path for AI-driven science is shifting
Google I/O showed how the path for AI-driven science is shifting
Google I/O 展示了人工智能驱动科学的路径正在发生怎样的转变
EXECUTIVE SUMMARY During Tuesday’s Google I/O keynote, Demis Hassabis, the CEO of Google DeepMind, proclaimed that we are currently “standing in the foothills of the singularity.” It was a striking statement—the singularity is the theoretical future moment when AI rapidly exceeds human intelligence and dramatically transforms the world. But what struck me as I listened in the audience was the context in which he said those words. He was on stage to close out the session with a segment on scientific AI, the centerpiece of which was a video detailing how the company’s weather prediction software provided an advance alert about Hurricane Melissa’s catastrophic landfall in Jamaica last year—and potentially saved lives.
执行摘要 在周二的 Google I/O 主题演讲中，Google DeepMind 首席执行官 Demis Hassabis 宣称，我们目前正“站在奇点（singularity）的山脚下”。这是一个引人注目的声明——“奇点”是指未来人工智能迅速超越人类智能并彻底改变世界的理论时刻。但当我在现场聆听时，令我印象深刻的是他说出这番话的语境。他当时在台上结束会议，进行关于科学人工智能的环节，其核心是一段视频，详细介绍了该公司的天气预报软件如何提前预警了去年飓风“梅丽莎”（Hurricane Melissa）在牙买加的灾难性登陆，并可能因此挽救了生命。
If that software, called WeatherNext, helped anyone escape the storm or better fortify their home, that’s an enormous and meaningful achievement. But it’s hardly evidence of an impending singularity. The juxtaposition of Hassabis’ lofty rhetoric with the real-world results of WeatherNext highlighted the tension between two very different approaches to AI for science. The first focuses on AI tools, like WeatherNext, that are designed and trained to solve specific scientific problems. The second is agentic, LLM-based systems that could one day execute cutting-edge research projects without human involvement.
如果这款名为 WeatherNext 的软件帮助任何人逃离了风暴或更好地加固了房屋，那是一项巨大且有意义的成就。但这很难说是奇点即将来临的证据。Hassabis 高谈阔论的修辞与 WeatherNext 的现实成果并置，凸显了两种截然不同的科学人工智能路径之间的张力。第一种专注于像 WeatherNext 这样设计并训练用于解决特定科学问题的人工智能工具；第二种则是基于大语言模型（LLM）的代理系统（agentic systems），它们有朝一日可能在无需人类参与的情况下执行前沿研究项目。
This second vision powers a great deal of AI enthusiasm right now, including recent excitement around recursive self-improvement, or the idea that AI systems could eventually become the primary drivers of AI advancement—a process that would get faster and faster as the AI systems grow smarter. And agentic systems are now making real research contributions, sometimes with limited human guidance. Just this week, Pushmeet Kohli, Google Cloud’s chief scientist, published a piece in a special AI and science issue of the journal Daedalus, writing: “We are moving toward AI that doesn’t just facilitate science but begins to do science.”
这种第二种愿景目前推动了大量的人工智能热情，包括最近围绕“递归自我改进”的兴奋点，即人工智能系统最终可能成为人工智能进步的主要驱动力——随着人工智能系统变得越来越聪明，这个过程将会越来越快。目前，代理系统正在做出真正的研究贡献，有时甚至是在有限的人类指导下完成的。就在本周，Google Cloud 首席科学家 Pushmeet Kohli 在《代达罗斯》（Daedalus）期刊的人工智能与科学特刊上发表文章写道：“我们正在迈向这样一种人工智能：它不仅能促进科学发展，而且开始亲自从事科学研究。”
With autonomous AI scientists on the horizon, it’s harder to justify massive efforts to develop super-specialized tools—even one like AlphaFold, for which DeepMind scientists won a Nobel Prize, or a potentially life-saving system like WeatherNext. It also heralds a far stranger future for science, in which humans and AI systems collaborate as peers—or AI even makes scientific progress on its own.
随着自主人工智能科学家的出现，投入巨大精力开发超专业化工具变得越来越难以证明其合理性——即使是像 AlphaFold（DeepMind 科学家凭借它获得了诺贝尔奖）或像 WeatherNext 这样可能挽救生命的系统。这也预示着科学将迎来一个更加陌生的未来：人类和人工智能系统作为同行进行协作，甚至人工智能可以独立取得科学进展。
To be clear, Google does not appear to be abandoning its work on specialized AI for science tools. AlphaGenome and AlphaEarth Foundations, which are trained for genetics and Earth science applications respectively, were released last summer, and the newest version of WeatherNext came out in November. What’s more, such tools remain extremely popular among scientists. Last year, for instance, Google reported that protein structure predictions from AlphaFold have been used by over three million researchers worldwide. And Isomorphic Labs, a Google subsidiary that aims to use AlphaFold and related technologies to develop new drugs, just raised a $2 billion Series B funding round.
需要明确的是，谷歌似乎并没有放弃其在专业科学人工智能工具方面的工作。去年夏天发布的 AlphaGenome 和 AlphaEarth Foundations 分别针对遗传学和地球科学应用进行了训练，而最新版本的 WeatherNext 也于 11 月发布。此外，这些工具在科学家中仍然非常受欢迎。例如，谷歌去年报告称，全球有超过 300 万研究人员使用了 AlphaFold 的蛋白质结构预测。旨在利用 AlphaFold 及相关技术开发新药的谷歌子公司 Isomorphic Labs，刚刚完成了 20 亿美元的 B 轮融资。
But there are concrete signs of realignment, in both enthusiasm and resources. Last month, the Los Angeles Times reported that Google fellow John Jumper, who won the Nobel for AlphaFold, is now working on AI coding, not on science-specific AI tools. It’s not surprising that Google is assigning its best minds to the coding problem, as the company has recently taken a reputational hit because its coding tools don’t currently stand up to those offered by Anthropic and OpenAI. But it may also signal a prioritization of agentic science on Google’s part, as coding abilities are key to the success of some of those systems.
但无论是在热情还是资源分配上，都有明显的调整迹象。上个月，《洛杉矶时报》报道称，凭借 AlphaFold 获得诺贝尔奖的谷歌研究员 John Jumper 目前正在从事人工智能编码工作，而不是科学专用的人工智能工具。谷歌将最优秀的人才分配到编码问题上并不令人意外，因为该公司最近声誉受损，其编码工具目前无法与 Anthropic 和 OpenAI 提供的工具相提并论。但这可能也标志着谷歌将代理科学（agentic science）置于优先地位，因为编码能力是其中一些系统成功的关键。
Across the industry, agentic researcher systems are showing real potential. This week, OpenAI announced that one of their models had disproved an important mathematics conjecture—perhaps the most meaningful contribution that generative AI has made to mathematics so far, according to some mathematicians. Importantly, the model used by OpenAI is not specialized for solving mathematical problems, or even for research; according to the company, it’s a general-purpose reasoning model in the vein of GPT-5.5. If general agents can make independent contributions to mathematical research, they might soon be able to do the same in science (though the fact that ideas in science must be verified experimentally makes it a tougher domain for AI).
在整个行业中，代理研究系统正在展现出真正的潜力。本周，OpenAI 宣布其模型之一推翻了一个重要的数学猜想——据一些数学家称，这可能是生成式人工智能迄今为止对数学做出的最有意义的贡献。重要的是，OpenAI 使用的模型并非专门用于解决数学问题，甚至不是专门用于研究；据该公司称，它是一个类似于 GPT-5.5 的通用推理模型。如果通用代理能够对数学研究做出独立贡献，它们可能很快也能在科学领域做到这一点（尽管科学观点必须经过实验验证这一事实使得该领域对人工智能来说更具挑战性）。
Google is certainly devoting a lot of attention toward an agent-driven scientific future. The big scientific announcement at I/O was the new Gemini for Science package, which unites several of the company’s LLM-based scientific systems under one brand. This includes the hypothesis-generating AI Co-Scientist and algorithm-optimizing AlphaEvolve, which are still not publicly available—but as Google is now allowing any researcher to apply for access to Gemini for Science, they may soon see wider adoption in the scientific community.
谷歌无疑正在将大量注意力转向代理驱动的科学未来。I/O 大会上重大的科学公告是全新的“Gemini for Science”套件，它将该公司旗下的几个基于大语言模型的科学系统统一在一个品牌下。这包括生成假设的 AI Co-Scientist 和优化算法的 AlphaEvolve，它们目前尚未公开——但由于谷歌现在允许任何研究人员申请访问 Gemini for Science，它们可能很快会在科学界得到更广泛的应用。
Scientists who were involved in early testing are enthusiastic about their potential: Gary Peltz, a Stanford geneticist, compared using the AI Co-Scientist to “consulting the oracle of Delphi” in a Nature Medicine article. Gemini for Science isn’t incompatible with specialized tools; to the contrary, agentic systems can be designed to call on such tools when they might be useful. And no agentic system can predict the structure that a protein will fold into without AlphaFold’s help (at least not yet). But the company seems to be shifting its public image—and at least some resources and personnel, such as Jumper—away from specifically developing those kinds of tools. Though it has only been five years since AlphaFold solved the protein-folding problem, both the technology and the discourse have quickly moved beyond that once-revolutionary achievement. Google has been careful to position this new set of scientific agents as an accelerant for human scientists, rather than a replacement for them—the choice of the name AI Co-Scientist.
参与早期测试的科学家对其潜力感到兴奋：斯坦福大学遗传学家 Gary Peltz 在《自然-医学》（Nature Medicine）的一篇文章中将使用 AI Co-Scientist 比作“咨询德尔斐神谕”。Gemini for Science 并不与专业工具不兼容；相反，代理系统可以被设计为在需要时调用这些工具。而且，如果没有 AlphaFold 的帮助，没有任何代理系统能够预测蛋白质的折叠结构（至少目前还不能）。但该公司似乎正在改变其公众形象——并将至少部分资源和人员（如 Jumper）从专门开发此类工具的工作中转移出来。尽管 AlphaFold 解决蛋白质折叠问题才过去五年，但技术和讨论都已经迅速超越了这一曾经革命性的成就。谷歌小心翼翼地将这套新的人工智能代理定位为人类科学家的加速器，而非替代者——从“AI Co-Scientist”（人工智能共同科学家）这一命名选择中便可见一斑。