内容来源：https://www.technologyreview.com/2026/05/22/1137813/google-i-o-showed-how-the-path-for-ai-science-is-shifting/

内容总结：

谷歌I/O大会揭示AI驱动科学新方向：从专用工具迈向自主智能体

在近日举行的谷歌I/O开发者大会上，谷歌DeepMind首席执行官德米斯·哈萨比斯宣称人类正“站在奇点的山脚下”，这一关于人工智能超越人类智能的宏大言论引发了广泛关注。然而，从大会现场的实际展示来看，AI在科学领域的应用路径正在发生微妙而深刻的转变。

大会科学AI环节的核心亮点是一款名为WeatherNext的天气预报软件。该工具去年成功预测了飓风“梅丽莎”在牙买加登陆的路径，提前发出预警，可能挽救了许多生命。这一成果固然意义重大，但与“奇点”的预言相去甚远。这恰恰凸显了当前AI科学应用的两条路线之争：一条是像WeatherNext、AlphaFold这样为解决特定科学问题而训练的专用工具路线；另一条则是基于大语言模型的智能体路线，即让AI系统在未来能自主执行尖端科研任务，无需人类干预。

后者正成为行业新热潮。谷歌云首席科学家普什米特·科利在最新论文中写道：“我们正走向不再仅仅是辅助科学、而是开始自主做科学的AI。”随着“自主AI科学家”的曙光初现，投入巨大资源开发超级专用工具（即便是赢得诺贝尔奖的AlphaFold或救命的WeatherNext）的合理性正受到挑战。这预示着未来人类与AI或将作为平等伙伴协作，甚至AI可能独立推动科学进步。

尽管谷歌并未放弃专用科学工具，去年夏天仍发布了针对遗传学和地球科学的AlphaGenome及AlphaEarth基础模型，但资源重组的迹象已十分明显。据《洛杉矶时报》报道，因AlphaFold获得诺贝尔奖的谷歌研究员约翰·詹珀，现已转攻AI编程领域。分析认为，此举既是为了应对谷歌编程工具落后于Anthropic和OpenAI的声誉危机，也标志着谷歌正将重心向智能体科学倾斜，因为编程能力是这类系统成功的关键。

在行业层面，智能体系统已展示出真实潜力。本周，OpenAI宣布其模型推翻了一个重要的数学猜想，被部分数学家视为生成式AI对数学领域迄今最有意义的贡献。值得注意的是，该模型并非数学专用工具，而是一个类似GPT-5.5的通用推理模型。如果通用智能体能独立贡献数学研究，那么在科学领域的突破可能也指日可待。

谷歌在I/O大会上发布的新产品“Gemini for Science”正是这一战略的集中体现。该品牌整合了多个基于大语言模型的科学系统，包括尚处于内测阶段的“假设生成AI联合科学家”和算法优化工具AlphaEvolve。目前，谷歌已开放申请，允许研究人员试用，早期测试者将其比作“咨询德尔斐神谕”。哈萨比斯强调，在接下来十年左右，AI仍是辅助科学家的绝佳工具，但“更长远地看，这些系统或将更像合作者”。

值得注意的是，如果哈萨比斯关于“奇点山脚”的论断并非空穴来风，那么未来AI科学家完全可能超越人类同行。他表示，当初投身AI正是因为观察到物理学自20世纪70年代以来的停滞，怀疑人类思维在特定领域已达极限。从这个角度看，打造超人类水平的自主科学智能体，正是谷歌瞄准的新顶峰。

中文翻译：

谷歌I/O大会揭示了人工智能驱动科学的发展路径正在转变。两年前，一款人工智能工具为谷歌DeepMind赢得了诺贝尔奖。如今，研究人员正朝着新的目标攀登。

在周二谷歌I/O大会的主题演讲中，谷歌DeepMind首席执行官德米斯·哈萨比斯宣称，我们目前“正站在奇点的山脚下”。这是一个引人注目的论断——奇点指的是未来那个理论上的时刻，届时人工智能将迅速超越人类智能，并彻底改变世界。但我在台下聆听时，真正触动我的是他说这番话的背景。

他登台为会议收尾，展示了一段关于科学人工智能的内容，其核心是一部视频，详细介绍了该公司的天气预报软件如何就去年飓风梅丽莎在牙买加灾难性登陆提前发出警报——并可能拯救了生命。如果这款名为WeatherNext的软件帮助任何人躲避了风暴或更好地加固了家园，那便是一项重大而有意义的成就。但这很难说是奇点即将来临的证据。

哈萨比斯高远宏大的言辞与WeatherNext的实际成果并置，凸显了两种截然不同的人工智能科学应用路径之间的张力。第一种路径侧重于人工智能工具，比如WeatherNext，它们被设计和训练用于解决特定的科学问题。第二种路径则是基于大语言模型的智能体系统，此类系统有朝一日无需人类参与即可执行前沿研究项目。

正是这第二种愿景，极大地激发了当前对人工智能的热情，包括近来围绕递归自我改进（即人工智能系统最终可能成为人工智能发展的主要驱动力——随着人工智能系统变得更聪明，这一过程将越来越快）的热潮。如今，智能体系统正做出实际的研究贡献，有时仅需有限的人类指导。

就在本周，谷歌云首席科学家普什米特·科利在《代达罗斯》期刊的一期人工智能与科学特刊上发表文章写道：“我们正迈向一种不仅能辅助科学、还能开始从事科学的人工智能。”随着自主人工智能科学家即将问世，投入巨大努力去开发超级专门化的工具——即便是像AlphaFold（DeepMind科学家因其获得诺贝尔奖）或像WeatherNext这样可能拯救生命的系统——就更显得难以自圆其说了。这也预示着科学界一个更为奇特的未来：人类与人工智能系统作为同伴合作——甚至人工智能自己就能取得科学进展。

需要明确的是，谷歌似乎并未放弃其为科学开发专用人工智能工具的工作。分别针对遗传学和地球科学应用训练的AlphaGenome和AlphaEarth Foundations已于去年夏天发布，而最新版本的WeatherNext则在去年11月问世。

更重要的是，此类工具在科学家中依然极受欢迎。例如，谷歌去年报告称，来自AlphaFold的蛋白质结构预测已被全球超过三百万名研究人员使用。而旨在利用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。

然而，在热情和资源配置方面，确已出现具体的调整迹象。上个月，《洛杉矶时报》报道称，因AlphaFold获得诺贝尔奖的谷歌研究员约翰·詹珀如今正致力于人工智能编码工作，而非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，因为该公司近期声誉受损，其编码工具目前不及Anthropic和OpenAI提供的产品。但这可能也表明，谷歌方面已将智能体科学置于优先地位，因为编码能力是其中一些系统成功的关键。

纵观整个行业，智能体研究系统正展现出真正的潜力。本周，OpenAI宣布其一个模型推翻了某个重要的数学猜想——据一些数学家称，这或许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所使用的模型并非专门为解决数学问题甚至研究而设计；据该公司称，它是一个类似于GPT-5.5的通用推理模型。如果通用智能体能够对数学研究做出独立贡献，它们或许很快也能在科学领域做到同样的事情（尽管科学中的观点必须通过实验验证，这使其成为人工智能更难攻克的领域）。

谷歌无疑正将大量注意力投入到智能体驱动的科学未来上。在I/O大会上，重大的科学发布是新的“Gemini for Science”套件，它将该公司多个基于大语言模型的科学系统统一在一个品牌之下。

这包括生成假说的人工智能“Co-Scientist”和优化算法的AlphaEvolve，它们目前尚未公开可用——但由于谷歌现已允许任何研究人员申请使用Gemini for Science，它们可能很快会在科学界得到更广泛的采用。参与早期测试的科学家们对其潜力充满热情：斯坦福大学的遗传学家加里·佩尔茨在一篇《自然·医学》文章中，将使用人工智能Co-Scientist比作“请教德尔斐神谕”。

Gemini for Science并非与专用工具不相容；相反，智能体系统可以被设计成在必要时调用这些工具。没有AlphaFold的帮助（至少目前还没有），任何智能体系统都无法预测蛋白质会折叠成什么结构。但该公司似乎正在转变其公众形象——并将至少部分资源和人员（如詹珀）从具体开发这类工具上转移开。尽管AlphaFold解决蛋白质折叠问题至今不过五年，但技术和讨论都已迅速超越了那项曾经革命性的成就。

谷歌一直小心翼翼地将其这套新的科学智能体定位为人类科学家的加速器，而非替代品——例如，选择“AI Co-Scientist”（人工智能合作科学家）而非“AI Scientist”（人工智能科学家）的名称，就显得相当刻意。哈萨比斯在谈论科学人工智能格局的变化时，也使用了同样的以人为中心的框架。“在未来十年左右的时间里，我们应该将人工智能视为帮助科学家的奇妙工具，”哈萨比斯在发表于《代达罗斯》特刊的一篇采访中说。“超出这个时间范围，就很难说清了，但也许这些系统会变得更像合作者。”

然而，一个人如果不首先是一位称职的科学家，就无法成为有效的科学合作者。而如果哈萨比斯关于“奇点的山脚下”的说法大致无误，那么人工智能科学家最终可能会超过其人类对应者的能力。

在I/O大会上与记者迈克·艾伦的一次讨论中，哈萨比斯谈到，当他观察到自20世纪70年代以来物理学进展停滞时，他最初是如何受到启发去追求人工智能的；他想知道人类思维是否在那个领域达到了极限，以及人工智能能否帮助克服这一障碍。超人类的智能体科学家无疑符合这一要求。我们或许永远无法接近那个境界，但谷歌似乎正朝着那个顶峰迈进。

深度阅读
人工智能
想了解人工智能的现状？看看这些图表。
根据斯坦福大学2026年人工智能指数，人工智能正在飞速发展，我们正努力追赶。

2026年人工智能领域值得关注的10件事
《麻省理工科技评论》权威概述了2026年人工智能领域的10项技术、新兴趋势、大胆创意和强大动能。

马斯克 v. 奥特曼 第一周：埃隆·马斯克称自己受骗，警告人工智能可能毁灭我们所有人，并承认xAI提炼了OpenAI的模型
马斯克保持冷静，OpenAI的律师则用尖锐的问题质疑他起诉公司的动机。

美国为基督徒推出的新手机网络旨在屏蔽色情和性别相关内容
该手机套餐将于下周在T-Mobile网络上推出，采取一种核弹级方法来应对在线安全。

保持联系
获取来自《麻省理工科技评论》的最新资讯
发现特别优惠、热门故事、即将举办的活动等更多内容。

英文来源：

Google I/O showed how the path for AI-driven science is shifting
Two years ago, an AI tool won Google DeepMind a Nobel. Researchers are now climbing toward a new goal.
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. 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.
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.” 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.
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.
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.
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).
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. 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 as opposed to AI Scientist, for instance, appears quite deliberate. Hassabis uses that same human-centric framing when he talks about changes in the landscape of scientific AI. “For the next decade or so, we should think about AI as this amazing tool to help scientists,” Hassabis said in an interview published in the Daedalus issue. “Beyond that timeframe, it is hard to say with any certainty, but perhaps these systems will become more like collaborators.”
But no one can be an effective scientific collaborator without also being a skilled scientist in their own right. And if Hassabis is anywhere near the mark when he talks about the “foothills of the singularity,” then AI scientists could eventually exceed the capabilities of their human counterparts.
In a discussion with the journalist Mike Allen at I/O, Hassabis spoke of how he was initially inspired to pursue AI when he observed how progress in physics had stagnated since the 1970s; he wondered whether the human mind had reached its limits in that domain, and if AI could help to overcome that barrier. Superhuman agentic scientists would certainly fit that bill. We might not ever get anywhere near there, but Google seems to be aiming itself toward that summit.
Deep Dive
Artificial intelligence
Want to understand the current state of AI? Check out these charts.
According to Stanford’s 2026 AI Index, AI is sprinting, and we’re struggling to keep up.
10 Things That Matter in AI Right Now
MIT Technology Review's authoritative overview of the 10 technologies, emerging trends, bold ideas, and powerful movements in AI in 2026.
Musk v. Altman week 1: Elon Musk says he was duped, warns AI could kill us all, and admits that xAI distills OpenAI’s models
Musk kept his cool, and OpenAI’s lawyer bulldozed him with piercing questions about his motivations for suing the company.
A new US phone network for Christians aims to block porn and gender-related content
Launching next week on T-Mobile's network, the cell plan takes a nuclear approach to online safety.
Stay connected
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.