发表机构
Google; Google DeepMind; University of Chicago; CUNEF Universidad; MIT FutureTech; University of Oxford; Carnegie Mellon University; University of Pennsylvania（谷歌; 谷歌DeepMind; 芝加哥大学; 西班牙金融研究大学; 麻省理工学院未来科技; 牛津大学; 卡内基梅隆大学; 宾夕法尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究基于多源数据揭示科学家广泛采用AI，LLM与专业模型互补，每周节省近7小时并重新投入研究，同时导致假设验证瓶颈，表明AI可提升科学生产力但需应对新挑战。
AI 中文摘要
科学进步是经济增长和繁荣的关键驱动力。关于AI对科学的影响，人们既充满热情也存在担忧，但迄今为止相关数据很少。我们从三个数据来源提供早期洞察：1500万次Gemini交互样本、跨学科超过2600个专业AI模型的清单，以及对600多名科学家的调查。我们将这些数据映射到一个新的科学任务分类法中，以研究科学家如何使用AI。主要发现有四方面。首先，我们发现广泛的采用和覆盖：科学家使用AI的频率高于大多数其他职业。专业AI模型具有广泛的学科覆盖且被高度引用。近半数的受访科学家报告每天使用某种形式的AI。其次，我们记录了证据表明LLM（以Gemini使用为代表）和专业模型起到互补作用——LLM用于一般分析、编码和手稿准备，而专业模型提供领域特定的预测、数据生成和分类。第三，科学家报告使用AI带来了巨大的生产力提升：每周节省近7小时，这些时间主要被重新投入到更多研究中。最后，我们表明AI已经在改变科学过程。随着科学研究某些阶段变得更容易，瓶颈向下游转移。科学家报告未经检验的假设积压增加，以及对输出验证的大量需求。我们的发现表明，AI具有显著提升科学生产力的潜力。然而，与其他部门一样，其最终影响将受到复杂任务相互依赖性和对消除新兴瓶颈的投资的制约。
英文摘要
Scientific progress is a key driver of economic growth and prosperity. There is great excitement - but also concerns - about the impacts of AI on science, but so far little data. We provide early insights on this from three data sources: a sample of 15 million Gemini interactions, an inventory of over 2,600 specialized AI models across disciplines, and a survey of over 600 scientists. We map these data to a new taxonomy of scientific tasks to study how scientists are using AI. Four main findings emerge. First, we find broad adoption and coverage: scientists use AI more than most other occupations. Specialized AI models have broad disciplinary coverage and are highly cited. Nearly half of the scientists surveyed report using some form of AI every day. Second, we document evidence that LLMs (proxied through Gemini usage) and specialized models act as complements-- LLMs are used for general analysis, coding, and manuscript preparation, while specialized models provide domain-specific predictions, data generation and classification. Third, scientists report large productivity gains from using AI: a saving of nearly 7 hours per week, time which is primarily re-invested in more research. Finally, we show that AI is already changing the scientific process. As some stages of scientific research become easier, bottlenecks shift downstream. Scientists report an increased backlog of untested hypotheses and substantial demand for output verification. Our findings suggest that AI holds significant potential to increase scientific productivity. However, as with other sectors, its ultimate impact will be governed by complex task interdependencies and investment into the elimination of emerging bottlenecks.
