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OpenAI:2026 AI:科研协作者——ChatGPT正加速从生物学到黑洞的科研进程研究报告(英文版)(20页).pdf

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1、 AI as a Scientific Collaborator From biology to black holes,ChatGPT is accelerating research January 2026 Introduction:Why AI for Science Matters 8.4 million average weekly messages on advanced topics in the hard sciences and mathematics Roughly 1.3 million weekly ChatGPT users focus on advanced ma

2、th&science topics worldwide The number of monthly advanced science messages grew nearly 50%last year OpenAI is building tools to help researchers generate insights,accelerate scientific discovery and translate those insights into real-world impact.Across ChatGPT,researchers,students,STEM faculty and

3、 engineers already use AI to read and synthesize technical literatures,debug and write code,analyze data,and plan experiments.Each week,ChatGPT sees almost 8.4 million messages on advanced topics in the sciences and mathematics.These now come from roughly 1.3 million weekly users worldwide.Only abou

4、t 0.1 percent of the global population identifies as scientists,according to UNESCO,and yet they have an outsized impact.Scientific research drives the engine of progress toward a healthier,more prosperous,and more resilient future.New medicines,new technologies,and new industries come from new know

5、ledge put to practical use.A small group of early twentieth-century physicists laid the foundations of quantum mechanics through abstract research that,decades later,would underpin much of the modern digital economy,now measured in the tens of trillions of dollars.Basic research work done before the

6、 payoff is clear was the source of that knowledge.In 1947,scientists at Bell Labs created the first working transistor,based on insights from quantum physics.The transistor became a building block of computers,phones,and todays digital technology.The Global Positioning System(GPS)relies on Einsteins

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1. **AI加速科研应用**:ChatGPT每周处理约840万条高级科学与数学主题消息,用户达130万,同比增长47%,成为科研重要工具。 2. **多领域突破**:GPT-5.2在数学(如AIME满分、解决Erdős问题)、科学(GPQA准确率超92%)及生物学(蛋白设计效率提升)等领域展现前沿能力。 3. **科研效率提升**:AI辅助文献综述、代码调试、实验规划,缩短研发周期(如药物研发从10-15年压缩至数月)。 4. **政策建议**:呼吁加强AI技能培训、开放科研数据、升级算力基础设施,并建立国家级AI资源分配机制。
AI如何加速科研? 科研为何需要AI? AI能解决哪些科研难题?
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