AI Expands Scientists' Impact but Contracts Science's Focus
- URL: http://arxiv.org/abs/2412.07727v1
- Date: Tue, 10 Dec 2024 18:24:17 GMT
- Title: AI Expands Scientists' Impact but Contracts Science's Focus
- Authors: Qianyue Hao, Fengli Xu, Yong Li, James Evans,
- Abstract summary: We analyze 67.9 million research papers across six major fields using a validated language model.<n>Scientists who adopt AI tools publish 67.37% more papers, receive 3.16 times more citations, and become team leaders 4 years earlier than non-adopters.
- Score: 11.634306888037273
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The rapid rise of AI in science presents a paradox. Analyzing 67.9 million research papers across six major fields using a validated language model (F1=0.876), we explore AI's impact on science. Scientists who adopt AI tools publish 67.37% more papers, receive 3.16 times more citations, and become team leaders 4 years earlier than non-adopters. This individual success correlates with concerning on collective effects: AI-augmented research contracts the diameter of scientific topics studied, and diminishes follow-on scientific engagement. Rather than catalyzing the exploration of new fields, AI accelerates work in established, data-rich domains. This pattern suggests that while AI enhances individual scientific productivity, it may simultaneously reduce scientific diversity and broad engagement, highlighting a tension between personal advancement and collective scientific progress.
Related papers
- Scaling Laws in Scientific Discovery with AI and Robot Scientists [72.3420699173245]
An autonomous generalist scientist (AGS) concept combines agentic AI and embodied robotics to automate the entire research lifecycle.
AGS aims to significantly reduce the time and resources needed for scientific discovery.
As these autonomous systems become increasingly integrated into the research process, we hypothesize that scientific discovery might adhere to new scaling laws.
arXiv Detail & Related papers (2025-03-28T14:00:27Z) - Unlocking the Potential of AI Researchers in Scientific Discovery: What Is Missing? [20.94708392671015]
We project that AI4Science's share of total publications will rise from 3.57% in 2024 to approximately 25% by 2050.
We propose structured and actionable strategies to position AI researchers at the forefront of scientific discovery.
arXiv Detail & Related papers (2025-03-05T09:29:05Z) - Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently.
Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
arXiv Detail & Related papers (2025-02-07T18:26:45Z) - Bridging AI and Science: Implications from a Large-Scale Literature Analysis of AI4Science [25.683422870223076]
We present a large-scale analysis of the AI4Science literature.
We quantitatively highlight key disparities between AI methods and scientific problems.
We explore the potential and challenges of facilitating collaboration between AI and scientific communities.
arXiv Detail & Related papers (2024-11-27T00:40:51Z) - Drivers and Barriers of AI Adoption and Use in Scientific Research [0.0]
We study the integration of AI in scientific research, focusing on the human capital of scientists and the external resources available within their network of collaborators and institutions.
Our results suggest that AI is pioneered by domain scientists with a taste for exploration' and who are embedded in a network rich of computer scientists, experienced AI scientists and early-career researchers.
arXiv Detail & Related papers (2023-12-15T14:49:13Z) - AI empowering research: 10 ways how science can benefit from AI [0.0]
This article explores the transformative impact of artificial intelligence (AI) on scientific research.
It highlights ten ways in which AI is revolutionizing the work of scientists, including powerful referencing tools, improved understanding of research problems, enhanced research question generation, optimized research design, stub data generation, data transformation, advanced data analysis, and AI-assisted reporting.
arXiv Detail & Related papers (2023-07-17T18:41:18Z) - The Future of Fundamental Science Led by Generative Closed-Loop
Artificial Intelligence [67.70415658080121]
Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole.
AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
arXiv Detail & Related papers (2023-07-09T21:16:56Z) - Artificial intelligence adoption in the physical sciences, natural
sciences, life sciences, social sciences and the arts and humanities: A
bibliometric analysis of research publications from 1960-2021 [73.06361680847708]
In 1960 14% of 333 research fields were related to AI, but this increased to over half of all research fields by 1972, over 80% by 1986 and over 98% in current times.
In 1960 14% of 333 research fields were related to AI (many in computer science), but this increased to over half of all research fields by 1972, over 80% by 1986 and over 98% in current times.
We conclude that the context of the current surge appears different, and that interdisciplinary AI application is likely to be sustained.
arXiv Detail & Related papers (2023-06-15T14:08:07Z) - Quantifying the Benefit of Artificial Intelligence for Scientific Research [2.4700789675440524]
We estimate both the direct use of AI and the potential benefit of AI in scientific research.
We find that the use of AI in research is widespread throughout the sciences, growing especially rapidly since 2015.
Our analysis reveals considerable potential for AI to benefit numerous scientific fields, yet a notable disconnect exists between AI education and its research applications.
arXiv Detail & Related papers (2023-04-17T08:08:50Z) - Learning from learning machines: a new generation of AI technology to
meet the needs of science [59.261050918992325]
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery.
The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data.
arXiv Detail & Related papers (2021-11-27T00:55:21Z) - Learnings from Frontier Development Lab and SpaceML -- AI Accelerators
for NASA and ESA [57.06643156253045]
Research with AI and ML technologies lives in a variety of settings with often asynchronous goals and timelines.
We perform a case study of the Frontier Development Lab (FDL), an AI accelerator under a public-private partnership from NASA and ESA.
FDL research follows principled practices that are grounded in responsible development, conduct, and dissemination of AI research.
arXiv Detail & Related papers (2020-11-09T21:23:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.