There is a long history of the science of intelligent machines and its potential to provide scientific insights have been debated since the dawn of AI. In particular, there is renewed interest in the role of AI in research and research policy as an enabler of new methods, processes, management and evaluation which is still relatively under-explored. This empirical paper explores interviews with leading scholars on the potential impact of AI on research practice and culture through deductive, thematic analysis to show the issues affecting academics and universities today. Our interviewees identify positive and negative consequences for research and researchers with respect to and . AI is perceived as helpful with respect to information gathering and other narrow tasks, and in support of impact and interdisciplinarity. However, using AI as a way of 'speeding up-to keep up' with bureaucratic and metricised processes, may proliferate negative aspects of academic culture in that the expansion of AI in research should assist and not replace human creativity. Research into the future role of AI in the research process needs to go further to address these challenges, and ask fundamental questions about how AI might assist in providing new tools able to question the values and principles driving institutions and research processes. We argue that to do this an explicit movement of meta-research on the role of AI in research should consider the effects for research and researcher creativity. Anticipatory approaches and engagement of diverse and critical voices at policy level and across disciplines should also be considered.
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http://dx.doi.org/10.1007/s00146-021-01259-0 | DOI Listing |
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School of Biological Sciences, University of Aberdeen, King's College, Aberdeen, UK.
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Institute of Science and Innovation in Mechanical and Industrial Engineering (INEGI), FEUP Campus, Rua Dr. Roberto Frias 400, 4200-465 Porto, Portugal.
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Center for Generic Aerospace Technology, Huanjiang Laboratory, Zhuji 311816, China.
This paper introduces Re-DQN, a deep reinforcement learning-based algorithm for comprehensive coverage path planning in lawn mowing robots. In the fields of smart homes and agricultural automation, lawn mowing robots are rapidly gaining popularity to reduce the demand for manual labor. The algorithm introduces a new exploration mechanism, combined with an intrinsic reward function based on state novelty and a dynamic input structure, effectively enhancing the robot's adaptability and path optimization capabilities in dynamic environments.
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January 2025
School of Civil Engineering Architecture and the Environment, Hubei University of Technology, Wuhan 430068, China.
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In recent years, some new "Qi-Nan" clones of with the characteristics of easy induction and high-quality agarwood have been obtained, through the cultivation and propagation of grafted seedlings. These clones are used for the intensive production of high-quality agarwood. The speed of resin formation and yield are crucial for the development of the agarwood industry.
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