Artificial intelligence: A powerful paradigm for scientific research
By Yongjun Xu, Qi Wang, Zhulin An, Fei Wang, Libo Zhang, Yanjun Wu, Fengliang Dong, Cheng-Wei Qiu, Xin Liu, Junjun Qiu and 37 more. The Innovation, 2021
1,476 citations recorded by the source corpus.
Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.
Subfields: Artificial Intelligence, Materials Chemistry, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/6e4d9cd2da1e3667da2cc303c5549a7773f07fa7
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Citations in the corpus
- Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting (2022, background): “With the availability of large-scale traffic data and the rise of artificial intelligence [41], Spatial-Temporal Graph Neural Networks (STGNNs) are proposed tomodel the complex spatial-temporal correlations in traffic data [21, 26, 35, 44, 48].”
- Computation (2020, neutral): “The last section contains the conclusions and future work for improving the model.”
- Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting (2022, background): “The accuracy ofmultivariate time series forecasting has been largely improved by artificial intelligence [37], especially deep learning techniques.”