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This paper provides a comprehensive survey of the applications of artificial intelligence (AI), particularly large-scale language models (LLMs), to scientific research. While advances in LLMs like OpenAI-o1 and DeepSeek-R1 have led to a surge in research on the application of AI to the innovation process of scientific research, a comprehensive survey of this field has been lacking. This paper presents a systematic taxonomy that categorizes five key challenges in AI-powered research (AI4Research). It highlights key research gaps and promising future directions, focusing on the rigor and scalability of automated experiments and their societal impact. It also compiles a wealth of resources, including relevant multidisciplinary applications, data corpora, and tools.
Takeaways, Limitations
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Takeaways:
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We provide the first comprehensive survey of the AI4Research field, providing useful information and resources for researchers.
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We present a systematic classification system that categorizes the five main tasks of AI4Research.
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It presents the future direction of AI4Research, including the rigor and scalability of automated experiments and their social impact.
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It provides a wealth of resources, including various applications, data corpora, and tools.
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Limitations:
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The classification system and future directions presented in this paper may require future revision and update as the field of AI4Research rapidly advances.
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The list of resources presented may not be complete and requires ongoing updating.
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There may be a lack of in-depth discussion of the ethical and social implications of AI4Research.