On scientific understanding with artificial intelligence
By Mario Krenn, R. Pollice, S. Guo, Matteo Aldeghi, Alba Cervera-Lierta, Pascal Friederich, Gabriel dos Passos Gomes, Florian Hase, A. Jinich, AkshatKumar Nigam and 2 more. Nature Reviews Physics, 2022
320 citations recorded by the source corpus.
An oracle that correctly predicts the outcome of every particle physics experiment, the products of every possible chemical reaction or the function of every protein would revolutionize science and technology. However, scientists would not be entirely satisfied because they would want to comprehend how the oracle made these predictions. This is scientific understanding, one of the main aims of science. With the increase in the available computational power and advances in artificial intelligence, a natural question arises: how can advanced computational systems, and specifically artificial intelligence, contribute to new scientific understanding or gain it autonomously? Trying to answer this question, we adopted a definition of ‘scientific understanding’ from the philosophy of science that enabled us to overview the scattered literature on the topic and, combined with dozens of anecdotes from scientists, map out three dimensions of computer-assisted scientific understanding. For each dimension, we review the existing state of the art and discuss future developments. We hope that this Perspective will inspire and focus research directions in this multidisciplinary emerging field. Scientific understanding is one of the main aims of science. This Perspective discusses how advanced computational systems, and artificial intelligence in particular, can contribute to driving scientific understanding.
Subfields: Artificial Intelligence, Biophysics, Information Systems and Management
View Paper: https://www.semanticscholar.org/paper/94e32a898cc8c62c5a25fb9fa01e94c21fd41da5
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In Artificial Intelligence
- Mark Yatskar
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- Sihao Chen
- Chaitanya Malaviya
- Elizabeth Sieber
- Subin Lee
- Yejin Choi
Citations in the corpus
- Computation (2020, unknown): “For example, in targeted marketing, the NPD metric can be used to reach only the customers most likely to buy in the next period, optimizing the campaign budget.”
- Artificial intelligence and illusions of understanding in scientific research (2024, unknown): “Although the enthusiasm for Oracles is largely shared by the authors of these publications, some researchers have observed how Oracles can detract from scientific understanding 33 , a point we discuss below.”
- ExpertQA: Expert-Curated Questions and Attributed Answers (2023, background): “For example, medical professionals are using these systems for performing differential diagnosis (Lee et al., 2023) and researchers are using them for faster literature surveys (Krenn et al., 2022; Birhane et al., 2023; Owens, 2023).”
- Characterizing LLM scientific concept generation: A multi-dimensional measurement study (2026, unknown): “Prior work on embedding-based novelty detection in scientific text [24] has typically relied on a single model, limiting the robustness of findings.”