Closed-form, interpretable mathematical models have been instrumental for advancing our understanding of the world; with the data revolution, we may now be in a position to uncover new such models for many systems from physics to the social sciences. However, to deal with increasing amounts of data, we need "machine scientists" that are able to extract these models automatically from data. Here, we introduce a Bayesian machine scientist, which establishes the plausibility of models using explicit approximations to the exact marginal posterior over models and establishes its prior expectations about models by learning from a large empirical corpus of mathematical expressions. It explores the space of models using Markov chain Monte Carlo. We show that this approach uncovers accurate models for synthetic and real data and provides out-of-sample predictions that are more accurate than those of existing approaches and of other nonparametric methods.
The Use of AI-Robotic Systems for Scientific Discovery
Chalmers University of Technology（查尔姆斯理工大学）；The University of Manchester（曼彻斯特大学）；KTH Royal Institute of Technology（皇家理工学院）；Vanderbilt University（范德比大学）；University of Cambridge（剑桥大学）
Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of California（加州大学）；Huazhong Agricultural University（华中农业大学）；Huazhong University of Science and Technology（华中科技大学）
Accelerating scientific discovery with Co-Scientist
Google Cloud AI Research（谷歌云人工智能研究）；Google DeepMind（谷歌DeepMind）；Google Research（谷歌研究）；Stanford University School of Medicine（斯坦福大学医学院）；Houston Methodist（休斯顿卫理公会医院）；Sequome；Fleming Initiative and Imperial College London（弗莱明倡议与伦敦帝国理工学院）
Deep learning (DL), a new-generation of artificial neural network research, has transformed industries, daily lives and various scientific disciplines in recent years. DL represents significant progress in the ability of neural networks to automatically engineer problem-relevant features and capture highly complex data distributions. I argue that DL can help address several major new and old challenges facing research in water sciences such as inter-disciplinarity, data discoverability, hydrologic scaling, equifinality, and needs for parameter regionalization. This review paper is intended to …
Rutherford Appleton Laboratory, Science and Technology Facilities Council（卢瑟福·阿普尔顿实验室，科学与技术设施委员会）；Oak Ridge National Laboratory（橡树岭国家实验室）；University of Virginia（弗吉尼亚大学）
The breakthrough in Deep Learning neural networks has transformed the use of AI and machine learning technologies for the analysis of very large experimental datasets. These datasets are typically generated by large-scale experimental facilities at national laboratories. In the context of science, scientific machine learning focuses on training machines to identify patterns, trends, and anomalies to extract meaningful scientific insights from such datasets. With a new generation of experimental facilities, the rate of data generation and the scale of data volumes will increasingly require the …
AI scientists produce results without reasoning scientifically
Laboratory of Organic and Macromolecular Chemistry (IOMC), Friedrich Schiller University Jena（有机与大分子化学实验室（IOMC），弗里德里希-席勒耶纳大学）；Department of Civil Engineering, Indian Institute of Technology Delhi（土木工程系，印度理工学院德里）；School of Interdisciplinary Research, Indian Institute of Technology Delhi（跨学科研究学院，印度理工学院德里）；Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi（Yardi人工智能学院，印度理工学院德里）；Center for Energy and Environmental Chemistry Jena, Friedrich Schiller University Jena（能源与环境化学中心（耶纳），弗里德里希-席勒耶纳大学）；Helmholtz Institute for Polymers in Energy Applications Jena (HIPOLE Jena), Lessingstraße 12–14（能源应用高分子研究所（耶纳，HIPOLE耶纳），Lessingstraße 12–14）
Georgia Institute of Technology（佐治亚理工学院）；Queensland University of Technology（昆士兰科技大学）；Jet Propulsion Laboratory（喷气推进实验室）
While anomaly detection stands among the most important and valuable problems across many scientific domains, anomaly detection research often focuses on AI methods that can lack the nuance and interpretability so critical to conducting scientific inquiry. In this application paper we present the results of utilizing an alternative approach that situates the mathematical framing of machine learning based anomaly detection within a participatory design framework. In a collaboration with NASA scientists working with the PIXL instrument studying Martian planetary geochemistry as a part of the sea…
Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy
Yale University（耶鲁大学）；National Library of Medicine, National Institutes of Health（国家医学图书馆，国立卫生研究院）；Mila-Quebec AI Institute（魁北克AI研究所）；Shanghai Jiao Tong University（上海交通大学）；OPPO Research Institute（OPPO研究院）；Reichman University（里奇曼大学）
