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Machine learning for science: state of the art and future prospects
1Machine Learning Systems Group, Jet Propulsion Laboratory/California Institute of Technology, Pasadena, CA, 91109, USA. mjolsness@jpl.nasa.gov
Science (New York, N.Y.)
|September 15, 2001
Summary
Modern machine learning (ML) methods offer powerful new scientific tools. This viewpoint explores ML characteristics and their relevance for scientific applications, suggesting future directions.
Area of Science:
- Computational Science
- Data Science
- Scientific Research Methodology
Background:
- Machine learning (ML) methods have advanced significantly.
- ML has demonstrated success in diverse scientific fields like planetary science and bioinformatics.
- These advances suggest ML can provide powerful new tools for scientists.
Purpose of the Study:
- To highlight useful characteristics of modern machine learning methods.
- To discuss the relevance of these ML characteristics to scientific applications.
- To speculate on near-term progress and future directions for ML in science.
Main Methods:
- This work is a viewpoint article, not based on empirical data.
- It synthesizes current knowledge on machine learning.
- It discusses the applicability of ML techniques to scientific challenges.
Main Results:
- Modern machine learning methods possess characteristics beneficial for scientific research.
- Specific ML features are relevant for various scientific applications.
- The potential for ML to transform scientific discovery is significant.
Conclusions:
- Machine learning offers a promising suite of tools for contemporary scientists.
- Continued development and application of ML are expected to accelerate scientific progress.
- Future research should focus on novel ML applications and methodological advancements in science.
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