Summary of Is Machine Learning Good or Bad For the Natural Sciences?, by David W. Hogg (nyu et al.
Is machine learning good or bad for the natural sciences?
by David W. Hogg, Soledad Villar
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG); Data Analysis, Statistics and Probability (physics.data-an)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the intersection of machine learning (ML) with the natural sciences, highlighting both the benefits and limitations of integrating ML methods into scientific research. Specifically, it discusses how ML’s strong ontology and epistemology can be valuable in certain contexts, such as causal inference, but also introduce unwanted biases when used to emulate physical simulations or label datasets. The authors encourage the scientific communities to critically evaluate the role and value of ML in their respective fields. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is helping scientists make new discoveries, but it’s not without its problems. This paper looks at how machine learning works and where it can be useful. For example, it can help us understand how things cause each other by reducing confusing factors. However, when we use machine learning to copy real-life simulations or give names to data sets, it can make our results less trustworthy. The authors are asking scientists to think about the good and bad of using machine learning in their work. |
Keywords
* Artificial intelligence * Inference * Machine learning
