Christoph Molnar, an expert in supervised machine learning, and Timo Freiesleben, a postdoctoral researcher in AI ethics, explore the intersection of machine learning and science. They discuss the skepticism scientists have towards predictive models and highlight the balance between accuracy and interpretability. The duo addresses the diverse levels of machine learning adoption across various scientific fields and the importance of domain knowledge. They also touch on how ML can enable scientists to test hypotheses and potentially discover new scientific laws.
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insights INSIGHT
Scientific Modeling Goals
Scientists aim to predict, control, explain, and reason about phenomena.
They traditionally build models from simple to complex, integrating data and knowledge.
insights INSIGHT
Machine Learning's Role in Science
Machine learning's prediction focus can be limiting for some scientific goals.
However, its clear benchmarks and ability to handle complex phenomena are advantageous.
volunteer_activism ADVICE
Leveraging Machine Learning's Convenience
Machine learning offers convenience by requiring fewer assumptions about the model.
Scientists can use it to create strong baselines and compare them to complex models.
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Machine learning is transforming scientific research across disciplines, but many scientists remain skeptical about using approaches that focus on prediction over causal understanding.
That’s why we are excited to have Christoph Molnar return to the podcast with Timo Freiesleben. They are co-authors of "Supervised Machine Learning for Science: How to Stop Worrying and Love your Black Box." We will talk about the perceived problems with automation in certain sciences and find out how scientists can use machine learning without losing scientific accuracy.
• Different scientific disciplines have varying goals beyond prediction, including control, explanation, and reasoning about phenomena • Traditional scientific approaches build models from simple to complex, while machine learning often starts with complex models • Scientists worry about using ML due to lack of interpretability and causal understanding • ML can both integrate domain knowledge and test existing scientific hypotheses • "Shortcut learning" occurs when models find predictive patterns that aren't meaningful • Machine learning adoption varies widely across scientific fields • Ecology and medical imaging have embraced ML, while other fields remain cautious • Future directions include ML potentially discovering scientific laws humans can understand • Researchers should view machine learning as another tool in their scientific toolkit
Stay tuned! In part 2, we'll shift the discussion with Christoph and Timo to talk about putting these concepts into practice.
The AI Fundamentalists 
