Part 2 of this series could have easily been renamed "AI for science: The expert’s guide to practical machine learning.” We continue our discussion with Christoph Molnar and Timo Freiesleben to look at how scientists can apply supervised machine learning techniques from the previous episode into their research.

Introduction to supervised ML for science (0:00)

• Welcome back to Christoph Molnar and Timo Freiesleben, co-authors of “Supervised Machine Learning for Science: How to Stop Worrying and Love Your Black Box”

The model as the expert? (1:00)

• Evaluation metrics have profound downstream effects on all modeling decisions
• Data augmentation offers a simple yet powerful way to incorporate domain knowledge
• Domain expertise is often undervalued in data science despite being crucial

Measuring causality: Metrics and blind spots (10:10)

• Causality approaches in ML range from exploring associations to inferring treatment effects

Connecting models to scientific understanding (18:00)

• Interpretation methods must stay within realistic data distributions to yield meaningful insights

Robustness across distribution shifts (26:40)

• Robustness requires understanding what distribution shifts affect your model
• Pre-trained models and transfer learning provide promising paths to more robust scientific ML

Reproducibility challenges in ML and science (35:00)

• Reproducibility challenges differ between traditional science and machine learning

Go back to listen to part one of this series for the conceptual foundations that support these practical applications.

Check out Christoph and Timo's book “Supervised Machine Learning for Science: How to Stop Worrying and Love Your Black Box” available online now.

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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.

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Unlock the secrets to AI's modeling paradigms. We emphasize the importance of modeling practices, how they interact, and how they should be considered in relation to each other before you act. Using the right tool for the right job is key. We hope you enjoy these examples of where the greatest AI and machine learning techniques exist in your routine today.

More AI agent disruptors (0:56)

• Proxy from London start-up Convergence AI
• Another hit to OpenAI, this product is available for free, unlike OpenAI’s Operator.

AI Paris Summit - What's next for regulation? (4:40)

• [Vice President] Vance tells Europeans that heavy regulation can kill AI
• US federal administration withdrawing from the previous trend of sweeping big tech regulation on modeling systems.
• The EU is pushing to reduce bureaucracy but not regulatory pressure

Modeling paradigms explained (10:33)

• As companies look for an edge in high-stakes computations, we’ve seen best-in-class rediscovering expert system-based techniques that, with modern computing power, are breathing new light into them.
  • Paradigm 1: Agents (11:23)
  • Paradigm 2: Generative (14:26)
  • Paradigm 3: Mathematical optimization (regression) (18:33)
  • Paradigm 4: Predictive (classification) (23:19)
  • Paradigm 5: Control theory (24:37)

The right modeling paradigm for the job? (28:05)

What did you think? Let us know.

Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics:

• LinkedIn - Episode summaries, shares of cited articles, and more.
• YouTube - Was it something that we said? Good. Share your favorite quotes.
• Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.