Supervised machine learning for science with Christoph Molnar and Timo Freiesleben, Part 2
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 decisionsData augmentation offers a simple yet powerful way to incorporate domain knowledgeDomain expertise is often undervalued in data science despite being crucialMeasuring causality: Metrics and blind spots (10:10)Causality approaches in ML range from exploring associations to inferring treatment effectsConnecting models to scientific understanding (18:00)Interpretation methods must stay within realistic data distributions to yield meaningful insightsRobustness across distribution shifts (26:40)Robustness requires understanding what distribution shifts affect your modelPre-trained models and transfer learning provide promising paths to more robust scientific MLReproducibility challenges in ML and science (35:00)Reproducibility challenges differ between traditional science and machine learningGo 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.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.
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Supervised machine learning for science with Christoph Molnar and Timo Freiesleben, Part 1
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 toolkitStay tuned! In part 2, we'll shift the discussion with Christoph and Timo to talk about putting these concepts into practice. 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.
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The future of AI: Exploring modeling paradigms
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 AIAnother 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 AIUS 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 pressureModeling 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.
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Agentic AI: Here we go again
Agentic AI is the latest foray into big-bet promises for businesses and society at large. While promising autonomy and efficiency, AI agents raise fundamental questions about their accuracy, governance, and the potential pitfalls of over-reliance on automation. Does this story sound vaguely familiar? Hold that thought. This discussion about the over-under of certain promises is for you.Show NotesThe economics of LLMs and DeepSeek R1 (00:00:03)Reviewing recent developments in AI technologies and their implications Discussing the impact of DeepSeek’s R1 model on the AI landscape, NVIDIA The origins of agentic AI (00:07:12)Status quo of AI models to date: Is big tech backing away from promise of generative AI?Agentic AI designed to perceive, reason, act, and learnGovernance and agentic AI (00:13:12)Examining the tension between cost efficiency and performance risks [LangChain State of AI Agents Report]Highlighting governance concerns related to AI agents Issues with agentic AI implementation (00:21:01)Considering the limitations of AI agents and their adoption in the workplace Analyzing real-world experiments with AI agent technologies, like Devin What's next for complex and agentic AI systems (00:29:27)Offering insights on the cautious integration of these systems in business practicesEncouraging a thoughtful approach to leveraging AI capabilities for measurable outcomesWhat 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.
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Contextual integrity and differential privacy: Theory vs. application with Sebastian Benthall
What if privacy could be as dynamic and socially aware as the communities it aims to protect? Sebastian Benthall, a senior research fellow from NYU’s Information Law Institute, shows us how privacy is complex. He uses Helen Nissenbaum’s work with contextual integrity and concepts in differential privacy to explain the complexity of privacy. Our talk explains how privacy is not just about protecting data but also about following social rules in different situations, from healthcare to education. These rules can change privacy regulations in big ways.Show notesIntro: Sebastian Benthall (0:03)Research: Designing Fiduciary Artificial Intelligence (Benthall, Shekman)Integrating Differential Privacy and Contextual Integrity (Benthall, Cummings)Exploring differential privacy and contextual integrity (1:05)Discussion about the origins of each subjectHow are differential privacy and contextual integrity used to enforce each other?Accepted context or legitimate context? (9:33)Does context develop from what society accepts over time?Approaches to determine situational context and legitimacyNext steps in contextual integrity (13:35)Is privacy as we know it ending?Areas where integrated differential privacy and contextual integrity can help (Cummings)Interpretations of differential privacy (14:30)Not a silver bulletNew questions posed from NIST about its applicationPrivacy determined by social norms (20:25)Game theory and its potential for understanding social normsAgents and governance: what will ultimately decide privacy? (25:27)Voluntary disclosures and the biases it can present towards groups that are least concerned with privacyAvoiding self-fulfilling prophecy from data and contextWhat 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.
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