It’s surprising that for centuries, scientists have left the study of how to do science largely to non-scientists. Not anymore – thanks to the young field of cognitive epistemology. In this episode, we discuss the exciting – and surprising – science of doing science with Marina Dubova, a postdoc at the Santa Fe Institute and soon a professor at UC Berkeley. Marina found, for example, that to get the most powerful theories, you should not plan the collection of data with a view to falsify or validate existing hypotheses; instead, try to get as diverse a dataset as possible. It adds a new angle to an idea that we discussed in many previous episodes: that discoveries often arise from exploratory data collection. Based on Marina’s research and her own process, we also discuss how science thrives on diversity at all levels – data, ideas, questions, scientists – and on the use of analogies and metaphors. 

The Night Science Podcast is produced by the Night Science Institute. For more information on Night Science, visit night-science.org .

Ken Stanley is a highly regarded researcher in machine learning and artificial intelligence. After leaving his professorship at the University of Central Florida, he cofounded Geometric Intelligence (now Uber AI Labs), and he is now Senior Vice President of Open-Endedness at LilaSciences. In this episode, Ken explains why ambitious objectives often backfire: the real stepping stones to breakthrough discoveries rarely look like progress toward the goal, so a direct pursuit can blind us to the opportunities that matter most. Together, we connect these ideas to night science, scientific intuition, and the pressure to justify research with hypotheses and deliverables, and we argue for a healthier system that funds exploration, curiosity, and the creation of new “playgrounds” for discovery.

The Night Science Podcast is produced by the Night Science Institute. For more information on Night Science, visit night-science.org .

Maria Leptin is the President of the ERC, the European Research Council, and Professor of genetics at the University of Cologne. In this episode, Maria describes her own path as one driven by observation and curiosity rather than long-term planning, and discusses why small, intellectually vibrant institutes often outperform large labs. We discuss how funding agencies can better support bold ideas, and we explore how to evaluate creativity in grant proposals and why a focus on feasibility can stifle innovation. We also consider the role of grant writing in shaping ideas, the differences between academia and industry, and the importance of stable yet non-complacent careers.

The Night Science Podcast is produced by the Night Science Institute. For more information on Night Science, visit night-science.org .®

Stephen Nachmanovitch is a musician celebrated for his free improvisations, and an educator whose books Free Play and The Art of Is have become classics on the creative process. With his training as an ecologist and his PhD in the history of consciousness, Stephen brings a unique philosophical view on art, science, and life to the podcast. In our discussion, Stephen reflects on how creativity is not a thing but a living process: the art of IS. He draws connections between artistic and scientific practice, emphasizing how both depend on careful attention to the world, openness to mistakes, and dialogue across perspectives. We discuss how fear of error inhibits creativity, and how improvisation can free us from perfectionism. We also touch upon the importance of chivalry in dialogue, the art of advancing each other’s ideas rather than blocking them.

The Night Science Podcast is produced by the Night Science Institute. For more information on Night Science, visit night-science.org .

Akiko Iwasaki, a Yale professor and Howard Hughes Investigator, was named one of Time Magazine’s 100 most influential people in 2024. Together, we reflect on how diverse backgrounds enrich research, allowing people to discover different things in the same data. Akiko explains how leading large collaborations requires managing expectations, not micromanaging the research. She compares her work of studying complex conditions to solving multilayered puzzles: each new piece of evidence must be placed in the right layer for the bigger picture to emerge. And she jokes about her own “terrible hairball analogy” and how, at the center of that mess, she searches for hidden gems.

The Night Science Podcast is produced by the Night Science Institute. For more information on Night Science, visit night-science.org .

To answer this question, we speak with Dr. Alan Karthikesalingam and Vivek Natarajan from Google DeepMind about their groundbreaking AI co-scientist project. Beyond their work at Google, Alan is an honorary lecturer in vascular surgery at Imperial College London, and Vivek teaches at Harvard’s T.H. Chan School of Public Health. Together, we discuss how their system has evolved to mirror parts of human hypothesis generation while also diverging in fascinating ways. We talk about its internal “tournaments” of ideas, its ability to be prompted to “think out of the box,” and whether it becomes too constrained by the need to align with every published “fact”. And we discuss how we still seem far away from a time when AI can not only answer our questions, but can ask new and exciting research questions itself.

The Night Science Podcast is produced by the Night Science Institute – for more information on Night Science, visit night-science.org .

Professor Eve Marder is a pioneering neuroscientist at Brandeis University. Drawing on decades of work with a small neural circuit in lobsters, she describes how discovery often emerges from intuition, puzzlement, and the courage to follow unexpected observations. Eve highlights the central role of personal tolerance for ambiguity in shaping a scientist’s questions and methods. She discusses the fine line between idiosyncrasies and general principles, and how deep familiarity with the literature shaped her scientific intuition – something hard to replicate in today’s information-saturated world. We also discuss how reading is a prerequisite for clear writing, and how rigid publishing norms led to “recipe science”, suppressing creativity.

​​For more information on Night Science, visit https://www.biomedcentral.com/collections/night-science .

Martin Schwartz, a professor at Yale, is known for his work on integrins and his influential essay “The importance of stupidity in scientific research”. He emphasizes that while learning science makes you feel smart, true scientific discovery often involves feeling stupid, because it means venturing into the unknown. We discuss how the ego can obstruct creativity, and how resilience, self-discovery, and the cultivation of "passionate indifference" – being deeply engaged but unattached to outcomes – are key to sustaining a productive and fulfilling life in science.

With Ethan Mollick, professor at Wharton and author of the bestselling “Co-Intelligence”, we explore how generative AI tools like ChatGPT can enhance scientific creativity. Ethan emphasizes that AI excels at idea generation through sheer volume and recombination, outperforming most humans in many creativity tasks – though it does have odd obsessions with VR and crypto. However, AI is most effective when integrated into a collaborative human–machine workflow rather than used as a replacement. Ethan describes AI as your tireless science buddy that never gets bored or judgmental during brainstorming. We discuss how AI’s "hallucinations" can be used for creativity, how AI can bridge disciplines by revealing hidden connections across fields, and how prompting strategies – such as chain-of-thought or playful personas – can guide AI toward more original outputs. Ethan stresses the need for scientists to actively experiment with these tools, share their methods openly, and reconsider scientific workflows in light of rapid AI progress.

​​For more information on Night Science, visit https://www.biomedcentral.com/collections/night-science .

David Baker, who was awarded the 2024 Nobel Prize in Chemistry for designing novel proteins with AI, is a professor at the University of Washington. In this episode, he explains how he socially engineers his lab’s "communal brain", where all individuals function like neurons, densely interconnected to maximize idea generation. We explore the role of AI in science, discussing whether AI can be truly creative. Finally, we discuss the current funding crisis in science, which disproportionately affects young researchers.

For more information about Night Science, visit https://www.biomedcentral.com/collections/night-science .