The Joy of Why

When an LLM answers a question, is it reasoning like humans, or just producing text that looks like reasoning? The distinction isn’t just philosophical, this determines what we can trust AI to do, how closely we need to supervise it, and ultimately what its real-world impact will turn out to be.
Melanie Mitchell at the Santa Fe Institute argues that we lack adequate methods for measuring machine cognition, and that AI is a form of “alien intelligence” that operates through non-human cognitive mechanisms. In this episode of The Joy of Why, Mitchell tells Steven Strogatz how methods that psychologists use to study cognition in other kinds of “alien intelligence” — babies and animals — can be adapted to probe AI, and she lays out six principles for better assessing machine cognition. Their conversation ranges from the challenge of interpreting what’s happening inside these systems, to recent AI-assisted breakthroughs in mathematics, to why a math-performing horse from the early 1900s offers a cautionary tale for how we assess intelligence.

Pain and pleasure seem like simple facts of life, but they are far from it. Neuroscientists still cannot say why physical pain differs from psychological pain, for instance, nor why a loved one’s touch soothes while a stranger’s touch repels.
To explore the science behind these sensations, Janna Levin talked to Ishmail Abdus-Saboor, a neuroscientist at Columbia University’s Zuckerman Institute. Their conversation covers how pain serves an evolutionary purpose, how researchers measure pain and pleasure in the lab despite the absence of any objective biomarker, and how touch functions as a social and emotional signal, not just a sensory one. Abdus-Saboor also describes his work with naked mole rats — a species that barely feels pain, shows no signs of aging, and lives in colonies built almost entirely on touch — and the ethical trade-offs when studying sensations in animals that cannot describe what they feel.

One of the biggest mysteries in cosmology seems to keep getting bigger. Astronomers have known since the 1930s that the universe is expanding, but in the 1990s, the discovery that this expansion is accelerating rather than slowing down came as a huge shock to the field. Something had to be driving that acceleration, and dark energy was proposed as the cause. What began as a seismic shock in cosmology eventually led to a Nobel prize.
But this story has a sequel, and it comes with another major plot twist. The two main methods for estimating the universe's present-day expansion rate are producing significantly different answers. As a result, how fast the universe is really expanding has become a matter of considerable debate — with no small amount of angst — and the discrepancy has become known as the Hubble tension. Perhaps most surprising of all is that one of the loudest voices raising concern is Adam Riess, the astrophysicist whose Nobel Prize-winning work helped ignite the acceleration debate in the first place.
Riess joined co-host Steven Strogatz on The Joy of Why to explain how we got to this point, what the Hubble tension might be telling us, and what may happen next.

Does intelligent life exist elsewhere in the universe? The question has captivated us for centuries, but despite decades of searching it remains frustratingly unanswered. Every so often a curious signal appears — fossilized structures in a meteorite, say, or an unusual gas in an exoplanet’s atmosphere — and for a moment it seems possible that we are not alone before the excitement gives way to a more mundane explanation. 
So what would it actually take to find life in the cosmos — and how would we know when we saw it?
David Kipping, an astronomer at Columbia University, has spent his career finding better ways to answer these questions. His approach is statistical: rather than chasing individual detections, he develops mathematical frameworks for reasoning about where habitable worlds are likely to exist and how confidently we can interpret the signals they produce. In this episode of The Joy of Why, Kipping joins co-host Janna Levin to discuss efforts to frame one of humanity’s oldest existential questions as a tractable scientific problem, why biosignatures have proved so difficult to interpret, and why he believes exomoons may be an overlooked place to search for life.

What links certain mathematical models of traffic flow, shallow-water waves, and quantum particle scattering? The surprising answer lies in a corner of the algebraic combinatorics world that goes by the name of positive Grassmannian. In simple terms, the positive Grassmannian is a shape that classifies other shapes. Remarkably, pieces of the positive Grassmannian can be reassembled in forms that reveal shared structures in these and many other seemingly unrelated mathematical systems.
That we know the positive Grassmannian crops up in many real-world settings is largely down to the theoretical work of Lauren Williams at Harvard University. In this latest episode of The Joy of Why, Williams talks to co-host Steven Strogatz about her work, how she realized the surprising pervasiveness of the positive Grassmannian, and how she has made a career of finding connections among fields that don’t at first sight seem connected. The conversation then switches to another project Williams is working on, called First Proof, which is trying to measure objectively how good AI systems are at coming up with proofs of research-level mathematical statements, and which leads to an exploration of whether AI may or may not take over mathematics.
Note: Since this conversation was recorded, results from the First Proof Second Batch project were released on June 10, 2026.