The Joy of Why

One of the most surprising and remarkable discoveries in recent scientific history has been CRISPR. Short for Clustered Regularly Interspaced Short Palindromic Repeats, CRISPR is a form of immune system that evolved in bacteria more than a billion years ago to defend against persistent viral threats. Under attack, bacteria can snip a small fragment of a virus’s DNA, store it in the CRISPR region of their genome, and then use it to recognize and destroy the same virus if it returns. The CRISPR-Cas9 system, to give it its longer name, consists of a short strand of guide RNA that identifies where to cut the DNA and a protein that acts as the molecular scissors.What made this system truly revolutionary was the demonstration in 2012 that it could be reprogrammed with different pieces of guide RNA to edit virtually any genome in any species, and at a level of precision and ease that far surpassed existing gene-editing tools. Since then, the editing capability of CRISPR has been tested on everything from developing disease treatments to engineering drought-resistant crops to resurrecting genes of extinct species. The possibilities have expanded so rapidly that researchers, ethicists, and regulators have found themselves struggling to keep up.One person acutely aware of the power of CRISPR is Jennifer Doudna, co-developer of the technology. Doudna, who received the Nobel Prize in Chemistry in 2020 with Emmanuelle Charpentier for this pioneering work, has been a prominent voice not only for its vast potential but also for its responsible and ethical use. In this episode of The Joy of Why, Doudna tells co-host Janna Levin how her early, “rebellious,” decision to study RNA led her on a serendipitous path to one of biology’s most transformative discoveries. They also discuss the breakthroughs, barriers, and frontiers that will define CRISPR’s true impact.

Birds are not merely descendants of dinosaurs — they are dinosaurs. For Yale evolutionary biologist and ornithologist Richard Prum, birds have been a lifelong passion and a window into some of evolution’s most intriguing mysteries.In a wide-ranging conversation with co-host Janna Levin, Prum traces the deep evolutionary origins of feathers, which he argues first emerged not for flight but for insulation, camouflage and display. Their colors — often invisible to the human eye — come into sharp focus under birds’ ultraviolet vision, suggesting a sensory world far richer than our own.Prum also explains why he champions Darwin’s once-marginalized theory of sexual selection, which proposes that traits such as the peacock’s tail evolved not for survival, but simply because they were attractive. Beauty, in other words, may shape life as powerfully as utility.

Most cosmologists agree that our universe had a beginning. But the finer details about the Big Bang remain a mystery. A history of everything would explain all, or so theoretical physicists hoped. In his final years, Stephen Hawking working with Thomas Hertog proposed a striking idea: The laws of physics were not precisely determined before the Big Bang; they evolved as the universe evolved. In this episode of The Joy of Why, Hertog speaks with co-host Janna Levin about his work and partnership with Hawking. Hertog, now at KU Leuven in Belgium, explains why they rejected the popular multiverse theory and instead explored the idea that the universe’s properties are a result of cosmological natural selection. According to Hertog and Hawking, these properties must be viewed through the lens of human observers, who are also the consequence of natural selection.So, how could the universe have created the conditions needed for life to emerge? Listen to the episode below to find out.

Born in the 18th century when Leonhard Euler solved the puzzle of the seven bridges of Königsberg, graph theory has become a foundational tool in mathematics. It studies relationships through nodes (vertices) and the links (edges) that connect them, transforming the complexity of systems — from friendship networks to airline routes — into elegant abstractions that reveal underlying structure and interaction.Maria Chudnovsky from Princeton University is a leading mathematician in the field. In this episode of The Joy of Why, Chudnovsky talks with co-host Janna Levin about how she got into graph theory, solved the decades-old perfect graph problem, and used it to plan her wedding seating chart. Chudnovsky also reflects on her appearance in commercials as a “superstar mathematician,” and how her background primed her for a discipline that transcends language, culture and time.

For decades, string theory has been hailed as the leading candidate for the theory of everything in our universe. Yet despite its mathematical elegance, the theory still lacks empirical evidence.One of its most intriguing, yet vexing, implications is that if all matter and forces are composed of vibrations of tiny strands of energy, then this allows for a vast landscape of possible universes with different physical properties, varieties of particles and complex spacetimes. How, then, can we possibly pinpoint our own universe within a field of almost infinite possibilities?Since 2005, Cumrun Vafa at MIT has been working to weed out this crowded landscape by identifying which hypothetical universes lie in a ‘swampland’ with properties inconsistent with the world we observe. In this episode of The Joy of Why, Vafa talks to co-host Janna Levin about the current state of string theory, why there are no more than 11 dimensions, how his swampland concept got an unexpected lift from the BICEP array, and how close we may be to testable predictions.

Large language models (LLMs) are becoming increasingly more impressive at creating human-like text and answering questions, but whether they can understand the meaning of the words they generate is a hotly debated issue. A big challenge is that LLMs are black boxes; they can make predictions and decisions on the order of words, but they cannot communicate the reasons for doing so.Ellie Pavlick at Brown University is building models that could help understand how LLMs process language compared with humans. In this episode of The Joy of Why, Pavlick discusses what we know and don’t know about LLM language processing, how their processes differ from humans, and how understanding LLMs better could also help us better appreciate our own capacity for knowledge and creativity.

Quantum computing promises unprecedented speed, but in practice, it’s proven remarkably difficult to find important questions that quantum machines can solve faster than classical ones. One of the most notable demonstrations of this came from Ewin Tang who rose to prominence in the field as a teenager. When quantum algorithms had in principle cracked the so-called recommendation problem, Tang designed classical algorithms that could match them.So began the approach of “dequantizing,” in which computer scientists look at quantum algorithms and try to achieve the same speeds with classical counterparts. To understand the ongoing contest between classical and quantum computing, co-host Janna Levin spoke to Tang on The Joy of Why podcast. The wide-ranging conversation covered what it was like for Tang to challenge the prevailing wisdom at such a young age, the role of failure in scientific progress, and whether quantum computing will ultimately fulfill its grand ambitions.

How did complex life evolve? Where did space-time come from? Will computers ever understand language like we do? How did geometry create modern physics? These are just a few of the big and bold questions that we’ll be exploring in the latest season of Quanta’s interview podcast, “The Joy of Why,” starting March 20, and released every other Thursday.As ever, we will be talking to researchers and exploring the latest thinking on great scientific and mathematical questions of our time. But this season sees a new format, with the two of us — mathematician Steven Strogatz and physicist Janna Levin — together in conversation for the first time to offer our own thoughts and questions about each topic.As a preview, we discuss why we are particularly excited about the new season, and we reveal our favorite standout moments from the previous season.

Death might seem like a pure loss, the disappearance of what makes a living thing distinct from everything else on our planet. But zoom in closer, to the cellular level, and death takes on a different, more nuanced meaning. There is a challenge in simply defining what makes an individual cell alive or dead. Scientists today are working to understand the various ways and reasons that cells disappear, and what these processes mean to biological systems. In this episode, cellular biologist Shai Shaham talks to Steven Strogatz about the different forms of cell death, their roles in evolution and disease, and why the right kinds and patterns of cell death are essential to our development and well-being.

Scientists routinely build quantitative models — of, say, the weather or an epidemic — and then use them to make predictions, which they can then test against the real thing. This work can reveal how well we understand complex phenomena, and also dictate where research should go next. In recent years, the remarkable successes of “black box” systems such as large language models suggest that it is sometimes possible to make successful predictions without knowing how something works at all. In this episode, noted statistician Emmanuel Candès and host Steven Strogatz discuss using statistics, data science and AI in the study of everything from college admissions to election forecasting to drug discovery.

When we think about medicine’s war on cancer, treatments such as surgery, radiation and chemotherapy spring to mind first. Now there is another potential weapon for defeating tumors: statistics and mathematical models that can optimize the selection, combination or timing of treatment. Building and feeding these models requires accounting for the complexity of the body, and recognizing that cancer cells are constantly evolving. In this episode, host Steven Strogatz hears from Franziska Michor, a computational biologist, about how our understanding of evolutionary dynamics is being used to devise new anticancer therapies.

As a treat to our listeners, we are posting a full episode of Sidedoor, a podcast that explores the treasures in the Smithsonian's vaults. Subscribe to Sidedoor from Smithsonian wherever you listen to podcasts!Black holes could unlock the mysteries of creation and live at the heart of nearly every galaxy. But these invisible balls of extremely dense matter have never been fully understood, especially when they were only a theory. We travel through a cosmic wormhole back to the 1930s to learn how the first astrophysicist to successfully theorize a black hole, Subrahmanyan Chandrasekhar, was ridiculed and rejected by his scientific community.

Observations of the cosmos suggest that unseen sources of gravity — dark matter — tug at the stars in galaxies, while another mysterious force — dark energy — drives the universe to expand at an ever-increasing rate. The evidence for both of them, however, hinges on assumptions that gravity works the same way at all scales. What if that’s not true? In this episode, theoretical physicist Claudia de Rham explains her work on an alternative explanation called “massive gravity” to host Janna Levin.