How Is AI Changing the Science of Prediction?

Quantum gravity is one of the biggest unresolved and challenging problems in physics, as it seeks to reconcile quantum mechanics, which governs the microscopic world, and general relativity, which describes the macroscopic world of gravity and space-time. Efforts to understand quantum gravity have been focused almost entirely at the theoretical level, but Monika Schleier-Smith at Stanford University has been exploring a novel experimental approach — trying to create quantum gravity from scratch. Using laser-cooled clouds of atoms, she is testing the idea that gravity might be an emergent phenomenon arising from quantum entanglement.In this episode of the Joy of Why podcast, Schleier-Smith discusses the thinking behind what she admits is a high-risk, high-reward approach, and how her experiments could provide important insights about entanglement and quantum mechanical systems even if the end goal of simulating quantum gravity is never achieved.

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.

At first, life on Earth was simple. Cells existed, functioned and reproduced as free-living individuals. But then, something remarkable happened. Some cells joined forces, working together instead of being alone. This transition, known as multicellularity, was a pivotal event in the history of life on Earth. Multicellularity enabled greater biological complexity, which sparked an extraordinary diversity of organisms and structures.How life evolved from unicellular to multicellular organisms remains a mystery, though evidence indicates that this may have occurred multiple times independently. To understand what could have happened, Will Ratcliff at Georgia Tech has been conducting long-term evolution experiments on yeast in which multicellularity develops and emerges spontaneously.In this episode of The Joy of Why podcast, Ratcliff discusses what his “snowflake yeast” model could reveal about the origins of multicellularity, the surprising discoveries his team has made, and how he responds to skeptics who question his approach.

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.

We have identified thousands of planets just in our neighborhood in the Milky Way, mostly from the way they impact their host stars. Basic calculations suggest that there are countless more across the galaxy, and that billions of them could potentially support life. But what kind of life they host, and how we would be able to detect the presence of those biological processes from Earth, remain big questions in the world of exoplanets and astrobiology. What technologies might lie ahead to help us answer the question of whether we are alone in the universe? Lisa Kaltenegger, an astrophysicist and astrobiologist at Cornell University, talks to Janna Levin about that search, the atmospheric fingerprints of life, and why an advanced alien civilization might decide not to talk to us. 

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.

It’s fair to say that enjoyment of a podcast would be severely limited without the human capacity to create and understand speech. That capacity has often been cited as a defining characteristic of our species, and one that sets us apart in the long history of life on Earth. Yet we know that other species communicate in complex ways. Studies of the neurological foundations of language suggest that birdsong, or communication among bats or elephants, originates with brain structures similar to our own. So why do some species vocalize while others don’t? In this episode, Erich Jarvis, who studies behavior and neurogenetics at the Rockefeller University, chats with Janna Levin about the surprising connections between human speech, birdsong and dance.

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. 

The “species” category is almost certainly the best known of all the taxonomic classifications that biologists use to organize life’s vast diversity. It’s a linchpin of both conservation policy and evolutionary theory, though in practice biologists have struggled to find a definition that works across the natural world. In this episode, Kevin de Queiroz, a zoologist and evolutionary biologist, talks with host Janna Levin about the variety of ways to conceive of a species, and ways to understand the relationships among living things.

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.