The Joy of Why

How Is AI Changing the Science of Prediction?

The Joy of Why

Show Notes

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.

More from The Joy of Why

Oct 15, 2026

People may think of soil as just dirt, but it is anything but inert. Beneath your feet, a vast network of mycorrhizal fungi is hard at work, trading minerals with plant roots. This trading system helps enrich the soil, and it sequesters 13 billion tons of atmospheric carbon dioxide each year — roughly equivalent to one-third of the world’s fossil-fuel emissions. Scientists know that these fungi form partnerships with 80% of all plant species on Earth, a relationship that evolved some 450 million years ago. What they have not understood is how organisms with no brain or central nervous system can decide whom to trade with, and on what terms.

Oct 1, 2026

We know all about how things exist in the three dimensions of length, width, and height. Physicists often talk about time as a fourth dimension, but what if there were a fourth spatial dimension — another direction entirely? How on earth would we picture that?

Sep 17, 2026

Quantum mechanics may be one of the most precisely tested paradigms in science, but there’s still no universally accepted interpretation of what it tells us about reality. How do we get from the wave-like behavior of quantum systems to the solid, macroscopic world of objects and the universe at large?

Sep 3, 2026

Mathematicians are witnessing a profound shift in their field. AI systems are now producing proofs, spotting connections between distant fields, and, in a few cases, solving problems that had stumped mathematicians for decades. The pace of progress over the past several months has raised challenging questions: What can AI systems actually do? And what happens to the more human, creative aspects of the field once machines can match – or exceed – people at problem-solving?

Aug 20, 2026

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.

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