In the final episode of the season, Abha sits down with Melanie to hear her perspective. They chat about Melanie’s career and research with Douglas Hofstadter, the author of Gödel, Escher, Bach. They also discuss her opinions on LLMs’ current capabilities, what she thinks of existential questions like the alignment problem, how sustainable the industry is, the difficulty of making claims about concepts like “intelligence” and “understanding,” and what she thinks future technological development should focus on.

When it comes to assessing intelligence, people have all kinds of tests — the SAT, IQ tests, and so on. There’s controversy over how fairly these tests really measure human intelligence, but at the very least, we know that they correlate with some general reasoning skills when people take them. That assumption breaks down when we try to assess intelligence in non-humans. What does it mean when a large language model passes an intelligence test meant for humans? Does it actually have the same reasoning skills that a human does, or is it doing something else? In today’s episode, with guests Erica Cartmill and Ellie Pavlick, we investigate the best ways to assess intelligence in non-humans, whether animals or machines.

There’s an argument to be made that if we train AI systems to learn the way babies do, we’ll get them closer to human-like intelligence. But how our own learning development functions in babyhood is still a mystery that researchers are untangling. We know that the information babies absorb is very different from how an LLM learns, and in today’s episode, with guests Linda Smith and Michael Frank we’ll attempt to look at the world through an infant’s eyes and examine why they’re able to do more with, seemingly, less information.

Large language models, like ChatGPT and Claude, have remarkably coherent communication skills. Yet, what this says about their “intelligence” isn’t clear. Is it possible that they could arrive at the same level of intelligence as humans without taking the same evolutionary or learning path to get there? Or, if they’re not on a path to human-level intelligence, where are they now and where will they end up? In this episode, with guests Tomer Ullman and Murray Shanahan, we look at how large language models function and examine differing views on how sophisticated they are and where they might be going.

Complex language is unique to the human species. It’s part of how we evolved, the backbone of our societies, and one of the primary ways we judge others’ intellect. Is it our intelligence that leads to our language abilities, or conversely, does our ability for language enhance our intelligence, or both? How do language and thinking interact? And can one exist without the other? Guests: Evelina Federenko, Steve Piantadosi, and Gary Lupyan.

Depending on whom you ask, artificial intelligence is either going to solve all humanity’s problems, or it’s going to kill us. Business leaders are getting ready for it to “disrupt” entire industries, and educators are re-thinking how to teach in the age of ChatGPT. It can feel like artificial intelligence is going to transform everything about the way we live. But in order to understand how to think about AI, it’s useful to take a step back. In today’s episode, we’re asking what it means to call anything "intelligent". What makes humans intelligent? And how do machines compare? Guests: John Krakauer and Alison Gopnik

Right now, AI is having a moment — and it’s not the first time grand predictions about the potential of machines are being made. But, what does it really mean to say something like ChatGPT is “intelligent”? What exactly is intelligence? In this season of the Complexity podcast, The Nature of Intelligence, we'll explore this question through conversations with cognitive and neuroscientists, animal cognition researchers, and AI experts in six episodes. Together, we'll investigate the complexities of human intelligence, how it compares to that of other species, and where AI fits in. We'll dive into the relationship between language and thought, examine AI's limitations, and ask: Could machines ever truly be like us?

In the final episode of this season, we hear from a NASA researcher whose expertise spans from studying samples in deep, untouched regions of our planet all the way to organic chemistry happening in space. We consider the possibility of other, past origins of life on Earth and look at the rich potential to learn from sample return missions, including the recent OSIRIS-REx mission that retrieved samples of the asteroid Bennu. Abha also sits down with Chris to hear his perspective on the podcast as a researcher who's collaborated with this season's guests on diverse research.

In this episode, we examine how the course of human history has shaped our scientific knowledge, why the physics community prioritizes some questions over others, and why progress in complex systems research is especially difficult. Academia continues to operate within set boundaries and students are taught certain concepts as fundamental and to skirt others completely. However, the history of science demonstrates that such concepts aren’t always set in stone. It’s possible that blowing open the “shackles of reality,” such as redefining the concept of life itself, and reprioritizing the problems that scientists want to tackle, might help scientists make more progress in this very difficult world of complexity research.

How do groups solve problems? Are there conditions that create a pathway to innovation and groundbreaking inventions? In today’s episode, we look at the science of collectives to learn about the patterns that emerge as human societies grow, the importance of a collective structure to foster ideas and create impact, and – from collectives like ants and immune systems – the importance of veering off the beaten path to become better at exploring and discovering.