What Most People Get Wrong About Evolution | Akarsh Kumar

SPEAKER_1 (0:00)

Your heart can tell you a lot about your health. Apple Watch Series 12 measures your heart rate every five seconds with the most accurate heart rate sensing in a wearable. So your vitals app, now with heart rate variability, can tell you when something is off. And your readiness score can let you know when to rest and when to push. Hear the story in every heartbeat with Apple Watch Series 12

The features described are for wellness purposes only and not for medical use. iPhone 11 or later required. Based on Apple conducted study of heart rate accuracy August 2026, visit apple.com/applewatchseries12.

SPEAKER_2 (0:31)

This episode is brought to you by Google Chrome. You think you know a browser, but Gemini and Chrome, that's new. It can help you with practically anything on the web, like restoring a vintage motorcycle from a 50 page restoration block or finally break down that long article you've had open for weeks. Gemini and Chrome is here for it. Ready to make anything online make sense? There's no place like Chrome. Check responses set up required compatibility and availability varies 18 plus.

Akarsh Kumar (0:56)

I would just want to clarify, evolution is anything but random.

So artificial life is really about understanding, like you said, life as it could be rather than life as it is. We need to understand the space of all possible intelligences, the space of all possible life forms that can be.

Conway's Game of Life is a super famous simulation by John Conway, where you have this grid of cells, this 2D grid of cells, and they can either be on or off, alive or dead. That's it. And from this super, super simple world, it's like possibly the simplest possible thing you can think of, you run this, you get insane amounts of complexity that no one would have thought it could do until you actually run it. You can do counterfactual analysis in other universes. In what worlds do we get this open-ended creation of complexity and emergence? And in what worlds do we not get that? Why does this physics, when you run it, it produces this amazing phenomenon? And when you run this physics, it doesn't do that. It produces just random noise. Instead of hard coding a single simulation, you parameterize a space of simulations, which is much, much easier. Just simulate it, see what happens, plug it into a foundation model and see what the foundation model thinks about what happened. There's 260,000 rules. We've plotted all of them. So we found that there's literally like a big island of solutions, which are not open-ended. And there's a small island of solutions where all of the coolest simulations lie. Some of them actually end up creating little patterns, which move around that look like cells. This object, this cell-like object is exploiting the physics in a way to create a persisting object in this world, right? If you train them on calculus, they'll learn calculus. You don't have to train them on arithmetic first. And it's such a weird alien way that we train these models. I really like to call them a statistical intelligence. And they're like basically perfect statistically. But this FER idea and this Pickbreeder line of work, it's basically like a different paradigm of intelligence that we're trying to study. So it's a difference between like a statistical intelligence and a regularity-based intelligence. And you can see this in like the Pickbreeder skulls. It's in the solutions, like the neural circuits that we find. The regularities are baked into the circuits.

We wanted to see what would happen if you used LLMs to evolve, basically small little assembly programs that compete against each other. So there's this programming game invented in like the 1980s that people used to play. It's called Core War.

What my program needs to do in order to do that is it needs to basically inject an invalid operation in front of your program's execution thread. It's going to halt your program by crashing it. Your program will crash.

Can we create this open-ended evolutionary arms race of programs from this?

Tim Scarfe (3:52)

It's like Game of Thrones Turing edition.

Akarsh Kumar (3:55)

Yeah, exactly. That's a good way to put it. The LLM is not good at red code. If you just try to zero-shot this language, it's terrible. But if you do this evolution and you have this verifier, it turns out to be really good. Evolution is probably the most powerful algorithm that we know to solve like discrete combinatorial problems. If you solve like even one part of a puzzle, and you hang on to that and you search for the other ones, you can turn your exponential search problem into a linear search problem.