#330 Sebastian Risi: Why AI Should Be Grown, Not Trained

AI has been trained like software. But what if it should be grown like life? In this episode of Eye on AI, Craig Smith sits down with Sebastian Risi, professor and leading researcher in neuroevolution and artificial life, to explore a fundamentally different approach to building intelligence, one inspired by how nature evolves, grows, and adapts. Sebastian explains why traditional AI systems are limited by fixed architectures and one-time training, and how evolutionary algorithms can create systems that continuously learn, self-organize, and even grow their own neural structures over time. They dive into concepts like plastic neural networks that keep updating during their lifetime, AI systems that can recover from damage, and models that develop from a single "cell" into complex structures, similar to biological organisms. The conversation also explores how combining large language models with evolutionary search could unlock more creative and open-ended problem solving, from merging specialized models to building AI systems capable of generating and testing scientific ideas. If you want to understand where AI is headed beyond today's transformer models, and why the future may look more like living systems than software, this episode offers a clear and thought-provoking perspective.

You can listen to #330 Sebastian Risi: Why AI Should Be Grown, Not Trained online on Radio and Podcast. Open the player on this page to stream the available audio.

#330 Sebastian Risi: Why AI Should Be Grown, Not Trained is an episode from Eye On A.I. by Craig S. Smith.

This episode is 01:00:43 long.

This episode was published on Apr 6, 2026.

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