The Long Frontier
What if the next generation of computers isn’t built with silicon — but grown from living neurons? In this episode of The Long Frontier, hosts Brannon Jones and Abe Murray explore one of the most unconventional frontiers in computing: biological intelligence. Joined by Dr. Hon Weng Chong, Founder & CEO of Cortical Labs, they unpack the emerging field of biological computers — systems that combine living neurons with silicon to create adaptive, energy-efficient computing platforms. From neurons learning to play Pong to the possibility of growing compute directly inside future data centers, this conversation explores whether biology may unlock capabilities traditional AI systems struggle to achieve. WHAT YOU'LL LEARN * Why the human brain performs extraordinary computation using only ~20 watts of power * How Cortical Labs taught lab-grown neurons to learn and play Pong * Why biological systems may be dramatically more sample-efficient than modern AI models * How reinforcement learning in living neurons differs from traditional machine learning * Why biological computers may be especially useful for robotics and embodied AI * The biggest technical challenges ahead: memory, reproducibility, scaling, and ethics * Why future data centers could potentially “grow their own compute” * The overlap between biological computing and brain-computer interfaces like Neuralink FEATURED GUEST: DR. HON WENG CHONG Dr. Hon Weng Chong is Founder & CEO of Cortical Labs, a Melbourne-based biotechnology company pioneering biological computing systems powered by living neurons. A trained medical doctor and software engineer, Chong previously co-founded CliniCloud, a medical technology startup backed by Tencent and Ping An Ventures. His work at Cortical Labs gained international attention after demonstrating that lab-grown neurons could learn to play Pong. The company has since introduced commercial biological computing platforms including the CL1. NOTABLE MOMENTS * “If Neuralink is trying to put a chip inside a brain, we’re doing the opposite — we start with the chip and grow the brain on top of it.” * The discussion explores whether biological systems can outperform traditional AI in real-world learning tasks because they adapt continuously instead of relying on massive pre-training datasets. * Hon also explains why neurons may fundamentally optimize for reducing “surprise” — an idea inspired by neuroscientist Karl Friston’s Free Energy Principle. CONNECT WITH THE HOSTS Follow Brannon Jones & Abe Murray: https://x.com/thelongfrontier [https://x.com/thelongfrontier] ABOUT THE PODCAST The Long Frontier explores the technologies shaping the next 30 years of human civilization — diving deep into the science, engineering tradeoffs, and market dynamics behind the world’s most important innovations. GLOSSARY Biological Computing — Computing systems that use living biological material to process information. Brain-Computer Interface (BCI) — Technology enabling direct communication between neural tissue and computers. Cortical Labs — Australian biotech company developing biological computers powered by living neurons. DishBrain — Cortical Labs’ experimental platform where lab-grown neurons learned to play Pong. Embodied AI — AI systems that interact with and learn from the physical world in real time. Free Energy Principle — Theory proposing biological systems minimize uncertainty or “surprise” about their environment. Microelectrode Array (MEA) — A chip containing electrodes used to read and stimulate neural activity. Neuromorphic Computing — Computing architectures inspired by biological neural systems. Organoid Intelligence — Research area exploring the computational potential of lab-grown neural tissue. Reinforcement Learning (RL) — Learning through rewards and penalties; adapted by Cortical Labs using living neurons. Synthetic Biological Intelligence (SBI) — Cortical Labs’ term for integrating living neurons with silicon hardware. ---------------------------------------- Hosted on Acast. See acast.com/privacy [https://acast.com/privacy] for more information.
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