It’s often said that artificial intelligence is modeled after the human brain, but what if the brain itself could inspire entirely new kinds of AI – ones that actually learn faster and more efficiently than our best machine learning algorithms? I recently came across a fascinating study that showed just that, using living neural cells to outpace traditional AI in learning tasks. This isn’t science fiction; it’s the cutting edge of biological computing.
How living brain cells outperform machine learning
The team behind this breakthrough, including the Melbourne startup Cortical Labs, developed a system called DishBrain that merges live human-derived neurons with silicon chips. This hybrid setup forms what they call Synthetic Biological Intelligence (SBI). What’s truly remarkable is that when these living neural cultures were put into a game environment – essentially a Pong simulation – their learning speed and adaptability beat some of the most advanced reinforcement learning (RL) algorithms, including DQN, A2C, and PPO.
Why does this matter? Because unlike AI systems that often require millions of training steps to improve, these biological networks reorganized in real-time, adapting rapidly to stimuli with far fewer samples. This sample efficiency mimics how real brains learn – quickly, flexibly, and with greater connectivity plasticity. It’s a huge leap in understanding how biological intelligence can potentially eclipse traditional AI in some areas.
These biological systems not only adapt faster but do so more efficiently and robustly when learning opportunities are limited – closer to how humans actually learn.
The birth of bioengineered intelligence: two paths, one exciting future
The implications extend beyond just beating AI at one game. Cortical Labs and partnering research institutes have articulated a new paradigm called Bioengineered Intelligence (BI). This approach uses engineered neural circuits within cultured brain cells to develop intelligence, contrasting with but complementing a related field called Organoid Intelligence (OI), which relies on brain organoids.
This dual-path framework essentially opens up a new frontier where biological substrates can be harnessed for computation and intelligent behavior. By combining living neurons’ dynamic plasticity with cutting-edge electronics and algorithms, BI aims to create systems that not only learn faster but can tackle problems that conventional AI struggles with, especially where adaptability and rapid reconfiguration matter.
Experts find this especially exciting because it integrates principles from neuroscience and machine learning, offering a more ethically sustainable and biologically faithful route toward developing intelligence in machines. It’s a field still in its infancy, but with huge potential for breakthroughs in both understanding the brain and developing revolutionary computing paradigms.
What this means for AI, neuroscience, and beyond
The proof-of-concept demonstrated with the DishBrain platform and the subsequent launch of the CL1 biological computer signal something profound: intelligence isn’t just code running on hardware; it’s deeply rooted in biological processes. The rapid, adaptive learning observed in living neural cultures suggests that actual intelligence may always remain biological at its core, even as we strive to build smarter machines.
For AI researchers, this doesn’t mean abandoning existing algorithms but rather enriching AI with biological insights that could lead to more sample-efficient, flexible systems. For neuroscientists, it offers a new window into how neural circuits organize, learn, and adapt—not just in brains, but in engineered systems capable of real-time, closed-loop interaction.
Moreover, the technology opens doors to studying neural disorders and brain function with unprecedented precision by creating living models of neural networks that reflect real-world dynamics. This can accelerate developing treatments for neurodegenerative diseases and cognitive conditions.
- Living neural networks outperform deep RL in learning speed and efficiency under real-world sample constraints.
- Bioengineered Intelligence emerges as a new paradigm coupling biology and machine intelligence.
- Understanding biological learning mechanisms can revolutionize AI design and neuroscience research.
Looking forward, the intersection of biology and AI promises a future where machines might not just simulate intelligence but actually embody living, adapting intelligence. This could redefine what we consider a computer, a brain, and the very nature of intelligence itself.
It’s an exciting, humbling reminder that while AI has made incredible strides, the biological brain still holds many keys that machines have yet to unlock. The journey of blending life and machine has only just begun.
