Beth Hall PhD
69 posts

Beth Hall PhD
@BethHallPhD
Vision scientist by trade, multimodal by heart ❤️








We are pleased to share our latest research, now published in Nature Communications: “Smart Cellular Bricks: Physical Modules That Recognize Their Own Shape and Repair Themselves.” Blog: sakana.ai/smart-cellular… Paper: nature.com/articles/s4146… A long-running theme in our work is collective intelligence: the idea that sophisticated, robust behavior can emerge from many simple parts following local rules, with no central controller, as it does in a colony, a tissue, or a brain. We had mostly studied this in software and simulation. So this time we asked a simple question. Do the same decentralized principles hold up in the physical world, where communication is noisy and modules fail? To find out, we built a collection of simple cubic bricks. Each brick runs the same small neural network and talks only to the bricks it is physically connected to. No brick is told its position, or which shape it is part of. Yet from these purely local exchanges, the collective converges on the correct global shape, locates where modules are missing or damaged, and can even guide its own repair, inspired by how living tissue self-organizes and regenerates after injury. For us, this is a first step in a broader direction: taking the principles of collective intelligence we have studied in software and letting them emerge, decentralized and robust, in the physical world. In the future, we imagine smart materials that let structures sense and report damage on their own, and LEGO-like systems that recognize their own configuration and adapt in real time, pointing toward environments that are more robust, adaptive, and regenerative. This work is a collaboration between Sakana AI, IT University of Copenhagen and Autodesk.








$65k to help build the future of biology in san francisco the first experiment is whether you can survive on that




Machines see the world fundamentally differently from humans, and it's costing us a lot of tokens. Where machines see uniform rectangles, our brain sees a variable resolution retinal array reformatted into a magnified manifold in primary visual cortex, enabling high-resolution and wide field-of-view at minimal computational cost. In our new ICML paper, we enable the same mechanism for deep vision models. 🧵 ↓









