What if we could open up an AI system and find a well-organized factory of components that work together? The article explores a new approach that combines two powerful concepts: sparse neural circuits and physics-inspired mathematics. By combining these different areas, we could find new approaches for analyzing and building AI systems. While neural networks appear to be elusive black boxes, researchers have uncovered something fascinating: they contain interpretable “circuits” that function similarly to machine components. Let me explain in simple terms.
What if, instead of trying to understand an entire neural network at once, we could examine it piece by piece, just as biologists study individual cells and neural pathways? This approach, inspired by neurology and cellular biology, was pioneered by Chris Olah in 2018, offering a more thorough way to understand neural networks.
Think about how we recognize a dog in a picture. Our brain processes different features: the curve of the ears, the texture of the fur, the roundness of the eyes. Neural networks…