What is a neural network and how does it work?

There's a moment in Cohere's walkthrough of neural networks (https://cohere.com/blog/what-is-a-neural-network) where the curtain lifts, and you realize that the most transformative technology of our era is, at its core, a glorified multiplication table. Each artificial neuron takes its inputs, multiplies them by weights, adds a bias, and applies an activation function — a tiny mathematical operation that decides whether to "fire" or stay silent. Stack thousands of these in layers, and you get something that can translate languages, diagnose diseases, and paint pictures. The gap between the simplicity of the components and the complexity of the behavior is one of the great scientific vertigo-inducers of our time. The learning process is where things get properly weird. Neural networks don't learn the way we do — no one hands them a textbook or explains the rules of grammar. Instead, they engage in a kind of blind descent: the network makes a prediction, measures how wrong it was, and then propagates that error backward through its layers, nudging each weight by a tiny amount in the direction that would have reduced the mistake. It's like stumbling down a foggy mountain at night, guided only by the slope beneath your feet. There's no map, no destination in the traditional sense — just an endless, incremental pursuit of "less wrong." And here's the philosophical gut-punch: nowhere in that process is there an instruction that says "understand" or "know." The network never grasps what a cat is, or what a sentence means. And yet, when you scale this simple error-correcting dance across billions of parameters and trillions of examples, coherent behavior emerges — grammar, reasoning, even something that looks startlingly like creativity. No single neuron knows anything; the knowledge is smeared across the entire web of weights, distributed like a ghost in the machine. It's the same mystery that haunts neuroscience: how does the collective firing of simple units give rise to something that feels like mind? What makes this so wild is that we built it — but we didn't design it. We designed the learning algorithm and then let the data sculpt the network into whatever it needed to become. Cohere's article is a reminder that intelligence, whether biological or synthetic, may be less about following rules and more about the relentless, blind adjustment of connections in response to experience. If that's true, then the boundary between our minds and our machines is thinner than we'd like to admit — and the universe has been running this experiment for billions of years.