Watch: Why deep learning works.

Why does artificial intelligence actually work? It turns out, the secret is not just in the algorithms, but in the structure of the real world itself. Think about all the possible ways you can arrange pixels on a screen. The number is astronomical. Yet, the real-world images we care about, like human faces or handwritten numbers, occupy only a tiny, highly structured fraction of that space. This is the manifold hypothesis. It suggests that all natural data lies on a lower-dimensional surface within a much larger space. We humans do this naturally. To survive, our brains evolved to compress the chaotic, high-dimensional world into simple symbols and concepts. Large language models do the exact same thing. They map complex ideas into what is called a latent space. Just as we might compress a complex person into a simple label, like a scientist or an expert, machine learning models organize massive amounts of data into simplified, multidimensional maps. By exploring this latent space, we can see how ideas connect. Two concepts that seem completely unrelated might actually be right next to each other when viewed from the right angle. It allows us to probe the hidden patterns of our world and discover the true nature of information.