Same Heart, New Scale

From Neural Networks to Machine Learning

What we now call machine learning (ML) — a part of the broader field known as artificial intelligence (AI) — has roots that go back decades, starting with the concept of the neural network (NN). It was, and still is, a powerful way to model systems that are too complex or poorly understood to be described with tidy mathematical formulas.

Classic neural networks are made up of layers of interconnected “neurons.” By feeding the network examples (inputs along with the correct outputs), it learns to recognize important patterns — a process known as supervised learning. Once trained, the model can make predictions on new, unseen data.
Modern ML systems are built on the same principle, but at a much larger and more complex scale. Where we once had relatively simple layers and somewhat understandable math, we now deal with millions of variables, tensors instead of matrices, and transformers instead of basic neurons. It’s no longer something you can easily sketch on a whiteboard — it has become a high-dimensional, abstract system.

At its core, though, the concept remains the same: learning from data when traditional models fall short. To understand modern ML, start by understanding neural networks — the foundation hasn’t changed, only the complexity has.