Machine Learning

Machine learning is the approach to building software that learns its own rules from data instead of having them hand-written by a programmer — a spam filter built the traditional way needs an engineer to list keyword rules one by one, while a machine-learned spam filter is shown thousands of labeled examples and derives its own rules for what separates spam from legitimate mail. That shift, from "a human writes the logic" to "the system fits a function to examples," is the single idea underneath every technique the field has produced since, from a simple linear regression to a trillion-parameter language model. At a high level, every machine learning system follows the same basic shape:

Machine learning splits into three learning styles based on one question: does the training data come with correct answers attached? Supervised learning uses labeled examples (an image tagged "cat," a house's sale price next to its features) and learns to predict the label from the input; unsupervised learning works on unlabeled data and finds structure on its own (clustering similar customers, compressing data to fewer dimensions); reinforcement learning learns through trial and error against an environment that returns a reward signal rather than a labeled answer (a game-playing agent that only ever learns "that move led to winning or losing," never "here is the objectively correct move"). Nearly every named technique in the field — from a decision tree to deep learning — is a specific way of solving one of these three problem shapes.

Deep learning is a subset of machine learning, not a separate field, and the relationship between the two is a strict nesting, not a rivalry. Machine learning is the whole umbrella — linear regression, decision trees, support vector machines, and neural networks are all machine learning. Deep learning specifically means neural networks with many stacked layers, and it's the subset responsible for essentially all of the AI capability that made headlines over the past decade (image recognition, large language models, image generation) because stacking layers lets a model learn increasingly abstract representations automatically, something shallower classical methods can't do without a human manually engineering those representations first. Classical machine learning methods (gradient-boosted trees especially) still dominate on smaller, tabular datasets where deep learning's data and compute appetite isn't justified by any accuracy gain.

Chip demand tracks which kind of machine learning is running, because the two major problem shapes have very different hardware profiles. Classical machine learning on tabular data (fraud detection, demand forecasting) typically runs efficiently on CPUs or modest GPU instances, since the datasets and models involved are small relative to deep learning's. Deep learning, especially at the scale of modern language and vision models, is why the AI chip industry exists in its current form: training a frontier model needs thousands of GPUs running in parallel for weeks, and serving that model to users needs enough inference capacity (GPU memory bandwidth, HBM capacity) to keep response latency acceptable at real traffic volume — machine learning as a field predates GPUs by decades, but the specific subset that dominates chip demand today is the deep-learning slice of it, trained and served on hardware built almost entirely around that one subset's needs.

Read machine learning as the parent category that deep learning, supervised learning, and reinforcement learning are all specific answers within, not synonyms for the whole field: every headline AI capability of the last decade is deep learning, which is itself one technique inside machine learning, which is itself one approach inside the broader discipline of building systems that learn rules from data instead of having them written by hand — the terminology collapses in casual conversation, but the actual nesting is what explains why a chip built for one slice of this stack (training a trillion-parameter model) looks nothing like the hardware that runs a classical fraud-detection model in production.