AI, ML & LLMs
What do AI, machine learning, and LLM actually mean?
AI is the broad goal of getting machines to do things we associate with intelligence. Machine learning is one way to get there, by learning patterns from data. A large language model is a machine-learning system trained on text.
People use AI, machine learning, and LLM as if they were the same thing. They are not. They are three different sizes of idea, and it helps to keep them apart.
Artificial intelligence (AI) is the broadest of the three. It is the capability of computer systems to perform tasks we usually associate with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making.[1] AI is a goal, not a single technique. A chess program that follows hand-written rules counts as AI, and so does a model that learned to recognize faces from millions of photos.
Machine learning (ML) is one way of building AI. Instead of a programmer writing out every rule by hand, the system is shown a lot of examples and works out the patterns itself. The standard definition calls it a field of study concerned with algorithms that learn from data and generalize to unseen data, performing tasks “without being explicitly programmed.”[2] That last phrase is the whole point: the behavior is learned, not typed in.
A large language model (LLM) is a specific kind of machine-learning system. It is a neural network trained on a vast amount of text, built for language tasks and especially for generating text.[3] ChatGPT, Claude, and Gemini are all LLMs. The next page shows how these three ideas fit inside one another.
References
- Artificial intelligence — Wikipedia
- Machine learning — Wikipedia
- Large language model — Wikipedia