What Is AI, ML, and Deep Learning?
You'll learn to
- -Correctly place AI, machine learning, and deep learning as nested categories, not synonyms
- -Give a precise, working definition of each term
These three terms get used interchangeably in casual conversation, which causes real confusion. They are not synonyms. They are nested categories, each one a subset of the one before it.
Artificial Intelligence: The Broadest Category
Artificial Intelligence is the broad field of building systems that perform tasks we associate with human intelligence: reasoning, perception, language, decision-making. This includes machine learning, but also things that involve no learning at all. A chess engine built entirely from hand-coded search and evaluation rules is AI, even though it never learns from data.
Machine Learning: Learning From Data
Machine Learning is the subset of AI where the system improves its behavior by learning patterns from data, rather than following only hand-written rules. A spam filter trained on labeled emails is machine learning. A chess engine using only hard-coded heuristics is not, even though both are AI.
Deep Learning: Learning With Neural Networks
Deep Learning is the subset of machine learning that uses neural networks with many layers, "deep" refers to layer count, to learn patterns directly from raw or lightly processed data such as pixels, audio waveforms, or raw text, instead of requiring a human to hand-engineer features first. Large language models are deep learning systems.
A common mistake is calling every AI system "deep learning." A simple linear regression model predicting house prices is machine learning, but it is not deep learning. There is no neural network involved.
A job posting asks for "deep learning experience" for a role that turns out to be building a simple linear regression model for pricing. Is that a red flag?
"No, deep learning and ML are basically the same thing, so it does not matter."
"It is a signal worth asking about, since deep learning specifically means neural networks with many layers, and a linear regression model has no neural network in it at all, it is classical ML. Either the posting is using the terms loosely, which is common and not necessarily disqualifying, or there is a real mismatch between what the role advertises and what it actually needs. I would ask directly what modeling techniques the team uses day to day, since the answer tells you a lot about whether the posting is accurate."
What is the correct relationship between AI, machine learning, and deep learning?