AI, machine learning and deep learning: how the three terms relate
Artificial intelligence is the broadest field, covering any technique that makes machines mimic human intelligence. Machine learning is a subset of AI that learns from data, and deep learning is a subset of machine learning using multilayered neural networks. The three are often confused, but they form a nested hierarchy.
The relationship between the three terms
The terms artificial intelligence, machine learning, and deep learning are often used interchangeably, but they are nested concepts. Artificial intelligence is the broadest category, machine learning is a subset of artificial intelligence, and deep learning is a subset of machine learning. Neural networks form the backbone of deep learning algorithms, and a neural network must have more than three layers to be considered a deep learning model. IBM's comparison page explains this hierarchy.
Artificial intelligence is an umbrella term that covers many techniques, including rule-based systems, search algorithms, and machine learning. Machine learning is only one branch of AI, and not every AI system uses machine learning. The AWS comparison of AI and machine learning notes that while all machine learning is AI, not all AI is machine learning.
Many everyday products blend these technologies. A voice assistant, for example, uses AI to understand natural language, machine learning to improve its responses, and deep learning to process the patterns in speech. The Microsoft Learn introduction to AI concepts lists natural language, speech, and computer vision as common AI workloads.
What is artificial intelligence?
Artificial intelligence is the field of computer science that aims to create systems capable of performing tasks that would normally require human intelligence, such as recognizing patterns, understanding language, and making decisions.
AI is often categorized by capability. Artificial Narrow Intelligence (ANI) handles a single specific task, such as image recognition or chess. Artificial General Intelligence (AGI) would perform any intellectual task at a human level, and Artificial Super Intelligence (ASI) would surpass human intelligence. AGI and ASI remain theoretical. The Google Cloud overview of AI vs. machine learning describes these categories.
What is machine learning?
Machine learning is a subset of artificial intelligence that uses algorithms and statistical models to enable a computer system to find patterns in data and make predictions or decisions without being explicitly programmed for each step. The NVIDIA machine learning glossary defines it as learning from data without explicit rules.
Machine learning often is divided into supervised learning, which uses labeled datasets to train a model, and unsupervised learning, which finds hidden patterns in unlabeled data. There is also semi-supervised learning that mixes both. The choice of algorithm depends on the problem and the quality of the training data.
What is deep learning?
Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers, often called deep neural networks. These networks are inspired by the structure of the human brain and are capable of learning from large amounts of data. The IBM deep learning page explains that deep learning models require at least four layers.
Deep learning models are often described as black boxes because their internal representations are difficult to interpret. They typically need very large datasets and substantial computing power, which is why practical advances arrived with improved graphics processing units.
| Term | Scope | Data types | Core technology |
|---|---|---|---|
| Artificial intelligence | Broadest category | All types: structured, semi-structured, unstructured | Logic, rules, search, machine learning, deep learning |
| Machine learning | Subset of AI | Structured and semi-structured data | Statistical models and algorithms |
| Deep learning | Subset of machine learning | Very large, usually labeled datasets | Multilayered neural networks |
Frequently asked questions
Is deep learning the same as machine learning?
No. Deep learning is a specific subset of machine learning that relies on neural networks with multiple layers. Machine learning includes many other algorithms such as decision trees and regression.
What is the difference between AI and machine learning?
AI is the broad field of making machines mimic human intelligence. Machine learning is a subset of AI that uses data to train models to learn and make predictions.
What are the three categories of artificial intelligence?
Artificial Narrow Intelligence (ANI), which handles specific tasks; Artificial General Intelligence (AGI), which would match human intellect; and Artificial Super Intelligence (ASI), which would surpass it. AGI and ASI are not yet realized.
Why do deep learning models need so much data?
Deep learning models have many parameters and multiple layers, so they require large datasets to tune those parameters accurately and avoid overfitting.
Can machine learning work without labeled data?
Yes. Unsupervised learning finds patterns in unlabeled data, and semi-supervised learning combines a small amount of labeled data with a larger unlabeled set.