What are the differences between AI, machine learning and deep learning?
AI is the general field, machine learning is the family of methods where machines learn from data, deep learning is the sub-family using deep neural networks, behind the spectacular progress since 2012. Three concentric circles, not three synonyms.
These three terms are not synonyms but three concentric circles. Artificial intelligence is the general field: making machines perform tasks associated with intelligence. Machine learning is the subset of methods where the machine learns from data rather than from written rules. Deep learning is the subfamily of machine learning that uses multi-layer neural networks, and is responsible for the spectacular progress since 2012.
The widest circle: AI
AI includes various approaches, some of which do not learn at all and follow rules written by experts — so-called symbolic systems. Historically, AI long relied on these rules. It is a part of the field, less visible today than learning-based approaches.
The intermediate circle: machine learning
Here, the machine does not receive its rules: it learns them from examples. It is shown data, it derives a model capable of generalizing to new cases. Most useful AI systems today fall under machine learning.
The inner circle: deep learning
Deep neural networks stack many processing layers, allowing them to capture very complex regularities in images, sound, and language. This technique made possible reliable image recognition, quality machine translation, and generative language models.
Why the distinction matters
It avoids confusion in discussions. When we talk about AI in general, we encompass very different things. When we talk about deep learning, we refer to a specific technique, hungry for data and computation. Understanding the nesting helps correctly interpret news and debates.
Frequently Asked Questions
Is deep learning always better?
No. For simple tabular data, lighter methods are often just as good and easier to explain.
Is a language model deep learning?
Yes. Large language models are deep neural networks.
Does deep learning require a lot of data?
Generally yes, which is one of its limitations compared to more data-efficient methods.
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