Understanding the Evolution and Differences of AI, Machine Learning, and Deep Learning

Artificial intelligence (AI) is a new technical science that studies and develops the theories, methods, technologies, and application systems used to simulate, extend, and augment human intelligence. Today, the article first presents a diagram of AI’s past and present, then discusses the differences and connections among AI, machine learning (ML), and deep learning (DL).

When talking about AI, the terms AI, ML, and DL have appeared more frequently in recent years. The article uses several pictures to quickly illustrate their relationships and differences, showing that the timeline of concept introduction also clarifies their connections.

Artificial Intelligence (AI): Simulates the human brain to recognize objects such as apples and oranges.

Machine Learning (ML): Based on features, it learns to buy oranges at a fruit stall; as it sees more oranges and other fruits, its ability to distinguish oranges improves, avoiding mistakes like confusing bananas with oranges.

Machine learning emphasizes “learning” rather than the program itself, using complex algorithms to analyze massive data, identify patterns, and make predictions without needing specific code. As the sample size grows, it self‑corrects and refines its learning objectives, improving recognition through experience.

Deep Learning (DL): In a supermarket with three types of apples and five types of oranges, data analysis links attributes such as color, shape, size, ripeness, and origin to distinguish regular oranges from blood oranges, enabling the selection of the specific orange variety a user needs.

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