What is the difference between AI, Machine Learning, and Deep Learning?

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are three closely related technologies that are often used interchangeably but have distinct differences.

1. Artificial Intelligence (AI): AI is the broadest concept among the three and represents any technique that enables computers to mimic human behavior. AI makes it possible for machines to learn from experience, adjust to new inputs, and perform human-like tasks. AI systems are designed to handle tasks that would typically require human intelligence, such as speech recognition, decision-making, translation between languages, and visual perception.

2. Machine Learning (ML): Machine Learning is a subset of AI and consists of methodologies and algorithms that enable machines to improve at tasks with experience. ML is about using data and algorithms to enable computers to learn how to perform tasks without being explicitly programmed to do so. It focuses on developing computer programs that can access data and use it to learn for themselves. The learning process is automated and improves with experience, making it more efficient as it is exposed to more data.

3. Deep Learning (DL): Deep Learning is a subset of Machine Learning, which in turn, is a subset of AI. It refers specifically to neural networks with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its capability—allowing it to “learn” from large amounts of data. Deep Learning techniques have led to significant breakthroughs in complex tasks such as