Artificial intelligence vs machine learning

The difference between Artificial Intelligence (AI) and Machine Learning (ML) lies in their scope and focus:

Artificial Intelligence (AI) is the broader concept of creating machines or systems that can perform tasks that typically require human intelligence. These tasks may include reasoning, problem-solving, understanding language, recognising patterns, and making decisions. AI aims to create systems that can mimic human cognitive functions like understanding, learning, reasoning, and interacting with the environment. It encompasses a wide range of techniques, not just limited to learning. It includes methods such as symbolic reasoning, rule-based systems, expert systems, natural language processing, robotics, and more.

Examples include things like self-driving cars, chatbots, virtual assistants (like Siri or Alexa), and decision-making systems.

Machine Learning (ML) is a subset of AI that focusses specifically on enabling machines to learn from data and improve over time without being explicitly programmed for every task. The goal of ML is to develop algorithms that can identify patterns in data, learn from them, and make predictions or decisions based on that learning. ML primarily revolves around data-driven approaches and algorithms. It includes supervised learning, unsupervised learning, reinforcement learning, and deep learning.

Applications of ML include recommendation systems (like Netflix or YouTube recommendations), spam filters, image and speech recognition, and predictive analytics.

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