Today  I am going to breakdown the differences between Artificial Intelligence (AI) and Machine Learning (ML), with practical and historical context to help explain where they diverge and overlap.

AI is the broader field focused on building systems that can simulate human intelligence—reasoning, learning, problem-solving, and even perception. IN doing so it mimics human cognition (e.g. decision-making, understanding language), can be rule-based (expert systems) or data-driven as well as encompasses multiple subfields: ML, robotics, natural language processing, etc.

Furthermore so called narrow AI is single task focused, general AI, while hypothetical operates on the same level as general intelligence and super AI is designed to surpass human intelligence. 

AI has multiple applications from chatbots, to self driving cars among other things.

Strictly speaking, machine learning is a subset of AI that enables systems to learn from data and improve over time without being explicitly programmed for every task and is characterised in the way it learns patterns from historical data, improves performance with more data and focuses on prediction, classification, and pattern recognition. It works on the principles of either supervised, unsupervised or reinforcement learning (through repetition) which are self-explanatory.

A simplified explanation of their characteristics is shown in the table below.

Finally and in in conclusion AI as a concept dates back to the 1950s, with early efforts focused on symbolic reasoning and logic whereas ML gained traction in the 1980s–90s as computing power and data availability grew, shifting focus from rules to learning algorithms and today, most practical AI applications (like facial recognition or voice assistants) are powered by ML.

Hope that clears it up for you :)

As always stay safe my friends.