AI and machine learning are related, but they are not interchangeable terms.

AI, short for artificial intelligence, is the broadest term we use to classify programs that can sense, reason, act, and adapt to data it receives. These systems use algorithms and high volumes of data to perform tasks that typically require human cognitive functions, such as recognizing patterns, making decisions, and understanding natural language.

Machine learning is a subset of AI in which algorithms improve their performance as they are exposed to more data over time. If AI is all-encompassing, then machine learning is a more controlled version, focused on a singular function: the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions based on data.

AI and machine learning are similar because they both:

• Make decisions based on data
• Can handle task automations
• Continuously improve their performance based on data input

AI and machine learning are different when it comes to:

• Scope — AI is broad and speaks to multiple functions while machine learning is hyperfocused on specific functions.
• Functionality — Again, AI is designed to mimic the cognitive function of the human brain. Machine learning is task-oriented and improves based on repetition of that task.
  

Examples of AI:

• Chatbots on websites
• Autonomous vehicles
• Game-playing agents
• Personal assistants on smartphones or computer operating systems

Examples of machine learning:

• Recommendation systems, such as those used in a streaming service
• Image recognition, such as identifying objects in photos on a smartphone
• Speech recognitions
• Predictive analytics