One of the notable differences between artificial intelligence (AI) and machine learning (ML) is that one is a subset of the other. ML is a subset of AI and depends on it to make predictions or decisions. Most people confuse AI and ML because they are closely related to each other and, therefore, non-professionals use them interchangeably.

ARTIFICIAL INTELLIGENCE

AI involves incorporating human intelligence into machines.
AI simulates natural intelligence to solve intricate problems.
When a machine or computer completes tasks by following a number of rules known as algorithms to solve issues, the intelligent behavior is referred to as artificial intelligence.
Programs with AI can reason, sense, act, and adapt. Therefore, machines with AI can solve complex problems and recognize human gestures.

AI-powered machines can be classified into two categories — general and applied. General AI refers to computers that can solve problems intelligently. General AI is yet to be fully exploited until machines can perform every task that a human can.
Conversely, computers with applied AI can complete specific tasks efficiently. They can trade stocks intelligently and are used in autonomous vehicles.
It is predicted that in the future, AI machines will become super intelligent. This means that they will be more intelligent than human beings.
AI is currently being used by companies such as Walmart to make audit processes more efficient. On the other hand, Penn State University uses AI to show students the best paths to graduation.

MACHINE LEARNING
ML is a subset of AI. It involves empowering computers to learn by themselves using available data and arrive at various conclusions.
ML entails training algorithms to learn how to make predictions. In ML, training involves providing the algorithm with huge volumes of data, which facilitate learning.
ML enables computers to learn without the need for programming. ML developed from computational learning theory and pattern recognition that was used in artificial intelligence. It explores the construction of algorithms that can learn and make predictions based on data.
 Neural networks are used in machine learning. These help machines understand different types of data.

Neural networks are algorithms that are developed to mimic the human brain. They can recognize patterns and interpret sensory data. The patterns neural networks recognize are numerical.
Today, ML applications can determine whether a message by a customer is a complaint or praise. ML programs can also listen to a music file and determine if it can make someone happy or sad.
Examples of companies that use ML include Google and on-demand streaming services such as Netflix.

WHY PEOPLE CONFUSE AI AND ML 


AI and ML are often confused because many non-professionals use the terms interchangeably.
In addition, the fact that ML is an application of AI confuses those who are not conversant with the term.

Since, AI and ML are closely related, some organizations use the former in their business plans, which confuses both software experts and laymen.

A learning model is one way to build an AI system

Machine learning is commonly treated as part of AI; it does not depend on a separate external 'AI' component to exist. A rule-based program, a learned classifier and a tool-using system describe different design choices. Human-like language is not a requirement for every useful AI application.

In a hypothetical spam filter, hand-written rules might flag certain patterns while a learned model estimates categories from examples. Compare them using the same held-out messages, including false positives. Then decide how uncertain cases are handled. The label matters less than the evidence about the particular task, the failure cases and who remains responsible for the result.