AI Versus ML

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AI Versus ML

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.


  • 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.


  • 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.



To find the requested information, we looked through company websites and industry reports such as Oracle, Towards Data Science, and Leanix. Using these sources, we gained useful insights on the differences between AI and ML. Also, we found information that revealed why people often confuse AI and ML.