December 04, 2017

The Difference Between AI and Machine Learning

Artificial Intelligence (AI) and Machine learning are both hot topics currently. With self-driving cars, smart home appliances, voice recognition solutions, and the potential application of AI/Machine Learning in almost every industry, it’s impossible to ignore these terms. However, while these words are widespread, often people are confused between the difference between the two because they are both related and the terminology is often used interchangeably.

Artificial Intelligence, while it sounds like a new term, has a longer history than machine learning. The real origin of the current understanding of AI starts in the 1940s and 1950s when some scientists put forth an idea of ‘creating an artificial brain”. This led to the foundation of the field of ‘Artificial Intelligence” as an academic discipline in 1956. During this period, Alan Turing published the Turing Test that speculates the possibility of creating machines that think. To pass the test, a computer must be able to carry on a conversation that is indistinctive from a conversation with a human being. This was the first serious proposal in the philosophy of AI, which was explained as: a science developing technology to mimic humans to respond in a circumstance.

The problem state of creating intelligence was broken down into a number of sub-problems (each which later became a separate field of study):

  • Deduction, Reasoning, Problem Solving
  • Knowledge Representation
  • Planning & Scheduling
  • Machine Learning
  • Natural Language Processing (NLP)
  • Perception
  • Creativity
  • General Intelligence


Thus, Machine learning is a subset of AI. While Artificial Intelligence is the whole idea of a technology that behaves like a human, machine learning algorithms are about finding patters and invariants in big data. These self-learning algorithms allow the machines to learn from data sets. They detect patterns in existing data, identify similar patterns in future data and make data-driven predictions.

Venn Diagram: Approaches to AI

Source: Deep Learning Book by Ian Goodfellow, page 9

One of the most common examples is a Google search. When you misspell a search query, Google machine learning algorithms ask you if you meant a different word or direct you to the correctly spelled word automatically. Their algorithms detect when something a user misspells a word and corrects it and keeps it in mind for future users you make similar spelling mistakes. Other examples or real-world machine learning applications, include recommendations engines (social media, shopping, Netflix movies, etc.), rankings, and knowledge navigators (Siri, Alexa, etc.).

Companies are using machine learning towards complicated solutions like risk analysis, gaining better customer insight, and improving reporting and dashboards or various technology.