The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning



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The Difference Between Artificial Intelligence, Machine Learning, and Deep Learning

A guide to understanding the distinct differences between Artificial Intelligence, Machine Learning, and Deep Learning

Photo by Pixabay on Pexels

We’ve all heard the terms Artificial Intelligence, Machine Learning, and Deep Learning as the concept of creating computers that can mimic human actions has become increasingly popular in today’s climate. These terms are even sometimes used interchangeably, seeming closely related to each other, despite the crucial differences between them. With this article, I will explain what each of these terms mean, along with some examples!

How Artificial Intelligence, Machine Learning, and Deep Learning are related (created by me!)

“Artificial intelligence is a set of algorithms and intelligence to try to mimic human intelligence. Machine learning is one of them, and deep learning is one of those machine learning techniques.” – Frank Chen

What is Artificial Intelligence?

Artificial Intelligence is a broad field, first coined in 1956 by a group of researchers, consisting of any technology that mimics human intelligence and is considered a branch of computer science. As Andrew Moore, Former-Dean of the School of Computer Science at Carnegie Mellon University states, “Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence.”. Although there is no universally accepted definition of Artificial Intelligence, Stuart Russell and Peter Norvig, authors of “Artificial Intelligence: A Modern Approach” identify 4 different approaches to define the field:

  • Thinking humanly,
  • Thinking rationally,
  • Acting humanly, and
  • Acting rationally

Typically, Artificial Intelligence is used for prediction, automation, and optimization, which are tasks humans have historically done and are categorized into two classes — narrow and general AI. Narrow AI, or sometimes called “Weak AI” are machines that can perform very specific tasks well, often better than humans. However, such machines are very limited in their capabilities, only operating within a very limited context. On the other side of the spectrum, there is General AI or “Strong AI”, which can follow tasks considered to be extremely human-like, and carry out complicated tasks. These machines can problem-solve and apply intelligence to anything without human intervention and are more close to those we see in movies like Star Trek and Westworld.

Examples of Artificial Intelligence

It is quite difficult to find any examples of Artificial Intelligence that does not include Machine Learning. However, here are some simple implementations of Artificial Intelligence!

The very first working AI programs were written in 1951 by Christopher Strachey and Dietrich Prinz, who made a checkers-playing program and chess-playing program respectively. Strachey wrote the program to be run on the Ferranti Mark I computer and was written in CPL, later revived in Python by Peter Norvig. By 1952, the program could play a complete game of checkers. Prinz’s chess-playing program solved simple problems of the mate-in-two variety. It found a solution by examining all possible moves; several thousand moves had to have been examined to solve a problem and was much slower than a human player

Another example of Artificial Intelligence without machine learning is chatbots. There are human-defined rules which allow the chatbot to answer questions. The intelligence is received by a large amount of manual data inputted and processed.

Natural Language Processing is also another example, and the rules of syntax and semantics of the language are inputted into a model to interpret and produce a sentence or phrase in a certain language. However, do note that most current-day Natural Language Processing models use Machine Learning and Deep Learning.

AI/ML

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