Defining the Difference between Deep Learning, Machine Learning, and Artificial Intelligence


This article will discuss three currently trending technology topics, namely deep learning, machine learning, and artificial intelligence. According to research, 80 percent of enterprises are actively investing in AI technologies. The machine learning market has a projected value of $8.81 billion by 2022, while the deep learning market size is expected to reach $10.2 billion by 2025.

Deep learning and Artificial Intelligence

A lot of media sources discuss these terms synonymously, often by referring to the umbrella term of artificial intelligence when talking about machine learning or deep learning technology. However, using the terms interchangeably causes confusion; there are subtle yet important differences between them,  which this article will clarify. You’ll also find out about some use cases and different ways in which companies are beginning to adopt them to their advantage.


Artificial Intelligence

Artificial intelligence (AI) refers to the demonstration of different forms of intelligence by machines. These forms of intelligence typically imitate human intelligence, and they include decision-making and visual recognition, among many other examples.

The concept of inanimate objects displaying traits that mimic human intelligence can trace its roots far in the past to Greek mythology and some vivid imaginations. However, the adoption of AI as a formal concept came much later in 1956 during a conference in New Hampshire.

AI remained relatively constrained to the world of science fiction books and movies during the latter part of the 20th century, with few advances in the field. However, AI really began to evolve rapidly at the beginning of the second decade of the 21st century, when computing power and technology, in general, began to grow at a ferocious pace as did the volume of data that could be fed into machines.

The case of IBM Watson defeating two of the world’s leading players at U.S. game show Jeopardy arguably exemplified just how far AI had managed to come.


AI Use Cases

  • Marketing—AI is applied extensively throughout marketing to automate tedious tasks and to gain a competitive edge by  automatically optimizing email campaigns and ads served to customers based on their activity on a website.
  • Consumers—particularly for elderly or disabled people, AI is proving extremely useful in the consumer industry by automating various tasks. Amazon Echo, via its inbuilt virtual intelligence assistant Alexa, can order pizza, switch on the lights, and call people, all thanks to the wonders of modern artificial intelligence.
  • Workplace—designing traditional customer-focused voice assistants like Siri or Alexa with a business focus can help to optimize workplace tasks such as scheduling meetings, logging people into meetings, and so forth. Cisco Spark is one such application already succeeding using AI in the workplace.


Machine Learning

In comparison to AI, you can regard machine learning as a narrow subfield of AI that specifically refers to using statistical techniques that enable computers to learn from data and progressively improve performance at specific tasks without needing to be programmed.

The term machine learning was coined back in 1959 by Arthur Samuel when he wrote a paper entitled, “Some Studies in Machine Learning Using the Game of Checkers”. Some analysts argue that machine learning has evolved enough to be considered its own domain. The tipping points in terms of machine learning accessibility have been the availability of cheap cloud computing power and crucially, the release of machine learning tools and frameworks like TensorFlow, Amazon Machine Learning and Accord.Net.


Machine Learning Use Cases

  • Healthcare—healthcare providers can use machine learning algorithms to improve the early diagnosis of cancers with computer-assisted diagnoses, potentially identifying many more cancer cases than the skilled professional can on his/her own.
  • Fraud Detection—Machine learning is being smartly used by payment merchants such as PayPal to analyze millions of transactions and potentially flag cases of fraud, including money laundering.
  • Finance—Machine learning techniques are being applied to financial trading to make more accurate decisions about when to buy and sell various stocks. Such algorithms enhance trading strategies as opposed to replacing human input entirely.

Deep Learning

Deep learning is a subfield of machine learning that revolves around building models with an architectural structure of artificial neural networks. In other words, these hierarchical networks are mathematical models inspired by the human brain, and they have many layers.

The design of such networks makes them extremely efficient at finding patterns in data that would be otherwise impossible to unearth. Like with machine learning, the algorithms learn without explicit programming, however, and this is the key—deep learning networks excel at unsupervised learning.

This means that data doesn’t need to be tagged/labeled for a neural network to identify things. On the contrary, machine learning only works when data are labeled; this is supervised learning. Because so-called Big Data is typically unstructured and unlabeled, deep learning software and frameworks are prime candidates from getting insights from this type of data.


Deep Learning Use Cases

  • Big Data Insights—deep learning software can find abstract patterns within huge Big Data datasets.
  • Cyber Security—deep learning can detect malware and network intruders more efficiently than rule-based or machine learning-based processes.
  • Image Classification—the neural networks underscoring deep learning models can accurately classify images without being told what the objects to look out for, making such models useful in a range of fields including autonomous driving, for example by recognizing stop signs.



Expect to see more news articles and features in the media discussing exciting use cases of AI, machine learning, and deep learning over the coming years, as the market for AI technologies expands.

Now that you are (hopefully) clear on the precise differences between these concepts and you have an idea on their various uses, you can approach the literature in a more informed manner.


By Ronan Mahony

Ronan is a freelance technology writer who discusses topics including SaaS, artificial intelligence, and web security.