The Differences Between AI, Deep Learning, Machine Learning & NLP

  • “Artificial intelligence is a technology or computer system designed to function in a way that simulates how the human brain thinks.
  • Machine learning is a subset of AI which involves ‘training’ machines to ‘learn’ from sets of data, enabling them to draw insights and make predictive decisions. It automates tasks and finds patterns or anomalies, learns from them and creates new rules for next time.
  • Deep learning is currently the most advanced subset of machine learning, and thereby a subset of AI, which intends to bring machines as close as possible to human levels of thinking. According to MIT Technology Review, “The software learns, in a very real sense, to recognize patterns in digital representations of sounds, images, and other data” by creating an artificial neural network.
  • Natural Language Processing (NLP) is an element of deep learning that involves translating text or human ways of speaking so that a computer is able to categorize and make sense of it.”

Image Source:  “); background-size: 1px 1px; caret-color: rgba(0, 0, 0, 0.541176); font-family: medium-content-sans-serif-font, “Lucida Grande”, “Lucida Sans Unicode”, “Lucida Sans”, Geneva, Arial, sans-serif; font-size: 16px; font-style: normal; font-variant-caps: normal; font-weight: 300; letter-spacing: normal; orphans: auto; text-align: center; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; background-position: 0px calc(1em + 1px); background-repeat: repeat no-repeat”>UnSplash

Artificial Intelligence vs. Machine Learning vs. Deep Learning

“Deep Learning, on the other hand, is a very young field of Artificial Intelligence that is powered by artificial neural networks.

It can be viewed again as a subfield of Machine Learning since Deep Learning algorithms also require data in order to learn to solve tasks. Although methods of Deep Learning are able to perform the same tasks as classic Machine Learning algorithms, it is not the other way round.

Artificial neural networks have unique capabilities that enable Deep Learning models to solve tasks that Machine Learning models could never solve”

A.I. 101: What is artificial intelligence and where is it going?

  • “‘In a recent Northeastern University and Gallup survey that found 71 percent of Americans feared the surge in AI would cause more job loss than gain.’
  • Computer scientist John McCarthy coined the phrase “artificial intelligence” in 1956,


Algorithms are mathematical formulas that amount to a set of processing instructions — akin to a recipe — that aim to solve a specific problem.

Machine learning

Finds patterns in a large amount of data. Machine learning comes in three forms: supervised, unsupervised and reinforcement learning.

Deep learning

Utilizes a web of computation models called “neural networks” that are designed to mimic human brains.

Natural language processing

NLP technology uses machine-learning algorithms that tag parts of speech and the relationships between words to analyze the meaning in text and audio.

Deepfake technology

Uses deep learning models to manipulate photos and videos to create realistic images of people doing or saying something they never did

  • Predictions are all over the map about whether technology will usher in new work to offset the loss of jobs to artificial intelligence in upcoming years.
  • A recent Oxford Economics report that found 20 million manufacturing jobs will be lost by 2030
  • On more positive note, a 2018 report from World Economic Forum — a nonprofit composed of the world’s 1,000 top companies — predicts that while 75 million jobs will be displaced by automation, it will generate 133 million new roles
  • A growing number of AI experts and politicians agree that the advancements in AI have outpaced government regulation.
  • Although the next stop remains unknown, the AI train isn’t stopping anytime soon. As World Economic Forum Founder and Executive Chairman Klaus Schwab aptly summarized the Fourth Industrial Revolution, ‘There has never been a time of greater promise or greater peril.'”