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AI vs Machine Learning vs Deep Learning: What’s the Difference?

Published on: Nov 6, 2022
By: Editorial Staff
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On LinkedIn, news sites, and classrooms and offices everywhere the terms artificial intelligence (AI), machine learning, and deep learning receive frequent mention. If you’re considering working with any of these for your career — maybe you’re thinking of becoming a machine learning engineer, an AI specialist, or a data scientist — you might be confused as to what exactly the difference is between each.

You wouldn’t be alone. In fact, some argue that the ambiguity in how the terms artificial intelligence and machine learning are used is deliberate, at least in part. In calling “AI” what is merely “machine learning,” so John Naughton argues in The Guardian, companies around the world are attempting to distract us from the potential ills of their technologies by invoking the romantic project of artificial intelligence: the quest to invent machines with the reasoning, emotion, and sentience of humans.

Weak vs Strong AI

Implicit in Naughton’s argument is yet another distinction, that between artificial general intelligence, or AGI, and artificial narrow intelligence, or weak AI. Far less ambitious than AGI — of which no examples exist, its plausibility even being doubted by some researchers — weak artificial intelligence refers to the kinds of task-based AI technologies ubiquitous today: Google Translate, IBM’s Deep Blue chess computer, or Uber’s ride-hailing algorithms. 

When companies use the term “AI” for these technologies, Naughton alleges, they are attempting to dress up weak AI in the trappings of AGI. Whether or not Naughton is correct in his analysis, it’s important for an aspiring AI professional to understand the distinction. It’s also important to understand that, while for some “artificial intelligence” might evoke memories of Spike Lee’s Her, Stanley Kubrick’s 2001: A Space Odyssey, James Cameron’s Terminator franchise, or the many other films that deal with imagined futures where intelligent machines threaten to surpass their human designers, when machine learning engineers and data scientists speak of AI in their day-to-day, they’re referring to the weak variant.

AI vs machine learning vs deep learning

But the question remains: how is artificial intelligence (here meaning weak AI) different from machine learning and deep learning? In truth, it’s not so much a question of how they’re different as what kinds of relationships exist between them. In the end, it’s quite simple.

Machine learning is a subdiscipline of artificial intelligence focused on the development of mathematical algorithms that allow computers to progressively improve their capabilities. Deep learning is a method of machine learning that employs artificial neural networks comprising layers of nodes inspired by the brain’s neurons. 

Below, we’ll dive deeper into each, including key aspects and use cases.

Artificial intelligence

The definition of AI remains up for debate, but a useful working definition would be that artificial intelligence is the ability of computers to think and act rationally in a given situation — “to do the right thing,” as Stuart Russell and Peter Norvig put it in their seminal textbook Artificial Intelligence: A Modern Approach. An “AI” is also the name given to a computer or a machine that has this capacity. Today, however, AI engineers are increasingly attempting to endow AIs with human-like abilities beyond mere logical reasoning such as creativity and empathy. Take, for example, the image generator DALL-E. 

A reminder, as we mentioned above, that when discussing artificial intelligence it’s useful to distinguish between “strong” AI, or artificial general intelligence (AGI), and “weak” or “narrow” AI. AGI refers to yet-unrealized AI systems with broad intelligence on par with, or even exceeding, human intelligence, while narrow AI refers to the more limited, task-oriented capabilities of today. Going forward, we will focus on the latter.

Key aspects and components

Computer vision

Computer vision is an AI subdiscipline with the goal of endowing computers with the ability to perceive, assess, and act on visual stimuli at a level equal to, or even exceeding, human vision. This ability is essential if computers are to become even more active in the physical world in the coming years.

Use case

Professionals in healthcare are increasingly employing computer vision to assist diagnostics. AI-enabled diagnostic imaging interpretation helps improve efficiency and accuracy in diagnosis and might reduce technician burnout.

scans of human brain

Image Credit: Emerj

Natural language processing

Borrowing from computer science, artificial intelligence, and linguistics, natural language processing (NLP) is a subdiscipline of artificial intelligence concerned with giving computers the ability to understand, and even employ, written and verbal language at a near-human level. 

Use case

Natural language processing is at the core of the autocorrect capabilities we encounter on our phones every day.

Robotic process automation

Robotic process automation (RPA) concerns the automation of complex business processes to drive efficiency and productivity. Rather than robots active in the physical world, in RPA the robots are metaphorical “software bots” that can learn business processes autonomously and then complete them automatically at scale.

Use case

RPA allows banks to easily process thousands and millions of daily transactions without need for human intervention.


In the context of artificial intelligence, robotics focuses on how intelligent technologies can engage with the physical world through a combination of sensors and effectors. Sensors provide an input of environmental information, while effectors — robotic arms, legs, grippers, and the like —  allow a robot informed by these inputs to produce physical outputs and effect change on the world around it.

Use case

In manufacturing, AI-enabled robots can train themselves to improve efficiency and schedule their own preventative maintenance, avoiding costly breakdowns.

production line with AI powered robot

Image Credit: DLabs.ai

Machine learning

See below.

Knowledge Representation and Reasoning

Knowledge representation and reasoning (KRR) is a subdiscipline of artificial intelligence concerned with communicating or representing the world in a way that computers can understand. KRR requires skills in formal logic, semantics, and so-called Semantic Web technologies, which focus on making the internet machine-readable.

Use case

KRR plays a crucial role in personal assistants like Apple’s Siri.

Machine learning

A subdiscipline of artificial intelligence, machine learning focuses on the development of mathematical algorithms that allow computers to progressively improve their capabilities — “learning” as they encounter more and more data. Stuart Russell and Peter Norvig describe the process as follows: “a computer observes some data, builds a model based on the data, and uses the model as both a hypothesis about the world and a piece of software that can solve problems.” 

At the core of machine learning are machine learning algorithms, bits of computer code that process input data and spit out usable output data. As you’ll learn below, these can be trained by being fed “training data,” relevant input data for which the output is already known (supervised learning). Alternatively they can be written to train themselves to find patterns and other signals in unlabeled data sets (unsupervised learning) or learn to maximize a numerical award aligned with a desired action or other output (reinforcement learning).

When fed machine learning algorithms are capable of “learning,” the end result being a machine learning model that can be used to make predictions and complete other tasks when fed with real-world data.

Machine learning models are central to predictive analytics, a component of business analytics used by businesses to identify trends from historical data to predict what might happen in the future. Machine learning also often overlaps with other subdisciplines of artificial intelligence, including computer vision, natural language processing, and robotics.

Key aspects and components

Supervised learning

Supervised machine learning entails using labeled data sets — where each piece of data is tagged and classified — to train a machine learning algorithm to give the correct output when fed an input. In supervised machine learning, algorithms essentially build an ML model by learning by example.

Use case

A common application of supervised learning is for image recognition. If you’ve ever identified a taxi for a reCAPTCHA, you’ve helped build a supervised learning training set.

recaptcha example of taxis

Unsupervised learning

Unsupervised machine learning entails using unlabeled data sets to train a machine learning algorithm. Rather than learning by example as in supervised learning, unsupervised ML algorithms make sense of the data themselves: discovering patterns and other signals, forming clusters, and identifying the most important data points. 

Use case

Unsupervised machine learning algorithms are essential for “Customers also bought” features on ecommerce sites like Amazon.

Reinforcement learning

Reinforcement learning entails writing ML algorithms to behave in a complex environment in ways that will maximize a numerical reward. Usually this is achieved through things called Markov decision processes.

Use case

Reinforcement learning is used to optimize traffic control systems, both in the streets and in the air.

Data mining

Data mining entails identifying patterns and other signals in big data sets. Descriptive data mining seeks merely to develop more knowledge about a given data set, while predictive data mining leverages a given data set to glimpse into what the future may hold. The latter frequently utilizes advanced machine learning.

Use case

Data mining is commonly employed for sentiment analysis on big data sets arising from social media. This sentiment analysis can then be used to predict how well a product will perform in a particular market.

Deep learning

Deep learning is a method of machine learning in which “artificial neural networks” (ANNs) of algorithms are employed to perform analysis and other tasks that involve big data sets with extreme accuracy. Neural networks comprise dozens of nodes (or more), arranged in layers (hence “deep”) and each with an associated weight and threshold. As information passes through these nodes, these thresholds and weights determine whether the node allows information to pass through to the next node and, if so, how this information should impact the next layer of calculation and, ultimately, the neural network’s final output. Deep learning algorithms are written to allow these thresholds and weights to become more precise over time as the neural network learns.

Deep learning has recently entered the public’s imagination — on Twitter, at least — through the images and text passages generated from user-submitted prompts by deep learning networks Dall-E 2 and GPT-3, respectively.

Chart of information flow
darth vader deep ai learning example

Key aspects and components


An autoencoder is a kind of artificial neural network used to discover ways to classify or detect features from unlabeled data sets. By encoding data — say, an image — and then attempting to reconstruct the original from the encoded version and comparing its “original” to the actual original, it can progressively improve its capabilities.

Use case

Autoencoders are frequently used for tasks such as image denoising, which removes non-essential information and nonsense (noise) from images to support computer vision tasks.

image transformation chart

Image Credit: Manthan Gupta

Generative adversarial network

A generative adversarial network sets two neural networks — a generator and a discriminator — against each other in a zero-sum game in order to improve the network’s outputs. As the generator is trained to output examples of real-world data like photographs, the discriminator determines the quality or authenticity of these examples. This continues until the two neural networks reach a stasis, with the discriminator fooled by the generator half of the time.

Use case

Generative adversarial networks (GANs) are used for tasks like generating realistic artificial photographs from prompts and performing facial aging. 

chart of image to text recognition

Image credit: Rajat Garg

Interested in learning more?

In discussing the relationships between AI, machine learning, and deep learning and diving deeper into each, we’ve previewed just a fraction of what these incredible technologies can do. If we’ve piqued your interest and you want to learn more, we have some suggestions for further reading.

If you want to know more about how artificial intelligence and machine learning are making a difference in industry and in our daily lives, check out:

If you’re interested in a potential career in artificial intelligence or machine learning, but you don’t know where to start, see our guides on the different kinds of AI & ML career paths out there. They’ll give you the low-down on what skills you need to have, what you can expect to work on, and how much you can expect to earn.

If you know you want to pursue a career in artificial intelligence or machine learning and want to start getting smarter, our education guides are for you. In each, you can learn more about what you need to apply to a particular kind of program, what you can expect to learn if you get accepted, and what career paths each program will open for you.