Key Takeaways
- ✓Artificial intelligence is the broadest concept: computer systems designed to perform tasks that appear intelligent or rational in a given situation.
- ✓Machine learning is a subdiscipline of AI that uses algorithms and data to improve performance without being explicitly programmed for every rule.
- ✓Deep learning is a machine learning method that uses layered artificial neural networks to work with large, complex datasets.
- ✓Most AI systems people use today are narrow or weak AI, not artificial general intelligence with human-level understanding across domains.
- ✓Understanding the differences can help students and career changers choose better learning paths, roles, and projects.
On LinkedIn, news sites, classrooms, and offices everywhere, the terms artificial intelligence, machine learning, and deep learning receive frequent mention. If you’re considering working with any of these technologies — perhaps as a machine learning engineer, AI specialist, or data scientist — the differences can be confusing.
You wouldn’t be alone. Some argue that 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,” John Naughton argues in The Guardian, companies may be invoking the romantic project of artificial intelligence: the quest to invent machines with the reasoning, emotion, and sentience of humans.
For a practical career or education decision, though, the relationship is simpler: artificial intelligence is the broad field, machine learning is a major subfield inside AI, and deep learning is a machine learning method. If you want to keep exploring after this overview, AIFwD also covers machine learning applications, machine learning in business, machine learning in medicine, artificial intelligence skills, and how to become a machine learning engineer.
“AI is the umbrella, machine learning is a major branch, and deep learning is one powerful method within machine learning.”— AIFwD Editorial Staff
Weak vs Strong AI
Implicit in Naughton’s argument is another distinction: artificial general intelligence, or AGI, versus artificial narrow intelligence, often called weak AI. Far less ambitious than AGI — of which no examples currently exist, and whose plausibility some researchers doubt — weak AI refers to task-based technologies that are already common today.
Examples of weak AI include Google Translate, IBM’s Deep Blue chess computer, recommendation systems, route optimization, and ride-hailing algorithms. These systems can perform specific tasks very well, but they do not possess broad human-like understanding.
When companies use the term “AI” for these technologies, critics argue they may be dressing up weak AI in the trappings of AGI. Whether or not that criticism is fair in every case, aspiring AI professionals should understand the distinction. In day-to-day technical work, machine learning engineers and data scientists usually mean the weak or narrow variant when they refer to AI.
AI vs Machine Learning vs Deep Learning
How is artificial intelligence, here meaning weak or narrow AI, different from machine learning and deep learning? It is less a question of complete difference and more a question of relationship.
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 uses artificial neural networks made of layers of nodes inspired by the brain’s neurons.
Put another way: all deep learning is machine learning, and machine learning is one important approach within AI. But not every AI system uses machine learning, and not every machine learning system uses deep learning.
Artificial Intelligence
The definition of AI remains up for debate, but a useful working definition is 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 textbook Artificial Intelligence: A Modern Approach.
An “AI” can also refer to a computer or machine with this capacity. Today, AI engineers increasingly attempt to build systems with capabilities beyond simple logical reasoning, including language generation, visual perception, planning, creativity, and forms of social interaction. One familiar example is image generation, including systems such as DALL-E.
As noted above, it helps to distinguish strong AI, or AGI, from weak or narrow AI. AGI refers to unrealized systems with broad intelligence on par with or exceeding human intelligence. Narrow AI refers to the more limited, task-oriented systems in use today.
Computer Vision
Computer vision is an AI subdiscipline that aims to give computers the ability to perceive, assess, and act on visual stimuli at a level equal to, or even exceeding, human vision.
Use case: Healthcare professionals increasingly employ computer vision to assist diagnostics. AI-enabled diagnostic imaging interpretation can help improve efficiency and accuracy in diagnosis and may reduce technician burnout. Image credit from the original article: Emerj.
Natural Language Processing
Borrowing from computer science, artificial intelligence, and linguistics, natural language processing, or NLP, is concerned with giving computers the ability to understand and even employ written and verbal language at a near-human level.
Use case: NLP is at the core of autocorrect features people encounter on phones every day. Image credits from the original article: Alphr and Alphar.com.
Robotic Process Automation
Robotic process automation, or RPA, concerns the automation of complex business processes to drive efficiency and productivity. In RPA, the robots are metaphorical software bots that can learn business processes and complete them automatically at scale.
Use case: RPA allows banks to process thousands or millions of daily transactions with less need for human intervention.
Robotics
In the context of artificial intelligence, robotics focuses on how intelligent technologies can engage with the physical world through sensors and effectors. Sensors provide environmental inputs, while effectors such as robotic arms, legs, or grippers allow a robot to produce physical outputs and affect the world around it.
Use case: In manufacturing, AI-enabled robots can train themselves to improve efficiency and schedule preventative maintenance, helping avoid costly breakdowns. Image credit from the original article: DLabs.ai.
Knowledge Representation and Reasoning
Knowledge representation and reasoning, or KRR, is a subdiscipline of artificial intelligence concerned with representing the world in a way computers can understand. KRR draws on formal logic, semantics, and Semantic Web technologies that help make information machine-readable.
Use case: KRR plays an important role in personal assistants like Apple’s Siri.
Machine Learning
A subdiscipline of artificial intelligence, machine learning focuses on mathematical algorithms that allow computers to progressively improve their capabilities — learning as they encounter more data. Russell and Norvig describe the process as a computer observing data, building a model based on that data, and using the model both as a hypothesis about the world and as software that can solve problems.
At the core of machine learning are algorithms: pieces of code that process input data and produce usable output data. These algorithms can be trained with labeled data, trained to find patterns in unlabeled data, or trained to maximize a reward in a complex environment.
The result is a machine learning model that can make predictions or complete other tasks when it receives real-world data. Machine learning models are central to predictive analytics and frequently overlap with computer vision, natural language processing, and robotics.
Supervised Learning
Supervised machine learning uses labeled datasets, where each piece of data is tagged and classified, to train an algorithm to return the correct output when given an input. In supervised learning, algorithms learn by example.
Use case: A common application is image recognition. If you have ever identified taxis for a reCAPTCHA, you have helped build a supervised learning training set.
Unsupervised Learning
Unsupervised machine learning uses unlabeled datasets to train an algorithm. Rather than learning by example, unsupervised algorithms discover patterns, form clusters, identify relationships, and surface important data points on their own.
Use case: Unsupervised machine learning algorithms are important for “Customers also bought” features on ecommerce sites such as Amazon.
Reinforcement Learning
Reinforcement learning involves writing machine learning algorithms to behave in a complex environment in ways that maximize a numerical reward. This is often achieved through Markov decision processes.
Use case: Reinforcement learning is used to optimize traffic control systems, both on streets and in the air. Image credit from the original article: National Air Traffic Services press office, via the Alan Turing Institute.
Data Mining
Data mining entails identifying patterns and other signals in large datasets. Descriptive data mining seeks to develop more knowledge about a dataset, while predictive data mining uses data to glimpse what the future may hold. The latter frequently relies on advanced machine learning.
Use case: Data mining is commonly used for sentiment analysis on large social media datasets. That sentiment analysis can help predict how well a product may perform in a particular market.
Deep Learning
Deep learning is a method of machine learning in which artificial neural networks, or ANNs, are used to perform analysis and other tasks involving large datasets with high accuracy.
Neural networks comprise nodes arranged in layers — hence “deep” — each with an associated weight and threshold. As information passes through those nodes, thresholds and weights determine whether information passes to the next node and how it affects the next layer of calculation and the network’s final output.
Deep learning algorithms are written so these thresholds and weights become more precise over time as the neural network learns. Deep learning entered the public imagination through images and text generated from prompts by systems such as DALL-E 2 and GPT-3.
Autoencoder
An autoencoder is a kind of artificial neural network used to discover ways to classify or detect features from unlabeled datasets. By encoding data, such as an image, then attempting to reconstruct the original from the encoded version, it can compare its reconstruction against the actual original and improve over time.
Use case: Autoencoders are frequently used for tasks such as image denoising, which removes non-essential information and noise from images to support computer vision tasks. Image credit from the original article: Manthan Gupta.
Generative Adversarial Network
A generative adversarial network, or GAN, sets two neural networks — a generator and a discriminator — against each other in a zero-sum game to improve outputs. As the generator learns to produce examples of real-world data such as photographs, the discriminator judges their quality or authenticity.
This process continues until the two networks reach a kind of stasis, with the discriminator fooled by the generator about half of the time. Use case: GANs are used for tasks such as generating realistic artificial photographs from prompts and performing facial aging. Image credit from the original article: Rajat Garg.
Interested in Learning More?
In discussing the relationships between AI, machine learning, and deep learning, we have previewed only a fraction of what these technologies can do. If you want to learn more about how artificial intelligence and machine learning are making a difference in industry and daily life, start with AIFwD’s guides to machine learning in business, machine learning in medicine, machine learning applications, and the future of machine learning.
If you’re interested in AI or machine learning careers, see our guides to machine learning careers, artificial intelligence careers, machine learning jobs, how to become a machine learning engineer, and artificial intelligence engineer careers.
If you know you want to study artificial intelligence or machine learning, explore machine learning bootcamps, master’s in artificial intelligence, online master’s in artificial intelligence, master’s in machine learning, online master’s in machine learning, and Ph.D. programs in artificial intelligence.
Frequently Asked Questions
What is the difference between AI, machine learning, and deep learning?+
AI is the broad field of building systems that can act intelligently. Machine learning is a subfield of AI that uses data and algorithms to improve performance. Deep learning is a machine learning method that uses layered neural networks.
Is machine learning the same as AI?+
No. Machine learning is one major approach within artificial intelligence, but AI also includes other areas such as knowledge representation, reasoning, planning, robotics, and expert systems.
Is deep learning always better than machine learning?+
Not always. Deep learning can be powerful for large, complex datasets such as images, audio, and language, but simpler machine learning methods may be faster, cheaper, easier to explain, and more practical for smaller datasets.
What is weak AI?+
Weak AI, or narrow AI, refers to systems designed for specific tasks, such as translation, recommendations, image recognition, or route optimization. It is different from artificial general intelligence, which would have broad human-level intelligence across domains.
Conclusion & Next Steps
The simplest way to remember the relationship is nested: artificial intelligence is the broad umbrella, machine learning is a major branch within it, and deep learning is a specialized machine learning method built around layered neural networks.
That distinction matters for careers, education, and product decisions. Someone learning AI fundamentals may study logic, search, planning, language, robotics, and ethics. Someone focused on machine learning will spend more time on data, algorithms, model training, and evaluation. Someone specializing in deep learning will go deeper into neural networks, large datasets, and high-compute model architectures.
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