Is deep learning always better than classical machine learning?

No, and assuming so is a common and expensive mistake. Deep learning wins where features are hard to specify by hand: images, audio, raw text. On tabular data with a few thousand rows, gradient-boosted trees usually beat a neural network, train in seconds rather than hours, and are far easier to explain to whoever has to sign off on the decision. Match the method to the data, not to the fashion.

What actually separates machine learning from ordinary programming?

Where the rules come from. In ordinary programming you write the rules and the computer applies them to data. In machine learning you supply data and the answers, and the algorithm derives the rules. That is why machine learning suits problems where you can recognise a correct answer but cannot write down how you recognised it, spam being the classic example.

Is AI the top of a pyramid or the bottom?

Neither, really, and the pyramid metaphor causes the confusion. Nested circles work better: AI is the outer circle, machine learning is a circle inside it, deep learning is a circle inside that. If you insist on a pyramid, AI is the wide base because it contains the most, and deep learning is the narrow tip. Drawing AI at the top inverts the containment and is worth correcting whenever you see it.

Do all AI systems learn?

No. Plenty of systems that qualify as AI use no learning at all: rule-based expert systems, search and planning algorithms, constraint solvers. A chess engine using tree search with a hand-written evaluation function is doing something that clearly requires intelligence in a human, without learning anything from data. Machine learning became dominant enough that people now use the terms interchangeably, but the older, broader definition is the accurate one.

Why does deep learning need so much more data?

Because it is learning the feature representation as well as the decision rule. Classical machine learning starts from features a human already chose, which encodes a lot of knowledge for free. A neural network working from raw pixels or characters has to discover which patterns matter before it can use them, and that discovery is what consumes the data and the compute.

Journal AI Integration January 1, 2023

AI Integration · Guide

Unraveling the Confusion: A Primer on AI, ML, and Deep Learning

Understand the key differences between artificial intelligence, machine learning, and deep learning with clear definitions, examples, and real-world applications.

Anurag Verma

3 min read

AI Machine Learning Deep Learning Beginners Data Science

Unraveling the Confusion: A Primer on AI, ML, and Deep Learning

Key takeaways

The three are nested, not parallel. Deep learning is a subset of machine learning, which is a subset of AI, so every deep learning system is a machine learning system but not the reverse.
AI is the outcome (behaviour that would need human intelligence). Machine learning is one method of getting there (learn the rules from data instead of writing them). Deep learning is one family of machine learning methods (multi-layer neural networks).
Narrow AI does one task; general AI would handle a wide range. Everything in production today is narrow, regardless of how the marketing reads.
A spam filter trained on labelled emails is machine learning. An image classifier learning features from raw pixels is deep learning. A self-driving car is an AI system that contains many of both.
The practical difference is where the features come from: classical machine learning needs you to engineer them, deep learning learns them from raw input, which is why it needs far more data and compute.

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are often used interchangeably, but they are actually three distinct fields within the broader field of AI. Understanding the difference between these three subfields can be confusing, but it’s important to know what sets them apart in order to understand the capabilities and limitations of each.

Artificial intelligence

Artificial intelligence refers to the ability of a machine to perform tasks that would normally require human intelligence, such as learning, problem-solving, and decision-making. AI can be divided into two main categories: narrow AI, which is designed to perform a specific task, and general AI, which is designed to perform a wide range of tasks.

Machine learning

Machine learning, on the other hand, is a subfield of AI that involves the development of algorithms that allow a machine to learn from data, rather than being explicitly programmed to perform a task. Machine learning algorithms use statistical models to make predictions or decisions based on data inputs.

Deep learning

Deep learning is a subfield of machine learning that involves the use of neural networks, which are complex mathematical models inspired by the structure and function of the human brain. Neural networks are made up of layers of interconnected “neurons,” which process and transmit information. Deep learning algorithms are able to learn and make decisions on their own by analyzing vast amounts of data and adjusting the connections between neurons as they learn.

How the three fit together

One way to think about the relationship between these three fields is to imagine a set of nested circles: AI is the outer circle, machine learning sits inside it, and deep learning sits inside machine learning. Every deep learning system is a machine learning system, and every machine learning system is an AI system, but not the other way around.

An example of each

Here are a few examples to illustrate the differences between these fields:

AI: A self-driving car is an example of a system that uses AI. The car’s sensors gather data about its surroundings, and an AI system processes that data to make decisions about how to navigate the road.
Machine learning: A spam filter is an example of a system that uses machine learning. The filter is trained on a large dataset of emails, and it uses machine learning algorithms to classify new incoming emails as spam or non-spam based on their characteristics.
Deep learning: A image recognition system is an example of a system that uses deep learning. The system is fed a large dataset of images and their corresponding labels (e.g., “cat,” “dog,” etc.), and it uses a deep learning algorithm to learn how to identify objects in new images based on the patterns it has learned from the training data.

The short version

In summary, AI refers to the ability of a machine to perform tasks that would normally require human intelligence, ML involves the development of algorithms that allow a machine to learn from data, and DL involves the use of neural networks to learn and make decisions on their own. Each of these fields has its own unique capabilities and limitations, and they all play important roles in the development of intelligent systems.

Frequently asked questions

Is deep learning always better than classical machine learning?: No, and assuming so is a common and expensive mistake. Deep learning wins where features are hard to specify by hand: images, audio, raw text. On tabular data with a few thousand rows, gradient-boosted trees usually beat a neural network, train in seconds rather than hours, and are far easier to explain to whoever has to sign off on the decision. Match the method to the data, not to the fashion.
What actually separates machine learning from ordinary programming?: Where the rules come from. In ordinary programming you write the rules and the computer applies them to data. In machine learning you supply data and the answers, and the algorithm derives the rules. That is why machine learning suits problems where you can recognise a correct answer but cannot write down how you recognised it, spam being the classic example.
Is AI the top of a pyramid or the bottom?: Neither, really, and the pyramid metaphor causes the confusion. Nested circles work better: AI is the outer circle, machine learning is a circle inside it, deep learning is a circle inside that. If you insist on a pyramid, AI is the wide base because it contains the most, and deep learning is the narrow tip. Drawing AI at the top inverts the containment and is worth correcting whenever you see it.
Do all AI systems learn?: No. Plenty of systems that qualify as AI use no learning at all: rule-based expert systems, search and planning algorithms, constraint solvers. A chess engine using tree search with a hand-written evaluation function is doing something that clearly requires intelligence in a human, without learning anything from data. Machine learning became dominant enough that people now use the terms interchangeably, but the older, broader definition is the accurate one.
Why does deep learning need so much more data?: Because it is learning the feature representation as well as the decision rule. Classical machine learning starts from features a human already chose, which encodes a lot of knowledge for free. A neural network working from raw pixels or characters has to discover which patterns matter before it can use them, and that discovery is what consumes the data and the compute.

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