What is the difference between artificial intelligence, machine learning, and deep learning?
In this article, we will break down these three concepts and address them from the roots until we reach real-world applications. We will understand how artificial intelligence is the overarching umbrella, while machine learning comes as one of its branches, and deep learning appears as a more specialized and complex branch within this context.
First: Artificial Intelligence (AI)—the old dream of scientists
1. The basic concept:
Artificial intelligence is simply an attempt to make machines "think" like humans, or at least perform tasks that require a degree of human intelligence. For example, can a machine play chess? Write a story. Drive a car? Diagnose a disease? These are all questions that artificial intelligence has tried to answer.2. Historical Overview:
The idea of artificial intelligence is not new. In the 1950s, scientists began developing programs that could solve simple mathematical problems or play logical games. However, due to the limited hardware at that time, artificial intelligence remained merely research and theories.3. Its types:
Artificial intelligence is divided into two main types:Weak AI (Narrow AI): This is what we use today. It performs specific tasks such as image classification, translation, or facial recognition.
General AI: It is the dream of the future. A machine that thinks, learns, and analyzes like a human in all fields, and we have not yet reached it.
Secondly: Machine Learning—How do machines learn?
1. What is machine learning?
Machine learning is a method within artificial intelligence that enables systems to learn and improve automatically through experience (data) without being explicitly programmed.Instead of writing code that says, "If the car is red, do this," we feed the algorithm thousands of images of red cars and let it deduce the rules itself.
2. How does it work?
Machine learning follows a methodology that is simple in essence:Data: We collect a lot of data.
Models: We build a mathematical model.
Training: We present the model to the data.
Improvement: We adjust the model to make it more accurate.
The test: We check its performance on new data.
3. Types of Machine Learning:
A. Supervised Learning:
The model learns from pre-labeled data. For example, providing the model with pictures of dogs and cats along with their types so it can distinguish between them later.B. Unsupervised Learning:
We don't give the model labels; it discovers the patterns by itself. For example, classifying customers based on purchasing behavior without pre-defining the categories.C. Reinforcement Learning:
The machine learns through trial and error, just like learning a video game through rewards and punishments.Third: Deep Learning—the electronic brain:
1. What is deep learning?
Deep learning is an advanced branch of machine learning. It relies on what is known as "artificial neural networks," which mimic the way the human brain works.2. Why is it "deep"?
Because it uses multiple layers of processing. Each layer extracts a specific feature from the data and then passes it to the next layer. Imagine you see a picture of a cat:The first layer captures the edges.
The second one defines the shapes.
The third sees the ears.
And in the end, she says, "Yes, this is a cat!"
3. Areas of its use:
Image recognition: like on Facebook when tagging friends.Audio: like Google Assistant or Siri.
Machine translation: Like in Google Translate.
Self-driving: Tesla cars heavily rely on deep learning.
4. What is the difference from machine learning?
The fundamental difference is that deep learning does not require significant human intervention in "feature extraction" from data, whereas traditional machine learning requires experts to determine what the model should learn.- Comparison between the three :
From real life: How do these concepts meet?
Let's take a practical example: a self-driving car.Artificial intelligence is the overall goal: making the car "think" and drive itself.
Machine learning allows it to learn from real data: images, maps, and previous experiences.
Deep learning helps it recognize traffic signals and people and control complex decisions.
All these layers work together, but each one serves a specific role.
Why is this distinction important?
With the widespread adoption of artificial intelligence concepts, it becomes essential to understand what these terms mean. Distinguishing between them helps you to:Understanding how the products around you work.
Identify the skills that need to be learned if you are interested in the field.
Differentiating between marketing promises and scientific reality.
Conclusion: Each layer leads to the next.
Artificial intelligence, machine learning, and deep learning are not opposing concepts but rather intertwined and complementary.Artificial intelligence is the goal.
Machine learning is the means.
Deep learning is the latest tool in this method.
Understanding this series gives you a clearer view of the present and makes you more prepared to deal with the future.Whether you are a developer, an entrepreneur, or just an interested user, understanding the difference between these concepts is the first step towards consciously engaging with the upcoming technological revolution.