{"id":13084,"date":"2025-06-06T11:47:35","date_gmt":"2025-06-06T11:47:35","guid":{"rendered":"https:\/\/www.yiaho.com\/what-are-convolutional-neural-networks-cnns-in-artificial-intelligence\/"},"modified":"2025-06-06T11:47:35","modified_gmt":"2025-06-06T11:47:35","slug":"what-are-convolutional-neural-networks-cnns-in-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/www.yiaho.com\/en\/what-are-convolutional-neural-networks-cnns-in-artificial-intelligence\/","title":{"rendered":"What are Convolutional Neural Networks (CNNs) in artificial intelligence?"},"content":{"rendered":"

The field of artificial intelligence is extremely vast, encompassing many technical aspects and sub-domains. At Yiaho, our goal is to make these concepts accessible and understandable! <\/p>\n

In this article written by the Yiaho team, today we are going to explore convolutional neural networks, commonly known as CNNs.<\/p>\n

These powerful tools, inspired by how the human brain works, are revolutionizing the way machines interpret visual, audio, and even textual data. We will explore what CNNs are, their history, how they work, their fascinating applications, and why they are so essential in modern AI. <\/p>\n

The History and Invention of CNNs<\/h2>\n

The history of CNNs begins in the 1980s, when AI pioneer Yann LeCun<\/a> drew inspiration from the human visual system to develop the foundations of these networks. In 1989, Yann LeCun and his team introduced LeNet, the first CNN, designed to recognize handwritten digits in zip codes. <\/p>\n

This innovation built on earlier work, such as Kunihiko Fukushima’s research on the neocognitron (1980), a model inspired by the way visual neurons detect patterns.<\/p>\n

However, CNNs didn’t truly take off until the rise of powerful computers and Big Data<\/a> in the 2010s. The victory of AlexNet (created by Alex Krizhevsky et al.) at the ImageNet competition in 2012 marked a turning point, demonstrating the superiority of CNNs for image recognition and paving the way for massive adoption in AI. <\/p>\n

What is a CNN in AI?<\/h2>\n

A convolutional neural network is a type of artificial neural network designed specifically to process data structured in grids, such as images or time series.<\/strong><\/p>\n

Unlike classic neural networks<\/a>, which process each piece of data independently, CNNs exploit the spatial structure of the data. For example, in an image, they recognize that neighboring pixels are related and can form patterns like edges, shapes, or textures. <\/p>\n

Imagine you show a photo of a car to a CNN:<\/strong><\/p>\n

Instead of analyzing each pixel in isolation, the CNN will first spot simple features (like the outlines of the wheels), then more complex patterns (like the shape of the hood), to finally identify the vehicle. This ability to learn hierarchically makes CNNs incredibly effective for tasks like image recognition or object detection. <\/p>\n

How does a CNN work?<\/h2>\n

A CNN is like a chef who transforms raw ingredients (the pixels of an image) into a refined dish (a precise prediction). Here are the key steps of how it works, explained simply: <\/p>\n

Convolution layer: the heart of the CNN<\/h3>\n

Convolution consists of applying filters (or kernels) to the image to detect specific features, such as lines or curves. Each filter slides over the image, performs a mathematical calculation, and produces a feature map. For example, one filter might spot the edges of the headlights, another the textures of the bodywork. <\/p>\n

These filters are learned automatically by the network during training, allowing it to adapt to varied data.<\/p>\n

ReLU activation: adding non-linearity<\/h3>\n

After convolution, an activation function, often ReLU (Rectified Linear Unit), is applied. It transforms negative values into zero, which helps the network capture complex relationships and avoid learning problems. <\/p>\n

Pooling layer: reducing size, keeping the essentials<\/h3>\n

Pooling (often max pooling) reduces the size of the feature maps while retaining important information. This makes the CNN faster and less sensitive to small variations, like a slight shift in the image. <\/p>\n

Fully connected layers: decision making<\/h3>\n

Once the features are extracted, they are flattened and sent to fully connected layers. These layers combine the information to produce a prediction, such as “it’s a car” or “it’s a truck.” <\/p>\n

Training: learning to see<\/h3>\n

CNNs are trained with algorithms like gradient descent, where the network adjusts its filters to minimize prediction errors. The more examples it sees (for example, thousands of images of cars), the more accurate it becomes. <\/p>\n

See also: RAG (Retrieval-Augmented Generation) in AI: definition, explanation, and example<\/a><\/p>\n

Why are CNNs so powerful?<\/h2>\n

CNNs shine because of their ability to automatically extract relevant features without human intervention. Previously, to recognize an object in an image, engineers had to manually define rules (for example, “a car has four wheels”). With CNNs, the network learns these features on its own from the data. <\/p>\n

In addition, CNNs are efficient and robust. Thanks to pooling, they can recognize an object even if it is slightly distorted or moved. They also require fewer parameters than classic fully connected networks, which reduces computational needs. <\/p>\n

Fascinating examples and applications of CNNs<\/h2>\n

CNNs are all around us, often without us even realizing it. Here are some concrete examples that show their impact: <\/p>\n