AI neural network glowing nodes and connections in 3D space

Artificial neural networks are computational systems inspired by the human brain, used in deep learning and artificial intelligence.

About this subject

Artificial neural networks are the backbone of deep learning, a subfield of artificial intelligence that has revolutionized areas such as computer vision, natural language processing, and pattern recognition. Inspired by biological neurons, these networks consist of layers of interconnected nodes that process information hierarchically. Each connection has an adjustable weight, optimized during training with large datasets.

The concept of neural networks is not new: the first mathematical model, the perceptron, was proposed by Frank Rosenblatt in 1958. However, it was only from the 2010s onward, with increased computational power and the availability of large datasets, that deep networks became viable. Today, architectures like convolutional neural networks (CNNs) and transformers are used in everyday applications, from virtual assistants to self-driving cars.

In Brazil, neural network research is active at universities such as USP, Unicamp, and UFRJ, with contributions in precision agriculture and medical image diagnosis. The country also has startups applying AI in agribusiness, finance, and healthcare. Globally, companies like Google, Meta, and OpenAI invest billions in increasingly large models, such as GPT-4 and Gemini.

A curiosity: the term "deep learning" was popularized by Geoffrey Hinton in 2006, but the first functional deep neural network was created by Alexey Ivakhnenko in the 1960s. Despite the name, neural networks are not as complex as the human brain: the brain has about 86 billion neurons, while the largest AI models have billions of parameters but still lack contextual understanding and common sense.

Frequently Asked Questions

What is an artificial neural network?

It is a computational model inspired by brain neurons, composed of layers of interconnected nodes that learn from data.

What is the difference between neural network and deep learning?

Deep learning is a subfield of machine learning that uses neural networks with many hidden layers to learn complex representations.

Where are neural networks used today?

In facial recognition, voice assistants, self-driving cars, medical diagnostics, content recommendations, and many other applications.