“Neural Networks” in Artificial Intelligence: What Do They Mean and What Is Their Role?

16th December 2024

2 minutes read

"Neural Networks" in Artificial Intelligence: What Do They Mean and What Is Their Role?

Neural networks are a fundamental component of machine learning technologies, particularly in the field of deep learning. They are mathematical models inspired by the way the human brain works.

These networks aim to simulate the processing of information and decision-making carried out in the brain through a network of neurons.

Types of Neural Networks

There are many types of neural networks used in artificial intelligence systems. Here are the most common ones:

Simple Neural Networks: These are the most basic neural networks and are used for linear data classification tasks.
Multilayer Neural Networks: These networks contain multiple hidden layers, enabling them to learn non-linear patterns.
Recurrent Neural Networks (RNNs): Used for processing sequential data such as texts or audio signals.
Transformer Networks: Employed in text processing and machine translation, they have proven effective in applications like ChatGPT.

How They Work

A neural network consists of several layers of neurons or computational units interconnected through links called weights.

The first layer (input layer) receives the initial data.
The middle layers (hidden layers) process the data and extract patterns or hidden knowledge.
The final layer (output layer) provides the final result based on the processed data.

Neural networks learn through a process called training, where they are fed data with known outcomes. During training, the network calculates the difference between the predictions made by the model and the actual results, then adjusts its parameters to minimize this difference or error.

These networks can perform various tasks such as image recognition, face detection, or object identification in images, voice recognition, speech-to-text conversion, machine translation, financial market prediction, and weather forecasting. They can also learn strategies in games or assist robots in making intelligent decisions.

In Conclusion

Neural networks are a powerful tool in artificial intelligence, enabling models to learn complex patterns from data using a structure inspired by the functioning of the human brain.

By learning the connections between neurons, these models continuously improve their performance, becoming more accurate and efficient across a wide range of applications.