Artificial Neural Networks: understanding the fundamentals of a technological revolution

Introduction
Technological progress, as often happens, draws inspiration from nature and its structures. By observing the behavior and functioning of living beings, humans have been able to replicate successful evolutionary patterns and models in multiple contexts. This strategy has proven particularly effective in artificial intelligence, where the imitation of human intelligence has led to the creation of artificial neural networks, technologies capable of learning, recognizing complex patterns, and making decisions automatically.
With the development of artificial neural networks (ANN – Artificial Neural Networks), a significant step has been taken toward replicating the human brain’s functioning, creating systems that can learn from data, improve over time, and operate in real-world contexts with increasing accuracy and complexity. This article explores the basics of neural networks, highlighting the principles of their operation, main derived technologies, and applications in industrial and scientific contexts.

The origin of artificial Neural Networks
Contrary to popular belief, neural networks are not a recent innovation. The first theoretical models of neural networks were developed in the 1940s by Warren McCulloch and Walter Pitts, who laid the foundation for a system inspired by biological neurons. Despite the pioneering insight, computing technology and data availability were still limited, and it was only in the 1980s, with the introduction of the backpropagation algorithm, that the first practical applications could begin to be explored.
However, the true development of neural networks exploded with the technological advancements of the 21st century, thanks to the availability of large amounts of data (big data) and increased computational power. These elements have enabled the creation of deep and complex neural networks (Deep Neural Networks) capable of addressing problems in areas ranging from image recognition to natural language understanding.

Structure and functioning of a Neural Network
Artificial neural networks are based on computing units called “neurons,” organized into layers. Generally, a neural network is structured in three main levels:

1. input layer: receives initial data and transmits it to the next layer.
2. hidden layers: perform most of the processing operations, executing intermediate data transformations through activation functions.
3. output layer: returns the final result of the process based on the information processed.

Each neuron receives one or more inputs, processes them through an activation function, and generates an output that is passed to the neurons in the next layer. Through this flow, a neural network can extract relevant features from input data, learn relationships, and produce responses that simulate the behavior of human cognitive processes.
One of the cornerstones of neural network functioning is backpropagation, which involves updating the weights associated with each neuron based on prediction error, allowing the network to “learn” and improve its performance. This iterative learning process optimizes results, adapting to different data and objectives.

Technologies based on Neural Networks
Today, neural networks have diversified, giving rise to specific technologies for different types of problems. Some of the most advanced include:

• Convolutional Neural Networks (CNNs): Designed for image recognition, CNNs find applications in fields such as computer vision, including facial recognition, medical imaging diagnostics, and video analysis. CNNs can extract high-level features from images and are particularly effective at detecting visual patterns.
• Recurrent Neural Networks (RNNs): Optimized for processing sequential data like text or time series, RNNs are particularly useful in areas such as speech recognition, automatic translation, and time-series analysis.
• Transformers: This architecture has revolutionized Natural Language Processing (NLP), enabling the understanding and generation of natural language. Models based on Transformers, like BERT and GPT, can analyze and respond to natural language commands with high accuracy, facilitating the development of advanced chatbots, virtual assistants, and translation systems.

The learning process in Neural Networks
Training is the fundamental phase in which a neural network learns to perform a specific task, and several approaches can be used, including:

• supervised learning: The network learns from labeled examples, meaning data sets in which inputs are associated with a desired output. This type of learning is the basis of many classification and prediction systems, such as image recognition or automatic diagnosis based on clinical data.
• unsupervised learning: Here, the network autonomously seeks to identify patterns in unlabeled data, useful for exploring large amounts of information and identifying hidden trends.
• reinforcement learning: The network learns through a system of rewards and penalties, typical in contexts requiring progressive adaptation. This approach is the basis of artificial intelligence systems that play chess, Go, or other rule-based games, where an agent learns to maximize successes by minimizing errors.

During the training process, the backpropagation algorithm progressively adjusts the network’s weights. The typical sequence of phases includes weight initialization, forward propagation of data, error calculation, and backward propagation, during which the weights are adjusted until an optimal result is achieved. In this way, the network “learns” from errors, improving its accuracy and predictive ability.

Applications of Neural Networks in business and industry
Artificial neural networks now have extensive and diversified applications, becoming a pillar of artificial intelligence in modern companies. Among the main uses are:

• medical diagnostics and bioinformatics: Neural networks support the analysis of medical images, disease prediction, and genetic research, speeding up diagnostic processes and improving personalized care.
• finance and trading: Used to analyze complex data flows, neural networks enable the identification of trends, market predictions, and the development of automated trading systems.
• marketing and customer insights: By analyzing customer data, neural networks allow companies to anticipate consumer needs, personalize marketing campaigns, and improve customer satisfaction.
• industrial automation: In managing production plants, neural networks can optimize processes, predict maintenance needs, and monitor workflows in real-time.

Conclusion
Understanding the basics of artificial neural networks provides a key to navigating the current technological landscape and assessing the potential of artificial intelligence. Although it is not necessary to know all the technical details to use a machine learning system, a basic understanding helps in comprehending how these systems “think” and learn.
Neural networks are revolutionizing data management and automation capabilities in many sectors. For companies, adopting neural network-based technologies represents not only a step forward in innovation but also a way to improve operational efficiency, product quality, and the services offered, adapting quickly to the dynamics of an increasingly competitive and complex market.