Artificial neural networks – How does technology learn from data?

Artificial neural networks – How does technology learn from data?

September 6, 2023

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Gone are the years when artificial intelligence was just fiction. With technology being improved at high speed, fiction became reality and it all started with the development of artificial neural networks.

In 1943, the world acknowledged, for the first time, the prototype of an artificial neural network, created by Warren McCulloh and Walter Pitts. In 1958, however, Frank Rosenblatt developed Percepton, which stored information in the same way as the human brain through a probabilistic model, although far from what exists today. Later, in 1975, Kunihiko Fukushima developed a true multilayer neural network.

At that time, the intention was to create a technological system that would work similarly to the human brain, mainly in solving problems and performing specific tasks. However, for these networks to be able to reproduce human logic, they have to learn and retain information as the human brain does.

In this article we will review what artificial neural networks are, how they work, their different types, and what are their real applications.


Table of Contents:

What are artificial neural networks?


What are artificial neural networks?

Just like the human brain, artificial neural networks have the capacity to acquire, organize and retain knowledge. Artificial neural networks are then a subcategory of Machine Learning technology, which was inspired by the functions and structure of the human brain.

As it is a computational technique, it uses a mathematical model to create layers of nodes that communicate with each other, process data and establish relationships between them, just like human neurons and neurotransmitters.

These nodes, or artificial neurons, which make up artificial neural networks, are able to learn and improve their own performance through the data to which they are exposed. In this way, algorithms are created, and the neural network is subjected to training.


How do artificial neural networks work?

In order for it to respond as expected to a question or request, input and output data (or, simply put, the “question” and the “answer”) are provided. Later, the neural network begins to identify patterns and structures and begins to respond to data input on its own.

To form an artificial neural network, there must be an input layer, an output layer and other hidden layers in between to process the information and extract the most important details. The more hidden layers there are in the neural network, the more capacity to retain knowledge and to respond correctly it will have.

The information enters the network through the input layer and travels from layer to layer in an ascending movement until the answer comes out in the output layer. In this process, called Forward Propagation, it is possible to control parameters such as synaptic coefficients and trends. Thus, these two parameters are adjusted so that the neural network predicts the response as accurately as possible.

Another algorithm widely used for the operation of neural networks is called Back Propagation. This algorithm calculates the error variance and adjusts the synaptic coefficient automatically.


Types of artificial neural networks

Currently there several types of neural network in operation, which later turns into Artificial Intelligence software, for example.

The most basic neural network is the one discussed in the section above and is called Forward Propagation, which has between one and three hidden neural layers. Within the basic neural networks, the Radial Artificial Neural Network distinguishes itself due to its fast-learning capacity with only three layers. It is mainly used for non-linear classification, function approximation and time series forecasting.

In contrast to basic networks, deep learning networks are those in which there are more than three hidden layers through which data is processed and recognized. These have the ability to apprehend information and process it in depth, managing to respond to data input more completely.

The Autoencoder neural network, by its turn, is a network that retains knowledge without being supervised. This reduces the size of the data and extracts the most important features. Finally, the information that entered the first layer passes through several hidden layers until it is reconstructed in the output layer.

Another common neural network is Feedforward, that only processes information from the input layer to the output layer.

Convolutional Artificial Neural Networks are also commonly used. They recognize objects and images by reducing their size. As in Autoencoder networks, convolutional networks extract the most relevant features of these images and objects and pass them through the various layers to end up being reconstructed in the output layer.

Recurrent Neural Networks, in turn, process more complex data sequences such as video, audio and text and then transmit them from one stage to another. Within recurrent neural networks, Long Short-Term Memory networks stand out within this type, which selectively discard or retain information, avoiding the gradient problems that recurrent networks can have. Long Short-Term Memory artificial neural networks are typically used for translation, voice and speech recognition, and time series forecasting.

Finally, Modular Artificial Neural Networks are networks composed of several other smaller networks. Each of these responds to a part of the problem, passes the information to an integrator, which delivers the final answer to the output layer. This type of complex artificial neural network is used for specific and large tasks, and normally all the networks that compose it are connected by a designer in a specific way.


Applications of artificial neural networks

Artificial neural networks are one of the technological advances that are most part of people’s and companies’ lives. The applications of these networks are almost endless and in almost all sectors, from finance to the health sector, including industry.

Because they exist as a constituent part of Artificial Intelligence software, the following are examples of neural network applications:

  • Detection of financial fraud.
  • Forecast of financial indicators.
  • Speech recognition and natural language processing.
  • Recognition of images and objects for surveillance systems.
  • Stock management through demand forecast.
  • Quality control and industrial processes.
  • Diagnosis of errors in industrial machinery.
  • Recognition of medical images for diagnostics.
  • Trend forecasting for marketing and data analysis.