More and more leading technology companies are investing in artificial intelligence and seeing a future-oriented market here. At the same time, negative headlines about modern industrialization keep popping up: Critics warn that people are being replaced by machines even where the human factor was previously considered a constant. It is, therefore, all the more important to get an accurate picture of the technology and its possibilities. This article will go over the essential workings of a neural network. Neural networks represent part of the basic technology for the development of artificial intelligence. The roots of these methods go back to the 1960s. But as with the term artificial intelligence, there are also many forms of neural networks. We focus on how the most modern methods from neural networks work and take a closer look at them.
Before we get into the technological aspects, let’s look at the domains of artificial intelligence. Roughly speaking, the generic term artificial intelligence summarizes three areas that we use consciously or unconsciously in everyday life: machine learning, deep learning, and reinforcement learning. But let’s start at the beginning.
Artificial intelligence began in the 1950s when the first chess program was developed. At that time, only a few people had the privilege of playing a game of chess with the computer. These people usually worked in scientific institutions and secretly in their spare time converted mainframes and mainframes, which served military or scientific purposes, into an artificially intelligent opponent with the help of a chess program.
The development in the computer industry took rapid steps forward, and only 30 years later, computer games of this kind were accessible to a broad group of people. When we talk about artificial intelligence today, we don’t just mean games, but above all, we also mean mathematical methods that help us to automate processes. Algorithms are used automatically for this purpose, which brings us to machine learning.
Machine learning is just a generic term for statistical methods that can carry out classification, regression, or clustering within a data set. This can be used to detect anomalies, for example. Machine learning is divided into two major domains, supervised and unsupervised learning.
- In supervised learning, we are dealing with what is known as statistical modeling. We first build a statistical model on a historical data set and select suitable methods for the data to be analyzed to classify all identically structured new data with a statistical model. Since we have modeled the past well, we also recognize statistical pairs in the data and conclude the future. Historical data with known class labels are used for this. Both the result of a particular data constellation and the data constellation itself are thus known.
- Unsupervised learning brings order to chaos. In this case, disorder means that we neither have historical data nor, in the case of new data, know the correlations between the initial situation and the result. This is where the unsupervised methods are used, which can cluster statistically similar pairs within a large amount of data. The classification can then be carried out with supervised learning in a second step. And that again and again.
This roughly explains the term machine learning. In the analysis methods of supervised learning, we encounter, among other things, a neural network.
A neural network consists of several levels; in the example, there are three. Each group can have multiple neurons, shown here as circles. In this model, each neuron can take on a value. All neurons are interconnected, and each connection is multiplied by a weighting factor. The weighting factor automatically adjusts with each iteration in the training process. With a similar constellation of input parameters, the neural network can weight the model correctly and recognize and specify the previously trained output.
The ability to learn from errors and readjust the weights to approximate the given output makes this technique very interesting. On the other hand, the network can also be “over-learned”. The model then seems to work perfectly and recognizes every detail that has been trained. However, when the model is confronted with new data, the classifications are no longer correct, and the results are incorrect. Another disadvantage of such models is that one does not know precisely how the weighting is distributed and which neurons will later influence the classification. Given an input and an output produced by the network, we cannot infer the input elements and therefore do not know how the result came about. Something happens, and after that, it works pretty well in some suitable areas. For example, noise can be filtered out of signals, or the pH value of water can be determined based on different measured values.
Also Read: Artificial Intelligence In The Digital World