Introduction to AI and Artificial Neural Networks (ANNs)

A computer is superior to the human brain in many respects. For example, it generally calculates much faster and more accurately than a human being. Computer-assisted information processing has very specific advantages over human capabilities. A computer can process, calculate and store data much faster and more accurately than a human being, and retrieve it at any time. Human memory is incomplete, and some stored information is eventually forgotten.

The term artificial intelligence (AI) was first coined in 1956 as part of a scientific research project (the Dartmouth Summer Research Project on Artificial Intelligence).

Artificial neural networks consist of a network of neurons characterised by a large number of connections between them. The strength of the connection between individual neurons in the neural network is determined by weights. When training the network, these weights are adjusted until a specific input produces the desired output.

Computing power is a crucial factor in the application of artificial neural networks. On the one hand, the computing power of computer systems has improved considerably in recent years; on the other hand, specific advances have also been made regarding the architecture required for neural networks.

In recent years, the volume of data worldwide has experienced explosive growth. The amount of data generated globally has risen remarkably; today, more than two and a half times as much data is generated globally as was the case five years ago.

Machine learning (ML) is a subfield of artificial intelligence (AI) in which algorithms learn patterns from data to make predictions or decisions, rather than following hard-coded rules. Instead of rigid rules, machines analyse large amounts of data, identify patterns, relationships and structures within it, and improve their performance based on these experiences. Artificial neural networks (ANNs) are a specific type of machine learning.

Artificial neural networks (ANNs) are modelled on the structure of the human brain and have seen a huge surge in popularity in recent years. In many applications, they simulate mental processes; however, the way their various elements function is often not based on the model of natural neurons. In most applications, what matters is not the similarity to the natural biological model, but rather the effective and efficient use of methods for data processing and analysis.

Artificial neural networks (ANNs) consist of interconnected ‘neurons’ arranged in layers. They form the backbone of machine learning by recognising complex patterns in large amounts of data, thereby enabling numerous applications. Artificial neural networks are based on a biological model and represent an abstraction of that model. However, they are not replicas of biological neural networks.

An artificial neural network consists of several layers of artificial neurons:

The input layer is the first layer of the neural network. It receives the raw data. Each neuron in this layer represents a feature of the input.

The hidden layers are the layers between the input and output layers. An artificial neural network can have one or more hidden layers, with the neurons in these layers often connected to all the neurons in the previous layer. The artificial neurons in the hidden layer enable the network to filter out increasingly complex and abstract features.

The output layer provides the final result. Depending on the task, the output may be, among other things, a single number (regression) or probabilities for different classes (classification).

A feedforward network (FNN) is the simplest and most basic type of artificial neural network. In this architecture, information flows in only one direction (from the input layer through one or more hidden layers to the output layer). There is no feedback, which means that the output of one layer is simply passed on to the next layer. Feedforward networks are suitable for tasks where the inputs are independent of one another and have no specific order. Feedforward networks have no memory.

A recurrent neural network (RNN) is a special type of neural network suited to processing sequential or time-dependent data. Unlike feedforward networks, RNNs have feedback connections that allow them to pass information from previous steps to the current step, giving the network a kind of short-term memory (though no long-term memory and therefore no ability to recognise long-term dependencies). These networks are well suited to time series forecasting (such as forecasts based on financial market data).

A neuron is a processing unit that aggregates the values received via weighted connections and determines an activation state by applying an activation function.

A neural network consists of a group of neurons connected to one another by directed and weighted links. Each link between the neurons has adjustable weights. Through a process known as training, these weights are continuously optimised until the network is able to make reliable and accurate predictions.

In supervised learning, the network is trained using labelled data, where each input is associated with a correct answer. The network learns to match inputs with the correct outputs by minimising errors.

In unsupervised learning, the network is trained using unlabelled data. The network learns to recognise hidden patterns, structures or groupings in the data without being given correct answers.

In reinforcement learning, the network learns through interaction with an environment. It receives rewards or penalties for its actions and attempts to maximise the total reward over time.

In transfer learning, a model that has already been trained on a dataset for a specific task is reused and then adapted to a new, related task. Instead of training a neural network from scratch, a pre-trained network is used and further refined using a smaller, specific dataset.

In the past, various AI techniques often proved to be nothing more than short-lived trends. The difference today is that many AI applications have reached market maturity or have proven themselves in tests and in practice. AI is already a useful tool and is finding its way into various sectors of the economy on an ever-increasing scale and at an ever-increasing pace.

The great flexibility of neural networks can prove to be both an advantage and a disadvantage. It is important to note that, under certain conditions, an algorithmic approach may prove to be more advantageous. If you see a need within your company to implement your own neural networks, or would like to explore this further, please feel free to contact us without obligation. We will carry out the necessary assessments for you and, if required, build the networks and train the models accordingly. Following model development, we routinely conduct an appropriate assessment of model quality and offer support with training and implementation.