New frontiers thanks to artificial neural networks
Artificial neural networks are in part similar to traditional software since they are also created using common programming languages. However, their code is not made up of a set of fixed instructions, but is the result of a complex combination of algorithms.
These mathematical models are able to learn which outputs to return according to user inputs, which often consist of large quantities of complex or unexpected data. This reflects their capacity to adapt, perfected by the neural network following a period of training.
How artificial neural networks are built
The striking thing about artificial neural networks is the fact that they were inspired by the make-up of the human brain. In general, almost all artificial models have something in common with biological neural networks. The most frequent characteristics are, for example, the presence of:
- an entrance layer (brain neurones);
- one or more intermediate layers (synapses);
- an exit layer.
These connections are replicated during code building (which constitutes the artificial neural networks), so that they can learn and update themselves autonomously. All this is possible thanks to machine learning algorithms and training cycles based on big data sets.
Furthermore, the geometry chosen to build the connections between internal algorithms also determines the main macro categories that neural networks can belong to:
Feed-forward: includes all networks in which the connections transmit signals, following a pre-established direction that starts from the input and ends with the output;
Recursive neural networks: they can be recognised by certain mathematical models they use which are inter-connected in a loop. This type is the most used for complex data processing and predictive analysis.
Another distinction useful for cataloguing artificial neural networks more accurately, can be made using the type of learning paradigms implemented (Machine Learning). The variants are:
- supervised learning;
- unsupervised learning;
- reinforcement learning.
There is also a hybrid version of the first two solutions known as semi-supervised, which is able to exploit the advantages of both variants by combining them.
Current applications and limits of neural networks
The natural adaptability of artificial neural networks makes them suitable for application in a wide range of areas. That is why so many different sectors have started to introduce them as part of their standard operations.
One good example is their use in finance, where neural networks are exploited for their predictive ability. They are in fact able to carry out tasks such as calculating reliable risk assessment, analysing market trends and other operations on which investors base their choices.
This ability is turning out to be particularly useful in the medical field, where certain algorithms are even able to make accurate patient diagnoses. They are also widely used for data mining and to build complex simulations in similar fields.
Of particular interest is their application in the processing of images and sound; some research teams are currently working on artificial neural networks which are able to recognise people’s faces or interpret spoken language correctly.
These complex mathematical models have enormous potential, which in future could really make a difference in some sectors. Despite this however, there are still many obstacles to be overcome, such as the difficulty of algorithm programming.
Setting up a relatively accurate machine learning process which is accurate and complete is not easy, as it requires a high level of skill on the part of programmers, a vast knowledge of the data to be analysed (data scientists are crucial) and, above all, a long period of training.
Translated by Joanne Beckwith
