In the past ten years, neural networks have moved into the public consciousness due to the discussions around artificial intelligence. However, the basic technology has already been around for many decades.
Talk of artificial neural networks can be dated back to the early 1940s. Back then, Warren McCulloch and Walter Pitts described a model that linked together elementary units and was based on the structure of the human brain. It was supposed to be able to calculate almost any arithmetic function. In 1949, Donald Hebb developed Hebb’s rule which is still used in many neural networks today.
In 1960, a neural network was developed which had worldwide commercial use for echo filtering in analog telephones. After that, research in the field ground to a halt. One reason for this was that leading scientists had come to the conclusion that the neural network model could not solve important problems. Another was that effective machine learning requires large amounts of digital data which was not available at the time. It was not until the emergence of big data that this situation changed: interest in artificial intelligence and neural networks was revived.
Since then, this field has seen tremendous growth. While the results are promising, neural networks are not the only technology to implement artificial intelligence in computers. They are just one possibility, even if they are often presented as the only viable option in the public discourse.