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  • Philip Osadebay - Tech Journalist

Artificial Neural Network Explained: How does it work, and why should you use it?

This story uncovers the role of artificial neural networks in creating algorithms.Artificial neural networks (ANN) are at the core of many computing systems. They operate with a close resemblance to the biological orchestration found in human brains. Artificial intelligence cuts through almost every computing system, changing the narrative and delivering immense digital transformation.

ANN are so powerful that they can organise complex numerical arrangements using a structure of connected “neurons” to “deep learn” and master unique patterns without human assistance. One famous example of ANN is its use in identifying objects in images. For instance, a system of neurons is constructed to identify the image of animals. The ANN learns from a set of data that constitute the image labeled as “cat” so that the label can be used as a reference point in future analysis.

We can compare this to how the human brain identifies pets based on their distinctive features, such as tongue, fur, and legs, breaking each image down into its

components, shapes, and colours.

This story will explain what you need to know about Artificial neural networks.

How does an Artificial neural network work?

As we discussed, ANN adapts its idealogy from the neurological operation of the human brain. Like the human brain, neurons are connected via a complex synapse, and ANN works similarly. This means that the connection of one neural node can ultimately affect the processes of other nodes within the network. Artificial intelligence has pushed the boundaries of neural networks beyond the conventional use of pre-defined processes and regular human intervention to obtain results.

ANN analyze data and spreads them across a net of several interconnected layers of nodes, just like a fishing net design. Once data is contextualised, it is passed along to the next node and moves across the layers so that ANN can build on existing data. This happens so that based on the already existing analytical load, the artificial neural networks can efficiently process and contextualise the following data entering the interconnected layers of nodes.

Consequently, ANN builds up the number of layers on top of each other to analyze data, resulting in an automated input and output flow of data from the first layer to the final layer at the bottom. The only difference between ANN and the human brain is that it connects nodes through a series of interconnected layers, while the human brain connects nodes using a 3D matrix system.

In the past, neural networks possessed shallow structures in which only one input and output layers were in operation. But today, with the development of AI, modern systems have a complex structure arrangement in which it possesses an input layer, multiple hidden layers (deep layers), and an output layer. This defines “deep learning,” which relates to a neural network with more than one hidden layer.

Why Should Businesses use Artificial Neural Networks?

The world is moving fast towards full-scale automation. Neural networks have the potential to learn independently. The inputs do not limit the expected results. ANN can generate inputs from existing analytical loads of data without human intervention.

They can explore shortcuts between data points to generate complex computational answers without expecting new data sources. One hallmark of ANN is the ability to process and cluster unstructured data, like audio files, video, numerical and picture data. The complex interconnected hierarchical layers of nodes give ANN the potential to analyse large amounts of unstructured data.

Examples of Neural Networks

One famous example of the neural network is its application to image recognition. ANN can improve photos' appearance when analyzing specific objects and structures. For instance, companies can use this unique algorithm to compare and differentiate between animals such as dogs and cats. Medical experts can also use it to identify abnormal growth and apoptosis in cells suffering from cancer.

In terms of the financial industry, Exchange rate authorities can deploy ANN to quickly forecast the latest exchange rates related to a country’s inflation and GDP outcomes. Also, telecommunication giants are adopting ANN to analyze network traffic in real-time to optimize routing and service quality.


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