How do Artificial Neural Networks Work?

As we have seen Artificial Neural Networks are made up of a number of different layers.

Each layer houses artificial neurons called units.

These artificial neurons allow the layers to process, categorize, and sort information.

Alongside the layers are processing nodes.

Each node has its own specific piece of knowledge.

This knowledge includes the rules that the system was originally programmed with.

It also includes any rules the system has learned for itself.

This makeup allows the network to learn and react to both structured and unstructured information and data sets.

Almost all artificial neural networks are fully connected throughout these layers.

Each connection is weighted.

The heavier the weight, or the higher the number, the greater the influence that the unit has on another unit.

The first layer is the input layer.

This takes on the information in various forms.

This information then progresses through the hidden layers where it is analysed and processed.

By processing data in this way, the network learns more and more about the information.

Eventually, the data reaches the end of the network, the output layer.

Here the network works out how to respond to the input data.

This response is based on the information it has learned throughout the process.

Here the processing nodes allow the information to be presented in a useful way

Educating Artificial Neural Networks

For artificial neural networks to learn they require a mass of information.

This information is known as a training set.

If you wanted to teach your ANN to learn how to recognise a cat your training set would consist of thousands of images of a cat.

These images would all be tagged “cat”.

Once this information has been inputted and analysed the network is considered trained.

From now on it will try to classify any future data based on what it thinks it is seeing.

So if you present it with a new image of a cat, it will identify the creature.

As a check, during the training period, the system’s output is matched against the description of the data it’s analysing.

What are Artificial Neural Networks Used for?

Artificial Neural Networks can be used in a number of ways.

They can classify information, cluster data, or predict outcomes.

ANN’s can be used for a range of tasks.

These include analyzing data, transcribing speech into text, powering facial recognition software, or predicting the weather.

There are many types of Artificial Neural Network.

Each has its own specific use.

Depending on the task it is required to process the ANN can be simple or very

complex.

The most basic type of Artificial Neural Network is a feedforward neural network.

This is a basic system where information can travel in only one direction, from input to output.

Online grocers Ocado are making the most of this technology.

Their smart warehouses rely on robots to do everything from stock management to fulfilling customer orders.

This information is used to power the trend of dynamic pricing.

Many companies, such as Amazon, use dynamic pricing to reproduced and increase revenue.

This application has spread beyond retail, service providers, such as Uber, even use this information to adjust prices depending on the customer.

Many retail organisations, such as Walmart, use Artificial Neural Networks to predict future product demand.

The network models analyse location, historical data sets, as well as weather forecasts, models and other pieces of relevant information.

This is used to predict an increase in sales of umbrellas or snow clearing products.

By predicting a potential rise in demand the company is able to increase stock in store.

This means that customers won’t leave empty-handed and also allows Walmart to offer product-related offers and incentives.

Recently, the Macau district in China has introduced ATM’s that are capable of reading the user’s face.

This negates the need for cards and pin numbers.

If proved to be successful it could lead to the end of paying with plastic

Meanwhile, companies such as Facefirst are developing software capable of identifying shoplifters.

When implemented this can cut loss to crime, saving money, and making stores safer.

The company is also looking to roll out its systems at airports and other public areas.

Microsoft and Nvidia are just two of the companies working with Facefirst technology.

Finally at the 2019 CES Proctor and Gamble revealed their idea of the store of the future.

Here cameras driven by Artificial Neural Networks recognize customer’s face.

The system then makes product suggestions based on the customer’s past history and information.

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