INDUSTRIAL USES CASES OF THE NEURAL NETWORKS
Artificial neural networks are a form of deep learning.
They are also one of the main tools used in machine learning.
Consequently ANN’s play an increasingly important role in the development of artificial intelligence.
The rise in importance of Artificial Neural Network’s is due to the development of “backpropagation”.
This technique allows the system’s hidden layers to become versatile.
Adapting to situations where the outcome doesn’t match the one originally intended.
The development of deep learning neural networks has also helped in the development of Artificial Neural Networks.
Deep learning neural networks are networks made up of multiple layers.
This allows the system to become more versatile.
Different layers are able to analyse and extract different features.
This process allows the system to identify new data or images.
It also allows for unsupervised learning and more complex tasks to be undertaken.
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.
If the information is the same, the learning process is validated.
If the information is different backpropagation is used to adjust the learning process.
Backpropagation involves working back through the layers, adjusting the set mathematical equations and parameters.
These adjustments are made until the output data presents the desired result.
This process, deep learning, is what makes the network adaptive.
The network is able to learn and adapt as more information is processed.
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.
Different Types of Neural Networks
The most commonly used type of Artificial Neural Network is the recurrent neural network.
In this system, data can flow in multiple directions.
As a result, these networks have greater learning ability.
Consequently, they are used to carry out complex tasks such as language recognition.
Each network is capable of carrying out a specific task.
The data you want to enter, and the application you have in mind, affect which system you use.
Complex tasks such as voice recognition may require more than one type of ANN.
Now that we’ve established what Artificial Neural Networks are here are 10 examples of how they are currently being applied.
Artificial Neural Networks are Improving Marketing Strategies
By adopting Artificial Neural Networks businesses are able to optimise their marketing strategy.
Systems powered by Artificial Neural Networks all capable of processing masses of information.
This includes customers personal details, shopping patterns as well as any other information relevant to your business.
Once processed this information can be sorted and presented in a useful and accessible way. This is generally known as market segmentation.
To put it another way segmentation of customers allows businesses to target their marketing strategies.
Businesses can identify and target customers most likely to purchase a specific service or produce.
This focusing of marketing campaigns means that time and expense isn’t wasted advertising to customers who are unlikely to engage.
This application of Artificial Neural Networks can save businesses both time and money.
It can also help to increase profits.
The flexibility of Artificial Neural Networks means that their marketing applications can be implemented by most businesses.
Artificial Neural Networks can segment customers on multiple characteristics.
These characteristics can be as diverse as location, age, economic status, purchasing patterns and anything else relevant to your business.
One company making the most of this flexibility is cosmetics brand Sephora.
The email marketing campaign is tailored to the interests of each customer on the mailing list.
This allows them to offer a seamless, targeted marketing campaign.
This approach means that at a time when many companies are struggling Sephora is flourishing.
Developing Targeted Marketing Campaigns
Through unsupervised learning, Artificial Neural Networks are able to identify customers with a similar characteristic.
This allows businesses to group together customers with similarities, such as economic status or preferring vinyl records to downloaded music.
Supervised learning systems allow Artificial Neural Networks to set out a clear aim for your marketing strategy.
Like unsupervised systems, they can also segment customers into similar groupings.
However supervised learning systems are also able to match customer groupings to the products they are most likely to buy.
This application of technology can increase profits by driving sales.
Starbucks has used Artificial Neural Networks and targeted marketing to keep customers engaged with their app.
The company has integrated its rewards system location and purchase history on their app.
This allows them to offer an incredibly personalised experience, helping to increase revenue by $2.56 billion.
Reducing Email Fatigue and Improving Conversion Rates
By only advertising relevant products to interested customers, you also reduce the chances of customers developing email fatigue.
In short, if your advertisements are relevant and interesting customers are more likely to interact.
This drives visits to your website, potentially increasing sales, and helps you to build a strong client-business relationship.
Applying Artificial Neural Networks in your marketing strategy can save your company both time and money.
By streamlining your marketing approach in this way you will only be targeting the customers most likely to purchase your product.
This streamlined approach of targeting the people most likely to engage can help to increase sales and profits.
Many companies who have adopted targeted or personalised marketing strategies have noticed clear, positive results.
For example, stationery retailers Paperstyle segmented their subscribers into two different groups.
Each group then received targeted emails.
Consequently, the business reported an open rate increase of 244%.
The traffic driven from emails to the website also increased by 161%.
These statistics show that personalised marketing campaigns can deliver real results, benefiting businesses.
Improving Search Engine Functionality
During 2015 Google I/O keynote address in San Francisco, Google revealed they were working on improving their search engine.
These improvements are powered by a 30 layer deep Artificial Neural Network.
This depth of layers, Google believes, allows the search engine to process complicated searches such as shapes and colours.
Using an Artificial Neural Network allows the system to constantly learn and improve.
This allows Google to constantly improve its search engine.
Within a few months, Google was already noticing improvements in search results.
The company reported that its error rate had dropped from 23% down to just 8%.
Google’s application shows that neural networks can help to improve search engine functionality.
Similar Artificial Neural Networks can be applied to the search feature on many e-commerce websites.
This means that many companies can improve their website search engine functionality.
This allows customers with only a vague idea of what they want to easily find the perfect item.
Amazon has reported sales increases of 29% following improvements to its recommendation systems.
Applications of neural networks in the pharmaceutical industry
Artificial Neural Networks are being used by the pharmaceutical industry in a number of ways.
The most obvious application is in the field of disease identification and diagnosis.
It was reported in 2015 that in America 800 possible cancer treatments were in the trial.
With so much data being produced, Artificial Neural Networks are being used to help scientists efficiently analyse and interpret it.
The IBM Watson Genomics is one example of smart solutions being used to process large amounts of data.
IBM Watson Genomics is improving precision medicine by integrating genomic tumour sequencing with cognitive computing.
With a similar aim in mind, Google has developed DeepMind Health.
Working alongside a number of medical specialists such as Moorfields Eye Hospital, the company is looking to develop a cure for macular degeneration.
Developing Personalised Treatment Plans
A personalised treatment plan can be more effective than adopting a standardised approach.
Artificial Neural Networks and supervised learning tools are allowing healthcare professionals to predict how patients may react to treatments based on genetic information.
The IBM Watson Oncology is leading this approach.
It is able to analyse the medical history of a patient as well as their current state of health.
This information is processed and compared to treatment options, allowing physicians to select the most effective.
MIT’s Clinical Machine Learning Group is advancing precision medicine research with the use of neural networks and algorithms.
The aim is to allow medical professionals to get a better understanding of how disease forms and operates.
This information can help to design an effective treatment.
The team at MIT are currently working on possible treatment plans for sufferers of Type 2 Diabetes.
Meanwhile, the Knight Cancer Institute and Microsoft’s Project Hanover is using networks and machine learning tools to develop precision treatments.
In particular, they are focusing on treatments for Acute Myeloid Leukemia.
Vast amounts of information and data are required to progress precision medicine and personalised treatments.
Artificial Neural Networks and machine learning tools are able to quickly and accurately analyse and present data in a useful way.
This ability makes it the perfect tool for this form of research and development.
Neural Networks in the Retail Sector
As we have noted, Artificial Neural Networks are versatile systems, capable of dealing reliably with a number of different factors.
This ability to handle a number of variables makes Artificial Neural Networks an ideal choice for the retail sector.
For instance, Artificial Neural Networks are, when given the right information, able to make accurate forecasts.
These forecasts are often more accurate than those made in the traditional manner, by analysing statistics.
This can allow accurate sales forecasts to be generated.
In turn, this information allows your businesses to purchase the right amount of stock.
This reduces the chances of selling out of certain items.
It also reduces the risk of valuable warehouse space being taken up by products you are unable to sell.
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.
Applications to Encourage Repeat Custom
As well as monitoring and suggesting purchases, Artificial Neural Network systems also allow you to analyse the time between purchases.
This application is most useful when monitoring individual customer habits.
For example, a customer may buy new ink cartridges every 2 months.
Systems powered by Artificial Neural Networks can identify and monitor this repeat custom.
You can then contact your customer and remind them to buy when the time to purchase the product approaches.
This friendly reminder increases the chances of the customer returning to your store to make their purchase.
Retailers that offer loyalty schemes are already taking advantage of this.
Beauty brand Sephora’s Beauty Insider program records every purchase a customer makes.
It also records how frequently these purchases are made.
This information allows the company to predict when a customer’s products may be running low.
At this point the company sends a “restock your stash” email, prompting the customer to make a repeat purchase.
This information can also be used to develop a personalised marketing approach offering incentives or discounts.
Keeping Customers Loyal to Your Company
Artificial Neural Networks can also identify customers likely to switch to a competitor.
By knowing which customers are most likely to defect you are able to target them with tailored marketing campaigns.
Offering incentives, or friendly reminders about your company, will encourage customers to stick around.
This predictive use of Artificial Neural Networks is already benefiting FedEx.
Forbes reports that FedEx can predict which customers are likely to leave with an accuracy of 60–90%.
By applying Artificial Neural Networks in this way we can enhance and personalise the consumer’s experience.
Encouraging repeat custom and helping to build a relationship between your business and your customers.
Artificial Neural Networks in Financial Services
When it comes to AI banking and finance, Artificial Neural Networks are well suited to forecasting.
This suitability largely comes from their ability to quickly and accurately analyse large amounts of data.
Artificial Neural Networks are capable of processing and interpreting both structured and unstructured data.
After processing this information Artificial Neural Networks are also able to make accurate predictions.
The more information we can give the system, the more accurate the prediction will be.
Forecasting Market Movements
Over a 2 year period, MJ Futures reported a 199.2% returns due to their use of neural network prediction methods.
LBS Capital Management has also reported positive results with a simplified neural network.
Their model uses 6 financial indicator inputs such as the average directional movement over the previous 18 days.
As networks become more advanced and are fed more detailed information, their predictions will only become more accurate.
READ MORE — 10 Applications of Machine Learning in Finance
Improving the way Banks Operate
The forecasting ability of Artificial Neural Networks is not just confined to the stock market and exchange rate situations.
This ability also has applications in other areas of the financial sector.
Mortgages, overdrafts and bank loans are all calculated after analysing an individual account holders statistical information.
Traditionally the software that analysed this information was driven by statistics.
Increasingly banks and financial providers are switching to software powered by Artificial Neural Networks.
This allows for a wider analysis of the applicant and their behaviour to be made.
Consequently, this means that the information presented to the bank or financial provider is more accurate and useful.
This allows the bank to make a better-informed decision that is more appropriate to both themselves and the applicant.
Forbes revealed that many mortgage lenders expect this application of systems powered by Artificial Neural Networks will boom in the next few years.