Industry use cases of Neural Networks
What are Neural Networks?
Neural networks also known as artificial neural networks (ANN) are a set of algorithms, they are designed to mimic the human brain, that is designed to recognize patterns. They interpret data through a form of machine perception by labeling or clustering raw input data.
The most groundbreaking aspect of neural networks is that once trained, they learn on their own. In this way, they emulate human brains, which are made up of neurons, the fundamental building block of both human and neural network information transmission.
Let’s take a moment to consider the human brain. Made up of a network of neurons, the brain is a very complex structure.It’s capable of quickly assessing and understanding the context of numerous different situations. Computers struggle to react to situations in a similar way. Artificial Neural Networks are a way of overcoming this limitation.
First developed in the 1940s Artificial Neural Networks attempt to simulate the way the brain operates.Sometimes called perceptrons, an Artificial Neural Network is a hardware or software system.
Consisting of a network of layers this system is patterned to replicate the way the neurons in the brain operate.
The network comprises an input layer, where data is entered, and an output layer.The output layer is where processed information is presented.Connecting the two is a hidden layer or layers.
The hidden layers consist of units that transform input data into useful information for the output layer to present.
In addition to replicating the human decision making progress Artificial Neural Networks allow computers to learn.Their structure also allows ANN’s to reliably and quickly identify patterns that are too complex for humans to identify.Artificial Neural Networks also allow us to classify and cluster large amounts of data quickly.
Tasks Neural Networks Perform
Neural networks are highly valuable because they can carry out tasks to make sense of data while retaining all their other attributes. Here are the critical tasks that neural networks perform:
- Classification: NNs organize patterns or datasets into predefined classes.
- Prediction: They produce the expected output from given input.
- Clustering: They identify a unique feature of the data and classify it without any knowledge of prior data.
- Associating: You can train neural networks to “remember” patterns. When you show an unfamiliar version of a pattern, the network associates it with the most comparable version in its memory and reverts to the latter.
Artificial Neural Networks are Improving Marketing Strategies.
By adopting Artificial Neural Networks businesses are able to optimize 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.
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 tumor 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.
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 behavior 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.
Facial Recognition Software
Technology companies have long been working toward developing reliable facial recognition software.
One company leading the way is Facebook.
For a number of years now they have been using the facial recognition technology to auto-tag uploaded photographs.They have also developed DeepFace.
DeepFace is a form of facial recognition software-driven by Artificial Neural Networks.
It is capable of mapping 3D facial features.Once the mapping is complete the software turns the information into a flat model.The information is then filtered, highlighting distinctive facial elements.
To be able to do this DeepFace implements 120 million parameters.
This technology hasn’t just emerged overnight.DeepFace has been trained with a pool of 4.4 million tagged faces.
These images were taken from 4,000 different Facebook accounts.
During the training process, tests were carried out presenting the system with side-by-side images.The system was then asked to identify if the images are of the same person.
In these tests, DeepFace returned an accuracy rating of 97.25%.
Human participants taking the same test scored, on average, 97.5%.
Facebook has also taken its software to computing and technology conferences.
This is done with the purpose of allowing academics and researchers to assess and inspect the technology.With all this work it’s little wonder that DeepFace may be the most accurate facial technology software yet developed.
Paying With Your Face
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.
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.
Deep neural networks are responsible for some of the greatest advance in modern computer science.
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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.