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How to Implement Neural Networks in Business and Enterprises?

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Alexander Safonov
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In the recent fifteen years, neural networks (artificial neural networks, ANNs) developed from a draft technology to the most promising tool that can sophisticate all human activity processes, from logistics optimization and forecasting the demand to drawing paintings and playing chess. The experts claim that the global neural network market will grow from $14.35 billion in 2020 to $152.61 billion by 2030. CAGR will reach 26.7% yearly. Governments and businesses understand the ANNs’ advantages and strive to implement them, optimize their processes, and outperform competitors.

This article will tell what neural networks are and how they can help your business. Also, it will provide instructions on how to implement neural networks in the business processes of your company or enterprise. 

What are neural networks?

Neural networks (NNs) are one of the artificial intelligence options; namely, these algorithms can imitate human brain activity. Neural networks employ unique mathematical models to reproduce human brain neurons' structure, interconnection, and functions of human brain neurons. Hence, the computer can learn and make conclusions. These networks can follow algorithms and formulas or use their former experience. 

Usually, the architecture of a neural network has three or more units: input, output, and one or more hidden units. Moreover, each unit has artificial neurons (computing blocks). Each digital neuron processes input unit data does straightforward computing, and passes it to another neuron. 

The most typical neural network architecture

The most typical neural network architecture
These three (or more) layers of NNs mimic the essentials of the human brain, performing all sorts of parallel calculations to predict some Y value or set of Y values for X values (much faster and more accurate than our brain).
  • Input. It functions likewise dendrites in the human brain. It is a set of data in artificial neural networks for making forecasts.
  • Hidden unit. This layer is similar to the cell body; it sits between the input and output units, like the synaptic connections in the brain. In NNs, the hidden unit is where the artificial neurons work with the data transformed by the previous layers based on the synaptic weight, which represents the amplitude or strength of the connection between nodes.
  • Output. The transfer function applied to this data creates the result. This is what you and your clients will see; the final forecast made by NNs.
A neural network takes a massive data set, splits it into tiny fragments, and distributes them to the units. Artificial neurons receive the pieces and somehow process them (no one yet understands how), then give the result. It will consider the quality of the result; the network discards poor outcomes and computing, while good-quality ones help the network learn and improve. Hence, the neural network reduces wrong computing and increases right ones, thus minimizing errors.  

The learning process scheme of a neural network

The learning process scheme of a neural network. Source

Basically, the learning process of artificial neural networks is likewise to how children learn, namely, try and fail (sometimes the teacher will help to understand the quality of the result). NNs algorithms randomly pick various solutions to find the most efficient one and then sophisticate it till it reaches an acceptable performance. 

Neural networks capabilities

Theoretically, neural networks can solve any task if you have enough actual data or resources for synth data to teach them. 

Self-arrangement. Neural networks can group and classify massive data volumes; therefore, they are a perfect tool for complex issues that require arranging and structuring data. 

Predictions. Predicting various processes: weather, exchange rates, traffic, sales, treatment efficiency, etc., is the most popular employment for neural networks. NNs can efficiently process massive data volumes for forecasting and defining unusual correlations. Moreover, neural networks function several-fold faster than people, a significant advantage in stocks and currency trading markets. 

Symbol and image recognition. Neural networks can process data and extract specific values and variables. It is perfect for recognizing signs, images, music, videos, and others. Neural networks can identify static data and create complex models to search for variable data, for instance, to detect people in my walk manner.

Collecting and analyzing information. Neural networks can efficiently analyze data; they make valuable data from unprocessed parameters. It can search for particular patterns, such as when the world will start the subsequent influenza outbreak, or get a photo of a black hole in our galaxy (even though it is hidden behind the nebulae and stars). 

Flexible learning. Neural networks do non-linear and complex interactions and use a former experiences like humans; therefore, NNs can learn and adjust to external conditions.

Fault tolerance. The other considerable advantage of artificial networks is performing even if one or several ANNs fail. Employing neural networks in critical systems that must work 24/7 without faults is beneficial. For example, equipment will inevitably fail in space exploration, yet NNs will function. 

Employing neural networks in business 

eCommerce. The most promising sector of implementing neural networks in business is eCommerce; NNs help to increase sales. Neural networks allow intelligent chatbots, recommendation systems, automated marketing tools, social eves-dropping systems, and many others.

Decent examples of neural network implementation are recommendation personalization at Amazon, Walmart, Google Play, and other marketplaces. These systems analyze former user behavior, purchases, and similar products to those the user viewed earlier and provide the most appropriate recommendations and discounts for a particular user.

PixelDTGAN is also a remarkable example. This application allows sellers to save funds on photograph services. PixelDTGAN neural networks automatically photograph models' clothes and create collages for online shop showcases. The sellers only have to change the photo size to 64 * 64 after PixelDTGAN NN. 

PixelDTGAN work examples

PixelDTGAN work examples

Retail. Neural networks will help this industry forecast demand. Furthermore, the forecasts will be much more accurate than human ones. Businesses will save on purchases, transportation services, and storage for goods for which demand will fall. Also, it will increase the sales ratio since the buyers will get the product right when needed. 

Moreover, artificial intelligence can replace the staff in retail shops to optimize them. Walmart's smart offline shop in Levittown is an explicit example. Artificial intelligence employs CCTV in real time to track particular products on the shelves and their expiration date. Not only that, but Walmart AI notifies the sellers when they have to resupply and prevents thefts. 

Finances and banking. Neural networks predict the markets and search for fundamentals and other patterns. Moreover, NNs identify, predict, and prevent fraud. For instance, SAS Real-Time Decision Manage software helps banks to find a solution for businesses whether to issue a loan to a particular client by analyzing the risks and potential income. Finprophet employs NN to forecast a broader range of financial instruments like fiat currencies, cryptocurrencies, stocks, and futures. 

The other case of neural network employment for preventing fraud. The bank created AI to identify and prevent fraudulent transactions. The artificial neural network uses a massive database with millions of user transactions and shows excellent results. 

Banks broadly employ neural networks to automatize repetitive and frequent tasks; hence, they reduce the chance of human errors and boost the process since the staff can focus on other methods. Ernst&Young claimed to minimize the expenses on these tasks by 50-70% with the help of the neural network. JPMorgan Chase uses artificial neural networks to collect and analyze data, follow KYC, and document flow.

Security of computer systems. Neural networks successfully fight online fraud, identify and eliminate malicious software and spam, moderate content, and fight DDoS attacks and other cyber threats. For example, ICSP Neural by Symantec finds and removes viruses and vulnerabilities of zero-day on USB devices. Also, Shape Security (F5 Networks bought this startup in 2019) provides several financial solutions for optimizing and protecting applications, especially if the organization requires hybrid or cloud storage. 

Shape Security solutions from fraud

Shape Security solutions from fraud. Source

Insurance. Insurance companies employ neural networks to forecast future loss ratios and bonus adjustments and identify fraud requirements. Allstate is an existing example; they use AI to identify accident-prone drivers and charge appropriate fees. 

Logistics. Neural networks can do everything from packing to delivering. In particular, they are perfect for counting products by photo or video, determining the best route, balancing the assembly line, assigning workplaces depending on the skill sets and experience, and finding a defect in the production line. 

For instance, Wise Systems allows the user to plan the route, track it, and adjust the delivery path in real time with the forecasting tool. ETA Windward Maritime AI by FourKites uses neural networks to optimize transport routes and forecast the delivery date.

ETA Windward Maritime AI™ is a solution for artificial intelligence that provides shippers, carriers, and 3PL the most accurate estimated arrival time for 100% of vessel transportation on any route globally

ETA Windward Maritime AI™ is a solution for artificial intelligence that provides shippers, carriers, and 3PL the most accurate estimated arrival time for 100% of vessel transportation on any route globally.Source

Health care. Neural networks can recognize the signs of disease from x-rays, blood analysis, etc., organize staff work, facilitate communication with clients, track the expiration date and storage condition of drugs, and develop medicines. 

IBM Watson artificial intelligence is health care's most famous neural network solution. They spent two years training it for actual employment. The system received millions of pages from academic magazines, medical cards, and other documents. IBM Watson can hint at the diagnosis and offer the best treatment scheme according to the patient’s complaints and anamnesis.

How Watson Works

How Watson Works.Source

Car industry. Despite optimization and automatization, neural networks help to create autopilots. For instance, Tesla employs NNs to recognize road markings and obstacles and plan safe routes. 

The sight of Tesla autopilot

The sight of Tesla autopilot. Source

Online cinemas and video streaming.  Neural networks make recommendation lists on YouTube based on your earlier views and reaction (duration, likes, subscription, comments, adding to favorites, etc.) Netflix employs a similar NN and a solution for improving the video quality; if your connection is poor, the AI analyses each scene compresses it, and provides a quality image. 

Call centers. Artificial neural networks perfectly classify and distribute client queries and allow voice and chatbots to communicate with clients like humans. If you message or call technical support, the neural network analyses the data (text, context, image, sounds) and provides a solution for your issue. 

The implementation process of neural network solutions in the business

The implementation process of neural network solutions in the business.Source

The integration process of artificial intelligence on neural networks into an enterprise requires data, characteristics, and algorithms. 

Data. Teaching a neural network requires massive data volumes. For instance, to prepare a network to recognize people in photos or count containers in a warehouse, it is necessary to provide many images of people or warehouses with containers. Hence, the developer asks the client whether they have the data set or can collect it. They can buy or synthesize data if there is no such opportunity: the more data, the better.

The business aim will determine the set of required data. If you want a system for identifying emails (spam, clients, partners, etc.), you will need hundreds of thousands of emails. If you need a system to control the rational distribution of the workforce, you need data on employees and their performance in all their positions. Suppose you require a recommendation system for an online store. In that case, you will need data on past purchases, site behavior, and reactions for the individual user, your store, and the market to recommend trending things.

In short, you will first need data to train a neural network and then to integrate a neural network into a business or enterprise.

And there are two rules you need to follow when collecting this data:

  • The more, the better — it will boost the learning process and increase the neural network accuracy and efficiency.
  • Trash on the input provides trash on the output. You must use quality, relevant, utmost complete data. 
Characteristics. The next essential step of neural network integration to business processes are the characteristics, criteria, or indicators that allow the neural network to track the efficiency of its work and thus develop. For instance, AI solutions in eCommerce may take the number and frequency of purchases, average checks, etc., as the success indicator that will show how efficiently the system interacts with the consumers. In a perfect world, a neural network will trace the data in real-time to understand what leads the buyer to purchase or leave the website. Also, following the rules above about garbage data and volumes is crucial.

Algorithms. When you have the data for training your neural network, and you have decided on the features that will allow you to evaluate its effectiveness, you can begin to choose a method for solving the business problem. This method determines the speed and accuracy of the result of initial data processing, the "teachability" of the neural network, and, ultimately, its effectiveness/accuracy.

The easiest way, in this case, is to take a ready-made neural network (or rather a library that allows you to model and create neural networks) and train it to solve your business task. There are a lot of such libraries: NeuroLab, ffnet, SciPy, TensorFlow, Scikit-Neural Network, Lasagne, pyrenn, NumPy, Spark MLlib, Scikit-Learn, Theano, PyTorch, Keras, Pandas, and others.

Final thoughts 

Neural networks are in the vanguard of advanced technologies. The Gartner report says that in recent years, the employment of neural networks in business grew 270%, and the process is unlikely to cease. The technology provides considerable competitive advantages. If you want to be a part of the trend and implement neural networks in your business, please contact us, and we will share details on how to do it. 


How to Implement Neural Networks in Business and Enterprises? key takeaways:
  1. What are neural networks?
  2. Neural networks capabilities
  3. Employing neural networks in business 
  4. Final thoughts 
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