Neural Networks and its Industrial Use Cases

One of the key parts of cutting-edge AI technology, Artificial Neural Networks (ANNs) are becoming too important and commonplace to ignore. However, Artificial Neural Networks and the role that they play can be difficult concepts to understand. In this article, I’ll explain exactly what Artificial Neural Network is and how they work and what are their use-cases.

Neural Networks

Neural networks are a set of algorithms, they are designed to mimic the human brain, which is designed to recognize patterns. They interpret data through a form of machine perception by labeling or clustering raw input data. 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.

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 analyzed 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.

Why Do We Use Neural Networks?

Neural networks are computing systems with interconnected nodes that work much like neurons in the human brain. Using algorithms, they can recognize hidden patterns and correlations in raw data, cluster and classify it, and — over time — continuously learn and improve.

Neural networks have the ability to identify anomalies. In the future, we can use them to give doctors a second opinion — for example, if something is cancer, or what some unknown problem is. And we’ll be able to provide these second opinions faster and with more accuracy.

Our first goal for these neural networks, or models, is to achieve human-level accuracy. Until you get to that level, you always know you can do better.

Types of Neural Networks:

The Neural Networks are divided into types based on the number of hidden layers they contain or how deep the network goes. Each type has its own levels of complexity and uses cases. Few types of neural networks are Feed-forward neural networks, recurrent neural networks, Convolutional neural networks, and Hopfield networks.

  • Feed-forward neural networks:
    Feed-forward neural networks are the basic type of neural networks. The information in this network travels in a unidirectional manner, that is, only from input to processing node to output. The hidden layers may or may not be present in this type, making it more explicable.
  • Recurrent neural networks:
    Recurrent neural networks are much more complex and most widely used networks. The data flows in multiple directions in this network. They store the output data of the processing nodes and learn to improve their functioning.
  • Convolutional neural networks:
    Convolutional neural networks are the ones that are popular today due to their specialty in being able to perform face recognition. They allow encoding attributes into the input, by assuming it to be an image.

Neural Network Applications

Neural networks are widely used in different industries. Both big companies and startups use this technology. Most often, neural networks can be found in all kinds of industries: from eCommerce to vehicle building.

So, let’s look at some examples of neural network applications in different areas. Mostly, in:

  • eCommerce;
  • Finance;
  • Healthcare;
  • Security;
  • Logistics.

1. Facebook — Chatbot Army

Although Facebook’s Messenger service is still a little…contentious (people have very strong feelings about messaging apps, it seems), it’s one of the most exciting aspects of the world’s largest social media platform. That’s because Messenger has become something of an experimental testing laboratory for chatbots. Some chatbots are virtually indistinguishable from humans when
conversing via text Any developer can create and submit a chatbot for inclusion in Facebook Messenger. This means that companies with a strong emphasis on customer service and retention can leverage chatbots, even if they’re a tiny startup with limited engineering resources. Of course, that’s not the only application of machine learning that Facebook is interested in.
AI applications are being used at Facebook to filter out spam and poor-quality content, and the company is also researching computer vision algorithms that can “read” images to visually impaired people.

2. Google — Neural Networks and ‘Machines That Dream’

These days, it’s probably easier to list areas of scientific R&D that Google — or, rather, parent company Alphabet — isn’t working on, rather than trying to summarize Google’s technological ambition.

Needless to say, Google has been very busy in recent years, having diversified into such fields as anti-aging technology, medical devices, and — perhaps most exciting for tech nerds — neural networks.

The most visible developments in Google’s neural network research have been the DeepMind network, the “machine that dreams.” It’s the same network that produced those psychedelic images everybody was talking about a while back.

According to Google, the company is researching “virtually all aspects of machine learning,” which will lead to exciting developments in what Google calls “classical algorithms” as well as other applications including natural language processing, speech translation, and search ranking and prediction systems.

3. Baidu — The Future of Voice Search

Google isn’t the only search giant that’s branching out into machine learning. Chinese search engine Baidu is also investing heavily in the applications of AI.

A simplified five-step diagram illustrating the key stages of
a natural language processing system

One of the most interesting (and disconcerting) developments at Baidu’s R&D lab is what the company calls Deep Voice, a deep neural network that can generate entirely synthetic human voices that are very difficult to distinguish from genuine human speech. The network can “learn” the unique subtleties in the cadence, accent, pronunciation, and pitch to create eerily accurate recreations of speakers’ voices.

Far from an idle experiment, Deep Voice 2 — the latest iteration of the Deep Voice technology — promises to have a lasting impact on natural language processing, the underlying technology behind voice search and voice pattern recognition systems. This could have major implications for voice search applications, as well as dozens of other potential uses, such as real-time translation and biometric security.


Industry: RegTech, Legal, Financial

Location: Chicago

What it does: Ascent is an AI-powered regulatory platform that identifies the regulations a company must comply with and keeps them updated as the rules change. It’s difficult for a person or team to keep track of every single new rule or regulation change in the financial sector. Ascent’s platform saves companies valuable employee time and money by using AI to constantly monitor for rule changes and quickly alert the proper people to any compliance issues.


Industry: Healthtech, Biotech, Big Data

Location: Chicago, Illinois

What it does: Tempus uses AI to gather and analyze massive pools of medical and clinical data at scale. The company, with the assistance of AI, provides precision medicine that personalizes and optimizes treatments to each individual’s specific health needs; relying on everything from genetic makeup to past medical history to diagnose and treat. Tempus is currently focusing on using AI to create breakthroughs in cancer research.


Classification software based on neural network simulations is an accessible tool that can be applied to VA data potentially outperforming other the data-derived techniques already studied for this purpose. As with other data-derived techniques, over-fitting to the training data leading to a compromise in the generalizability of the models is a potential limitation of ANN. Increasing the number of training examples is likely to improve the performance of neural networks for VA. However, ANN algorithms with particular operating characteristics would be site-specific. Thus optimal algorithms need to be identified for use in a variety of settings.

Pursuing Computer Science and Engineering at KIET Group of Institutions Interested in android development, Cloud Computing and Machine Learning.