Understanding Neural Networks: The Brain of AI

Artificial intelligence, machine learning, neural networks are the latest buzz words in the field of technology. Neural networks are the cornerstone for artificial intelligence (AI) to function. The idea is to mimic the capabilities of the human brain and create a computational system which could resolve problems like a human brain does. Neural networks are applied in various areas and help in – speech recognition, computer vision, machine-based translation, social network filtering, gaming, and medical diagnosis. 

In today’s topic we will learn about the role of neural networks in artificial intelligence, why neural network is important? types of neural networks, use cases for neural networks. 

What are Neural Networks?

The very first neural network was conceived in 1943 by Warren McCulloch and Walter Pitts. They wrote a paper on how neurons work and created a simple neural network using electrical circuits. This advancement in the domain of neural networks paved the way to further enhance research in two areas: human brain Biological processes and application of neural networks in artificial intelligence (AI). 

Quick acceleration of artificial intelligence (AI) research happened with Kunihiko Fukushima who developed the first multi-layered neural network in 1975. The original goal of neural network approach was creation of a computation system which could solve problems as the human brain does but later the focus is shifted to perform specific tasks such as speech recognition, computer vision, machine-based learning and translation, games and in the area of medical diagnosis. 

What is the importance of Neural Networks?

Neural networks help people to solve complex problems in real life situations. Neural networks can learn and model the relationship between input/outputs which are non-linear and complex, make generalization and inferences, disclose hidden relationships, predictions and patterns, model highly volatile data such as time series data and variances required in prediction of rare events (Such as frauds) and can improve decision making in areas such as: 

• Credit card and fraud detection in claim processing in healthcare 
• Logistics optimization in transportation
• Disease diagnosis
• Targeted marketing 
• Financial predictions related to stocks, currency, options, 
• Robotics systems

And many more areas. 

How Neural Networks work?

A neural network is a network of artificial neurons in a software. They try to simulate the human brain and it has many layers of neurons. The first layer of neuron receives inputs such as images, voice, video, sound, text etc. This input goes through all layers and output of one layer is fed to the next layer. For example, suppose you have a neural network that is trained to identify shapes square and circle.

The first layer of neuron will break up shape into areas of light and dark. This data will be fed to the next layer to recognize the edges of shape. The next layer would try to recognize the shape formed by a combination of edges. The data will pass through several layers to finally recognize the shape according to data it is being trained on. 

Types of Neural Networks 

There are different types of deep neural networks. Let’s look at them more in depth. 

• Convolutional Neural Networks (CNNs) have five types of layers – input, convolution, pooling, output and fully connected. Each layer is meant for a specific purpose such as summarization, connecting and activation. Convolutional neural networks are used in natural language processing, image classification and object detection.
• Recurrent Neural Networks (RNNs) – make use of sequential information from sensors such as time stamped data. All inputs to this type of neural networks are not independent of each other, output of each element depends on preceding elements computation. They are used in forecasting and time series applications, sentiment analysis and text-based applications
• Feedforward Neural Networks – each perception is connected to every perception in the next layer and there are no feedback loops here. Information feeding happens in forward direction only.
• Autoencoder Neural Networks – used for creation of abstractions called encoders with a given set of inputs. Autoencoders desensitize irrelevant and sensitize the relevant. These can be used by linear or non-linear classifiers.

Uses of Neural Networks

• biomedical imaging and for monitoring of health 
• Energy and manufacturing companies use them for supply chain optimization, automated defect detection and forecasting energy needs
• Banks use neural networks for fraud detection, conducting credit analysis and automation of financial advisory services
• Public sector enterprises use them to support smart cities, facial recognition and security intelligence
• Retail and consumer industry use them to power chatbots, analyse customer preferences 

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