Neural Network and AI

A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain. It creates an adaptive system that computers use to learn from their mistakes and improve continuously. Thus, artificial neural networks attempt to solve complicated problems, like summarizing documents or recognizing faces, with greater accuracy.

The idea of NNs is based on the belief that working of human brain by making the right connections, can be imitated using silicon and wires as living neurons and dendrites.

The human brain is composed of 86 billion nerve cells called neurons. They are connected to other thousand cells by Axons. Stimuli from external environment or inputs from sensory organs are accepted by dendrites. These inputs create electric impulses, which quickly travel through the neural network. A neuron can then send the message to other neuron to handle the issue or does not send it forward.

NNs are composed of multiple nodes, which imitate biological neurons of human brain. The neurons are connected by links and they interact with each other. The nodes can take input data and perform simple operations on the data. The result of these operations is passed to other neurons. The output at each node is called its activation or node value.

Neural networks have several use cases across many industries, such as the following:

• Aerospace − Autopilot aircrafts, aircraft fault detection.
• Automotive − Automobile guidance systems.
• Military − Weapon orientation and steering, target tracking, object discrimination, facial recognition, signal/image identification.
• Electronics − Code sequence prediction, IC chip layout, chip failure analysis, machine vision, voice synthesis.
• Financial − Real estate appraisal, loan advisor, mortgage screening etc.
• Industrial − Manufacturing process control, product design and analysis, quality inspection systems etc.
• Medical − Cancer cell analysis, EEG and ECG analysis, prosthetic design, transplant time optimizer.
• Speech − Speech recognition, speech classification, text to speech conversion.
• Telecommunications − Image and data compression, automated information services, real-time spoken language translation.
• Transportation − Truck Brake system diagnosis, vehicle scheduling, routing systems.
• Software − Pattern Recognition in facial recognition, optical character recognition, etc.
• Time Series Prediction − ANNs are used to make predictions on stocks and natural calamities.
• Signal Processing − Neural networks can be trained to process an audio signal and filter it appropriately in the hearing aids.
• Control − ANNs are often used to make steering decisions of physical vehicles.