Artificial Neural Network

Artificial neural networks, usually simply called neural networks, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain.

The purpose of a neural network is to learn to recognize patterns in your data. Once the neural network has been trained on samples of your data, it can make predictions by detecting similar patterns in future data. Software that learns is truly “Artificial Intelligence”.

Neural Networks are the epitome of Artificial Intelligence. And then there is Unsupervised Learning which is regarded as the Holy Grail of Deep Learning.

An Unsupervised ANN is the cosmos of Artificial Intelligence. Unsupervised learning is done without the supervision of a teacher. This learning process is independent. During the training of ANN under unsupervised learning, the input vectors of similar type are combined to form clusters.

Unsupervised Artificial Neural Network capable of learning and adapting to new information over time will be the breakthrough we all are working for.

It would be something like Data’s positronic brain.

Something completely conscious of its surroundings and capable of making decisions based not only on 0s and 1s but on a quantum level.

The most enthralling achievement would be to create such an artificial intelligence system using quantum technology.

P.S. This is the most thrilling work I have ever given a try.