Human beings themselves have invented the computer, yet they are more dynamic than us. It can perform complex calculations instantly. Sometimes it sounds so weird, but still needs to accept it. But the computer cannot meet the human brain’s horizon in terms of common sense, creativeness, and inventiveness. ANN’s concept was inspired by the human brain, whose aim was to give the machine some degree of intelligence.
What is an Artificial Neural Network?
ANN is the computing program designed to mimic the way the human brain analyzes and processes the data. It is also considered the foundation of AI, which is believed to solve the extreme problems hard to process by the human brain. Also, ANN’s are build up with self-learning capabilities that empower them to deliver better outcomes as more information is fed into it.
How ANN works?
ANN is set up with hundreds of processing units which are interconnected to each other through the nodes. Those processing units are basically classified as the input and the output node. Once the chunk of the data is fed into the ANN through the input nodes, it goes through the training phase, where it tries to search and recognize different unique patterns. At this stage, ANN gives the output based on the normal observation, which is later compared to the actual output. If the desired output has not got a satisfactory result, it is adjusted by the back-propagation process. Back-propagation refers to movement from the output node towards the input node to adjust the appropriate weight. The appropriate weight is adjusted between the various unit so that the outcome produces the lowest possible error.
For example, a bank is adopting AI to detect internet banking fraud in real-time. To detect this, it may have four input units to feed the collected response from the user.
- Was the login done from the different countries?
- Was the password attempted several times?
- Was the transaction amount greater than 50,000?
- Did the transaction PIN match within the second attempt?
The expected result mentioned by the bank for the fraud transaction could be Yes Yes Yes No, which would be 1 1 1 0 in the binary format. If the ANN actual outcome is 1 0 1 0, it tries to adjust it until the result accords with 1 1 1 0. After the accuracy is maintained almost 100%, the model is deployed to the bank to detect the fraud transactions.
Application area of Neural Networks
The following are a few examples where ANN is being used in the commercial field.
- Google uses 30 layered neural networks to power Google photos
- The recommendation of the video on YouTube based on past usage is obtained with the implementation of ANN.
- Facebook using ANN to classify the different faces with an accuracy rate of around 97%.
- Lastly, real-time language translation by Skype is also the power of ANN.