What is an Artificial Neural Networks?
Artificial neural networks (ANN) give machines the ability to process data similar to the human brain and make decisions or take actions based on the data. While there’s still more to develop before machines have similar imaginations and reasoning power as humans, ANNs help machines complete and learn from the tasks they perform.
Do you know what facial recognition, real-time translation, Google photos and autonomous cars have in common? They are all applications of artificial neural networks (ANN). While there’s no doubt machines can outperform humans in a variety of ways, our human brains are still ahead when it comes to imagination and reasoning. However, with the advancement in artificial neural networks, machines are now closer than ever to thinking and acting like humans.
What else can artificial neural networks do?
Artificial neural networks are a main component of machine learning and they are designed to spot patterns in data. This makes ANNs an optimal solution for classifying (sorting data into predetermined categories), clustering (finding like characteristics among data and pulling that data together into categories) and making predictions from data (such as helping determine infection rates for COVID, the next catastrophic weather event or box-office smash). In everyday life, ANNs are powering the “watch next” feature of YouTube videos, creating realistic CGI faces, helping detect fraud, giving us the ability to chat with chatbots and more. In fact, there are probably not many tasks an artificial neural network can’t do as long as it’s trained to do it.
Ultimately, ANNs try to replicate how our human brains process information and make decisions. While ANNs are based on mathematical theory created in the 1940s, it wasn’t until the last couple of decades that it became a focus for artificial intelligence. When backpropagation was developed to help these networks learn and adjust actions based on outcomes its development and adoption really began to accelerate.
When a human brain receives an input, it processes it through a series of neurons. Different neurons of the human brain are responsible for processing different aspects of input in a hierarchical fashion. ANNs try to replicate this through artificial neurons called units that are arranged in layers and connected to each other to create a web-like structure.
ANNs have an input layer and output layer. Between these two layers there are other hidden layers that perform the mathematical computations that help determine the decision or action the machine should take. Ultimately, these hidden layers are in place to transform the input data into something the output unit can use.
The data is processed by each hidden layer and then moves on to the next based on connections that are weighted.
