Artificial intelligence is growing at a rapid speed in today’s time. With artificial intelligence, a number of Technologies are working alongside to produce a meaningful output in the near future. Almost every other smart device that we used today, uses artificial intelligence Technologies. There are even smartphones, like Google pixel 3 and Huawei mate 20 Pro, which uses an artificial intelligent camera. Machine learning is an important aspect of artificial intelligence. Moreover, there is a process of machine learning, called the artificial neural network. So let’s discuss more artificial neural network.
What is An Artificial Neural Network?
An artificial neural network, as the name suggests, means a process of machine learning, just like human brains which use neurons to store and interpret data. There are multiple layers in this network which is involved in the process of machine learning. The input layout tries to interpret all the data and produce a meaningful suggestion for the output layers. Moreover, this is a very essential tool which helps who developed the artificial intelligence Technology further.
It is not manually possible to teach the computer about the suggestions and other machine learning processes as such. However, tools like artificial neural networks help machine learning automatically by following Complex algorithms. In simple terms, the input layer interprets all the various activities of users and tries to analyze and forecast the future action of the user.
Types Of Artificial Neural Networks
There are many types of artificial neural networks which works according to the system complexity. The most basic neural network is the feedforward network. In this type of network, the input travels only in one direction. In Complex types of artificial neural networks, information travel in multiple directions depending upon a suitable permutation and combination. Hence, a meaningful output is obtained by this process.
The recurrent neural network is another kind, which allows two ways information flow. They are involved in much complex machine learning processes. Recurrent networks have the ability to interpret and produce an output of multiple things simultaneously.
What can artificial neural networks do?
Artificial neural networks can do almost anything you want it to. You should rather ask that what are the things that artificial neural networks cannot do. It can drive a car autonomously on the roads and read your brain too. It can interpret each and every input and convert it into an appropriate machine language. No wonder, it is being used extensively in the artificial intelligence process which is the possible fifth generation of computers. Moreover, it can cluster and combine any data to provide meaningful output in terms of machine learning.
How do they learn?
It gains knowledge as we do with time and experience. Reports say that more the data you give to this network, the better it will get over time. Hence, artificial intelligence is a gradual learning process and gets better with time. It simply analyses your behavior over time and helps you in selecting and predicting things based on this date. Hence, you cannot expect the device to predict accurate leave from the first day itself.
For a practical example, you can consider the Google keyboard or the dashboard of Google Now. Google keyboard is enabled with artificial intelligence and observed everything which you type. When you use it on a new device it will show irregular suggestions. However, over time you will find that it is reading your mind and predicting each and everything correctly when she wants to type. Hence, the artificial neural network helps the computer or device in learning. However, it is just in its early stage and has a long way to go.
