Several efforts in advanced AI concepts in past years have brought great innovations. Big data, autonomous vehicles or medical research are just some of the incredible applications of AI.
AI is in fact a wider concept. To understand some of them, you need to know three basic AI concepts – machine learning, deep learning, and neural networks.
Simply put, machines are given a large amount of data and examples for a certain task. As they go through these inputs, machines learn and adapt their strategy to achieve set goals.
For example, an image-recognition robot may be given millions of pictures for analysis. After going through huge amounts of permutations, robot acquires the ability to recognize faces, patterns, shapes, …
If we want this robot, who have learned to recognize photos, to use that knowledge to analyze different data sets – it is necessary to formulate general-purpose learning algorithms that help machines learn more than just one task.
Deep Learning concept is actually powered by neural networks. They are assembled from hundreds, thousands, or millions of artificial brain cells that learn and behave in a similar way as human brains.
They use math and computer science principles to simulate the processes of interconnected brain cells but these neurons(nodes) are built in code.
Neural networks consist of input and output layers and in most cases a hidden layer(s)
Very briefly – Input layer takes input values and passes them on to the next layer. In hidden layer (can be more) all nodes are interconnected with each other. They apply different transformations to the input data.
Output layer receives inputs from the last hidden layer and we can get desired number of values and in a desired range
AI has a huge potential to transform the business, in some use cases we can already benefit from it now. It empowers and enriches the digital transformation but still – it has a long way to go before it can think like a human being.