A Graph Neural Networks (GNN) is a class of artificial neural networks for processing graph data. Here we need to define what a graph is, and a definition is a quite simple – a graph is a set of vertices (nodes) and a set of edges representing the connections between the vertices. There are many data sources which produce data that can be organically presented in a graph form. For example, we can consider social network users as graph vertices where two vertices are connected if corresponding users are friends.Read More
The AI industry is usually remembered for post-apocalyptical scenarios where robots invade earth and rid of humanity altogether. That is far from the reality, as a matter of fact, AI has been playing a vital role in helping different industries as they improve to better answer their business needs and problems. Companies like ISS Art have been at the forefront of this industry by providing incredible AI development services globally.Read More
Artificial intelligence (AI) is directly related to Data Science – the science about data that aims to extract value from a big mass of information. This value may reside in, for example, enhanced forecasting capabilities, discovering patterns, better decision-making. Basically, AI concerns with information processing algorithms and methodologies. Artificial intelligence operates on huge amounts of data, analyzes it, and uses gathered insight to develop solutions.Read More
Why your Customer Service, Marketing and HR departments needs to use Natural Language Processing Solutions?
Command Alexa to sing you some super hit songs, and in no time, Alexa will be belting out a few musical numbers.
You bet! The exciting combination of Artificial intelligence and Natural Language Processing has helped machines like Alexa to offer ready-service to customers.
Much as we praise Alexa for helping us in our daily chores, the fact is machines like Alexa don’t readily understand human language.
Unlike humans, language learning is a tedious, time-consuming, and complicated process for machines. Why? For one, they tend to look at language in the form of data- loads of indecipherable, upended, and unstructured data. Another thing being, machines don’t understand grammar and context.