Applications of artificial intelligence: The applications of machine learning are lots, from the recent advances in robotics to autonomous cars, from e-commerce to customer service. User experiences are such that they are often improving as the amount of data grows and as the complexity of the algorithms increase. Most applications that you’ll encounter are automated systems, which use machine learning algorithms to help people accomplish complex tasks and requirements.
Reinforcement learning: The methods of reinforcement are based on a reward system, where the machine gets rewarded for the right actions made. Here the machine learns over time what it has to do to win.
Solutions on knowledge graphs: Knowledge graphs can be considered as the next generation of knowledge repository, where people can get the power of deep learning algorithms, large datasets, and also their structured representation. With knowledge graphs you can represent patterns and data using the complex nodes and their inter-connections. This approach can provide a powerful way to improve the speed at which data is analyzed and use the capability of deep learning to learn and make decisions
You can also use knowledge graphs to make sense of some of your data sets, enrich your visualization and discover innovative ways to understand your data sets. Knowledge graphs are a good basis for a bot too, which is trained to answer new user questions. Knowledge graphs are a great resource and can be used to make sense of some of your data sets, enrich your visualization and even discover innovative ways to understand your data sets. d2c66b5586