Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of data. This interesting information came to us from datanami in their article, “How Data-Centric AI Bolsters Deep Learning for the Small-Data Masses.”

In the physical world, where the majority of aspects that humans interact with are based on dynamic relationships with various intangible data, the human mind performs many simple data-driven calculations every day. Similarly, computation is based on data or labeled training data in machine learning, which helps an artificial intelligence (AI)-based program work to add value.

Machine learning as a concept is related to enhancing a computer’s ability to learn using algorithms and neural network models and perform various tasks faster and more efficiently. Machine learning helps in building models by using data or data sets to make decisions.

The use of algorithms is far more reliable and fast than writing the code of a program to automate a process or conduct deep findings on a large set of data.

Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. “Explainable AI” is used to describe an AI model, its expected impact and potential biases.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.