Artificial intelligence (AI) brings with it the possibility of genuine human-to-machine interaction. When machines become intelligent, they can understand requests, connect data points, and draw conclusions. They can reason, observe, and plan. It’s very common to hear the terms “machine learning” and “AI” thrown around interchangeably, but they are often used in the wrong context. They are similar concepts and are closely related, but they are not the same. This interesting topic came to us from Unite AI in their article, “Machine Learning vs Artificial Intelligence: Key Differences.”

While AI is the broad science of mimicking human abilities, machine learning is a specific subset of AI regarding the training of machines to independently learn new things. With AI, you can ask a machine questions – out loud – and get answers. In health care, treatment effectiveness can be more quickly determined. In retail, add-on items can be more quickly suggested. In finance, fraud can be prevented rather than simply detected.

Business leaders would greatly benefit from learning more about these skills and how AI systems make their decisions. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.

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.