Artificial intelligence (AI) brings with it a promise 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 in the wrong context, but they are not the same. They are similar concepts that are closely related. 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 that trains a machine how to learn. 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 instead of just detected.

Business leaders would greatly benefit from learning more about these skills and how AI systems make the decisions they do. 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.