Machine learning is a field related to artificial intelligence (AI) that focuses on the development of algorithms and models enabling computers to learn to make predictions and decisions based on data. This is as opposed to the AI having been explicitly, procedurally programmed to perform specific tasks at a macro level. This interesting topic came to us from The Scholarly Kitchen in their article, “The Best Explanation I’ve Seen for How Machine Learning Works.”

Machine learning involves the use of data to train models that can make predictions or decisions. It’s a versatile field with various algorithms and techniques, and the choice of the right approach depends on the specific problem and data at hand. Successful machine learning projects require careful data preparation, model selection, and ongoing maintenance to ensure the best possible performance.

Machine learning is being used in a wide range of applications across various industries. A few examples include face recognition, chatbots, virtual assistants, and disease prediction. Machine learning continues to advance, opening up new possibilities for innovation in numerous fields.

The real challenge is that most organizations have little knowledge on how AI systems make 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.