Artificial intelligence (AI) gets a lot of air time these days, but few people fully understand what it is and how it achieves the results it does. CIO Insight brought this interesting information to our attention in their article, “Is Machine Learning the Same as Predictive Analytics?

AI isn’t a single technology. Instead, it’s an umbrella term for various approaches that can train machines to solve problems in ways that mimic human intelligence. 

Machine learning is one of these approaches, and predictive analytics are used in machine learning algorithms. It is used to predict outcomes based on historical data and make smarter decisions. Both machine learning and predictive analytics involve collecting and analyzing data from past events to make better decisions about the future. 

By using predictive analytics software, companies can understand how customer behavior will likely change in response to business strategies or other variables. 

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. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.

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.