Festo recently announced a new artificial intelligence (AI) platform for predictive maintenance, predictive quality, and energy optimization. Using advanced analytics, Festo AX maps data to learn a component, machine, product, or energy system’s healthy state. Festo AX provides actionable information to correct anomalies when data begins trending away from nominal. This interesting topic came to us from Today’s Medical Development in their article, “AI solution improves machine utilization, product quality, energy efficiency.”
Predictive analytics is the use of mathematical and statistical methods, including AI and machine learning, to predict the value or status of something of interest. Organizations—as well as the software vendors that supply their needs—have largely tapped analytics to provide deeper information beyond basic indexed searching, which typically involves applying Boolean logic to keywords, date ranges, and data types.
Within the information governance space are analytics (or predictive analytics) and AI/machine learning. These are here to stay, and we are just beginning to scratch the surface of their many uses. Most organizations don’t have a complete understanding of their operation, and as a result, they know nearly nothing about how the results can apply across AI and machine learning fields. 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 its 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
Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.