Emerging technologies like artificial intelligence (AI) and machine learning are constantly being integrated in different industries and applications. In this case, it is in the world of ecology and science. This interesting information came to us from NewsWise in their article, “Using Machine Learning to Better Understand How Water Behaves.”

Machine learning is being used to help scientists understand how water reacts with extreme temperatures, combined with other liquids and other quantum chemistry situations. To better understand how water interacts, the researchers ran molecular simulations on supercomputers.

Even a decade ago, running such long and detailed simulations wouldn’t have been possible, but machine learning today offered a shortcut. 

These discoveries and new uses for emerging technologies are great. But the biggest challenge is understanding the technology. Most organizations have little knowledge on how AI systems make the decisions they do, and as a result, how the results are being applied in the various fields that AI and machine learning are being applied. 

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, changing search to found.