Explainability is a fascinating topic. It covers a research field where a wide variety of experts come together: mathematicians, engineers, psychologists, philosophers and regulators, which makes it one of the most interesting. I have been involved in quite some AI projects where explainability -- or XAI -- turned out to be crucial. So, I decided to gather and share my experiences, and the experiences of my colleagues at Deeploy. AI is one of the biggest innovations of our time. It can change the way we live, work, care, teach and interact with each other.

Robots have moved off the assembly line and into warehouses, offices, hospitals, retail shops, and even our homes. ZDNet explores how the explosive growth in robotics is affecting specific industries, like healthcare and logistics, and the enterprise more broadly on issues like hiring and workplace safety. But machine learning (ML), which many people conflate with the broader discipline of artificial intelligence (AI), is not without its issues. ML works by feeding historical real world data to algorithms used to train models. ML models can then be fed new data and produce results of interest, based on the historical data used to train the model.