Thanks to advances in technology, we might soon be able to use sensitive data for machine learning without customers having to reveal their confidential information. Machine learning systems need access to huge volumes of data in order to learn thoroughly. But how secure is the data used to train the machine, especially if it's confidential information? Can it be traced or even hacked? Should we even use sensitive data for machine learning at all? SAP reported on the launch of SAP's guiding principles on artificial intelligence (AI) in 2018. One example of how SAP lives by these principles itself is homomorphic encryption.

SAP SE (NYSE: SAP) today introduced three initiatives to make its business applications more intelligent and empower its ecosystem to build machine learning (ML) applications for customers. Spanning its own solutions, partner programs and educational offerings, these programs will help accelerate ML adoption across SAP's global customer base. This announcement was made at the SAP TechEd conference, being held November 8-10, 2016, in Barcelona. First, SAP has unveiled new intelligent business applications. A new solution, "brand intelligence," is supposed to analyze brand exposure in video and images by leveraging deep learning.

An historic event unfolded in March 2016. The victory of the program AlphaGo over professional gamer Lee Sedol in the Google DeepMind Challenge demonstrated how far artificial intelligence (AI) has come: "Go's simple rules and elaborate possibilities have made it one of the most sought-after milestones in the field of AI research," writes Sam Byford of The Verge. The idea of computers learning autonomously has been around for decades. Why has machine learning gained so much ground in recent years? Increased computing power has made machine learning possible, at last.