Learning something new takes lots of time and even more effort. However, for machines that time is significantly reduced. Machine learning has become an important focus in an ever-increasingly technology-filled economy. As systems become more complex so too must the machine.

Joel Horwitz, director of Corporate & Business Development at IBM Analytics, and Srisatish Ambati (@srisatish), cofounder and CEO of H2O.ai, talked with John Walls and George Gilbert (@ggilbert41), cohosts of theCUBE, from the SiliconANGLE Media team, during the Apache Spark Maker Community event about the future of data and machine learning.

Coding is a commodity

Though machine learning can be a complex matter, Horwitz and Ambati kept it very straightforward. “The moneyball is on,” according to Ambati, and there will be a change in business both horizontally and vertically.

Data transformation is bringing technology to a much wider audience, and as such the focus is no longer on the product, but the system. There has been a shift of importance from physical assets to digital, and with all this shifting and transformation, it can be hard to determine where the value of a company lies. Horwitz and Ambati believe that this shift will simply help companies be more concerned with the community their brand creates. More integrated systems and open platforms will create a much larger “maker” culture.

Horwitz and Ambati are leaders, innovators and experts when it comes to machine learning, and they stated that as scary as it may sound, we are in the age of increasingly useful AI. We are “moving away from rule-based machine learning and into the gray area,” said Horwitz. As such, it is important to trust other leaders in the field and manage the many models of the future that are pouring in.

Watch the full interview below, and be sure to check out more of SiliconANGLE and theCUBE’s coverage of the Apache Spark Maker Community 2016.

Photo by SiliconANGLE