The evolution of machine learning: fusing human thought with algorithmic insights #IBMML - SiliconANGLE

As machine learning becomes more accessible through avenues such as Intel's BigDL and IBM opening Watson's core machine learning components up to businesses, some developers and industry insiders are cautioning against getting too dazzled by the potential without considering the human role. However much data those programs can process, in the end, "what you do with the results of algorithms is key," said Jean-Francois Puget, Ph.D. (pictured), distinguished engineer, machine learning and optimization, IBM Analytics, at IBM. Puget spoke with Dave Vellante (@dvellante) and Stu Miniman (@stu), co-hosts of theCUBE, SiliconANGLE Media's mobile live streaming studio, at the IBM Machine Learning Launch Event in New York, NY. He offered his perspective on machine learning and its applications. "For most people, machine learning equals machine learning algorithms," Puget said. "When you look at newspapers or blogs, social media, it's all about algorithms. Our view [is] that sure, you need algorithms for machine learning, but you need steps before you run algorithms, and after."