Startup aims to optimize algorithms for machine learning - AI Trends

Those who have gained some experience with machine learning, trying to digest the huge volumes of data that make it meaningful, quickly get to grappling with algorithms needed to optimize how the data is processed. And no matter how brilliant is the AI worker or data scientist trying to tame the data, it's usually a laborious, trial-and-error and often expensive process. That's what Scott Clark observed when he was a student at Cornell University, and later when he worked on the ad targeting team at Yelp. To help himself, he worked on some tools to put a degree of automation into the process of optimizing data used for machine learning. Now a PhD in applied mathematics, Scott is the CEO and co-founder of SigOpt, in the business of helping companies get to machine learning in a more practical way.