Despite its surg­ing pop­u­lar­i­ty, machine learn­ing isn’t new. It’s been around for more than 50 years, first intro­duced when a neu­ro­phys­i­ol­o­gist and math­e­mati­cian co-wrote a paper on how human neu­rons might work. To illus­trate the the­o­ry, they mod­eled a net­work with elec­tri­cal cir­cuits, and soon after, com­put­er sci­en­tists began apply­ing the idea to their work.

Today, machine learn­ing cer­tain­ly has evolved. Com­pa­nies use machine learn­ing to tap into big data to make fore­casts that will dri­ve their busi­ness­es for­ward in areas such as prod­uct devel­op­ment, cus­tomer sat­is­fac­tion, and employ­ee recruit­ment or reten­tion, just to name a few.

Sim­ply stat­ed, machine learn­ing allows an orga­ni­za­tion to con­duct busi­ness more intel­li­gent­ly. We sat down with Markus Noga, senior vice pres­i­dent of SAP Leonar­do Machine Learn­ing, to bet­ter under­stand how it can be used and how it will con­tin­ue to evolve.