When AI Gets Leaner And Meaner

Widely regarded as the father of marketing science, he developed algorithms to automatically analyse scanner data--sales information obtained by scanning product barcodes at the cash register--and provide managers with informed insights. This problem-driven approach--a departure from early computer programmes, which mostly used classical statistics to analyse data--is also what underpins the artificial intelligence (AI) and machine learning strategies in use today, said Professor Phil Parker, chaired professor of management science at INSEAD. "Today, if you don't start with a very concrete objective, you may find yourself in a situation where you invest a lot of money in big data, and two years later, you wonder how to monetise it," said Professor Parker. "The best way is to start with a problem and then reverse engineer the proper algorithms." Professor Parker was speaking on 4 December 2017 at the Artificial Intelligence and Machine Learning Festival, a three-day event organised by INSEAD, SGInnovate and Impact Hub.