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By Pedro URIA-RECIO, Head of Axiata Analytics Center, Axiata Group Berhad
When I worked as a McKinsey consultant, I served the CEO of a bank regarding his small business strategy. I wanted to run regressions on the bank’s data but I was advised against it: “They don’t even understand statistics. How are you going to explain a regression to them?”
CEOs have always needed to deeply understand human intelligence and emotion to manage enterprise teams. Now machines and algorithms are increasingly becoming part of these very teams. The job of a CEO in the age of artificial intelligence (AI) is to design new combinations of technologies, assets as well as human and machine skills to revolutionize business models. Understanding human intelligence alone is no longer enough.
Most recent advances in AI have been achieved in the area of machine learning (ML). Vision systems, speech recognition technologies or recommendation engines would not have been possible without ML. ML is a technological breakthrough which allows data scientists to write relatively generic software that can learn how to solve a problem without needing to specify a myriad of detailed step-by-step instructions.
There are three major types of ML methodologies: supervised, unsupervised and reinforcement learning.
Supervised Learning: Preparing Math ExamsOne learning method I enjoyed at university was reviewing solved math problems to prepare for the exam. Similarly, supervised learning algorithms learn how to solve a problem by looking at a lot of examples of the correct answer. Regressions are by far the most popular supervised algorithm. Regressions try to fit a formula, for example linear or quadratic, in your data. If you think there is a linear relation between your sales and factors such as competition prices, your advertising spend, promotions or even the weather, you can train a linear regression algorithm with historical data for which you already know your sales. The algorithm will learn the right parameters of the formula, which will be used to predict future sales. A special kind of regression, called logistic regression, which uses a simple exponential formula, can be used to categorize items into groups instead of providing a continuous numeric prediction. For example, a logistic regression could learn to estimate whether a potential customer will default on a loan or not, by looking at characteristics of historical customers and their loans, for which we already know the outcome. Without any doubt the most interesting kind of supervised algorithm are neural networks, which mimic how our brain works.
The job of a CEO in the age of artificial intelligence is to design new combinations of technologies, assets as well as human and machine skills to revolutionize business models