The Pivotal Management Challenge Of The AI Era

Managers and leaders have nothing to fear from AI – except missing out

- Theodoros Evgeniou

History indicates that major technological changes can take about half a century to go from the first lab drawings to society. Alan Turing first proposed the Turing machine laying the foundations of computation in 1936; the first general-purpose “Turing-complete” system was built in 1945; and “The Computer” was only named “Machine of the Year” by Time in 1982, about half a century later. The foundations of the internet were laid out in the 1960s, but consumers did not get to broadly use and benefit from it until the mid-to-late 1990s.

For most people, artificial intelligence was strictly a sci-fi concept until recent years. Yet, if you go by the above timeline, the AI revolution may actually be running at least two decades late. It has been more than 70 years since the famous 1956 Dartmouth workshop with Newell, Simon, McCarthy and Minsky – the last of them passing away only three years ago (in 2016) – at which the first AI programme was officially unveiled. But statistical learning theory, the foundation of modern AI and machine learning, arrived a little ahead of the 50-year deadline. The field (whose luminaries included Vladimir Vapnik, Tommy Poggio and Steve Smale) cross-pollinated statistics, mathematics and computer science to produce a flowering of breakthroughs, leading directly to today’s AI revolution.

AI and its impact on business, governments and society may be today where physics was at the turn of the previous century. After breakthroughs in physics at that time, and later in biology and other fields, the world became very different from what it was for thousands of years before.