Technological innovations can seem relentless. In computing, some have proclaimed that “a year in machine learning is a century in any other field.” But how do you know whether those advancements are hype or reality?
Failures quickly multiply when there’s a deluge of new technology, especially when these developments haven’t been properly tested or fully understood. Even technological innovations from trusted labs and organizations sometimes result in spectacular failures. Think of IBM Watson, an AI program the company hailed as a revolutionary tool for cancer treatment in 2011. However, rather than evaluating the tool based on patient outcomes, IBM used less relevant measures – possibly even irrelevant ones, such as expert ratings rather than patient outcomes. As a result, IBM Watson not only failed to offer doctors reliable and innovative treatment recommendations, it also suggested harmful ones.
