It’s perhaps an indication of how much the pendulum has swung towards machine learning within the artificial-intelligence community that the concept of 'data-centric AI' seems practically a tautology.
Back in the dim distant past, otherwise known as the late 20th century, a lot of the AI work being done then focused a lot more on building systems from scratch that could reason for themselves about the world. Then along came deep learning and, though there are people still working on the reasoning-heavy style of AI, most of the attention has gone into the approach of showing computers pictures or descriptions of stuff and expecting them to learn to identify the stuff.
In 2012, Geoffrey Hinton, professor of computer science at the University of Toronto, and colleagues demonstrated a rapid advance in the ability of computers, thanks to the increased calculating power of GPUs, to label correctly a thousand objects pulled from the ImageNet dataset...