Article was touched by pr dept, but still has actually information.
longer tldr:
They did the same thing that has been done for thousands of years. Back then the hot area of research was how to stage advance food and resource caches along a route for long journeys. They came up with algorithms to optimize cache hits.
In this case, the problem is GPUs can be fast for ML, but usually only have 16GB ram when dataset can be terabytes.
Simple chunk processing would seem to solve the problem, but it’s turns out overhead of cpu/gpu transfers badly degraded performance.
Their claim here is they can on the fly determine how important different samples are, and make sure samples that yield better results are in the chance more often than those with less importance.