The problem is that there's no intrinsic scoring system for Go, nothing specific to maximize, so it's difficult to tell a computer whether a given outcome is "good" or "bad". So early versions of AlphaGo used a collection of human-played Go games to get an idea of what constitutes "good" and what is "bad", so it can then train its model to predict whether a move will make things better or worse.
This new system forgoes that step, and instead has the model play itself starting at random and looking for patterns that end up winning games. It's as if you gave the rules to the game of Go to a culture that's never heard of it before, and they evolved their own play style entirely in isolation.
Their result is a model that is better than the one that was developed with human influence, and that's the interesting bit.