The article doesn't mention Google's Knowledge graph by name. But that is what the reporter is referring in sentences such as these, which mention "a strict set of rules set by humans":
> But for a time, some say, he [Singhal] represented a steadfast resistance to the use of machine learning inside Google Search. In the past, Google relied mostly on algorithms that followed a strict set of rules set by humans.
I know because I spoke with Metz at length and was quoted in the article.
The Knowledge Graph was, by definition, a rules engine. It was GOFAI in the tradition of Minsky, the semantic web and all the brittleness and human intervention that entailed.
What he's saying here is that Google has relied on machine learning in the form of RankBrain to figure out which results to serve when it's never seen a query before. And the news, in this case, is that statistical methods like RankBrain will take a larger and larger role, and symbolic scaffolding like the Knowledge Graph will take a smaller one.
You are right that the most powerful, recent demonstrations of AI combine neural nets with other algorithms. In the case of AlphaGo, NNs were combined with reinforcement learning and Monte Carlo Tree Search. I don't think a rules engine (the symbolic system you refer to) was involved at all there. Nor is it necessary, if by studying the world our algorithms can intuit its statistical structure and correlations without having them hard coded by humans before hand. It turns they do OK learning from scratch, given enough data.
So in many cases we don't need the massive data entry of a rules engine created painstakingly by humans, which is great, because those are brittle and adapt poorly to the world if left to themselves.
The Knowledge Graph is just a way of encoding the world's structure. The world may reveal its structures to our neural networks, given enough time, data and processing power.