This is the story of how Google Search died, and the people responsible for killing it.
The story begins on February 5th 2019, when Ben Gomes, Google’s head of search, had a problem. Jerry Dischler, then the VP and General Manager of Ads at Google, and Shiv Venkataraman, then
This is the story of how Google Search died, and the people responsible for killing it.
The story begins on February 5th 2019, when Ben Gomes, Google’s head of search, had a problem. Jerry Dischler, then the VP and General Manager of Ads at Google, and Shiv Venkataraman, then the VP of Engineering, Search and Ads on Google properties, had called a “code yellow” for search revenue due to, and I quote, “steady weakness in the daily numbers” and a likeliness that it would end the quarter significantly behind.
As an programmer, I want to think out loud about possible technical solutions.
I would have kept the understandable / hand-made algorithm as the core of search results. If you want to do fancy machine learning, do it on the periphery and we can include the machine output in our algorithm and weight its importance by hand. This would allow us to back out of the decision, because we could lower the weight of the machine learning output as needed.
It sounds like Google jumped strait to including the machine learning in the core algorithm though, and now with a decade of complexity in the core algorithm they are no longer able to go back without huge effort.
In general, it's important to consider "is this a decision we can easily back out of?".
Exactly, and that's something my company is aggressively moving toward, even though our userbase is nothing like Google's. It's just good engineering to be able to rapidly undo an unfavorable rollout.
Yeah, they seem to do "easy roll-foward." Any service is subject to replacement, given a sufficiently motivated project manager. So if there's a problem in deployment, they just replace the whole thing.