Artificial Intelligence Is Transforming Google Search. The Rest of the Web Is Next (GOOG)
From Wired:
Yesterday, the 46-year-old Google
veteran who oversees its search engine, Amit Singhal, announced his
retirement. And in short order, Google revealed that Singhal’s rather
enormous shoes would be filled by a man named John Giannandrea. On one
level, these are just two guys doing something new with their lives. But
you can also view the pair as the ideal metaphor for a momentous shift
in the way things work inside Google—and across the tech world as a
whole.
Giannandrea, you see, oversees Google’s work in artificial intelligence. This includes deep neural networks,
networks of hardware and software that approximate the web of neurons
in the human brain. By analyzing vast amounts of digital data, these
neural nets can learn all sorts of useful tasks, like identifying
photos, recognizing commands spoken into a smartphone, and, as it turns
out, responding to Internet search queries. In some cases, they can
learn a task so well that they outperform humans. They can do it better.
They can do it faster. And they can do it at a much larger scale.
This approach, called deep learning, is rapidly reinventing so many
of the Internet’s most popular services, from Facebook to Twitter to
Skype. Over the past year, it has also reinvented Google Search, where
the company generates most of its revenue. Early in 2015, as Bloomberg recently reported,
Google began rolling out a deep learning system called RankBrain that
helps generate responses to search queries. As of October, RankBrain
played a role in “a very large fraction” of the millions of queries that
go through the search engine with each passing second.
As Bloomberg says, it was Singhal who approved the roll-out
of RankBrain. And before that, he and his team may have explored other,
simpler forms of machine learning. But for a time, some say, he
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. The concern—as described
by some former Google employees—was that it was more difficult to
understand why neural nets behaved the way it did, and more difficult to
tweak their behavior.
These concerns still hover over the world of machine learning. The
truth is that even the experts don’t completely understand how neural
nets work. But they do work. If you feed enough photos of a
platypus into a neural net, it can learn to identify a platypus. If you
show it enough computer malware code, it can learn to recognize a virus.
If you give it enough raw language—words or phrases that people might
type into a search engine—it can learn to understand search queries and
help respond to them. In some cases, it can handle queries better than
algorithmic rules hand-coded by human engineers. Artificial intelligence
is the future of Google Search, and if it’s the future of Google
Search, it’s the future of so much more.
Sticking to the Rules
This past fall, I sat down with a former Googler who asked that I
withhold his name because he wasn’t authorized to talk about the
company’s inner workings, and we discussed the role of neural networks
inside the company’s search engine. At one point, he said, the Google
ads team had adopted neural nets to help target ads, but the “organic
search” team was reluctant to use this technology. Indeed, over the
years, discussions of this dynamic have popped up every now and again on Quora, the popular question-and-answer site.
Edmond Lau, who worked on Google’s search team and is the author of the book The Effective Engineer,
wrote in a Quora post that Singhal carried a philosophical bias against
machine learning. With machine learning, he wrote, the trouble was that
“it’s hard to explain and ascertain why a particular search result
ranks more highly than another result for a given query.” And, he added:
“It’s difficult to directly tweak a machine learning-based system to
boost the importance of certain signals over others.” Other ex-Googlers
agreed with this characterization.
Yes, Google’s search engine was always driven by algorithms that
automatically generate a response to each query. But these algorithms
amounted to a set of definite rules. Google engineers could readily
change and refine these rules. And unlike neural nets, these algorithms
didn’t learn on their own. As Lau put it: “Rule-based scoring metrics,
while still complex, provide a greater opportunity for engineers to
directly tweak weights in specific situations.”
But now, Google has incorporated deep learning into its search engine.
And with its head of AI taking over search, the company seems to believe
this is the way forward....MORE