Indeed it works very well. But we all experience examples where it falls flat. Often because it does not know the context of its use. We cheerfully act as the intelligent editor of the process, unless it induces risk or chaos or takes far more time than we expect.
Think Search Is Solved? Think Again
Alex Woodie in DataNami
Search is one of the oldest technologies around. Ever since the dawn of the World Wide Web, a search engine has been the portal through which we obtain information. The search for a better search engine index kick started the Hadoop craze, and it continues to drive Google to push the limits of technology. But don’t for a second think that search has been solved.
“Who said it’s solved?” barked Coveo’s Director of AI Ciro Greco in a recent interview with Datanami. “Search is far from being solved. It’s the hardest thing we do. It’s the hardest thing everybody does.”
Coveo is one of a handful of companies building the next generation of search engines, although even calling it that seems to be a disservice to someone. Forrester calls the field “Cognitive Search,” thanks to the abundance of machine learning, natural language understanding, and deep learning embedded in these products. Gartner, in turn, calls them “Insight Engines,” because they deliver more context than mere search engines.
“In contrast to search engines that provide links to original source materials such as documents and videos,” Gartner analysts write in a September 2019 Magic Quadrant report, “insight engines can also provide contextual information about the fact or entity in question.”
Today’s enterprise search engines use the index as the starting point for surfacing insights. But beyond just returning information based on the degree to which an entered term matches a predefined keyword stored in an index, modern search engines bring other data and technology to the party, including text analytics and machine learning technology that try to predict what the user is trying to find.
For Greco, the nature of search itself opens up such a rich field for exploration that it could never possibly be perfectly solved. For example, if people could always be relied upon to enter the perfect search term, there wouldn’t be much of a need for more elaborate search technology. But, of course, we’re all human, and so we can’t be relied upon to do that.
“If you go in a website because you have a problem with a product and you want to use a search engine to find the right information in your knowledge base, what are you going to do?” he asks. “Do you know exactly what you’re looking for? No, because you have a problem right. Do you know exactly where that thing is stored and how it is expressed in the documentation that this company has? Obviously not.
“So what you’re trying to do when you do search is actually you’re trying to the best of your knowledge to guess the intent of the human being through different layers that separate you from this person,” he continues. “It is as hard as trying to understand the map of New York City from the people that go in and out from JFK, if that is the little information that you have.” ... '
No comments:
Post a Comment