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AI in Google Search

As overseer of Google’s search engine, Amit Singhal, announced his retirement, the overseer of Google’s artificial intelligence, John Giannandrea, stepped up to fill Singhal’s place. Google’s approach to artificial intelligence which involves deep neural networks approximating to the neurons in a human brain is known as deep learning. Deep learning analyzes a lot of data, creating neural nets to learn tasks like recognizing photos, spoken words, and now search queries. Google’s deep learning system, known as RankBrain, interprets human language and query and learns what people prefer whereas the hundreds of other methods for returning a search result use inputted data and tons of experiments. This grows this “gut feeling” and “guessability” of people. Computers generally have trouble with colloquial language, but RankBrain will take in this colloquial language, as humans do, and guess at the meaning based on some past experience and the large net of data to which it has access.

In class, we talked about coming up with the query with the “best” answers. This involved taking weighted votes via in-links and constantly updating the authority based on the hub scores and thereby updating the quality of the list. Understandably, Google already seems to have that without any form of artificial intelligence taking care of this. However, RankBrain seems to take this searching a step further by understanding human language on a deeper more “human” level on top of constantly improving its quality of search queries. From my limited understanding of artificial intelligence and deep learning, RankBrain now has the potential to make the search not only more effective and efficient but more informed to make future decisions and predictions.



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