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![\"Google](\"\/wpe-content\/uploads\/2019\/11\/when-was-london-founded.jpg\")
<\/p>\nFor all its myriad patents, products, services, and initiatives, Google’s goal is neatly summed up in a concise mission statement: to organize the world’s information and make it universally accessible and useful.<\/p>\n
The search giant’s latest venture\u2014an AI endeavor named BERT\u2014encapsulates this effort perfectly, representing the next logical step in Google’s attempts to interpret exactly what users are looking for. But how will it work? And will there be any practical implications for marketers?<\/p>\n
To provide users with the best possible results, Google has always invested heavily in its ability to understand the true meaning behind search queries.<\/p>\n
On the surface, we have seen this manifested in the form of quick-fix rich snippets in SERPs. Answer Boxes, for instance, instantly satisfy short queries.<\/p>\n
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Definition-type searches also usually serve up an Answer Box. Most “how to” queries are answered with video results, while searches comprising “comparisons”, “best of”, and “top 10” are regularly addressed by easy-to-digest charts and tables.<\/p>\n
![\"Google](\"\/wpe-content\/uploads\/2019\/11\/nba-top-scorers.jpg\")
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Google’s desire to best answer user queries can be seen in many of its patents and updates. We’ve seen the importance of user signals grow steadily over the past five years, culminating in the March 2019 Core Algorithm Update<\/a> earlier this year.<\/p>\n Google has also long attempted to better understand human language itself, evolving from Latent Semantic Indexing (now pretty much debunked) through to machine learning and their Natural Language API<\/a>. The latest chapter in this story is their recent BERT update, which is currently being tested in North America\u2014as we touched upon in our October LPM Roundup<\/a>\u2014and is expected to roll out continuously in the foreseeable future.<\/p>\n Although the meaning behind the acronym is rather dry (Bidirectional Encoder Representations from Transformers), BERT might just be Google’s most pioneering attempt yet to understand conversational language.<\/p>\n It places a special emphasis on important-but-often-ambiguous prepositions such as “from” and “to”; words that form the common building blocks of language but are notoriously difficult for computers to understand.<\/p>\n In Google’s official example above, the query is slightly ambiguous\u2014even for humans\u2014but it is clear enough that the searcher is trying to research the requirements for a Brazilian traveling to the US. The second SERP (after BERT came into effect) has extrapolated this essential intent and served up a much more useful, practical, and relevant result.<\/p>\n According to Google, BERT is the beginning of an ongoing attempt to rid the net of “keywordese”<\/a>, the unnatural way that users currently search. Their aim is to gradually replace the common preference for using arbitrary strings of words…<\/p>\n ..with queries that are more conversational and nuanced.<\/p>\n These efforts to understand colloquial language are likely tied to the anticipated widespread integration of digital voice. As we covered in our free voice search white paper<\/a>, the growing anthropomorphization of digital assistants is increasing the need for these interfaces to understand casual questions. Users increasingly expect context-sensitive answers to their informal questions and requests.<\/p>\n \n Get exclusive insights into digital So… Who is BERT?<\/h2>\n
![\"BERT](\"\/wpe-content\/uploads\/2019\/10\/bert-in-action.jpg\")
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