MEB, for “Make Antarctica Email List feature Binary”, is Bing’s new algorithm, presented by Microsoft in August 2021. The MEB algorithm is already operational on the search engine, and should make it possible to obtain better results. relevant thanks to artificial intelligence. But, why a new algorithm? What should we take away from the Make Every feature Binary project? Released in 2008, and made public in 2009, Bing is now the second largest search engine in France after Google. If it is in second place, behind the web giant, Bing brings together only 3.44% of French Internet users against 92.5% of French for Google.
Today, Bing brings together more French Internet users than Yahoo (1.09%), Ecosia (0.97%), Qwant (0.88%), and the other search engines in terms of the number of visitors. So, to keep its second place, Bing is looking to innovate and optimize its algorithm. In this drive towards innovation, Bing released the MEB algorithm. For what efficiency? The MEB project, “Make Every feature Binary”, is an algorithm that should improve research thanks to AI at Scale which is based on years of work on systems by Microsoft researchers, in particular in the field of parallel computing, that enable machine learning models to be trained on an unprecedented scale faster.
Bing, the second largest search engine
The AI at Scale initiative is pioneering a new approach that translates into next-generation AI capabilities and data classification that will be scaled up across business AI products and platforms. MEB has several advantages, including identifying negative relationships between words or phrases, revealing what users don’t want to see for a query. For example, MEB learned that “Hotmail” correlates strongly with “Microsoft Outlook”, even though they are not close to each other in terms of semantic meaning. MEB notes a nuanced relationship between these words: Hotmail was a free webmail service provided by Microsoft which later changed its name to Microsoft Outlook.
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MEB: the new algorithm from Bing
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