Microsoft explains in detail how it has improved Bing’s automatic bidding recommendations with AI


Microsoft at the beginning of the year detailed ways Bingo was helpful PG and scale, an initiative to apply large-scale artificial intelligence and supercomputing to language processing across all Microsoft applications, services and managed products. Scale AI has greatly enhanced the search engine’s ability to answer questions directly and generate image captions, but a blog article Microsoft claims today that this has led Bing to improve things like auto-completion offers.

Bing and its competitors have a lot to gain from artificial intelligence and machine learning, especially in the field of natural language. Search engines need to understand queries, no matter how confusing they are, but have historically struggled to combine or separate search terms based on Boolean operators (simple words such as “and”, “or”, and “no”). But with the advent of PG, like Google ETRI and the Microsoft Thuringian family, search engines may become more understandable in terms of conversation and context than perhaps ever before.

For example, Bing’s automatic bidding feature, which recommends relevant completed searches that match a partial search, can now better manage next-phrase predictions for new language generation models. When another phrase prediction appears, all phrase suggestions are presented and generated in real time, which means that they are not limited to previously seen data or the word currently being collected.

Another feature of Bing – people are also asking – has improved the latest innovations in artificial intelligence and machine learning. As before, People Also Ask allows users to expand their search scope by finding answers to questions related to their initial search query. But now people are also asking that they can better understand questions they haven’t seen before, using a billions of document-taught model that creates pairs of questions and answers in documents. When the same documents appear on a search results page, Bing uses previously generated pairs to populate the PAA block next to existing previously asked questions.

Microsoft also says that Bing now provides better search in more than 100 languages ​​and more than 200 regions. This is thanks to Microsoft’s Turing Universal Language Representation (T-ULR), which includes a pre-built model called InfoXLM, which promotes multilingual understanding and generation tasks, scenarios, and workloads. The same model has been reused to improve captions in all Bing markets; it provides semantic highlighting, a new feature that extends highlighting to more than just simple keyword matching. For example, instead of highlighting the words “Germany” and “population” in response to the search for “German population,” semantic highlighting would emphasize the actual number of 81,453,631, as the German population in 2019.


“Progress in understanding natural language continues to be very rapid,” Bing wrote. “Bing users make hundreds of millions of search queries every day around the world. These queries vary in a variety of ways, from user aspirations to the languages ​​and regions in which these queries are submitted. To manage such a dynamic range of uses, Bing’s artificial intelligence models need to be constantly evolving. “

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