Microsoft details how it improved Bing’s autosuggest recommendations with AI

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Earlier in the year, Microsoft detailed the ways Bing has benefited from AI at Scale, an initiative to apply large-scale AI and supercomputing to language processing across Microsoft’s apps, services, and managed products. AI at Scale chiefly bolstered the search engine’s ability to directly answer questions and generate image captions, but in a blog post today, Microsoft says it has led to Bing improvements in things like autocomplete suggestions.

Bing and its competitors have a lot to gain from AI and machine learning, particularly in the natural language domain. Search engines need to comprehend queries no matter how confusingly they’re worded, but they’ve historically struggled with this, leaning on Boolean operators (simple words like “and,” “or,” and “not”) as band-aids to combine or exclude search terms. But with the advent of AI like Google’s BERT and Microsoft’s Turing family, search engines have the potential to become more conversationally and contextually aware than perhaps ever before.

Bing’s autosuggest feature, for instance, which recommends relevant completed searches matching a partial search, can now better handle next phrase prediction thanks to new language generation models. With next phrase prediction, full phrase suggestions are surfaced and generated in real time, meaning they’re not limited to previously seen data or the current word…

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