Bing users around the globe perform hundreds of millions of search queries every day. These queries are diverse in many ways, from the intent the users are seeking to fulfill, to the languages and regions where these queries are issued. To handle such a dynamic range of usage, AI models in Bing must continuously evolve and therefore Bing is the prime example of Microsoft AI at Scale. This blog post introduces recent updates to Bing that are making...
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Today we share more about continued evolution of Bing Image Search towards a more intelligent search engine through multi-granularity matches, improved understanding of user queries, images and webpages, as well as the relationships between them.
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Tablets are an important part of the Bing success story, with many users looking for the latest news, checking out sport scores and searching for entertainment. To better optimize for such user intent across devices of varying screens sizes and aspect ratios while providing fluid and aesthetically pleasing experience we at Microsoft completely redesigned the Bing homepage on Amazon Fire tablets using React + Redux.
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This post describes the various machine learning techniques used in GIF search by Bing. To provide the best GIF search experience Bing employs techniques such as sentiment analysis, OCR, and even pose modeling of subjects appearing in GIF flicks to reflect subtle intent variations of your queries. Read on to find out more about how we made it work.
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This blog post describes how Bing does video summarization at internet scale using machine learning. Videos account for some of the richest and most delightful content on the web. But it can be difficult to tell which cat video is really going to make you LOL. That can be a frustrating and time-consuming process and it’s why we decided to help by building a smart preview feature to improve our search results and help our users more...
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Searching for businesses along a route, like gas stations and coffee shops, is one of the many useful features provided by Bing Maps. See how Bing quickly compares thousands of business points along a route using the deviation in trip time, with help from pre-processing huge amounts of geospatial data.
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As announced earlier this week on the Bot Framework Blog, the Microsoft Cognitive Services QnA Maker, is a free, easy-to-use, REST API- and web-based service that trains AI to respond to users’ questions in a conversational way. QnA Maker works in three steps: extraction, training, and publishing. To start, feed it anything from existing FAQ URLs to documents and editorial content.
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Bing Search APIs (including Web Search, Image Search, Video Search, News Search as one collection) give developers the ability to bring the knowledge and intelligence of web search right into their experiences, and intelligence is always at the forefront of our conversations with partners.
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We are entering an exciting new era of more personal, natural computing, enabled by Artificial Intelligence (AI). In this first post on how Bing technology is being used by developers across the industry, we touch upon our work to enable more natural and conversational experiences that are designed around people.
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Experimenting at large scale is fundamental for improving Bing. Last June, we published a blog in this Search Quality Insights series titled Experimentation and Continuous Improvement at Bing, which covered a specific type of experiments known as interleaving. In this blog, Dr. Ronny Kohavi describes our broader online experimentation efforts at large scale and includes compelling examples that illustrate the power of these efforts, e.g., he shows...
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