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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Have you ever tried to remember the title of paper you read in an academic journal? You know the author’s name, but not how to spell it? Or, you’re sure that a certain actor starred in a movie directed by a particular director, but can’t remember the title? Wouldn’t it be great if your favorite search engine could help with that? Now it can.
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Over the past few months Bing shopping and recipes experiences have benefited from multiple enhancements impacting visual richness and overall quality. In the last multimedia post (The Image Graph - Powering the Next Generation of Bing Image Search) we talked about how we construct the Image Graph offline powering the backend of our experiences.So let’s talk more about the online and front-end part of this equation—features we created...
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In a never-ending quest to delight users with fresh, relevant features, Bing has been improving their innovation rate and code velocities. Four years ago, Bing engineers deployed new features once a month. To accelerate feature deployment and innovation, we have invested much effort in overcoming software engineering challenges. The monthly deployment cadence has been gone for some time, taking with it both the old culture and most of the...
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This month, we released a new Bing image search experience designed to help customers be inspired, learn more, and do more with image search. We discover billions of images on the Internet, but understanding the searches and providing helpful information for each image is an enormous challenge. In this blog post, my colleagues Arun Sacheti and Eason Wang provide a deeper look at how we built the image graph that allows us to automatically derive...
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In the past, we’ve provided an in-depth view of how Bing.com improves relevance for users in web and image search, along with ways we’ve improved experimentation. Leveraging technology similar to search, the team has embarked on the relatively new area we call “Bing Predicts.” This feature analyzes web activity, social sentiment, and other signals to predict the outcomes of events. My colleague, Walter Sun, the technical lead for the project...
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In today’s post we take a look at how the Bing ranking algorithm considers content quality for ranking web documents. There are often many web documents on the web that are relevant for any given user query and an effective ranking algorithm should consider not only relevance but also the authority, utility and presentation of the content in order to provide the best search results. My colleague Michael Basilyan will provide some insights on how...
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A few months ago we provided a behind the scenes look into how Bing is improving image search quality. In this post we wanted to explore some additional techniques we are employing to deliver high quality image results. My colleague Eason Wang will give you a closer look at how we are incorporating aesthetics to deliver more beautiful image results in Bing.
– Dr. Jan Pedersen, Chief Scientist, Bing and Information Platform R&D
As you can...
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