Groups Similar Look up By Text Browse About

Similar articles
Article Id Title Prob Score Similar Compare
135362 ARSTECHNICA 2019-5-15:
Microsoft open sources algorithm that gives Bing some of its smarts
1.000 Find similar Compare side-by-side
135354 TECHCRUNCH 2019-5-15:
Microsoft open-sources a crucial algorithm behind its Bing Search services
0.967 0.627 Find similar Compare side-by-side
135440 VENTUREBEAT 2019-5-15:
Microsoft open-sources key Bing Search search algorithm
0.974 0.624 Find similar Compare side-by-side
135364 TECHREPUBLIC 2019-5-15:
How to add cognitive services to your Microsoft Azure resources
0.435 Find similar Compare side-by-side
135336 TECHCRUNCH 2019-5-15:
Oh no, there’s A.I. whiskey now
0.404 Find similar Compare side-by-side
135495 VENTUREBEAT 2019-5-16:
Microsoft makes Google’s BERT NLP model better
0.379 Find similar Compare side-by-side
135228 TECHREPUBLIC 2019-5-14:
Feniks: Microsoft's cloud-scale FPGA operating system
0.363 Find similar Compare side-by-side
135383 THENEXTWEB 2019-5-15:
Microsoft’s AI hallucinates unique whisky flavors
0.337 Find similar Compare side-by-side
135795 TECHCRUNCH 2019-5-17:
Microsoft aims to train and certify 15,000 workers on AI skills by 2022
0.323 Find similar Compare side-by-side
135002 THEVERGE 2019-5-13:
Use this cutting-edge AI text generator to write stories, poems, news articles, and more
0.322 Find similar Compare side-by-side
135553 TECHCRUNCH 2019-5-16:
Rivals in gaming, Microsoft and Sony team up on cloud services
0.317 Find similar Compare side-by-side
135034 THENEXTWEB 2019-5-13:
Here’s why I’m genuinely excited about GitHub’s new package registry
0.316 Find similar Compare side-by-side
135312 TECHCRUNCH 2019-5-15:
7 accessibility-focused startups snag grants from Microsoft
0.314 Find similar Compare side-by-side
135592 ENGADGET 2019-5-16:
Microsoft invests in seven AI projects to help people with disabilities
0.312 Find similar Compare side-by-side
135193 TECHREPUBLIC 2019-5-14:
Beginner's guide for TensorFlow: The basics of Google's machine-learning library
0.308 Find similar Compare side-by-side
135643 THEVERGE 2019-5-16:
Microsoft and Sony form cloud gaming and AI partnership
0.306 Find similar Compare side-by-side
135108 VENTUREBEAT 2019-5-14:
Transform 2019: Hear from the movers and shakers in AI
0.305 Find similar Compare side-by-side
135358 THEVERGE 2019-5-15:
Angry Redditors are trying to Google bomb Game of Thrones writers
0.301 Find similar Compare side-by-side
135467 VENTUREBEAT 2019-5-15:
Microsoft announces latest AI for Accessibility grant recipients
0.297 Find similar Compare side-by-side
135733 VENTUREBEAT 2019-5-17:
ProBeat: Microsoft and Sony deal validates Google Stadia
0.292 Find similar Compare side-by-side
135594 THENEXTWEB 2019-5-16:
Designing products for people with disabilities has never been so important
0.280 Find similar Compare side-by-side
135645 VENTUREBEAT 2019-5-16:
Sony and Microsoft, kissing in a tree, working on AI and cloud technology
0.276 Find similar Compare side-by-side
135470 TECHCRUNCH 2019-5-15: wants to bring order to service meshes with centralized management hub
0.275 Find similar Compare side-by-side
135186 TECHCRUNCH 2019-5-14:
Algorithmia raises $25M Series B for its AI automation platform
0.272 Find similar Compare side-by-side
135526 TECHREPUBLIC 2019-5-16:
AI and machine learning: Top 6 business use cases
0.272 Find similar Compare side-by-side


ID: 135362


Date: 2019-05-15

Microsoft open sources algorithm that gives Bing some of its smarts

You can ask "How tall is the tower in Paris?" and it knows what you're talking about. Search engines today are more than just the dumb keyword matchers they used to be. You can ask a question—say, "How tall is the tower in Paris?"— and they'll tell you that the Eiffel Tower is 324 meters (1,063 feet) tall, about the same as an 81-story building. They can do this even though the question never actually names the tower. How do they do this? As with everything else these days, they use machine learning. Machine-learning algorithms are used to build vectors—essentially, long lists of numbers—that in some sense represent their input data, whether it be text on a webpage, images, sound, or videos. Bing captures billions of these vectors for all the different kinds of media that it indexes. To search the vectors, Microsoft uses an algorithm it calls SPTAG ("Space Partition Tree and Graph"). An input query is converted into a vector, and SPTAG is used to quickly find "approximate nearest neighbors" (ANN), which is to say, vectors that are similar to the input. This (with some amount of hand-waving) is how the Eiffel Tower question can be answered: a search for "How tall is the tower in Paris?" will be "near" pages talking about towers, Paris, and how tall things are. Such pages are almost surely going to be about the Eiffel Tower. Microsoft has released today the SPTAG algorithm as MIT-licensed open source on GitHub. This code is proven and production-grade, used to answer questions in Bing. Developers can use this algorithm to search their own sets of vectors and do so quickly: a single machine can handle 250 million vectors and answer 1,000 queries per second. There are some samples and explanations in Microsoft's AI Lab, and Azure will have a service using the same algorithms. Microsoft CEO Satya Nadella has spoken on a number of occasions of his desire to "Democratize AI" and make it available to everyone, creating not just a centralized, specialized tool that demands considerable expertise but something that a wide range of developers, solving a wide range of problems, can use as part of their toolkit. The release of SPTAG is an example of how Microsoft is putting those words into practice; the combination of an Azure service and open source means that developers can start with the more constrained, easy-to-use service, and as their expertise or requirements grow more complex, they can use SPTAG to build their own services.