Azure Cognitive Search is a cloud search service that gives developers APIs and tools to build rich search experiences over private, heterogeneous content in web, mobile, and enterprise applications. It has multiple components, including an API for indexing and querying, seamless integration through Azure data ingestion, deep integration with Azure Cognitive Services, and persistent storage of user-owned indexed content. At the heart of Azure Cognitive Search is its full text, keyword-based search engine built on the BM25 algorithm—an industry standard in information retrieval.   We’ve found that what customers desire next is higher-quality results out of the box with less effort, enabling […]

The purpose of the Association for the Advancement of Artificial Intelligence, according to its bylaws, is twofold. The first is to promote research in the area of AI, and the second is to promote the responsible use of these types of technology. The result was a 35th AAAI Conference on Artificial Intelligence (AAAI-21) schedule that broadens the possibilities of AI and is heavily reflective of a pivotal time in AI research when experts are asking bigger questions about how best to responsibly develop, deploy, and integrate the technology.   Microsoft and its researchers have been pursuing  

In video games, nonplayer characters, bots, and other game agents help bring a digital world and its story to life. They can help make the mission of saving humanity feel urgent,  

From a research point of view, games offer an amazing environment in which to develop new machine learning algorithms and techniques. And we hope, in due course, that those new algorithms will feed back not just into gaming, but into many other domains. Beyond the very technical machine learning techniques themselves, gaming is an environment in which we can explore the relationship between AI and people, and see how they can work in partnership. It’s a very rich environment in […]

Under now-standard techniques, such as over-parameterization, batch-normalization, and adding residual links, “modern age” neural network training—at least for image classification tasks and many others—is usually quite stable. Using standard neural network architectures and training algorithms (typically SGD with momentum), the learned models perform consistently well, not only in terms of training accuracy but even in test accuracy, regardless of which random initialization or random data order is used during the training. For instance, if one trains the same WideResNet-28-10 architecture […]