Recent progress in natural language understanding (NLU) has been driven in part by the availability of large-scale benchmarks that provide an environment for researchers to test and measure the performance of AI models. Most of these benchmarks are designed for academic settings--typically datasets that feature…
As part of Microsoft AI at Scale (opens in new tab), the Turing family of NLP models are being used at scale across Microsoft to enable the next generation of AI experiences. Today, we are happy to announce that the latest Microsoft Turing model (T-NLRv5)…
Editor’s note: The researchers would like to acknowledge co-authors Christopher Takahashi, Gagan Gupta, Jake Smith, Richard Rouse, Paul Berndt, Sergey Yekhanin, David Ward, Siena Ang, Patrick Garvan, Hsing-Yeh Parker, Rob Carlson, Douglas Carmean, and Luis Ceze for their contributions to this work. Current estimates by…
Generative image models offer a unique value by creating new images. Such images can be sharp super-resolution versions of existing images or even realistic-looking synthetic photographs. Generative Adversarial Networks (GANs) and their variants have demonstrated pioneering success with the framework of training two networks against…
Mixture of experts (MoE) is a deep learning model architecture in which computational cost is sublinear to the number of parameters, making scaling easier. Nowadays, MoE is the only approach demonstrated to scale deep learning models to trillion-plus parameters, paving the way for models capable…
Today, we’re excited to announce the release of SynapseML (previously MMLSpark), an open-source library that simplifies the creation of massively scalable machine learning (ML) pipelines. Building production-ready distributed ML pipelines can be difficult, even for the most seasoned developer. Composing tools from different ecosystems often…
Machine learning (ML) offers tremendous opportunities to increase productivity. However, ML systems are only as good as the quality of the data that informs the training of ML models. And training ML models requires a significant amount of data, more than a single individual or…
Today, the Microsoft Turing team (opens in new tab) is thrilled to introduce Turing Bletchley, a 2.5-billion parameter Universal Image Language Representation model (T-UILR) that can perform image-language tasks in 94 languages. T-Bletchley has an image encoder and a universal language encoder that vectorize input image and text respectively…
The natural association between visual observations and their corresponding sounds has exhibited powerful self-supervision signals for learning video representations, which makes the ever-growing amount of online video an attractive data source for self-supervised learning. However, online videos often provide imperfectly aligned audio-visual signals because of…
Object recognition systems have made spectacular advances in recent years, but they rely on training datasets with thousands of high-quality, labelled examples per object category. Learning new objects from only a few examples could open the door to many new applications. For example, robotics manufacturing…
For 30 years, Microsoft Research has brought together great minds from around the world to take on the biggest research challenges facing society. As we enter our fourth decade, the need for collaborative research—and the opportunities it presents—have never been greater. That’s why we’re so…
Today, we’re excited to announce that Microsoft Translator has added 12 new languages and dialects to the growing repertoire of Microsoft Azure Cognitive Services Translator, bringing us to a total of 103 languages! The new languages, which are natively spoken by 84.6 million people, are…
