In the news | OctoML

OctoML engineering collaborated with Microsoft Research on the “Watch For” project, an AI system for analyzing live video streams and identifying specified events within the streams. The collaboration sped up inference for the deep learning algorithms that analyze video streams,…

CodeBERT

In the news | Microsoft Innovation

A Pre-Trained Model for Programming and Natural Languages.

DeepSpeed MoE powers eight times bigger models using expert-parallelism + ZeRO-Offload compared with expert-parallelism only. A graph shows supported model sizes on NVIDIA A100 GPUs. DeepSpeed MoE scales near-linearly with respect to the number of GPUs. Z-code MoE (10B) consistently outperforms other systems on BLEU scores for an in-house 50 language test dataset. Read more in the blog post. 
Microsoft Research Blog

| DeepSpeed Team and Z-code Team

Today, we are proud to announce DeepSpeed MoE, a high-performance system that supports massive scale mixture of experts (MoE) models as part of the DeepSpeed (opens in new tab) optimization library. MoE models are an emerging class of sparsely activated…

hand holding a book bridging the gap in primary education for children passing by
Articles

Our guidelines for informing inclusive product design for students are a work in progress. We began with Microsoft’s universal design principles and the Universal Design for Learning guidelines and supplemented those with research other teams within the company conducted with…

In the news | Microsoft Educator Developer Blog

The Microsoft Learn Student Ambassadors community is for students who want to use tech to solve real-world problems with like-minded peers, establish themselves as mentors and leaders in their community, and amplify their impact. The Microsoft Learn Student Ambassadors, Health League…

comodgan

In the news | AI Lab

CoModGAN is an image completion tool that uses AI to complete an image that is missing significant amounts of visual information. Two neural networks—a generator tasked with filing in missing information and a discriminator that analyzes the realism of the…

Portraits of Microsoft researchers Sid Suri and Jaime Teevan photographed in black and white. Both smile and look forward. Teevan, on the right, is holding a cell phone in the lower right of the frame.
Microsoft Research Podcast

In this episode of The New Future of Work series, Chief Scientist Jaime Teevan and Senior Principal Researcher Siddharth Suri explore the many ways people were impacted by work shifts during the COVID-19 pandemic. They talk about how race, gender, income, and other factors are indicative of how…

An illustration of resolving a bad merge into a safe merge. Moving from left to right, circles on a continuous line represent code commits in a version control system. A circle labeled “Base” is the most common ancestor of the commits marked A and B, respectively. All three commits pass the project’s quality gates, denoted by green check marks alongside each of these commits. The subsequent merge results in a failure of some quality gate, denoted by a blue circle labeled “Bad merge” with a red x above it. The repair uses machine learning, denoted by an abstract image of a neural network, and program verification and synthesis, denoted by a formal inference rule containing math symbols, to construct a safe merge that passes the quality gate, denoted by a circle outlined in green with a green check mark above it.   
Microsoft Research Blog

| Shuvendu Lahiri

Since the computing world began embracing an open-source approach to programming, building software has become increasingly collaborative. Members of development teams with as few as two developers and as many as thousands are simultaneously editing different components in creating software…