{"id":804280,"date":"2021-12-15T12:54:30","date_gmt":"2021-12-15T20:54:30","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?p=804280"},"modified":"2022-09-20T13:44:31","modified_gmt":"2022-09-20T20:44:31","slug":"research-at-microsoft-2021-collaborating-for-real-world-change","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/research\/blog\/research-at-microsoft-2021-collaborating-for-real-world-change\/","title":{"rendered":"Research at Microsoft 2021:\u202fCollaborating for real-world change"},"content":{"rendered":"\n
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Over the past 30 years, Microsoft Research has undergone a shift in how it approaches innovation, broadening its mission to include not only advancing the state of computing but also using technology to tackle some of the world\u2019s most pressing challenges. That evolution has never been more prominent than it was during this past year.<\/p>\n\n\n\n

Recent events underscore the urgent need to address planet-scale problems. Fundamental advancements in science and technology have a crucial role to play in addressing ongoing societal challenges such as climate change, healthcare equity and access, supply chain logistics, sustainability, security and privacy, and the digital divide. Microsoft Research is increasing focus on these areas and others to help accelerate transformational change and build trust in technology as it evolves. However, these challenges are too large for any single organization to meet alone. They require broader and more diverse coalitions across the global science and technology community, including businesses, scholars, governments, nongovernmental organizations, and local communities.<\/p>\n\n\n\n

This year, Microsoft Research hosted the first-ever Microsoft Research Summit<\/a>, a virtual event that embodied our aspiration to catalyze collaboration and innovation across traditional boundaries. The summit brought together experts from around the world\u2014a mix of speakers from Microsoft and external organizations\u2014to critically examine the way technology can increase understanding and further drive advancement; support creativity and achievement; build a resilient, sustainable society; and open healthcare advances to all while maintaining ethical practices that put people first.<\/p>\n\n\n\n

This post explores just some of the work that\u2019s been done this year by Microsoft Research, alongside its partners and collaborators, to drive real-world impact in critical areas, and our aspirations for further impact in the years to come.<\/p>\n\n\n\n

Leading the way for real-world impact<\/h2>\n\n\n\n

Advancing human knowledge and foundational technologies<\/h3>\n\n\n\n

Fundamental insights into technology and computing can inspire breakthroughs and new computing paradigms while helping to drive scientific discovery forward. In his plenary talk at Research Summit<\/a>, Peter Lee, Corporate Vice President, Microsoft Research & Incubations, cited \u201cThe Usefulness of Useless Knowledge,\u201d an essay published in Harper\u2019s Magazine in 1939 by pioneering educator Abraham Flexner. Among other things, the essay stresses the role that curiosity and exploration play in game-changing technological leaps. It\u2019s at this root of invention and innovation, Flexner argues, where patience and belief in shared knowledge is key.<\/p>\n\n\n\n

LAMBDA<\/a>, one of this year\u2019s first big announcements, shows how the Microsoft research community can make significant contributions to products and customers when given the time and freedom to follow their curiosities. In this case, the product was Microsoft Excel<\/a>\u2014a program that has benefitted from the efforts of research teams over time. The feature, which resulted from collaboration between members of the Calc Intelligence<\/a> and Excel teams, gives users the ability to define custom worksheet functions in Excel\u2019s formula language, making the program Turing-complete<\/em>, that is, allowing any computation to be written in the Excel formula language.<\/p>\n\n\n\n

Podcast: Advancing Excel as a programming language with Andy Gordon and Simon Peyton Jones<\/a><\/figcaption><\/figure>\n\n\n\n
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To make networks in data centers more scalable to future needs, researchers in Optics for the Cloud<\/a> explored how optical circuit switches<\/a> could replace resource-heavy electrical switches at a network\u2019s core. They demonstrated the system\u2019s potential to switch between wavelengths at nanosecond speeds\u2014a necessity for supporting low-latency networks at the scale required\u2014using a microcomb and semiconductor optical amplifiers.<\/p>\n\n\n\n

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A research team moved the bar forward for DNA storage, introducing a proof-of-concept molecular controller<\/a> in the form of a tiny DNA storage writing mechanism on a chip. The chip demonstrated the ability to pack DNA-synthesis spots three orders of magnitude more tightly than before and results in much higher DNA writing throughput than current systems.<\/p>\n\n\n\n

AI at Scale<\/a> continued to gain momentum in 2021. With exponential growth this year, large artificial intelligence (AI) models trained using deep learning are one example of fundamental science where applications in the real world are becoming more ubiquitous. Microsoft Research teams were recognized for advancing the state of the art and developing new multilingual capabilities to build more inclusive language technologies using AI as well as pushing the boundaries of natural language processing (NLP) and computer vision.<\/p>\n\n\n\n

In June 2021, Microsoft Research\u2019s LReasoner system set a new standard for logical reasoning ability among pretrained language models. It reached the top of the official leaderboard for ReCLor, a dataset built using questions from the LSAT and GMAT, two standardized admissions tests.<\/p>\n\n\n\n

Microsoft Turing\u2019s T-ULRv5<\/a> achieved breakthrough performance on the XTREME leaderboard in September. A few weeks later, Microsoft Turing\u2019s model T-NLRv5 reached the top of the SuperGLUE and GLUE leaderboards.<\/a> Ultimately, these benchmarks and respective leaderboards help to measure progress toward creating AI that better understands language and better converses with people within and across language boundaries. To understand how quickly these advances are happening, one need only look to Megatron-Turing NLG<\/a>, the language generation model with 530 billion parameters trained to convergence\u2014a collaboration between Microsoft and NVIDIA.<\/p>\n\n\n\n

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Trend of sizes of state-of-the-art NLP models over time<\/figcaption><\/figure>\n\n\n\n

To train the Megatron-Turing model, DeepSpeed (opens in new tab)<\/span><\/a> and NVIDIA Megatron-LM (opens in new tab)<\/span><\/a> paired up to create an efficient and scalable 3D-parallel system harnessing data parallelism, pipeline parallelism, and tensor slicing\u2013based parallelism. Beyond this achievement, the DeepSpeed optimization library added a number of features and tools this year, including DeepSpeed Inference<\/a>, its first foray into improving inference latency and cost using multiple graphic processing units (GPUs). The team also introduced DeepSpeed MoE<\/a>, supporting five types of parallelism and training 8x larger models when compared with existing systems. Zero-Infinity<\/a> allowed for scaling of large model training from one to thousands of GPUs, furthering its effort to democratize model training for everyone.<\/p>\n\n\n\n

These large AI models are impressive in their own right, but it\u2019s what they\u2019re able to do to support people and democratize innovation that makes them especially valuable. Advances in language technologies resulted in the expansion of Microsoft translation<\/a> and spelling-correction<\/a> technologies into over 100 languages, breaking down language barriers in products like Microsoft Bing and Microsoft Translator<\/a>. The Microsoft Turing Team also introduced Turing Bletchley<\/a>, a 2.5 billion-parameter Universal Image Language Representation model (T-UILR) that can perform image-language tasks in 94 languages. <\/p>\n\n\n\n

Meanwhile, researchers from Microsoft Research Asia<\/a> worked on bridging the gap between computer-language and computer-vision modeling. In October, members of the Visual Computing group<\/a> won the Conference on Computer Vision (ICCV) 2021 award for their paper on the Swin Transformer<\/a>. This vision transformer surpasses the state of the art with its high performance, flexibility, and linear complexity, making it compatible with a broad range of vision tasks. With this work, the research team hopes to inspire additional research in this area that will ultimately enable joint modeling between the computer vision and language domains. Research teams in the same lab examined the potential for transformers to find success beyond language and vision, demonstrating the neural network architecture can be applied to graph representation learning. With their standard transformer architecture Graphormer, the researchers achieved state-of-the-art performance in the KDD Cup 2021 graph-level prediction track and topped popular graph-level prediction leaderboards<\/a>.<\/p>\n\n\n\n

Amplifying human creativity and achievement<\/h3>\n\n\n\n

People are multidimensional, pursuing goals and tasks across different areas of their lives, under a variety of circumstances. Microsoft researchers are dedicated to not only helping individuals accomplish more in their personal, professional, and creative lives, but also to helping them feel more confident doing so. <\/p>\n\n\n\n

Over the past year and a half, researchers and product teams throughout Microsoft have responded swiftly to workplace challenges and opportunities arising from the pandemic. Supporting organizations in executing hybrid work models, they explored technology as an intermediary between people who are physically in the room and those who are not, with some researchers presenting their findings in a hybrid meeting prototype during Research Summit<\/a>. Researchers investigated remote and hybrid work from a variety of angles\u2014from longitudinal studies on multitasking behavior<\/a> to workplace communication insights<\/a> gleaned using network machine learning\u2014to understand where technology needs to grow to help people thrive under these fluid working conditions. Previous and ongoing work in this area is captured by the New Future of Work Initiative<\/a> and the annual Work Trend Index<\/a>.<\/p>\n\n\n\n

As ML techniques and approaches advance, so does the potential for applications to empower individuals in the workplace and beyond does, too. Research teams are leveraging few-shot learning to help support AI that is truly more customizable to the individual<\/a> with the ORBIT dataset and benchmark<\/a>. The dataset and benchmark are inspired by a real-world application for people who are blind or have low vision called teachable object recognizers. The dataset strives to reflect the variance within object types and input quality that recognition systems will encounter in the day-to-day, while the benchmark challenges models to identify objects for single users from a few, high-variation examples. <\/p>\n\n\n\n

Earlier in the year, at the CHI 2021 Conference on Human Factors in Computing Systems<\/a>, researchers presented tools and learnings guided by a changing definition of accessibility, one that focuses on helping individuals rise above limitations imposed by a world built to accommodate the majority<\/a> to realize their full capabilities. Also, members of the Enable Group<\/a> explored the continuing evolution of Soundscape<\/a>, an app that uses 3D spatial audio to elevate users\u2019 perception of an environment they\u2019re navigating.<\/p>\n\n\n\n

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