Research at Microsoft 2020: Addressing the present while looking to the future

Microsoft researchers pursue the big questions about what the world will be like in the future and the role technology will play. Not only do they take on the responsibility of exploring the long-term vision of their research, but they must also be ready to react to the immediate needs of the present. This year in particular, they were asked to use their roles as futurists to address pressing societal challenges. In early 2020, as countries began responding to COVID-19 […]

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‘Seeing’ on tiny battery-powered microcontrollers with RNNPool

Computer vision has rapidly evolved over the past decade, allowing for such applications as Seeing AI, a camera app that describes aloud a person’s surroundings, helping those who are blind or have low vision; systems that can detect whether a product, such as a computer chip or article of clothing, has been assembled correctly, improving quality control; and services that can convert information from hard-copy documents into a digital format, making it easier to manage personal and business data. All […]

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MPNet combines strengths of masked and permuted language modeling for language understanding

Pretrained language models have been a hot research topic in natural language processing. These models, such as BERT, are usually pretrained on large-scale language corpora with carefully designed pretraining objectives and then fine-tuned on downstream tasks to boost the accuracy. Among these, masked language modeling (MLM), adopted in BERT, and permuted language modeling (PLM), adopted in XLNet, are two representative pretraining objectives. However, both of them enjoy their own advantages but suffer from limitations. Therefore, researchers from Microsoft Research Asia, […]

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NeurIPS 2020: Moving toward real-world reinforcement learning via batch RL, strategic exploration, and representation learning

As human beings, we encounter unfamiliar situations all the time—learning to drive, living on our own for the first time, starting a new job. And while we can anticipate what to expect based on what others have told us or what we’ve picked up from books and depictions in movies and TV, it isn’t until we’re behind the wheel of a car, maintaining an apartment, or doing a job in a workplace that we’re able to take advantage of one […]

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Research Collection – Reinforcement Learning at Microsoft

Reinforcement learning is about agents taking information from the world and learning a policy for interacting with it, so that they perform better. So, you can imagine a future where, every time you type on the keyboard, the keyboard learns to understand you better. Or every time you interact with some website, it understands better what your preferences are, so the world just starts working better and better at interacting with people. John Langford, Partner Research Manager, MSR NYC Fundamentally, […]

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Utilizing consumer cameras for contact-free physiological measurement in telehealth and beyond

Our research is enabling robust and scalable measurement of physiology. Cameras on everyday devices can be used to detect subtle changes in light reflected from the body caused by physiological processes. Machine learning algorithms are then used to process the camera images and recover the underlying pulse and respiration signals that can then be used for health and wellness tracking. According to the CDC WONDER Online Database, heart disease is currently the leading cause of death for both men and […]

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A Microsoft custom data type for efficient inference

AI is taking on an increasingly important role in many Microsoft products, such as Bing and Office 365. In some cases, it’s being used to power outward-facing features like semantic search in Microsoft Word or intelligent answers in Bing, and deep neural networks (DNNs) are one key to powering these features. One aspect of DNNs is inference—once these networks are trained, they use inference to make judgments about unknown information based on prior learning. In Bing, for example, DNN inference […]

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Adversarial machine learning and instrumental variables for flexible causal modeling

We are going through a new shift in machine learning (ML), where ML models are increasingly being used to automate decision-making in a multitude of domains: what personalized treatment should be administered to a patient, what discount should be offered to an online customer, and other important decisions that can greatly impact people’s lives. The machine learning revolution was primarily driven by problems that are distant from such decision-making scenarios. The first scenarios include predicting what an image depicts, predicting […]

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The human side of AI for chess

As artificial intelligence continues its rapid progress, equaling or surpassing human performance on benchmarks in an increasing range of tasks, researchers in the field are directing more effort to the interaction between humans and AI in domains where both are active. Chess stands as a model system for studying how people can collaborate with AI, or learn from AI, just as chess has served as a leading indicator of many central questions in AI throughout the field’s history. AI-powered chess […]

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Project InnerEye evaluation shows how AI can augment and accelerate clinicians’ ability to perform radiotherapy planning 13 times faster

Up to half of the population in the United States and United Kingdom will be diagnosed with cancer at some point in their lives. Of those, half will be treated with radiotherapy (RT), often in combination with other treatments such as surgery, chemotherapy, and increasingly immunotherapy. Radiotherapy involves focusing high-intensity radiation beams to damage the DNA of deep-seated cancerous tumors while avoiding surrounding healthy organs (known as organs at risk or OARs). Around 40% of successfully treated cancer patients undergo […]

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