Consider for a moment what it takes to visually identify and describe something to another person. Now imagine that the other person can’t see the object or image, so every detail matters. How do you decide what information is important and what’s not? You’ll need to know exactly what everything is, where it is, what it’s doing in relation to other objects, and note other attributes like color or position of objects in the foreground or background. This exercise shows […]

The goal of neural architecture search (NAS) is to have computers automatically search for the best-performing neural networks. Recent advances in NAS methods have made it possible to build problem-specific networks that are faster, more compact, and less power hungry than their handcrafted counterparts. Unfortunately, many NAS methods rely on an array of tricks that aren’t always documented in a way that’s easy to discover. While these tricks result in neural networks with greater accuracy, they often cloud the performance […]

Is FashionMNIST, a dataset of images of clothing items labeled by category, more similar to MNIST or to USPS, both of which are classification datasets of handwritten digits? This is a pretty hard question to answer, but the solution could have an impact on various aspects of machine learning. For example, it could change how practitioners augment a particular dataset to improve the transferring of models across domains or how they select a dataset to pretrain on, especially in scenarios […]

For over a decade, the Project InnerEye team at Microsoft Research Cambridge has been developing state-of-the-art machine learning methods for the automatic, quantitative analysis of three-dimensional medical images. An important application is to assist clinicians for image preparation and planning tasks for radiotherapy cancer treatment. This task involves a radiation oncologist or specialist technician manually examining and marking up dozens of 3D Computed Tomography (CT) image scans. This may take one or more hours currently, depending on the type of […]

By the Semantic Machines research team “Easier said than done.” These four words reflect the promise of conversational AI. It takes just seconds to ask When are Megan and I both free? but much longer to find out manually from a calendar. Indeed, almost everything we do with technology can feel like a long path to a short goal. At Microsoft Semantic Machines, we’re working to bridge this gap—to build conversational AI experiences where you can focus on saying what […]

Over the years at Microsoft Research, we’ve studied how to build AI systems that perceive, understand, and act in a human-filled world in real time. Our motivation has been to create computing systems that can support interactive experiences akin to what we expect when we talk to or collaborate with people. This research line has involved the development of several physically situated interactive applications, including embodied conversational agents that serve as personal assistants, robots that give directions in our building, […]