Microsoft Research Blog

Under now-standard techniques, such as over-parameterization, batch-normalization, and adding residual links, “modern age” neural network training—at least for image classification tasks and many others—is usually quite stable. Using standard neural network architectures and training algorithms (typically SGD with momentum), the learned models perform consistently well,…

Humans understand the world by perceiving and fusing information from multiple channels, such as images viewed by the eyes, voices heard by the ears, and other forms of sensory input. One of the core aspirations in AI is to develop algorithms that endow computers with…

Natural language understanding (NLU) is one of the longest running goals in AI, and SuperGLUE is currently among the most challenging benchmarks for evaluating NLU models. The benchmark consists of a wide range of NLU tasks, including question answering, natural language inference, co-reference resolution, word…

Many of the items and objects we use in our daily lives were designed with people in mind. In October, the Reserve Bank of Australia put out into the world its redesigned $100 banknote. Some design elements remained the same—such as color and size, characteristics…

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…

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…

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…

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…

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…

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…

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…

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…