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, […]

Since its launch in November 2019, Microsoft HoloLens 2 has helped enterprises in manufacturing, construction, healthcare, and retail onboard employees more quickly, complete tasks faster, and greatly reduce errors and waste. It sets the high-water mark for intelligent edge devices by leveraging a multitude of sensors and a dedicated ASIC (Application-Specific Integrated Circuit) to allow multiple real-time computer vision workloads to run continuously. In Research Mode, HoloLens 2 is also a potent computer vision research device. (Note: Research Mode is […]

To unearth a diamond in the block-based open world of Minecraft requires the acquisition of materials and the construction of tools before any diamond mining can even begin. Players need to gather wood, which they’ll use to make a wood pickaxe for mining stone underground. They’ll use the stone to fashion a stone pickaxe and, with the tool upgrade, mine iron ore. They’ll build a furnace for smelting the iron and use that to make the iron pickaxe they need […]

Editor’s note: This post and its research are the collaborative efforts of our team, which includes Andrew Ilyas (PhD Student, MIT), Logan Engstrom (PhD Student, MIT), Aleksander Mądry (Professor at MIT), Ashish Kapoor (Partner Research Manager). In practical machine learning, it is desirable to be able to transfer learned knowledge from some “source” task to downstream “target” tasks. This is known as transfer learning—a simple and efficient way to obtain performant machine learning models, especially when there is little training […]

Reinforcement learning (RL) provides exciting opportunities for game development, as highlighted in our recently announced Project Paidia—a research collaboration between our Game Intelligence group at Microsoft Research Cambridge and game developer Ninja Theory. In Project Paidia, we push the state of the art in reinforcement learning to enable new game experiences. In particular, we focus on developing game agents that learn to genuinely collaborate in teams with human players. In this blog post we showcase three of our recent research […]