DeepSpeed: Extreme-scale model training for everyone

In February, we announced DeepSpeed, an open-source deep learning training optimization library, and ZeRO (Zero Redundancy Optimizer), a novel memory optimization technology in the library, which vastly advances large model training by improving scale, speed, cost, and usability. DeepSpeed has enabled researchers to create Turing Natural Language Generation (Turing-NLG), the largest language model with 17 billion parameters and state-of-the-art accuracy at the time of its release. In May, we released ZeRO-2—supporting model training of 200 billion parameters up to 10x […]

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Platform for Situated Intelligence: An open-source framework for multimodal, integrative AI

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, […]

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Domain-specific language model pretraining for biomedical natural language processing

COVID-19 highlights a perennial problem facing scientists around the globe: how do we stay up to date with the cutting edge of scientific knowledge? In just a few months since the pandemic emerged, tens of thousands of research papers have been published concerning COVID-19 and the SARS-CoV-2 virus. This explosive growth sparks the creation of the COVID-19 Open Research Dataset (CORD-19) to facilitate research and discovery. However, a pandemic is just one salient example of a prevailing challenge to this […]

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Microsoft HoloLens 2: Improved Research Mode to facilitate computer vision research

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 […]

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MineRL sample-efficient reinforcement learning challenge—back for a second year—benefits organizers, as well as larger research community

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 […]

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Adversarial robustness as a prior for better transfer learning

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 […]

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Three new reinforcement learning methods aim to improve AI in gaming and beyond

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 […]

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