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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Issue #96 – Using Annotations for Machine Translating Named Entities

27 Aug20 Issue #96 – Using Annotations for Machine Translating Named Entities Author: Dr. Carla Parra Escartín, Global Program Manager @ Iconic Introduction Getting the translation of named entities right is not a trivial task and Machine Translation (MT) has traditionally struggled with it. If a named entity is wrongly translated, the human eye will quickly spot it, and more often than not, those mistranslations will make people burst into laughter as machines can, seemingly, be very creative. To a […]

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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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Issue #94 – Unsupervised Parallel Sentence Extraction with Parallel Segment Detection Helps Machine Translation

13 Aug20 Issue #94 – Unsupervised Parallel Sentence Extraction with Parallel Segment Detection Helps Machine Translation Author: Dr. Chao-Hong Liu, Machine Translation Scientist @ Iconic Introduction Curating corpora of quality sentence pairs is a fundamental task to building Machine Translation (MT) systems. This resource can be availed from Translation Memory (TM) systems where the human translations are recorded. However, in most cases we don’t have TM databases but comparable corpora, e.g. news articles of the same story in different languages. […]

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Research Collection: The Unseen History of Audio and Acoustics Research at Microsoft

Audio and Acoustics Research at Microsoft Getting the sound right is a crucial ingredient in natural user interfaces, immersive gaming, realistic virtual and mixed reality, and ubiquitous computing. Audio also plays an important role in assistive technologies for people who are blind or have low vision, and speech recognition and processing can help support those who are deaf or hard of hearing. Although computers have been capable of playing and processing high-fidelity audio for many decades, there are many frontiers […]

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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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Issue #93 – Semantic Neural Machine Translation using AMR

06 Aug20 Issue #93 – Semantic Neural Machine Translation using AMR Author: Dr. Karin Sim, Machine Translation Scientist @ Iconic Introduction Semantic representations were part of the very early Machine Translation (MT) systems, yet have had little role in recent Neural MT (NMT) systems. Given that a good translation should reflect the meaning of the source text, this seems an important area to focus on, particularly since the abstraction could potentially help handle data sparsity. In today’s blog post, we […]

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ICML 2020 highlights: A Transformer-based RL agent, causal ML for increased privacy, and more

With over 50 papers from Microsoft accepted at this year’s International Conference on Machine Learning (ICML 2020), a number of which were presented in virtual workshops, Microsoft researchers are in full summer swing when it comes to advancing machine learning in accessibility, privacy, healthcare, and other areas. As Microsoft Partner Research Manager and ICML President John Langford puts it, “ICML is a very broad conference, so its specialty is in some sense ‘all of the above.’” But Langford goes on […]

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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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Issue #90 – Tangled up in BLEU: Reevaluating how we evaluate automatic metrics in Machine Translation

16 Jul20 Issue #90 – Tangled up in BLEU: Reevaluating how we evaluate automatic metrics in Machine Translation Author: Dr. Karin Sim, Machine Translation Scientist @ Iconic Introduction Automatic metrics have a crucial role in Machine Translation (MT). They are used to tune the MT systems during the development phase, to determine which model is best, and to subsequently determine the accuracy of the final translations. Currently, the performance of these automatic metrics is judged by seeing how well they […]

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