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他是计算机领域最高奖项“图灵奖”的获得者，也是在你面前为你答疑解惑的和蔼长者。10 月 26 日，图灵奖获得者 John Hopcroft 教授受邀至微软亚洲研究院与实习生面对面交流。在这场圆桌讨论中，实习生们就职业生涯、研究评价、科研合作等方向提出了他们所关心或被困扰的问题。John Hopcroft 则从个人经历出发，解答了实习生们的疑惑，并与他们展开了富有启迪性的对话。 在国际化的环境中与顶...

In the news | Tech Ninja

Included in the curated list of OpenSource AutoML frameworks, Microsoft FLAML is a robust AutoML framework with a lightweight Python library that automatically finds accurate machine learning models.

Awards | ISO.org

Gary Sullivan, Chair of the ISO/IEC JTC 1/SC 29, and Microsoft Research Principal Standards Program Manager, is part of the expert group behind the structure of multimedia files such as MP4 and 3GP.  They were honored with the prestigious Technology…

In the news | GZERO

GZERO, November 2021 | Microsoft’s Lucas Joppa says the private sector is moving beyond lofty pledges to talk about performance.

Awards | National Academy of Television Arts & Sciences

The File Format Subgroup of MPEG Systems, Working Group 3 of the ISO/IEC JTC 1/SC 29 organization led by Gary Sullivan, Principal Standards Program Manager, was recognized on by the National Academy for Television Arts and Sciences (NATAS) on November…

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Articles

We’re told to celebrate failure, because that’s how we learn – and while that’s correct, many of these failures could have been avoided. How about if we could fail before our idea reached production? Remember, that each idea that reaches…

An illustration of how the image text contrastive and translation text contrastive tasks work together to help align the space of images, English text and non-English text. On the left side of the illustration, the three domains—Image Domain, English Domain, and Non-English Domain--are segregated. An arrow labeled “Image-Captions training data” points to another depiction of the three domains where the image domain and the English domain intersect but the non-English domain is still separate and shown in gray to show that it’s not significantly affected. A two headed arrow with the label “Image-Text contrastive loss” is drawn between the image and English domains. Towards the bottom of the image, an arrow labeled “Parallel corpus training data” points to another depiction of the three domains where the English domain and the non-English domain intersect but the image domain is separate and shown in gray to indicate that it is not significantly affected. A two-headed arrow with the label “Translated Text Contrastive loss” is drawn between the English and non-English domains. Finally, a third arrow with the label “Resulting Effect” is drawn to the right of the image which points to a depiction of all three domains intersecting.
Microsoft Research Blog

| Saurabh Tiwary

Today, the Microsoft Turing team (opens in new tab) is thrilled to introduce Turing Bletchley, a 2.5-billion parameter Universal Image Language Representation model (T-UILR) that can perform image-language tasks in 94 languages. T-Bletchley has an image encoder and a universal language encoder that vectorize…