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Research at Google
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Announcing tf.Transform, a library for #TensorFlow that allows users to define preprocessing pipelines and run these using large scale data processing frameworks, while also exporting the pipeline in a way that can be run as part of a TensorFlow graph.

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Virtual Reality (VR) enables remarkably immersive experiences, offering new ways to view the world and the ability to explore novel environments, both real and imaginary. However, compared to physical reality, sharing these experiences with others can be difficult, as VR headsets make it challenging to create a complete picture of the people participating in the experience.

Google Machine Perception researchers, in collaboration with #DaydreamLabs and YouTube Spaces, have been working on solutions to address this problem wherein we reveal the user’s face by virtually “removing” the headset and create a realistic see-through effect. Learn more on the Google Research blog, linked below.

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Feeling musical? Check out A.I. Duet, an experiment that lets you make music through machine learning. A neural network was trained on many examples and it learns about musical concepts, building a map of notes and timings. You just play a few notes, and see how the neural net responds.

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Pursuing research in video understanding? Today, we are releasing an update to the YouTube-8M dataset, and in collaboration with Google Cloud Machine Learning and kaggle.com, we are also organizing a video understanding competition and an affiliated CVPR’17 Workshop.


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Today, as part of the first annual #TensorFlow Developer Summit, hosted in Mountain View and livestreamed around the world, we’re announcing TensorFlow 1.0 - Its faster, more flexible, and more production-ready than ever! Learn more, below.

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Yesterday, we announced the launch of Android Wear 2.0, along with brand new wearable devices, that will run Google's first entirely “on-device” ML technology for powering smart messaging.

This on-device ML system enables technologies like Smart Reply to be used for any application, including third-party messaging apps, without ever having to connect with the cloud…so now you can respond to incoming chat messages directly from your watch, with a tap. Learn more, below.

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Today we are releasing #TensorFlow Fold, which makes it easy to implement #deeplearning models that operate over data of varying size and structure. Furthermore, TensorFlow Fold brings the benefits of batching to such models, resulting in a speedup of more than 10x on CPU, and more than 100x on GPU, over alternative implementations. 

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Today, in order to facilitate progress in video understanding research, we are introducing YouTube-BoundingBoxes, a dataset consisting of 5 million bounding boxes spanning 23 object categories, densely labeling segments from 210,000 YouTube videos. To date, this is the largest manually annotated video dataset containing bounding boxes, which track objects in temporally contiguous frames.

The dataset is designed to be large enough to train large-scale models, and be representative of videos captured in natural settings. Importantly, the human-labelled annotations contain objects as they appear in the real world with partial occlusions, motion blur and natural lighting. Learn more, and get the data, from the Google Research blog, linked below.


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Last week, we launched a new feature for Google Maps for Android across 25 US cities that offers predictions about parking difficulty close to your destination so you can plan accordingly. Today, we talk about the machine learning + crowdsourcing techniques that makes this feature possible. Learn more on the Google Research blog, below.
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