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The latest news from Google AI
The latest news from Google AI

1,098,047 followers
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Two years ago, we announced our inaugural work in training deep learning models for diabetic retinopathy. Today, we talk about how we've continued our efforts to improve the model’s performance, explainability, and applicability in clinical settings.
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Introducing Grasp2Vec, a method of acquiring effective object-centric representations for robotic manipulation tasks, without human labeling, by using autonomous robot interactions with the environment.
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Recently, we announced that we’re taking the first step at reducing gender bias in our translations. Today, we share some of the state-of-the-art techniques involved in supporting gender-specific translations for phrases and sentences in Turkish.
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Today, we’re announcing Open Images Extended, intended to be a collection of datasets that brings a greater geographic diversity of images/captions to Open Images. Learn more about the Crowdsourced extension, seeded with images from around the world, on the Google AI Blog.
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We are excited to share TF-Ranking, a scalable TensorFlow-based library that provides a unified framework for learning-to-rank algorithms, giving #MachineLearning researchers the ability to evaluate and choose among an array of different ranking models.
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Congratulations to Léon Bottou (Facebook AI Research) and Olivier Bousquet (Google AI, Zürich), recipients of the #NeurIPS2018 Test of Time Award for their 2008 paper “The Trade-Offs of Large Scale Learning”!
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Got some free time for reading? Then check out some of the publications, workshops and tutorials being presented by Google this week at #NeurIPS2018 in Montréal.
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Every year, Google hosts a PhD Fellowship Summit, where our Fellows are given the opportunity to network with Google’s research community as well as other recipients from around the world. Check out some highlights from our most recent summit on the Google AI blog!
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Learn how we used #MachineLearning to improve single-camera depth estimation on the Pixel 3, allowing for even better Portrait Mode results.
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Check out some new robotics research that addresses unsupervised learning of depth and ego-motion from a monocular camera, tackling the problem of highly dynamic scenes while achieving results comparable to stereo camera sensors. It's open source too!
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