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Dec 17, 2018 Exploring Quantum Neural Networks
Posted by Jarrod McClean, Senior Research Scientist and Hartmut Neven, Director of Engineering, Google AI Quantum Team Since its inception, the Google AI Quantum team has pushed to understand the role of quantum computing in machine learning . The existence of algorithms with provable advantages for global optimization suggest that quantum computers may be useful for training existing models within machine learning more quickly, and we are building experimental quantum computers to ...
Dec 14, 2018 Improving the Effectiveness of Diabetic Retinopathy Models
Posted by Rory Sayres PhD and Jonathan Krause PhD, Google AI, Healthcare Two years ago, we announced our inaugural work in training deep learning models for diabetic retinopathy (DR), a complication of diabetes that is one of the fastest growing causes of vision loss. Based on this research, we set out to apply our technology to improve health outcomes in the world .
Dec 11, 2018 Grasp2Vec: Learning Object Representations from Self-Supervised Grasping
Posted by Eric Jang, Software Engineer, Robotics at Google and Coline Devin, Berkeley PhD Student and former Research Intern From a remarkably young age, people are capable of recognizing their favorite objects and picking them up, despite never being explicitly taught how to do so. According to cognitive developmental research , the ability to interact with objects in the world plays a crucial role in the emergence of object perception and manipulation ...
Dec 10, 2018 Providing Gender-Specific Translations in Google Translate
Posted by Melvin Johnson, Senior Software Engineer, Google Translate Over the past few years, Google Translate has made significant improvements to translation quality by switching to an end-to-end neural network-based system . At the same time, we realized that translations from our models can reflect societal biases, such as gender bias.
Dec 07, 2018 Adding Diversity to Images with Open Images Extended
Posted by Anurag Batra and Parker Barnes, Product Managers, Google AI Recently, we introduced the Inclusive Images Kaggle competition , part of the NeurIPS 2018 Competition Track , with the goal of stimulating research into the effect of geographic skews in training datasets on ML model performance, and to spur innovation in developing more inclusive models.
Dec 05, 2018 The NeurIPS 2018 Test of Time Award: The Trade-Offs of Large Scale Learning
Posted by Anna Ukhanova, Program Manager, Google AI Zürich Progress in machine learning (ML) is happening so rapidly, that it can sometimes feel like any idea or algorithm more than 2 years old is already outdated or superseded by something better. However, old ideas sometimes remain relevant even when a large fraction of the scientific community has turned away from them.
Dec 05, 2018 TF-Ranking: A Scalable TensorFlow Library for Learning-to-Rank
Posted by Xuanhui Wang and Michael Bendersky, Software Engineers, Google AI Ranking, the process of ordering a list of items in a way that maximizes the utility of the entire list, is applicable in a wide range of domains, from search engines and recommender systems to machine translation , dialogue systems and even computational biology.
Dec 03, 2018 Google at NeurIPS 2018
Posted by Slav Petrov, Principal Scientist, Google This week, Montréal hosts the 32 nd annual Conference on Neural Information Processing Systems (NeurIPS 2018), the biggest machine learning conference of the year. The conference includes invited talks, demonstrations and presentations of some of the latest in machine learning research.
Nov 30, 2018 Highlights from the 2018 Google PhD Fellowship Summit
Posted by Susie Kim, Program Manager, University Relations Google created the PhD Fellowship Program to recognize and support outstanding graduate students doing exceptional research in Computer Science and related disciplines. This program provides a unique opportunity for students pursuing a graduate degree in Computer Science (or related field) who seek to influence the future of technology.
Nov 29, 2018 Learning to Predict Depth on the Pixel 3 Phones
Posted by Rahul Garg, Research Scientist and Neal Wadhwa, Software Engineer Portrait Mode on the Pixel smartphones lets you take professional-looking images that draw attention to a subject by blurring the background behind it. Last year, we described, among other things, how we compute depth with a single camera using its Phase-Detection Autofocus (PDAF) pixels (also known as dual-pixel autofocus ) using a traditional non-learned stereo algorithm .
Nov 27, 2018 A Structured Approach to Unsupervised Depth Learning from Monocular Videos
Posted by Anelia Angelova, Research Scientist, Robotics at Google Perceiving the depth of a scene is an important task for an autonomous robot — the ability to accurately estimate how far from the robot objects are, is crucial for obstacle avoidance, safe planning and navigation. While depth can be obtained (and learned) from sensor data, such as LIDAR , it is also possible to learn it in an unsupervised manner from a ...
Nov 16, 2018 Improved Grading of Prostate Cancer Using Deep Learning
Posted by Martin Stumpe, Technical Lead and Craig Mermel, Product Manager, Healthcare, Google AI Approximately 1 in 9 men in the United States will develop prostate cancer in their lifetime, making it the most common cancer in males . Despite being common, prostate cancers are frequently non-aggressive, making it challenging to determine if the cancer poses a significant enough risk to the patient to warrant treatment such as surgical removal of the ...
Nov 14, 2018 Night Sight: Seeing in the Dark on Pixel Phones
Posted by Marc Levoy, Distinguished Engineer and Yael Pritch, Staff Software Engineer Night Sight is a new feature of the Pixel Camera app that lets you take sharp, clean photographs in very low light, even in light so dim you can't see much with your own eyes.
Nov 12, 2018 Accurate Online Speaker Diarization with Supervised Learning
Posted by Chong Wang, Research Scientist, Google AI Speaker diarization , the process of partitioning an audio stream with multiple people into homogeneous segments associated with each individual, is an important part of speech recognition systems. By solving the problem of “who spoke when”, speaker diarization has applications in many important scenarios, such as understanding medical conversations , video captioning and more.
Nov 02, 2018 Open Sourcing BERT: State-of-the-Art Pre-training for Natural Language Processing
Posted by Jacob Devlin and Ming-Wei Chang, Research Scientists, Google AI Language One of the biggest challenges in natural language processing (NLP) is the shortage of training data. Because NLP is a diversified field with many distinct tasks, most task-specific datasets contain only a few thousand or a few hundred thousand human-labeled training examples.
Oct 31, 2018 Google at EMNLP 2018
Posted by Manaal Faruqui, Senior Research Scientist and Emily Pitler, Staff Research Scientist, Google AI Language This week, the annual conference on Empirical Methods in Natural Language Processing (EMNLP 2018) will be held in Brussels, Belgium. Google will have a strong presence at EMNLP with several of our researchers presenting their research on a diverse set of topics, including language identification, segmentation, semantic parsing and question answering, additionally serving in various levels ...
Oct 30, 2018 Introducing AdaNet: Fast and Flexible AutoML with Learning Guarantees
Posted by Charles Weill, Software Engineer, Google AI, NYC Ensemble learning , the art of combining different machine learning (ML) model predictions, is widely used with neural networks to achieve state-of-the-art performance, benefitting from a rich history and theoretical guarantees to enable success at challenges such as the Netflix Prize and various Kaggle competitions .
Oct 30, 2018 Acoustic Detection of Humpback Whales Using a Convolutional Neural Network
Posted by Matt Harvey, Software Engineer, Google AI Perception Over the last several years, Google AI Perception teams have developed techniques for audio event analysis that have been applied on YouTube for non-speech captions , video categorizations, and indexing. Furthermore, we have published the AudioSet evaluation set and open-sourced some model code in order to further spur research in the community.
Oct 24, 2018 Curiosity and Procrastination in Reinforcement Learning
Posted by Nikolay Savinov, Research Intern, Google Brain Team and Timothy Lillicrap, Research Scientist, DeepMind Reinforcement learning (RL) is one of the most actively pursued research techniques of machine learning, in which an artificial agent receives a positive reward when it does something right, and negative reward otherwise.
Oct 23, 2018 Fluid Annotation: An Exploratory Machine Learning–Powered Interface for Faster Image Annotation
Posted by Jasper Uijlings and Vittorio Ferrari, Research Scientists, Machine Perception The performance of modern deep learning–based computer vision models, such as those implemented by the TensorFlow Object Detection API , depends on the availability of increasingly large, labeled training datasets, such as Open Images .