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Feb 06, 2019 Announcing the Second Workshop and Challenge on Learned Image Compression
Posted by Nick Johnston, Software Engineer, Machine Perception Last year, we announced the Workshop and Challenge on Learned Image Compression (CLIC), an event that aimed to advance the field of image compression with and without neural networks. Held during the 2018 Computer Vision and Pattern Recognition conference ( CVPR 2018 ), CLIC was quite a success, with 23 accepted workshop papers , 95 authors and 41 entries into the competition .
Feb 04, 2019 Real-time Continuous Transcription with Live Transcribe
Posted by Sagar Savla, Product Manager, Machine Perception The World Health Organization (WHO) estimates that there are 466 million people globally that are deaf and hard of hearing. A crucial technology in empowering communication and inclusive access to the world's information to this population is automatic speech recognition (ASR), which enables computers to detect audible languages and transcribe them into text for reading.
Jan 24, 2019 Natural Questions: a New Corpus and Challenge for Question Answering Research
Posted by Tom Kwiatkowski and Michael Collins, Research Scientists, Google AI Language Open-domain question answering (QA) is a benchmark task in natural language understanding (NLU) that aims to emulate how people look for information, finding answers to questions by reading and understanding entire documents. Given a question expressed in natural language ("Why is the sky blue?"), a QA system should be able to read the web (such as this Wikipedia page ) ...
Jan 22, 2019 Expanding the Application of Deep Learning to Electronic Health Records
Posted by Alvin Rajkomar, MD and Eyal Oren, PhD, Google AI, Healthcare In 2018 we published a paper that showed how machine learning, when applied to medical records, can predict what might happen to patients who are hospitalized: for example, how long they would need to be in the hospital and, if discharged, how likely they would be to come back unexpectedly.
Jan 19, 2019 Looking Back at Google’s Research Efforts in 2018
Posted by Jeff Dean, Senior Fellow and Google AI Lead, on behalf of the entire Google Research Community 2018 was an exciting year for Google's research teams, with our work advancing technology in many ways, including fundamental computer science research results and publications, the application of our research to emerging areas new to Google (such as healthcare and robotics), open source software contributions and strong collaborations with Google product teams, all aimed ...
Jan 18, 2019 Soft Actor-Critic: Deep Reinforcement Learning for Robotics
Posted by Tuomas Haarnoja, Student Researcher and Sergey Levine, Faculty Advisor, Robotics at Google Deep reinforcement learning (RL) provides the promise of fully automated learning of robotic behaviors directly from experience and interaction in the real world, due to its ability to process complex sensory input using general-purpose neural network representations.
Jan 08, 2019 Top Shot on Pixel 3
Posted by Li Zhang and Wei (Alex) Hong, Software Engineers Life is full of meaningful moments — from a child’s first step to an impromptu jump for joy — that one wishes could be preserved with a picture. However, because these moments are often unpredictable, missing that perfect shot is a frustrating problem that smartphone camera users face daily.
Dec 18, 2018 Google AI Princeton: Current and Future Research
Posted by Elad Hazan and Yoram Singer, Research Scientists, Google AI and Princeton University Google has long partnered with academia to advance research, collaborating with universities all over the world on joint research projects which result in novel developments in Computer Science, Engineering, and related fields.
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 ...