Favorite Posted by Maxim Tabachnyk, Staff Software Engineer and Stoyan Nikolov, Senior Engineering Manager, Google Research The increasing complexity of code poses a key challenge to productivity in software engineering. Code completion has been an essential tool that has helped mitigate this complexity in integrated development environments (IDEs). Conventionally, code
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Shared by Google AI Technology July 26, 2022
Favorite Posted by Winnie Xu, Student Researcher and Kuang-Huei Lee, Software Engineer, Google Research, Brain Team Current deep reinforcement learning (RL) methods can train specialist artificial agents that excel at decision-making on various individual tasks in specific environments, such as Go or StarCraft. However, little progress has been made to
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Shared by Google AI Technology July 21, 2022
Favorite Posted by Akib Uddin, Product Manager and Andrew Sellergren, Software Engineer, Google Health Every year, nearly a billion chest X-ray (CXR) images are taken globally to aid in the detection and management of health conditions ranging from collapsed lungs to infectious diseases. Generally, CXRs are cheaper and more accessible
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Shared by Google AI Technology July 19, 2022
Favorite Posted by Cat Armato, Program Manager, University Relations Google is a leader in machine learning (ML) research with groups innovating across virtually all aspects of the field, from theory to application. We build machine learning systems to solve deep scientific and engineering challenges in areas of language, music, visual
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Shared by Google AI Technology July 18, 2022
Favorite Posted by Dustin Tran and Balaji Lakshminarayanan, Research Scientists, Google Research Deep learning models have made impressive progress in vision, language, and other modalities, particularly with the rise of large-scale pre-training. Such models are most accurate when applied to test data drawn from the same distribution as their training
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Shared by Google AI Technology July 14, 2022
Favorite Posted by Soravit Beer Changpinyo and Doron Kukliansky, Senior Software Engineers, Google Research Visual Question Answering (VQA) is a useful machine learning (ML) task that requires a model to answer a visual question about an image. What makes it challenging is its multi-task and open-ended nature; it involves solving
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Shared by Google AI Technology July 13, 2022
Favorite Posted by Qihang Yu, Student Researcher, and Liang-Chieh Chen, Research Scientist, Google Research Panoptic segmentation is a computer vision problem that serves as a core task for many real-world applications. Due to its complexity, previous work often divides panoptic segmentation into semantic segmentation (assigning semantic labels, such as “person”
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Shared by Google AI Technology July 12, 2022
Favorite Posted by Danijar Hafner, Student Researcher, Google Research Research into how artificial agents can make decisions has evolved rapidly through advances in deep reinforcement learning. Compared to generative ML models like GPT-3 and Imagen, artificial agents can directly influence their environment through actions, such as moving a robot arm
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Shared by Google AI Technology July 8, 2022
Favorite Posted by Been Kim, Research Scientist, Google Research, Brain Team, and Alison Lentz, Senior Staff Strategist, Google Research, Mural Team Advances in computer vision and natural language processing continue to unlock new ways of exploring billions of images available on public and searchable websites. Today’s visual search tools make
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Shared by Google AI Technology July 7, 2022
Favorite Over the last year, we’ve seen artificial intelligence (AI) systems advance our work in areas like inclusive product development and support for small businesses and job seekers. We’ve also seen its potential to be helpful in addressing major global needs — like forecasting and planning humanitarian responses to natural
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Shared by Google AI Technology July 6, 2022
Shared by Google AI Technology July 26, 2022
