Favorite Posted by Ruofei Du, Interactive Perception & Graphics Lead, Google Augmented Reality, and Na Li, Tech Lead Manager, Google CoreML Recent deep learning advances have enabled a plethora of high-performance, real-time multimedia applications based on machine learning (ML), such as human body segmentation for video and teleconferencing, depth estimation

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Shared by Google AI Technology April 21, 2023

Favorite Bard can now help with programming and software development tasks, across more than 20 programming languages. View Original Source (blog.google/technology/ai/) Here.

Favorite Posted by Rajat Sen and Abhimanyu Das, Research Scientists, Google Research Time-series forecasting is an important research area that is critical to several scientific and industrial applications, like retail supply chain optimization, energy and traffic prediction, and weather forecasting. In retail use cases, for example, it has been observed

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Shared by Google AI Technology April 20, 2023

Favorite We announced some changes that will accelerate our progress in AI and help us develop more capable AI systems more safely and responsibly. View Original Source (blog.google/technology/ai/) Here.

Favorite Posted by Lauren Wilcox, Senior Staff Research Scientist, on behalf of the Technology, AI, Society, and Culture Team Google sees AI as a foundational and transformational technology, with recent advances in generative AI technologies, such as LaMDA, PaLM, Imagen, Parti, MusicLM, and similar machine learning (ML) models, some of

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Shared by Google AI Technology April 19, 2023

Favorite Posted by Badih Ghazi, Staff Research Scientist, and Nachiappan Valliappan, Staff Software Engineer, Google Research Recently, differential privacy (DP) has emerged as a mathematically robust notion of user privacy for data aggregation and machine learning (ML), with practical deployments including the 2022 US Census and in industry. Over the

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Shared by Google AI Technology April 18, 2023

Favorite Posted by Matthew Streeter, Software Engineer, Google Research Derivatives play a central role in optimization and machine learning. By locally approximating a training loss, derivatives guide an optimizer toward lower values of the loss. Automatic differentiation frameworks such as TensorFlow, PyTorch, and JAX are an essential part of modern

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Shared by Google AI Technology April 14, 2023

Favorite Posted by Sergey Levine, Research Scientist, and Alexander Herzog, Staff Research Software Engineer, Google Research, Brain Team Reinforcement learning (RL) can enable robots to learn complex behaviors through trial-and-error interaction, getting better and better over time. Several of our prior works explored how RL can enable intricate robotic skills,

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Shared by Google AI Technology April 13, 2023

Favorite Posted by Sherry Yang, Research Scientist, and Yilun Du, Student Researcher, Google Research, Brain Team Building models that solve a diverse set of tasks has become a dominant paradigm in the domains of vision and language. In natural language processing, large pre-trained models, such as PaLM, GPT-3 and Gopher,

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Shared by Google AI Technology April 12, 2023

Favorite Posted by Sara Ahadi, Research Fellow, Applied Science, and Andrew Carroll, Product Lead, Genomics Aging is a process that is characterized by physiological and molecular changes that increase an individual’s risk of developing diseases and eventually dying. Being able to measure and estimate the biological signatures of aging can

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Shared by Google AI Technology April 11, 2023