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

Favorite A Google AI expert answers common questions about generative AI, large language models, machine learning and more. View Original Source (blog.google/technology/ai/) Here.

Favorite Posted by Thomas Lew, Research Intern, and Montserrat Gonzalez Arenas, Research Engineer, Google Research, Brain Team Over the past several years, the capabilities of robotic systems have improved dramatically. As the technology continues to improve and robotic agents are more routinely deployed in real-world environments, their capacity to assist

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Shared by Google AI Technology April 7, 2023

Favorite Posted by Oren Gilon, Software Engineer, and Grey Nearing, Research Scientist, Google Research Floods are the most common type of natural disaster, affecting more than 250 million people globally each year. As part of Google’s Crisis Response and our efforts to address the climate crisis, we are using machine

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Shared by Google AI Technology April 7, 2023

Favorite Posted by Greg Blascovich and Eric Gomez, User Researchers, Google As companies settle into a new normal of hybrid and distributed work, remote communication technology remains critical for connecting and collaborating with colleagues. While this technology has improved, the core user experience often falls short: conversation can feel stilted,

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Shared by Google AI Technology April 6, 2023

Favorite Posted by Zi Wang and Kevin Swersky, Research Scientists, Google Research, Brain Team Bayesian optimization (BayesOpt) is a powerful tool widely used for global optimization tasks, such as hyperparameter tuning, protein engineering, synthetic chemistry, robot learning, and even baking cookies. BayesOpt is a great strategy for these problems because

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Shared by Google AI Technology April 6, 2023

Favorite Posted by Piotr Padlewski and Josip Djolonga, Software Engineers, Google Research Large Language Models (LLMs) like PaLM or GPT-3 showed that scaling transformers to hundreds of billions of parameters improves performance and unlocks emergent abilities. The biggest dense models for image understanding, however, have reached only 4 billion parameters,

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Shared by Google AI Technology March 31, 2023