Introducing a novel mixture-of-experts routing algorithm, called Expert Choice, that can achieve optimal load balancing between experts while allowing heterogeneous token-to-expert mapping. Learn how it’s done at https://
goo.gle/3OdpO9t
@googleai
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Expert Choice: Novel Mixture-of-Experts Routing Algorithm
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Federated Learning: Training ML Models While Protecting User Privacy
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While most #ML models are trained by collecting data on a central server, federated learning makes it possible to train models without any user's raw data leaving their device. Check out the latest AI Explorable on how federated learning protects privacy→ https://t.co/5hDzyWomiO pic.twitter.com/xwd4xrn4Wi
— Google AI (@GoogleAI) 11 novembre 2022While most #ML models are trained by collecting data on a central server, federated learning makes it possible to train models without any user's raw data leaving their device. Check out the latest AI Explorable on how federated learning protects privacy→ https://
pair.withgoogle.com/explorables/fe
derated-learning/
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Google Releases Universal Video Quality Model Open-Source
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Today we announce the open-source release of the Universal Quality (UVQ) model. Read the blog to learn more, and be sure to check out the model → https://
github.com/google/uvq -
Emergent Capabilities in Large-Scale Language Models Explained
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As language models increase in scale they sometimes exhibit various useful emergent capabilities. In today’s post, we explore this phenomenon to better understand its dependence on model properties and its potential impact in applications. Read more at https://
goo.gle/3hvJMAb -
SegCLR: Contrastive Learning for Cellular Morphology Analysis
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Announcing Segmentation-Guided Contrastive Learning of Representations (SegCLR), a method that trains rich, generic representations of cellular morphology and ultrastructure without manual effort and can identify cell types from only small cell fragments→ https://t.co/7Ol91ZAhdx pic.twitter.com/Ff7qCYczxp
— Google AI (@GoogleAI) 9 novembre 2022Announcing Segmentation-Guided Contrastive Learning of Representations (SegCLR), a method that trains rich, generic representations of cellular morphology and ultrastructure without manual effort and can identify cell types from only small cell fragments→ https://
goo.gle/3G2uIEh -
ReAct: Synergizing Reasoning and Acting in Language Models
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Learn how ReAct, a general paradigm for synergizing reasoning and acting in language models, presents more interpretable, diagnosable, and controllable task-solving trajectories while outperforming reasoning and acting only paradigms. Read the blog → https://
goo.gle/3TiNcDp -
Infinite Nature: AI generates detailed landscape flythroughs from single images
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Read about our latest work from a research effort called Infinite Nature, which can produce high-quality flythroughs of richly detailed natural landscapes starting from a single seed image, using a system trained only on still photographs → https://t.co/sYKGpz1ivC pic.twitter.com/jQ3hZ5s0aO
— Google AI (@GoogleAI) 7 novembre 2022Read about our latest work from a research effort called Infinite Nature, which can produce high-quality flythroughs of richly detailed natural landscapes starting from a single seed image, using a system trained only on still photographs → https://
goo.gle/3hqHty4 -
Code as Policies: Robot Helper Executes Natural Language Tasks
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From our demo floor at AI@, check out Code as Policies at work. This helper robot is able to compute and execute a task given via natural language. Read more → https://t.co/hTJ9kaZJbq pic.twitter.com/XBTQAqg0nS
— Google AI (@GoogleAI) 4 novembre 2022From our demo floor at AI@, check out Code as Policies at work. This helper robot is able to compute and execute a task given via natural language. Read more → https://
goo.gle/3U5CmCg -
Reincarnating RL: Efficient Reinforcement Learning from Prior Computation
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Training #ReinforcementLearning algorithms from scratch is computationally intensive and time consuming. We propose an alternate approach, Reincarnating RL, that integrates prior computation into the RL training workflow. Learn more and grab the code at https://t.co/XwfX0uakJZ pic.twitter.com/ktaQznCD1a
— Google AI (@GoogleAI) 3 novembre 2022Training #ReinforcementLearning algorithms from scratch is computationally intensive and time consuming. We propose an alternate approach, Reincarnating RL, that integrates prior computation into the RL training workflow. Learn more and grab the code at https://
goo.gle/3Ws2TLk -
Google Leaders Discuss Responsible AI Innovation Strategy
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Starting now: James Manyika, SVP of Technology and Society, and Marian Croak, VP of Engineering @Google will discuss the importance of innovating responsibly and what factors we keep top of mind in our research. Tune in ↓