Want to learn more about Med-PaLM? Read about it and watch the video at https://
sites.research.google/med-palm/
@googleai
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Google Med-PaLM: Advanced Medical AI Language Model
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ML Technologies Enable Human Pangenome Reference Map
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Our contributions to the Human Pangenome Research Consortium helped create a more representative map of the human genome. Learn how our #ML technologies helped enable the pangenome by combining 47 individual genome references → https://t.co/90NRQqkLXY pic.twitter.com/7h34Wu7fRZ
— Google AI (@GoogleAI) 10 mai 2023Our contributions to the Human Pangenome Research Consortium helped create a more representative map of the human genome. Learn how our #ML technologies helped enable the pangenome by combining 47 individual genome references → https://
goo.gle/3O0yoKY -
Google Introduces MaMMUT Compact Multimodal AI Model
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Introducing MaMMUT, a compact multimodal model that trains across contrastive, text generative, and localization-aware objectives, and outperforms the state of the art or achieves competitive performance on a range of tasks. Learn more → https://
goo.gle/42lZuk5 -
Improving Language Tech for Swahili African Languages
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Kiswahili, also known as Swahili, is the native language of the Swahili people who are found primarily in Kenya, Tanzania, & Mozambique. Watch this episode of the African Language series to learn about Swahili and how to improve language tech for it ↓
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Reinforcement Learning Algorithm Enables Outdoor Robotic Navigation
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Presenting an algorithm that uses #ReinforcementLearning to train a navigation policy in simulated indoor environments and then transfers that same policy to real outdoor settings in order to enable efficient long-range robotic navigation outdoors → https://t.co/Gome7XTHXb pic.twitter.com/AqMMBZZwIx
— Google AI (@GoogleAI) 3 mai 2023Presenting an algorithm that uses #ReinforcementLearning to train a navigation policy in simulated indoor environments and then transfers that same policy to real outdoor settings in order to enable efficient long-range robotic navigation outdoors → https://
goo.gle/3VvV9Ii -
Text-Blueprint Demo at Google Booth ICLR2023
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Miss yesterday's #ICLR2023 demo on Text-Blueprint? You'll have another chance to catch it today! Just drop by the Google booth at 2:30pm and learn all about it. https://t.co/sZn6EvPYKt
— Google AI (@GoogleAI) 3 mai 2023Miss yesterday's #ICLR2023 demo on Text-Blueprint? You'll have another chance to catch it today! Just drop by the Google booth at 2:30pm and learn all about it.
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DreamFusion: Text-to-3D AI System Wins ICLR2023 Award
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At 2:30pm today at the Google booth hear @poolio describe DreamFusion, winner of the #ICLR2023 Outstanding Paper Award. This text-to-3D system was built by optimizing a NeRF from scratch using the Imagen text-to-image diffusion model. No 3D data needed! https://t.co/ylHkN6WuzY pic.twitter.com/ZhKONGea5n
— Google AI (@GoogleAI) 3 mai 2023At 2:30pm today at the Google booth hear @poolio describe DreamFusion, winner of the #ICLR2023 Outstanding Paper Award. This text-to-3D system was built by optimizing a NeRF from scratch using the Imagen text-to-image diffusion model. No 3D data needed! http://
dreamfusion3d.github.io -
Learned Optimizers: Future, Challenges, Current Capabilities
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Don't miss today's #ICLR2023 keynote talk at 1:30pm by @jaschasd
, called "Learned optimizers: why they're the future, why they’re hard, and what they can do now". -
Google Presents JaxPruner: Open-Source JAX Pruning Library
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Drop by the #ICLR2023 Google booth at 9:30am today where @utkuevci will demo JaxPruner, an open-source JAX-based pruning and sparse training library for #ML. Grab the code at https://t.co/f2H2Aj6uUL and try it for yourself! pic.twitter.com/bfe92Mhnc9
— Google AI (@GoogleAI) 3 mai 2023Drop by the #ICLR2023 Google booth at 9:30am today where @utkuevci will demo JaxPruner, an open-source JAX-based pruning and sparse training library for #ML. Grab the code at https://
github.com/google-researc
h/jaxpruner
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Scaling Model Capacity in Reinforcement Learning at ICLR2023
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Today at 2:30pm, @agarwl_ will be at the #ICLR2023 Google booth to present how scaling model capacity in #ReinforcementLearning — along with appropriate design choices — can lead to remarkable results, analogous to supervised learning.