Using JAX to accelerate our research

Here we share our experience of working with JAX, outline why we find it useful for our AI research, how we are supporting JAX development, and give an overview of the ecosystem we are building to support researchers everywhere.Read More

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December 4, 2020

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Google AI@GoogleAI·

14 Apr

Today, in partnership with @StanfordSVL, we are proud to announce the iGibson Challenge on Interactive and Social Navigation for simulating robot navigation in cluttered and busy environments. Grab the dataset and submit your model solution by May 31st. https://goo.gle/2PW79pg

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Google AI@GoogleAI·

9 Apr

Check out Monster Mash, an open source sketch-based tool that allows experts and amateurs alike to draw soft, dynamic 3D characters and animate them in minutes. Learn more, grab the code, and try out the demo. https://goo.gle/2Qf4iar

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Google AI@GoogleAI·

7 Apr

Last year we launched the Research Scholar Program to strengthen long-term collaboration with the academic community. Today we are pleased to announce the inaugural award recipients, representing 15+ countries and 50+ universities. See the full list ↓ https://goo.gle/3uBmbzn

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Google AI@GoogleAI·

25 Mar

Many Transformer #NLP models have computational requirements that scale quadratically with input sequence length. Learn how sparse attention methods reduce the dependency from a quadratic to linear and achieve state-of-the-art results on challenging tasks. https://goo.gle/2PxgxPe

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Google AI@GoogleAI·

24 Mar

Predicting the function of proteins using #neuralnetworks can greatly aid medical research. We are proud to collaborate with @emblebi to extend the @PfamDB database of functionally annotated proteins by 10% as part of the v34.0 release. Learn more below! https://xfam.wordpress.com/2021/03/24/google-research-team-bring-deep-learning-to-pfam/

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Google AI@GoogleAI·

24 Mar

Introducing a new #ReinforcementLearning algorithm for teaching agents how to solve tasks by providing only examples of success, avoiding the need for hand-crafted reward functions while outperforming prior imitation learning approaches. Read more →https://goo.gle/3vVksX1

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Google AI@GoogleAI·

23 Mar

Open-domain long-form question answering can provide a testbed to measure the factuality of generative text models in #NLP. Check out a new system that leverages both sparse attention and retrieval-based models to achieve state-of-the-art results. https://goo.gle/3cYRBs9

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Google AI@GoogleAI·

19 Mar

Today we present an approach for iterative digital game design that leverages #MachineLearning to train agents as play-testers, allowing designers to efficiently balance a game and align it with their original vision. Learn more at http://goo.gle/3rZyggK

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Google AI@GoogleAI·

18 Mar

This week @TensorFlow Quantum (#TFQ), the open-source library for the rapid prototyping of quantum #MachineLearning models, celebrates its one year anniversary. Learn more about the original release at http://goo.gle/2Q34oPM, and key results from the past year in the post below!

TensorFlow@TensorFlow

⚡ It's been a year since we released TensorFlow Quantum. In this post, we’ll take a look back at the results, papers, and next steps.

Learn more → http://goo.gle/3vDVdbB

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Google AI@GoogleAI·

18 Mar

Developing efficient algorithms for distributed computation on very large graphs is an ongoing challenge. Check out a new model that augments the capabilities of #MapReduce, providing a way to solve distributed graph algorithms in fewer computation rounds. https://goo.gle/3rWn2K4

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