Causal Bayesian Networks: A flexible tool to enable fairer machine learning

Decisions based on machine learning (ML) are potentially advantageous over human decisions, but the data used to train them often contains human and societal biases that can lead to harmful decisions.Read More

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October 3, 2019

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Google AI@GoogleAI·

25 Feb

Lyra is a new high-quality, very low-bitrate speech codec that leverages advances in #MachineLearning to make voice communication possible even on the slowest networks. Read all about it below: http://goo.gle/3ksSSLl

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Google AI@GoogleAI·

23 Feb

Today we present the technology behind the new Cinematic photos feature in Google Photos, where #MachineLearning is used to add simulated camera motion and parallax to a single-frame still photo, transforming a 2D photo into a more immersive 3D scene. https://goo.gle/3aKeRKr

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Google AI@GoogleAI·

19 Feb

We are excited to announce the release of Model Search, an open-source, domain-agnostic #AutoML platform that helps researchers to efficiently and automatically develop optimal #MachineLearning models. Learn more at http://goo.gle/3dtl2o5

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Google AI@GoogleAI·

18 Feb

Presenting DreamerV2, the first world model-based #ReinforcementLearning agent to achieve top-level performance on the Atari benchmark, learning general representations from images to discover successful behaviors in latent space. Read more at http://goo.gle/3ub8iZe

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Google AI@GoogleAI·

16 Feb

Can models more efficiently learn rearrangement tasks by overlaying 3D space instead of using object-centric representations? Check out Transporter Nets, an open-source framework for sample-efficient robot manipulation, with related benchmark tasks. See ↓ http://goo.gle/37k9KOW

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Google AI@GoogleAI·

11 Feb

Announcing the release of TensorFlow 3D, a set of training and evaluation pipelines for state-of-the-art 3D semantic segmentation, object detection and instance segmentation, with support for distributed training. Check it out and download the code at http://goo.gle/3pchcSG

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Google AI@GoogleAI·

11 Feb

Introducing the CATS4ML Data Challenge, a competition to identify “unknown unknowns”, data classes that are difficult for #ML models to evaluate due to the lack of real-world examples in training data. Learn more and join the challenge ↓ http://goo.gle/3tQvWKs

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Google AI@GoogleAI·

5 Feb

Introducing TracIn, a simple, scalable approach to understand the influence of training data on #MachineLearning models by tracing the changes in prediction as individual training examples are visited during the training process. Learn all about it below: https://goo.gle/3jkclgT

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Google AI@GoogleAI·

4 Feb

Check out new work on ML-driven design and exploration of custom accelerators, showing how #MachineLearning facilitates architecture exploration by rapidly identifying high-performing configurations across a range of applications. Learn more ↓ http://goo.gle/3rngiEh

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Google AI@GoogleAI·

3 Feb

Introducing the World Models Library, an open-source, platform-agnostic suite of tasks and tools for examination of world model design and performance in visual model-based reinforcement learning. Learn more and grab the code at http://goo.gle/36EXY1t

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