Google AI
@GoogleAI
Google AI is focused on bringing the benefits of AI to everyone. In conducting and applying our research, we advance the state-of-the-art in many domains.
Tweets by Google AI
Today we are releasing the Open Buildings Dataset, a new open-source dataset containing the locations and footprints of >500M buildings with coverage across Africa, which can support numerous scientific and humanitarian applications. Read more at https://t.co/ZAFeD3mWQt
Natural speech often has disruptions and complexities that are difficult for #NLP models to understand. Today we introduce two benchmark datasets that challenge models on temporal reasoning (TimeDial) and contextual disfluencies (Disfl-QA). Details below: https://t.co/kD65S8TYwx
While cochlear implants improve the listening experience for many people who are hard of hearing, they can be less effective in noisy environments. Learn how an #ML-based preprocessor can be used to suppress noise, leading to enhanced speech understanding. https://t.co/BCZAThTZ2g
Introducing Pathdreamer, a new world model that generates high-resolution 360º visual observations of areas of a building unseen by a navigational agent in order to help better predict how to successfully navigate the space. Learn more at https://t.co/U5egwDxcmg
Classical algorithms have proved useful for innovation in computing routes, but their computational burden can increase quickly at larger scales. Today we share how a new algorithm breaks road networks into smaller components for greater efficiency ↓ https://t.co/azOrQ9h8qt
Check out Translatotron 2, an updated version of the Translatotron direct speech-to-speech neural translation model, which exhibits improved translation performance, while also applying a new, more secure approach for voice retention. https://t.co/N7Ie1YQN3u
Language models can perform a variety of tasks, but often require careful engineering to perform well on specific tasks. Today we share a simple technique that fine-tunes a model to perform new tasks by following instructions. Learn more → https://t.co/aO1tbe3LNv
#ReinforcementLearning (RL) agents have shown promising results across various activities, but often cannot generalize to new tasks, even if they are similar. Learn about a new approach that incorporates RL’s sequential structure to enhance generalization→https://t.co/BSOciuybj5
Announcing Uncertainty Baselines, a collection of high-quality implementations of standard state-of-the-art deep learning methods for a variety of tasks that help research on uncertainty and robustness to be more reproducible. Read more and get the code ↓ https://t.co/GXZlFxXaef
Medical image classification models often pre-train on natural image datasets. Today, we present alternative approaches that use additional pre-training on medical images, along with metadata-based data augmentation, to significantly improve performance. https://t.co/DHy0XojZwm
Introducing a minimalist and effective approach for vision language model pre-training that learns a single representation from both visual and language inputs and efficiently leverages scaled datasets to achieve state-of-the-art performance. Learn more ↓ https://t.co/U9DY2CZbqR
Discovering new crystalline metal oxides critical for continued technological advancement is made difficult by the extremely large search space. Check out a new approach that prints thousands of distinct compositions quickly for follow-up #ML analysis. https://t.co/2RambPF7cY
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