Researchers have developed a novel method called REGLE, which can harness hidden information in high-dimensional clinical data for genetic discovery. This approach is computationally efficient and requires no disease labels. It can also incorporate information from expert-defined knowledge. The method aims to uncover hidden information in high-dimensional clinical data, which can help study the connection between genes and data.

Source: https://blog.research.google/

In fall 2023, a high-performance computational fluid dynamics framework called SWIRL-FIRE was developed and released, optimized for TPU architecture. This framework, integrated with the optimization platform Vizier, enabled the generation of the 1.36 PiB FireBench dataset in less than a week using 200k TPU hours. The dataset was generated by considering 117 different wind speeds.

Source: https://blog.research.google/

Researchers propose using large language models to assess the meaning preservation of automatic speech recognition (ASR) transcripts, which is an alternative metric to word error rate (WER) for low-resource scenarios and atypical speech. The current WER and word accuracy metrics do not measure comprehensibility, which is a critical aspect of ASR performance, especially for users with atypical speech patterns.

Source: https://blog.research.google/

Recent text-to-image generation models have made significant progress, but still suffer from issues like artifacts, misalignment with text descriptions, and low aesthetic quality. To improve these models, we propose rich human feedback for text-to-image generation and release a dataset to support this approach.

Source: https://blog.research.google/

We release the MISeD dataset of information-seeking dialogs focused on meeting transcripts, with corresponding baseline models. Meeting recordings have helped people worldwide catch missed meetings, focus instead of taking notes during calls, and review information. An agent that supports natural language conversations with meeting recordings could enable efficient navigation of recordings.

Source: https://blog.research.google/

The Kardar-Parisi-Zhang (KPZ) universality class describes the macroscopic behavior common to a variety of randomly growing interfaces, including growing wildfires and snow falling and clumping together. This class was introduced in 1985 by Kardar, Parisi, and Zhang.

Source: https://blog.research.google/

Researchers evaluated the performance of PaLM2 models in multilingual tasks, comparing direct inference with pre-translation. They found that direct inference in the source language outperformed pre-translation to English. This study focused on generative tasks like text summarization and attributed QA, where the output needs to be in the source language.

Source: https://blog.research.google/

Human I/O is a unified approach that detects situational impairments and assesses a user's ability to interact in a given situation. These impairments, known as situationally induced impairments and disabilities (SIIDs), can be caused by environmental factors like noise, lighting, temperature, stress, and social norms.

Source: https://blog.research.google/

We strive to create an environment conducive to many different types of research across many different time scales and levels of risk.

Source: https://blog.research.google/

We strive to create an environment conducive to many different types of research across many different time scales and levels of risk.

Source: https://blog.research.google/