Google Research strives to create an environment conducive to various research types across different time scales and risk levels.

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

Google Research strives to create an environment conducive to various research across different time scales and levels of risk. The organization focuses on creating a conducive environment for research.

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

Google's research philosophy has expanded to incorporate open-ended, long-term research driven by scientific curiosity rather than current product needs. The company believes successful industry research requires managing a portfolio of projects with varying time horizons, levels of risk, and goals. Google's research has become more important than ever to its mission, and the company strives to create an environment conducive to various types of research across different time scales and levels of risk.

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

Google's code repository, the Google monorepo, reveals that 25% of pasted code is immediately modified, with edits ranging from small syntactic fixes to sophisticated adaptations to the surrounding code context. This analysis aims to improve efficiency in code development by identifying patterns in user behaviors.

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

Mobile and wearable devices provide continuous data on an individual's physiological state and behaviors, such as step counts, heart rate variability, and sleep duration. This data can be used for personal health monitoring and motivation. Generative AI models can be used to provide personalized insights and recommendations to help individuals reach their health goals.

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

Google engineers have made significant progress in using AI in software engineering, with many using machine learning-based autocomplete tools, including Google's internal code completion, and commercially available products. The advancements have been made in just five years, from 2019 to 2024. Google is a key organization involved in these developments.

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

The authors thank various individuals and organizations for their aid in investigating medical imagery using generative AI. These include Dr. Sreenivasa Raju Kalidindi and his team at Apollo Radiology International, Andrew Sellergren and Zaid Nabulsi, Dr. Jorge Cuadros and Dr. Lauren P. Daskivich, and others. The authors also acknowledge the Apollo dataset, CXR modeling infrastructure, and EyePACS/LACDHS dataset.

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

Researchers developed a software library to optimize maritime safety by applying heuristics and mathematical optimization techniques to optimize the movement of ships and containers. The library predicts which columns are best to generate to solve large optimization problems.

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

Generative text-to-image models have achieved high-quality imagery synthesis, but their creativity can generate harmful content. To address this, data-centric challenges and red teaming efforts from HuggingFace, NVIDIA, and Microsoft are testing AI models for safety and probing large pre-trained models.

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

CodecLM is a method for fine-tuning large language models (LLMs) to better align with intended objectives. This involves instruction tuning, where the model is trained on varied instructions paired with desired outputs, enabling it to generalize across tasks and formats, ultimately improving performance in understanding and responding to user instructions.

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