Posted by Julian Eisenschlos, Research Software Engineer, Google Research Visual language is the form of communication that relies on pictorial symbols outside of text to convey information. It is ubiquitous in our digital life in the form of iconography, infographics, tables, plots, and charts, extending to the real world in street signs, comic books, food labels, etc. For that reason, having computers better understand this type of media can help with scientific communication and discovery, accessibility, and data transparency. While computer vision models have made tremendous progress usi ..read more

Posted by Ken Caluwaerts and Atil Iscen, Research Scientists, Google Creating robots that exhibit robust and dynamic locomotion capabilities, similar to animals or humans, has been a long-standing goal in the robotics community. In addition to completing tasks quickly and efficiently, agility allows legged robots to move through complex environments that are otherwise difficult to traverse. Researchers at Google have been pursuing agility for multiple years and across various form factors. Yet, while researchers have enabled robots to hike or jump over some obstacles, there is still no genera ..read more

Posted by Vincent Cohen-Addad and Alessandro Epasto, Research Scientists, Google Research, Graph Mining team Clustering is a central problem in unsupervised machine learning (ML) with many applications across domains in both industry and academic research more broadly. At its core, clustering consists of the following problem: given a set of data elements, the goal is to partition the data elements into groups such that similar objects are in the same group, while dissimilar objects are in different groups. This problem has been studied in math, computer science, operations research and stati ..read more

Posted by James Manyika, SVP Google Research and Technology & Society, and Jeff Dean, Chief Scientist, Google DeepMind and Google Research Wednesday, May 10th was an exciting day for the Google Research community as we watched the results of months and years of our foundational and applied work get announced on the Google I/O stage. With the quick pace of announcements on stage, it can be difficult to convey the substantial effort and unique innovations that underlie the technologies we presented. So today, we’re excited to reveal more about the research efforts behind some of the many ex ..read more

Posted by Alexander Frömmgen, Staff Software Engineer, and Lera Kharatyan, Senior Software Engineer, Core Systems & Experiences Code-change reviews are a critical part of the software development process at scale, taking a significant amount of the code authors’ and the code reviewers’ time. As part of this process, the reviewer inspects the proposed code and asks the author for code changes through comments written in natural language. At Google, we see millions of reviewer comments per year, and authors require an average of ~60 minutes active shepherding time between sending changes fo ..read more

Posted by Natalia Ponomareva and Alex Kurakin, Staff Software Engineers, Google Research Large machine learning (ML) models are ubiquitous in modern applications: from spam filters to recommender systems and virtual assistants. These models achieve remarkable performance partially due to the abundance of available training data. However, these data can sometimes contain private information, including personal identifiable information, copyright material, etc. Therefore, protecting the privacy of the training data is critical to practical, applied ML. Differential Privacy (DP) is one of the m ..read more

Posted by Lucas Dixon and Michael Terry, co-leads, PAIR, Google Research PAIR (People + AI Research) first launched in 2017 with the belief that “AI can go much further — and be more useful to all of us — if we build systems with people in mind at the start of the process.” We continue to focus on making AI more understandable, interpretable, fun, and usable by more people around the world. It’s a mission that is particularly timely given the emergence of generative AI and chatbots. Today, PAIR is part of the Responsible AI and Human-Centered Technology team within Google Research, and our w ..read more

Posted by Jerry Wei, Student Researcher, and Denny Zhou, Principal Scientist, Google Research There have recently been tremendous advances in language models, partly because they can perform tasks with strong performance via in-context learning (ICL), a process whereby models are prompted with a few examples of input-label pairs before performing the task on an unseen evaluation example. In general, models’ success at in-context learning is enabled by: Their use of semantic prior knowledge from pre-training to predict labels while following the format of in-context examples (e.g., seeing ex ..read more

Posted by Candice Schumann, Software Engineer, and Gbolahan O. Olanubi, User Experience Researcher, Google Research Skin tone is an observable characteristic that is subjective, perceived differently by individuals (e.g., depending on their location or culture) and thus is complicated to annotate. That said, the ability to reliably and accurately annotate skin tone is highly important in computer vision. This became apparent in 2018, when the Gender Shades study highlighted that computer vision systems struggled to detect people with darker skin tones, and performed particularly poorly for wo ..read more

Posted by Weicheng Kuo and Anelia Angelova, Research Scientists, Google Research Detection is a fundamental vision task that aims to localize and recognize objects in an image. However, the data collection process of manually annotating bounding boxes or instance masks is tedious and costly, which limits the modern detection vocabulary size to roughly 1,000 object classes. This is orders of magnitude smaller than the vocabulary people use to describe the visual world and leaves out many categories. Recent vision and language models (VLMs), such as CLIP, have demonstrated improved open-vocabul ..read more