Generative AI

Climate & Sustainability

Results We compare the performance of the four methods on manually annotated ground truth data, then apply the best-performing method to a large corpus of Web datasets in order to understand the prevalence of different provenance relationships between those datasets. We generated a corpus of dataset metadata by crawling the Web to find pages with

Research Published 30 October 2024 Authors Zalán Borsos, Matt Sharifi and Marco Tagliasacchi Our pioneering speech generation technologies are helping people around the world interact with more natural, conversational and intuitive digital assistants and AI tools. Speech is central to human connection. It helps people around the world exchange information and ideas, express emotions and

Digital note-taking is gaining popularity, offering a durable, editable, and easily indexable way of storing notes in a vectorized form. However, a substantial gap remains between digital note-taking and traditional pen-and-paper note-taking, a practice still favored by a majority of people. Bridging this gap by converting a note taker’s physical writing into a digital form

Applications in mental health research In our second paper, “Evidence of Differences in Diurnal Electrodermal, Temperature, and Heart Rate Patterns by Mental Health Status in Free-Living Data”, we analyze data for a more specific context, mental health and wellbeing. EDA is a measure of sympathetic arousal that has been linked to depression in laboratory experiments.

This work was made possible by core research and engineering efforts from Andrea Agostinelli, Zalán Borsos, George Brower, Antoine Caillon, Cătălina Cangea, Noah Constant, Michael Chang, Chris Deaner, Timo Denk, Chris Donahue, Michael Dooley, Jesse Engel, Christian Frank, Beat Gfeller, Tobenna Peter Igwe, Drew Jaegle, Matej Kastelic, Kazuya Kawakami, Pen Li, Ethan Manilow, Yotam Mann,

Most systems we regularly interact with, such as computer operating systems, are faced with the challenge of providing good performance, while managing limited resources like computational time and memory. Since it is challenging to optimally manage these resources, there is increasing interest in the use of machine learning (ML) to make this decision-making data driven

Examining DeepVariant To better understand what DeepVariant is learning from its training data, we used a set of simple clustering and visualization methods to summarize the information captured in the model’s high dimensional data. In partnership with collaborators on the Google Genomics team, we first loaded examples into the Integrated Genomics Viewer (IGV), a widely-used

Over recent years, developers and researchers have made progress in efficiently building AI applications. Google Research has contributed to this effort by providing easy-to-use embedding APIs for radiology, digital pathology and dermatology to help AI developers train models in these domains with less data and compute. However, these applications have been restricted to 2D imaging,