Posted by Isaac Noble, Software Engineer, Google Research, and Anelia Angelova, Research Scientist, Google DeepMind When building machine learning models for real-life applications, we need to consider inputs from multiple modalities in order to capture various aspects of the world around us. For example, audio, video, and text all provide varied and complementary information about…
The structure of Ghostbuster, our new state-of-the-art method for detecting AI-generated text. Large language models like ChatGPT write impressively well—so well, in fact, that they’ve become a problem. Students have begun using these models to ghostwrite assignments, leading some schools to ban ChatGPT. In addition, these models are also prone to producing text with factual…
This is not the first time that I’ve written about AI creativity, and I doubt that it will be the last. It’s a question that comes up repeatedly, and that is very much in the current mind, with events like the strikes by the Writers’ Guild of America and the Screen Actors Guild, in which…
This year the Earth has been hit by a record number of unpredictable extreme weather events made worse by climate change. Predicting them faster and with greater accuracy could enable us to prepare better for natural disasters and help save lives. A new AI model from Google DeepMind could make that easier. In research published…
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. “This moment in AI is an inflection moment,” Fei-Fei Li told me recently. Li is co-director of Stanford’s Human-Centered AI Institute and one of the most prominent computer science researchers of…
The following is excerpted from BROKEN CODE: Inside Facebook and the Fight to Expose Its Harmful Secrets by Jeff Horwitz. Reprinted by permission of Doubleday, an imprint of The Knopf Doubleday Publishing Group, a division of Penguin Random House LLC. Copyright © 2023 by Jeff Horwitz. In 2006, the U.S. patent office received a filing…
Asymmetric Certified Robustness via Feature-Convex Neural Networks TLDR: We propose the asymmetric certified robustness problem, which requires certified robustness for only one class and reflects real-world adversarial scenarios. This focused setting allows us to introduce feature-convex classifiers, which produce closed-form and deterministic certified radii on the order of milliseconds. Figure 1. Illustration of feature-convex classifiers…
Posted by Chintan Ghate, Software Engineer, and Diana Mincu, Research Engineer, Google Research As consumer technologies like fitness trackers and mobile phones become more widely used for health-related data collection, so does the opportunity to leverage these data pathways to study and advance our understanding of medical conditions. We have previously touched upon how our…
Posted by Katherine Heller, Research Scientist, Google Research, on behalf of the CAIR Team Artificial intelligence (AI) and related machine learning (ML) technologies are increasingly influential in the world around us, making it imperative that we consider the potential impacts on society and individuals in all aspects of the technology that we create. To these…
Posted by Kevin Miao and Matt McEwen, Research Scientists, Quantum AI Team The qubits that make up Google quantum devices are delicate and noisy, so it’s necessary to incorporate error correction procedures that identify and account for qubit errors on the way to building a useful quantum computer. Two of the most prevalent error mechanisms…