• Introducing the iNaturalist 2018 Challenge

    Posted Yang Song (Staff Software Engineer) and Serge Belongie (Visiting Faculty), Google Research Thanks to recent advances in deep learning, the visual recognition abilities of machines have improved dramatically, permitting the practical application of computer vision to tasks ranging from pedestrian detection for self-driving cars to expression recognition in virtual reality. One...

  • Announcing the winners of the Facebook Hardware & Software Systems research awards – Facebook Research

    Continued research into hardware and systems is essential to Facebook as we develop algorithms to maximize impact and every day experiences. By sponsoring research, we extend our knowledge and share findings. We are especially interested in collaborating and sponsoring research at the intersection of computer systems and machine learning and are pleased...

  • Open Sourcing the Hunt for Exoplanets

    Posted by Chris Shallue, Senior Software Engineer, Google Brain Team Recently, we discovered two exoplanets by training a neural network to analyze data from NASA’s Kepler space telescope and accurately identify the most promising planet signals. And while this was only an initial analysis of ~700 stars, we consider this a successful...

  • Geneva Motor Show Highlights AI, Electric Cars

    Flashy, fun, beautiful. The Geneva Motor Show has long been the showcase for outrageous luxury and exotic cars that make headlines around the world. As it kicks off its 88th edition this week, opening its doors to tens of thousands of attendees, the show is bringing more electric cars — and more AI...

  • Why diversity matters in AI research – Facebook Research

    Dr. Joelle Pineau is head of the Facebook AI Research (FAIR) lab in Montreal and co-director of the Reasoning and Learning Lab at McGill University’s School of Computer Science. On International Women’s Day, Pineau discusses the importance of diversity in the field of AI research and the progress being made to bring...

  • The Building Blocks of Interpretability

    Posted by Chris Olah, Research Scientist and Arvind Satyanarayan, Visiting Researcher, Google Brain Team (Crossposted on the Google Open Source Blog) In 2015, our early attempts to visualize how neural networks understand images led to psychedelic images. Soon after, we open sourced our code as DeepDream and it grew into a small...

  • A Preview of Bristlecone, Google’s New Quantum Processor

    Posted by Julian Kelly, Research Scientist, Quantum AI Lab The goal of the Google Quantum AI lab is to build a quantum computer that can be used to solve real-world problems. Our strategy is to explore near-term applications using systems that are forward compatible to a large-scale universal error-corrected quantum computer. In...

  • Making Healthcare Data Work Better with Machine Learning

    Posted by Patrik Sundberg, Software Engineer and Eyal Oren, Product Manager, Google Brain Team Over the past 10 years, healthcare data has moved from being largely on paper to being almost completely digitized in electronic health records. But making sense of this data involves a few key challenges. First, there is no...

  • Research Blog: Mobile Real-time Video Segmentation

    Valentin Bazarevsky and Andrei Tkachenka, Software Engineers, Google Research Video segmentation is a widely used technique that enables movie directors and video content creators to separate the foreground of a scene from the background, and treat them as two different visual layers. By modifying or replacing the background, creators can convey a...

  • A New Dataset and Challenge for Landmark Recognition

    Posted by André Araujo and Tobias Weyand, Software Engineers, Google Research Image classification technology has shown remarkable improvement over the past few years, exemplified in part by the Imagenet classification challenge, where error rates continue to drop substantially every year. In order to continue advancing the state of the art in computer...