As an undergraduate at MIT, Jana Saadi had to find a way to fulfill her humanities class requirements. Little did she know that her decision would heavily shape her academic career. On a whim, Saadi had joined a friend in a class offered through MIT D-Lab, a project-based program aimed at helping poor communities around…
Posted by AJ Piergiovanni and Anelia Angelova, Research Scientists, Google Research, Brain Team Video is an ubiquitous source of media content that touches on many aspects of people’s day-to-day lives. Increasingly, real-world video applications, such as video captioning, video content analysis, and video question-answering (VideoQA), rely on models that can connect video content with text…
It is a problem that has beguiled scientists for a century. But, buoyed by a $625,000 Distinguished Early Career Award from the U.S. Department of Energy (DoE), Matteo Bucci, an associate professor in the Department of Nuclear Science and Engineering (NSE), hopes to be close to an answer. Tackling the boiling crisis Whether you’re heating a…
In a previous article, I wrote about how models like DALL-E and Imagen disassociate ideas from technique. In the past, if you had a good idea in any field, you could only realize that idea if you had the craftsmanship and technique to back it up. With DALL-E, that’s no longer true. You can say,…
Amid a growing epidemic of gun violence, can AI be part of the solution? In this episode we look at some of the weapons detection technologies schools are using in an effort to try to keep students safe. We Meet: Gary Hough, superintendent of Fayette County schools Mark Keierleber, investigative reporter at The 74Mike Ellenbogen, Founder,…
I Was There When is an oral history project that’s part of the In Machines We Trust podcast. It features stories of how breakthroughs and watershed moments in artificial intelligence and computing happened, as told by the people who witnessed them. In this episode we meet Gustav Söderström, who helped create algorithms aiming to understand…
Machine-learning researchers make many decisions when designing new models. They decide how many layers to include in neural networks and what weights to give inputs at each node. The result of all this human decision-making is that complex models end up being “designed by intuition” rather than systematically, says Frank Hutter, head of the machine-learning…
Posted by Bingyi Cao, Software Engineer, Google Research, and Mário Lipovský, Software Engineer, Google Lens Computer vision models see daily application for a wide variety of tasks, ranging from object recognition to image-based 3D object reconstruction. One challenging type of computer vision problem is instance-level recognition (ILR) — given an image of an object, the…
Humans are notoriously poor at judging distances. There’s a tendency to underestimate, whether it’s the distance along a straight road with a clear run to the horizon or the distance across a valley. When ascending toward a summit, estimation is further confounded by false summits. What you thought was your goal and end point turns…
Posted by Qifei Wang, Senior Software Engineer, and Feng Yang, Senior Staff Software Engineer, Google Research Deep learning models for visual tasks (e.g., image classification) are usually trained end-to-end with data from a single visual domain (e.g., natural images or computer generated images). Typically, an application that completes visual tasks for multiple domains would need…