Posted by Sara Beery, Student Researcher, and Jonathan Huang, Research Scientist, Google Research, Perception Team Over four billion people live in cities around the globe, and while most people interact daily with others — at the grocery store, on public transit, at work — they may take for granted their frequent interactions with the diverse…
Hyper-realistic virtual worlds have been heralded as the best driving schools for autonomous vehicles (AVs), since they’ve proven fruitful test beds for safely trying out dangerous driving scenarios. Tesla, Waymo, and other self-driving companies all rely heavily on data to enable expensive and proprietary photorealistic simulators, since testing and gathering nuanced I-almost-crashed data usually isn’t…
Posted by Shaina Mehta and Kristen Borg, Program Managers This week marks the beginning of the premier annual Computer Vision and Pattern Recognition conference (CVPR 2022), held both in-person in New Orleans, LA and virtually. As a leader in computer vision research and a Platinum Sponsor, Google will have a strong presence across CVPR 2022 with over…
Upon looking at photographs and drawing on their past experiences, humans can often perceive depth in pictures that are, themselves, perfectly flat. However, getting computers to do the same thing has proved quite challenging. The problem is difficult for several reasons, one being that information is inevitably lost when a scene that takes place in…
Upon looking at photographs and drawing on their past experiences, humans can often perceive depth in pictures that are, themselves, perfectly flat. However, getting computers to do the same thing has proved quite challenging. The problem is difficult for several reasons, one being that information is inevitably lost when a scene that takes place in…
Many of us easily recognize emotions expressed in others’ faces. A smile may mean happiness, while a frown may indicate anger. Autistic people often have a more difficult time with this task. It’s unclear why. But new research, published June 15 in The Journal of Neuroscience, sheds light on the inner workings of the brain…
Posted by Laura Downs and Anthony Francis, Software Engineers, Robotics at Google Many recent advances in computer vision and robotics rely on deep learning, but training deep learning models requires a wide variety of data to generalize to new scenarios. Historically, deep learning for computer vision has relied on datasets with millions of items that…
Imagine a more sustainable future, where cellphones, smartwatches, and other wearable devices don’t have to be shelved or discarded for a newer model. Instead, they could be upgraded with the latest sensors and processors that would snap onto a device’s internal chip — like LEGO bricks incorporated into an existing build. Such reconfigurable chipware could…
Posted by Basil Mustafa, Research Software Engineer and Carlos Riquelme, Research Scientist, Google Research, Brain team Sparse models stand out among the most promising approaches for the future of deep learning. Instead of every part of a model processing every input (“dense” modeling), sparse models employing conditional computation learn to route individual inputs to different…
From their early days at MIT, and even before, Emma Liu ’22, MNG ’22, Yo-whan “John” Kim ’22, MNG ’22, and Clemente Ocejo ’21, MNG ’22 knew they wanted to perform computational research and explore artificial intelligence and machine learning. “Since high school, I’ve been into deep learning and was involved in projects,” says Kim,…