Posted by Soravit Beer Changpinyo and Doron Kukliansky, Senior Software Engineers, Google Research Visual Question Answering (VQA) is a useful machine learning (ML) task that requires a model to answer a visual question about an image. What makes it challenging is its multi-task and open-ended nature; it involves solving multiple technical research questions in computer…
The entirety of the known universe is teeming with an infinite number of molecules. But what fraction of these molecules have potential drug-like traits that can be used to develop life-saving drug treatments? Millions? Billions? Trillions? The answer: novemdecillion, or 1060. This gargantuan number prolongs the drug development process for fast-spreading diseases like Covid-19 because…
Posted by Qihang Yu, Student Researcher, and Liang-Chieh Chen, Research Scientist, Google Research Panoptic segmentation is a computer vision problem that serves as a core task for many real-world applications. Due to its complexity, previous work often divides panoptic segmentation into semantic segmentation (assigning semantic labels, such as “person” and “sky”, to every pixel in…
PARIS — This is as close as you can get to a rock concert in AI research. Inside the supercomputing center of the French National Center for Scientific Research, on the outskirts of Paris, rows and rows of what look like black fridges hum at a deafening 100 decibels. They form part of a supercomputer…
Radiologists assisted by an AI screen for breast cancer more successfully than they do when they work alone, according to new research. That same AI also produces more accurate results in the hands of a radiologist than it does when operating solo. The large-scale study, published this month in The Lancet Digital Health, is the…
In cooperative multi-agent reinforcement learning (MARL), due to its on-policy nature, policy gradient (PG) methods are typically believed to be less sample efficient than value decomposition (VD) methods, which are off-policy. However, some recent empirical studies demonstrate that with proper input representation and hyper-parameter tuning, multi-agent PG can achieve surprisingly strong performance compared to off-policy…
Posted by Danijar Hafner, Student Researcher, Google Research Research into how artificial agents can make decisions has evolved rapidly through advances in deep reinforcement learning. Compared to generative ML models like GPT-3 and Imagen, artificial agents can directly influence their environment through actions, such as moving a robot arm based on camera inputs or clicking…
Posted by Been Kim, Research Scientist, Google Research, Brain Team, and Alison Lentz, Senior Staff Strategist, Google Research, Mural Team Advances in computer vision and natural language processing continue to unlock new ways of exploring billions of images available on public and searchable websites. Today’s visual search tools make it possible to search with your…
Exactly two weeks after Russia invaded Ukraine in February, Alexander Karp, the CEO of data analytics company Palantir, made his pitch to European leaders. With war on their doorstep, Europeans ought to modernize their arsenals with Silicon Valley’s help, he argued in an open letter. For Europe to “remain strong enough to defeat the threat…
Using a novel fabrication process, MIT researchers have produced smart textiles that snugly conform to the body so they can sense the wearer’s posture and motions. By incorporating a special type of plastic yarn and using heat to slightly melt it — a process called thermoforming — the researchers were able to greatly improve the…