Latest from Google AI – Google at ICML 2022

Posted by Cat Armato, Program Manager, University Relations Google is a leader in machine learning (ML) research with groups innovating across virtually all aspects of the field, from theory to application. We build machine learning systems to solve deep scientific and engineering challenges in areas of language, music, visual processing, algorithm development, and more. Core…

Latest from Google AI – Towards Reliability in Deep Learning Systems

Posted by Dustin Tran and Balaji Lakshminarayanan, Research Scientists, Google Research Deep learning models have made impressive progress in vision, language, and other modalities, particularly with the rise of large-scale pre-training. Such models are most accurate when applied to test data drawn from the same distribution as their training set. However, in practice, the data…

Latest from MIT : Teaching AI to ask clinical questions

Physicians often query a patient’s electronic health record for information that helps them make treatment decisions, but the cumbersome nature of these records hampers the process. Research has shown that even when a doctor has been trained to use an electronic health record (EHR), finding an answer to just one question can take, on average,…

Latest from Google AI – Rewriting Image Captions for Visual Question Answering Data Creation

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…

Latest from MIT : Artificial intelligence model finds potential drug molecules a thousand times faster

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…

Latest from Google AI – Revisiting Mask Transformer from a Clustering Perspective

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…

Latest from MIT Tech Review – Inside a radical new project to democratize AI

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…

Latest from MIT Tech Review – Doctors using AI catch breast cancer more often than either does alone

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…

UC Berkeley – Why do Policy Gradient Methods work so well in Cooperative MARL? Evidence from Policy Representation

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…