MIT News – Artificial intelligence The Singapore MIT-Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, has launched a new interdisciplinary research group aimed at tackling key social and institutional challenges around the rise of artificial intelligence and other new technologies. The group, known as Mens, Manus and Machina: How AI Empowers People, […]Continue reading
Google AI Blog Posted by Greg Corrado, Head of Health AI, Google Research, and Yossi Matias, VP, Engineering and Research, Google Research Medicine is an inherently multimodal discipline. When providing care, clinicians routinely interpret data from a wide range of modalities including medical images, clinical notes, lab tests, electronic health records, genomics, and more. Over […]Continue reading
Google AI Blog Posted by Sanjay Subramanian, PhD student, UC Berkeley, and Arsha Nagrani, Research Scientist, Google Research, Perception Team Visual question answering (VQA) is a machine learning task that requires a model to answer a question about an image or a set of images. Conventional VQA approaches need a large amount of labeled training […]Continue reading
Coronavirus | The Guardian Research suggests those aged 18 to 24 have been deeply affected by pandemic and then cost of living and healthcare crisesYoung adults are bearing the brunt of a “growing mental health crisis” in England, according to researchers who warn that levels of severe distress have risen steadily in adults since the […]Continue reading
MIT News – Artificial intelligence Researchers from MIT, the MIT-IBM Watson AI Lab, IBM Research, and elsewhere have developed a new technique for analyzing unlabeled audio and visual data that could improve the performance of machine-learning models used in applications like speech recognition and object detection. The work, for the first time, combines two architectures […]Continue reading
Apple Machine Learning Research *= Equal Contributors Artificial Intelligence (AI) and Machine Learning (ML) have made tremendous progress in the recent decade and have become ubiquitous in almost all application domains. Many recent advancements in the ease-of-use of ML frameworks and the low-code model training automations have further reduced the threshold for ML model building. […]Continue reading
Google AI Blog Posted by Shekoofeh Azizi, Senior Research Scientist, and Laura Culp, Senior Research Engineer, Google Research Despite recent progress in the field of medical artificial intelligence (AI), most existing models are narrow, single-task systems that require large quantities of labeled data to train. Moreover, these models cannot be easily reused in new clinical […]Continue reading
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