You’ve likely heard that “experience is the best teacher”—but what if learning in the real world is prohibitively expensive? This is the plight of roboticists training their machines on manipulation tasks. Real-world interaction data is costly, so their robots often learn from simulated versions of different activities.You’ve likely heard that “experience is the best teacher”—but what if learning in the real world is prohibitively expensive? This is the plight of roboticists training their machines on manipulation tasks. Real-world interaction data is costly, so their robots often learn from simulated versions of different activities.Robotics[#item_full_content]

To best assist humans in real-world settings, robots should be able to continuously acquire useful new skills in dynamic and rapidly changing environments. Currently, however, most robots can only tackle tasks that they have been previously trained on and can only acquire new capabilities after further training.To best assist humans in real-world settings, robots should be able to continuously acquire useful new skills in dynamic and rapidly changing environments. Currently, however, most robots can only tackle tasks that they have been previously trained on and can only acquire new capabilities after further training.Robotics[#item_full_content]

Forensic experts in the U.K. are taking new steps to identify criminals caught on CCTV using the shoes they are wearing.Forensic experts in the U.K. are taking new steps to identify criminals caught on CCTV using the shoes they are wearing.Security[#item_full_content]

Deep machine learning has achieved remarkable success in various fields of artificial intelligence, but achieving both high interpretability and high efficiency simultaneously remains a critical challenge. Shi-Ju Ran of Capital Normal University and Gang Su of the University of the Chinese Academy of Sciences have reviewed an innovative approach based on tensor networks, drawing inspiration from quantum mechanics. This approach offers a promising solution to the long-standing challenge of reconciling interpretability and efficiency in deep machine learning.Deep machine learning has achieved remarkable success in various fields of artificial intelligence, but achieving both high interpretability and high efficiency simultaneously remains a critical challenge. Shi-Ju Ran of Capital Normal University and Gang Su of the University of the Chinese Academy of Sciences have reviewed an innovative approach based on tensor networks, drawing inspiration from quantum mechanics. This approach offers a promising solution to the long-standing challenge of reconciling interpretability and efficiency in deep machine learning.Computer Sciences[#item_full_content]

Image recognition technology has come a long way since 2012 when a group of computer scientists at the University of Toronto created a convolutional neural network (CNN)—dubbed “AlexNet” after its creator Alex Krizhevsky—that correctly identified images much better than others. Its findings have propelled successful use of CNNs in related fields such as video analysis and pattern recognition, and now researchers are now focusing on 3D deep learning networks.Image recognition technology has come a long way since 2012 when a group of computer scientists at the University of Toronto created a convolutional neural network (CNN)—dubbed “AlexNet” after its creator Alex Krizhevsky—that correctly identified images much better than others. Its findings have propelled successful use of CNNs in related fields such as video analysis and pattern recognition, and now researchers are now focusing on 3D deep learning networks.Computer Sciences[#item_full_content]