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Experiments with robots, or using machines to understand how natural systems work, are still in their infancy. But they are building on experience with software and hardware development. Deep-learning systems have been used in several commercial applications, such as voice and image recognition. The former has been used by computer-assisted diagnosis in medicine, since such a diagnosis can be made by looking at a set of images or text. These systems, however, have made some fundamental errors in understanding spoken language. The system can label the sound as being similar to a word from its database of similar sounds, but it can't determine the actual content of the speech. To make matters worse, some of the algorithms use a mathematical function to understand speech that is wildly at odds with real perception. Deep-learning methods have also been used to classify images, picking up visual features that have been trained for object classification. Deep-learning methods excel in image and speech recognition but struggle in other domains, such as text. A young person is uniquely moving in these spaces. It
Scientists at Hanyang University used a neural network to predict human behavior. A deep-learning system trained on 800 hours of "talking head" video chat data quickly learned that people's faces and hands are extremely deceptive. They don't really do what they pretend they do in front of a camera, the scientists found.
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