Deep learning advances are boosting computer vision ? but there?s still clear limits

The efforts have led to the emergence of computer vision, a vast subfield of AI and computer science that deals with processing the content of visual data.In recent years, computer vision has taken great leaps thanks to advances in deep learning and artificial neural networks. Deep learning is a branch of AI that is especially good at processing unstructured data such as images and videos.These advances have paved the way for boosting the use of computer vision in existing domains and introducing it to new ones. In many cases, computer vision algorithms have become a very important component of the applications we use every day.Before becoming too excited about advances in computer vision, it’s important to understand the limits of current AI technologies. Adobe’s Enhance Details technology, featured in Lightroom CC, uses machine learning to create sharper zoomed images.Adobe uses deep learning to enhance the details of zoomed images.Image editing tool Pixelmator Pro sports an ML Super Resolution feature, which uses a convolutional neural network to provide crisp zoom and enhance.Until not long ago, facial recognition was a clunky and expensive technology limited to police research labs. But in recent years, thanks to advances in computer vision algorithms, facial recognition has found its way into various computing devices.iPhone X introduced FaceID, an authentication system that uses an on-device neural network to unlock the phone when it sees its owner’s face. A very remarkable example is Google Lens, which uses computer vision algorithms to perform a variety of tasks, such as reading business cards, detecting the style of furniture and clothes, translating street signs, and connecting your phone to wi-fi networks based on router labels.Thanks to advances in deep learning, computer vision is now solving problems that were previously very hard or even impossible for computers to tackle. The neural network then finds the relevant features associated with each outcome and can then diagnose future images with impressive accuracy.Computer vision has found its way into many areas of medicine, including cancer detection and prediction, radiology, diabetic retinopathy.Some AI researchers have gone as far as saying deep learning will soon replace radiologists.