Computer Vision (CV)
Computer vision is the branch of artificial intelligence that enables machines to interpret and understand digital images or videos. It allows computers to process, analyze, and comprehend visual data in a way similar to how humans do – using algorithms to identify objects, faces, scenes, text and more. Computer vision can be used for a wide array of tasks such as recognizing faces, tracking objects in motion, reading license plates on cars, detecting cancerous cells and more. Additionally, computer vision technology can be used to understand customer behavior by analyzing facial expressions and body language. This technology has the potential to revolutionize the way we interact with machines and automate complex processes that before would require human judgement.
Computer vision is an exciting and ever-evolving field that has countless applications in the modern world. It holds great promise for improving many aspects of our lives and is likely to have a huge impact on how machines are used in the near future. With its potential to create more efficient and accurate processes, computer vision promises to be one of the most influential technologies in the next decade.
Deep learning for computer vision (CV) has progressed rapidly in recent years, with networks able to identify objects in images and generate realistic images based on text input. With the exploding availability of high-quality, high-resolution data, researchers must find ways to train deep neural networks on large images and take advantage of their rich contextual information.
As high-resolution data becomes more available in fields such medical imaging, the ability to process large enough images to capture the contextual information present in these images becomes increasingly important. Currently however, the maximum image sizes that can be used for training are often limited by the memory available on the training system, which in turn limits the insights that can be gained from such images. The Cerebras CS-2 system is uniquely designed to overcome the limitations of GPUS and handle the high-memory and high-computational demands that will allow researchers to more fully extract the information contained in these high-resolution images.
