Computer Vision

Computer vision is a branch of artificial intelligence focused on giving machines the ability to see and understand visual content. This includes tasks like image classification (what is in this image), object detection (where are specific objects), semantic segmentation (labeling every pixel), face recognition, optical character recognition (OCR), and video analysis. Computer vision systems power everything from smartphone cameras to autonomous vehicles.

The field has been transformed by deep learning, particularly convolutional neural networks (CNNs) and more recently vision transformers (ViTs). These models learn to recognize visual features at multiple levels of abstraction, from edges and textures to objects and scenes, by training on millions of labeled images. Modern computer vision systems can match or exceed human accuracy on many visual recognition tasks.

The convergence of computer vision with large language models has created multimodal AI systems that can both see and reason about images. Models like GPT-4V, Claude 3, and Gemini can describe images, answer questions about visual content, extract text from screenshots, analyze charts, and even generate code from UI mockups. This multimodal capability is opening new applications in healthcare imaging, quality control, accessibility, and creative design.