Natural Language Processing (NLP) is a technique in AI & ML that helps bridge the gap and remove the parity in communication between a Computer and a Human. The origin of Natural Language Processing dates back to the second world war when Machine Translation came into being.
Computer Vision refers to the branch of computer science that is dedicated to the creation and building of digital systems that are given the capability to analyze, process, and comprehend data in multiple formats such as images, videos, and other visual inputs.
Data Annotation is typically the term used to define the process of data labeling in multiple data formats such as images, video, text, to train machines for understanding the data and completing specific tasks.
Video annotation refers to the process of training computers to track and identify specific objects from videos. Video annotation is one of the several data annotation methods that deal with Computer Vision, Artificial Intelligence, and Machine Learning.
Data labeling is an integral part of the workflow for preparing data, building reliable AI models, and training machine learning models to execute a specific set of tasks.
Data Labeling refers to the process of identifying unorganized and raw data in formats including images, text files, videos, etc.) and adding one or more relevant labels/tags to every data element. The labels provide information to help machine learning models learn the context of every data element.
Cybersecurity is a serious concern in the current digital age, much so that cybersecurity has grown to become a whole industry since the advent of the internet and computers. Every other day we hear news of cyber attacks and organizations losing out on valuable and confidential data. Data Annotation, Image Annotation and Video Annotation powered by AI and ML technologies are only making cybersecurity more efficient with time.
Machine Learning has evolved with time, and the way this advanced technology works is quite different now as compared to when ML was first introduced. Machine Learning principles revolve around ‘iterative learning’ and data engineering mechanisms to help machines adapt to any new data acquired or interacted with.
