Data annotation, or the process of adding labels to images, text, audio and other forms of sample data, is typically a key step in developing AI systems. The vast majority of systems learn to make predictions by associating labels with specific data samples, like the caption "bear" with a photo of a black bear. A system trained on many labeled examples of different kinds of contracts, for example, would eventually learn to distinguish between those contracts and even extrapolate to contracts that it hasn't seen before. The trouble is, annotation is a manual and labor-intensive process that's historically been assigned to gig workers on platforms like Amazon Mechanical Turk. But with the soaring interest in AI -- and in the data used to train that AI -- an entire industry has sprung up around tools for annotation and labeling. Dataloop, one of the many startups vying for a foothold in the nascent market, today announced that it raised $33 million in a Series B round led by Nokia Growth Partners (NGP) Capital and Alpha Wave Global.

Data annotation, or the process of adding labels to images, text, audio and other forms of sample data, is typically a key step in developing AI systems. The vast majority of systems learn to make predictions by associating labels with specific data samples, like the caption "bear" with a photo of a black bear. A system trained on many labeled examples of different kinds of contracts, for example, would eventually learn to distinguish between those contracts and even extrapolate to contracts that it hasn't seen before. The trouble is, annotation is a manual and labor-intensive process that's historically been assigned to gig workers on platforms like Amazon Mechanical Turk. But with the soaring interest in AI -- and in the data used to train that AI -- an entire industry has sprung up around tools for annotation and labeling.

Data annotation is the way toward labelling images, audio, video frames, and text information primarily utilized in directed ML to prepare and train the datasets that assist a machine with understanding the input info and act as needs are. There are many kinds of annotations: bounding boxes, landmark annotation, semantic division, polyline annotation, polygon annotation, key issues, named entity recognition, and 3D point cloud annotations. With the headways in deep learning algorithms, NLP and computer vision have extraordinarily developed and done miracles around the world of Artificial Intelligence. Alongside this, AutoML has additionally developed. It has driven numerous enterprises to adopt AI quickly and use it in original use cases.

AI data management and annotation startup Dataloop today announced that it raised $16 million in funding, a combination of an $11 million series A round and a previously undisclosed $5 million seed round. A spokesperson says the funds will enable Dataloop to increase its recruitment efforts and grow its presence in the U.S. and Europe. Training AI and machine learning algorithms requires plenty of annotated data. But data rarely comes with annotations. The bulk of the work often falls to human labelers, whose efforts tend to be expensive, imperfect, and slow. Dataloop claims to solve the annotation challenge with a platform for automating data prep and data operations.