Until recently, a majority of data annotation for training AI models was carried out manually, which invited the usual challenges that come with human intervention. Manual data annotation is prone to a variety of biases and errors and is also time-consuming.
Garbage in, garbage out (GIGO) is a popular concept in computer programming. The GIGO principle applies in machine learning and artificial intelligence, too. If you feed your model rubbish (read: inaccurate or irrelevant data), it will turn out to be rubbish.
Video annotation is essential to improve the performance of many AI-based models and projects. Video annotation helps in achieving correct datasets to train machines and different models. In this blog post, we have talked about what is video annotation and how different types of video annotation techniques can benefit various organizations and businesses.
Artificial intelligence is deeply impacting industries and the lives of people and is going to have a long-lasting impact on almost everything. Thus, we cannot deny that artificial intelligence is the future as it supports businesses in harnessing and managing large amounts of data. It has captured nearly every sector including healthcare, education, media, customer service, transportation, manufacturing, and more.
Almost every organisation engaged in the process of developing machine learning algorithms dabbles with the idea of setting up an in-house team for data annotation requirements. Companies feel that assigning the seemingly easy task of data annotation to their employees will save them of both time and money.