AI and Data Annotation
Data annotation is the process of labeling data available in various formats such as text, video, or images. Labeling refers to classifying, categorizing, organizing, and ordering this data.
There are two types of data annotation: manual and automatic.
Manual annotation is a task that requires extreme concentration and skilled personnel. It makes the process both time-consuming and expensive. Therefore, automatic data labeling using AI exists. Once the annotation task is specified, a trained machine learning model can be applied to a set of unlabeled data. The AI will then be able to predict appropriate labels for the new and unseen dataset. This is called machine learning.
However, if the model fails to label correctly, humans can intervene, examine, and correct the mislabeled data. The corrected and revised data can then be used to retrain the labeling model.
At GOLDENS DEAL, we believe that AI and humans are complementary.
Human in the Loop – the importance of the human element in AI projects
The most critical element of any AI project is one that is rarely considered: humans.
AI will always need humans because it mimics human intelligence. The amount of manual annotation will never be zero, and the models are constantly improving.
The effectiveness of AI depends on humans, who are responsible for extracting large amounts of data and training the AI algorithms to perform the intended tasks. More and more companies are turning to AI and machine learning to update their customer experience and back-office operations. This is why human involvement in data capture and system maintenance over time is extremely important.