Annotation support can provide symbiosis of the human and AI that can create an effective annotation services for the huge amount of data because both are good in creating and choosing, but AI can work faster than human beings. This collaboration process can be broken down into several steps:
1. Defining annotation tasks and objectives: First, it is necessary to work out what type of annotation is under discussion and what aims are set for the annotation. This includes what the data of interest is, why it is necessary to annotate and what should result from annotating the data. The data could be text, picture, voice recording or even recorded video based on the task that the system is performing.
2. Selecting appropriate AI algorithms: Following that, AI algorithms should be selected according to the type of the data and the peculiarities of the annotation process. Dependent on the task, it may use anything from machine learning, deep learning, natural language processing, computer vision etc.
3. Preparing the data: To ensure that data are in a format which can be processed by AI, data should be pre-processed. It can include washing or scaling or even mapping of the data depending on the challenge ahead.
4. Initial AI-assisted annotation: The AI model developed is trained on a labelled dataset and then used to work on new data for the purpose of annotation. The annotations that have been generated by means of the machine learning and AI ways can be rechecked by the human to determine that there are some wrong content or the certain areas where the AI model fails to catch the right information.
5. Human-in-the-loop annotation: Finally, corrections to the work done for the AI model come in the form of advice or feedback that attempts to refine the correct annotations. AI results can be reviewed intuitively via human interaction to tell AI the correct input if it is incorrect.
6. Iterative refinement of AI algorithms: Whenever human feedback is pumped into the AI, the algorithms get tweaked in a way that improves performance. This process of approximation is repeated several steps until the required degree of confidence is attained.
7. Automated annotation with AI support: After it is properly tuned, the AI algorithms can be trained for enough and good amount of time such that the annotation of new data can be automated. These annotations can be checked by humans before annotation is completed however most of the work is done by the AI which makes the process of annotation much cheaper and less time consuming.
8. Continuous improvement: There is an ongoing improvement of the AI algorithms and human collaboration patterns as soon as new data is available or as the annotation task is modified.
To sum up, annotation support represents a manner of proper cooperation between humans and AI in annotation tasks based on the profitable features of each of the parties involved. The resulting is an ensemble of tasks formulation where the goals are set, algorithm choice where the correct AI algorithms for the task are picked, data preprocessing where the data is pre-processed and processed, and feedback where human feedback is integrated into the AI enchainment loop to improve the AI algorithms. In the long run, this can produce highly accurate and automatic annotation procedures which lower the time and cost for manual annotations greatly.