The process of adding labels or metadata to text or other types of data is named annotation. It can be useful in tasks such as text classification, sentiment analysis and entity recognition. In today’s world annotation is a valuable tool for improving the accuracy and effectiveness of machine language processing.
Four stages of annotation:
The very first stage starts with deciding the goals and purpose of the annotation project, identifying the type of data that needs to be annotated, selecting the annotation tools and software, and finally developing proper instructions for annotators.
In this stage, a subset of the data is pre-annotated to avail guide and train annotators. Pre-annotation can be done utilizing automated implements or manually by domain experts.
This is the primary stage of the annotation process, where trained annotators review and label the data according to the guidelines and ordinant dictations. The process may involve multiple rounds of review, feedback, and quality checks to ascertain consistency and precision.
In this final stage, the annotated data is reviewed and evaluated for precision and completeness. The data may be arbitrarily sampled and re-annotated to ascertain that the annotations are consistent and meet the project’s goals.
Overall, these stages are crucial for engendering high-quality annotated data that can be acclimated to train and amend machine learning models like me. Efficacious annotation requires conscientious planning, attention to detail, and constant monitoring to ascertain that the annotated data meets the project’s objectives and quality standards.
Why do we need AI?
As we have seen the stages and types of annotation we can clearly identify some major benefits that AI provides us. Sharing some of them:
i) AI can automate repetitive and tedious tasks
ii) Helps businesses personalize their products and services for individual customers
iii) Navigate doctors and researchers to analyze large amounts of medical data
iv) Provide personalized and immediate customer service through chatbots and virtual assistants
v) AI innovating autonomous vehicles, robotics and natural language processing.
According to a report by McKinsey Global Institute, AI has the potential to create between $3.5 trillion and $5.8 trillion in value annually across nine business functions in 19 industries. The report estimates that by 2030, AI could contribute an additional 1.2% to 1.4% to global GDP growth annually.
Industries that have started using AI and machine learning
There are certain industries that have already started using AI technology and using annotation to train their machine learning algorithms.
Those industries include-
In healthcare industry the use of AI and technology has been rapidly increasing. In terms of brain MRI test or scan, by the help of annotation, doctors now can pin point the tumor or cancer cells quite easily and efficiently.
Artificial Intelligence has taken the responsibility to improve the traffic system of a city. As time is very precious and limited. With the help of technology, the cameras now can detect the movement of vehicles and track their speeds also checks whether they are crossing the limits. Furthermore, this device can control the traffic system.
In the early stage of human civilization, agriculture is considered as one of the oldest form of occupation in the world. By the use of drones and GPS sensors we can get a clear footage of the crop health and soil condition, where using image annotation and train machine learning we can decide what measure to take in terms of irrigation and taking care of crops.
By the use of machine learning and artificial intelligence, we can now implement the security measures with the help of image annotation. In a warehouse for instance, the responsible people can track the theft, differentiate between normal and unusual movements, fire and so on. If this happens, they can easily detect and raise an alarm to the security team.
The pandemic has resulted in an increase in self driven distribution vehicles that are capable of delivering goods. Some image annotation techniques like polygon, box and semantic segmentation is used.
Every detail is annotated carefully that is teachable to machine learning to provide accurate results and avoid small errors which can lead to damage to life or property.
Retail sector is also evolving with the help of artificial intelligence. In terms of self checkouts on a large shopping mall, these payment systems are making our lives easier. When the data is fed to ML via annotation then it can track the customer and recognize them thereby charge them directly from their credit card. This process help mall owners save money on hiring staffs and gaining accuracy.
In sports arena, the skeletal annotation plays an important role in athlete training, track body movements and identify player’s performance specially in golf or football.
Annotation in financial data helps to identify market fluctuations and help provide suggestions based on data to their clients.
Idea and written by: Nawshin Khan
Operations Manager, Datacrete