Data annotation is a very important aspect of machine learning and AI projects. However, it is a very time-consuming process which is why a lot of companies choose to outsource such work to a dedicated service provider. Let’s first take a look at what data annotation is and how it is used in AI development.
What is Data Annotation?
Data annotation is the process of preparing training datasets with various techniques and methods that allow the machines to learn from the data. We can divide data annotation into further subgroups like image annotation, text annotation, audio annotation, and many others. Now that we received a brief overview of data annotation, let’s move on to some examples.
1. Facilitating the Development of Autonomous Vehicles
AI vehicles are trained on various image and video training sets that require data annotators to label various elements of the image. This can be something as simple as placing a bounding box around another object or more advanced forms of AI data annotation such as semantic segmentation and LiDAR and 3D point Cloud Annotation. Since there are so many scenarios the AI system needs to account for, large volumes of data annotation work are required.
2. Increasing Robot Process Automation
A lot of the routine processes that take place in warehouses, factories, agriculture farms, and other sectors can be automated to take this burden off human workers. However, these robots rely on LiDAR and 3D Point Clouds to see and interact with the physical world around them. This 3D Point Cloud needs to be annotated to help the machine learning algorithm identify all of the objects.
3. Improving Crop Yield
Farmers have the difficult task of growing crops and they need to be able to utilize the farmland they have to the maximum. Drones powered by computer vision technology can help them identify areas of the farmland that need more cultivation. The images produced by the drone need to be annotated with semantic segmentation to provide increased accuracy for the AI model.
4. Creating Next-Gen Personal Assistants
We all use voice assistants like Alexa and Siri, but they require a lot of text annotation. The reason for this is because there is so much nuance in human speech that the annotators need to label all kinds of information in the text that will help the system understand all of these nuances. For example, a human data annotator would need to label keywords and phrases and any relevant metadata to allow the NLP model to converse with humans. A great example of achievement in this field is IBM’s Watson which was able to win the game show Jeopardy and defeat the best human opponents.
5. Enhancing Medical Care
Doctors would much rather spend more time with patients than analyze medical images. This is why a lot of companies have created various products in healthcare that help analyze the medical images and provide a diagnosis much faster than human doctors. However, human data annotators usually need to label all of the various objects located in the image. They usually need to have a medical degree or some type of medical background to perform this job to the highest level.
Start Outsourcing Your Data Annotation Today
If you are working on an AI or machine learning model and you are having difficulties annotating your data, consider outsourcing such work to a service provider. Not only will this be a lot cheaper, but they will also have the necessary experience to get the job done right the first time. They will also be responsible for sourcing, recruiting, office space rental, and many other overhead costs. This is why outsourcing data annotation is a win-win for both sides.
