Data forms the cornerstone of all artificial intelligence (AI) and machine learning (ML) models. Yet, with the vast amount of data around us, unstructured even so, it doesn’t quite help the purpose of training models. Structuring vast amounts of data, including videos, images, and emails, requires precise annotation. Data annotation is the process of labeling unstructured datasets, making it much easier for a machine to comprehend them.
Annotated data is one of the primary factors that has led to the widespread development of AI and ML in recent years. By providing structured, labeled data, one can enable language models to learn patterns, make predictions, and perform tasks with increasing accuracy.
In this blog, we will elaborate further on data annotations and how its role has become so crucial.
Key Takeaways
- Data annotation refers to analyzing datasets and labeling them appropriately for training machines.
- Based on the kind of data sets, one can classify them into image, text, audio, or video annotation.
- High cost, errors, and annotators’ bias are some of the challenges in data annotation, although it could only grow more relevant with the usage of AI and ML.
What Is Data Annotation?
The increasing hype around the above subject brings us to a primary deliberation: What is data annotation?
Data annotation is the procedure of labeling and categorizing raw data to make it understandable to machines. Annotated data makes it possible for algorithms to recognize patterns, process them, and respond accordingly.
Let’s suppose you’re building a language model for medical imaging. The data you feed the models has to include accurate labels of the image, video, or any kind of dataset you feed it, which will help it learn and identify it for any eventual use case in medical science. Simply put, annotated data enables a machine to make sense of different types of data and analyze it suitably.
Moreover, data annotations could be done both manually or automatically, usually with predetermined guidelines or standards. Market research firms estimate the size of the data annotation market to grow by a CAGR of over 28% between 2023 and 2030!
Types Of Data Annotation
The types of data annotation depend on the kind of datasets you feed a machine and its eventual purpose. Depending on it, the types of data annotations are broadly categorized into:
Image annotation: It involves assigning labels to entire images based on what they represent. This helps AI models to classify images and categorize them accordingly. In more specific annotations, labeling objects within an image using bounding boxes is usually done to enable machines to detect objects. Segmentation is another key aspect of image annotation, where ML models are trained to analyze images at the pixel level. A very common application of this is in facial recognition software built into smartphone cameras, which is primarily supported by image annotation.
Text annotation: The semantics in text and their wide usage for businesses and other institutions or individuals make it one of the most important types of data annotation. For a business, text reviews on social media sites could be very important.
But how can businesses analyze several of such reviews and for any insights or specific information? Similarly, say a law enforcement agency wants to analyze anonymous threats posted online. Is it possible to train machines to analyze such texts and decode their intent or context?
Here’s where text annotation comes into play. Machines cannot decipher emotions like humor or sadness behind text. It is necessary to create ML models using annotated text along the lines of semantics, intent, sentiment, or general entity recognition. Pioneering work in this domain has led to the emergence of evolved AI chatbots today that can decrypt and interpret text prompts almost similar to a human.
Audio annotation: As its name suggests, audio annotation is associated with labeling datasets in the form of audio. These datasets could tend to get even more complex than texts due to multiple factors like dialects, tones, and non-verbal features like noise and silence. Transcription and classification of audio using annotated data generally form the basis of audio annotation.
Video annotation: Every frame in videos could suggest a different object or event, for which video annotation has to be meticulous. Video annotation involves a few facets, like tracking, detecting, and classifying objects/events/actions. An example of annotations in videos is in their key usage in differentiating explicit content online and securing the experience for certain categories of users, like minors.
Where Is Data Annotation Headed?
It is not that data annotation doesn’t come with its set of bottlenecks. The cost of manually annotating huge datasets could be costly and time-consuming, with the possibility of human error. Scalability of data annotations is another key concern, with resource availability and effectiveness being an issue. Annotation bias is another challenge, with annotators’ personal influences and opinions affecting training models.
However, automation in data annotation is gaining pace, with large language models dictating it. The impact of data annotation will likely grow stronger in the future, considering it forms the backbone of AI and ML. With the wide adoption of such emerging technologies, annotated data will form the criterion for testing future models and certifying their performance. Data annotation also leads to algorithms working more effectively, signifying their gravity in all spheres of technology.
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FAQs
What are some data annotations tool?
Several data annotation tools are available today. Amazon SageMaker, Intel’s CVAT, and VoTT by Microsoft are a few popular data annotation tools.
What do data annotators do?
Data annotators mainly label, review, and maintain datasets for training machines. They could also monitor model outputs and validate them accordingly.
