Data annotation refers to the process of labeling and categorizing data, such as text, images, video, audio, or sensor readings, to prepare it for use in machine learning and artificial intelligence systems. Data annotation helps “teach” artificial intelligence models by providing the labeled examples needed for a model to learn to recognize patterns, classify data, and make predictions.
The importance and adoption of data annotation have risen dramatically in recent years alongside the growth in AI. As machine learning algorithms become more powerful, they require ever-larger sets of high-quality training data to learn from.
Data annotation provides the fuel to train today’s complex deep-learning models. Tech giants like Google, Amazon, and Meta, as well as countless startups, now employ large teams of human annotators and make use of annotation automation tools to produce the massive labeled datasets needed for today’s AI systems.
Key Takeaways
AI data annotation is important for transforming raw data into relevant data that can be used to train AI models effectively across various types of AI data.
Tools like a data annotation platform or data annotation tool, combined with professional annotation services, streamline the process of intent annotation and entity annotation to improve AI accuracy.
With data labeling services, organizations can efficiently prepare high-quality datasets, ensuring their AI systems are equipped with the structured information needed to perform complex tasks.
Structured vs Unstructured Data
Data comes in different forms and can be categorized into two main types – structured and unstructured data.
Structured data is organized and standardized with a defined data model. It is formatted to be easily searchable and analyzable. Examples of structured data include data stored in relational databases with predefined fields, spreadsheets with column headings, and JSON data. Structured data has a high level of organization that lends itself well to automated analysis.
On the other hand, unstructured data does not have a predefined model or organization. Examples include images, videos, audio, emails, PDFs, scanned documents, and social media posts. The lack of structure makes unstructured data difficult for computers to interpret and process automatically. Additional steps need to be taken to extract meaningful information from unstructured data.
Since unstructured data does not have an inherent structure, more extensive data annotation is required to train AI models. Data annotation is the process of labeling and structuring unstructured data so that AI algorithms can interpret and learn from the data.
Unstructured data needs human annotation to add semantic labels, transcriptions, bounding boxes, segmentation maps, and other markup. Annotating unstructured data is more labor-intensive compared to structured data but it enables unstructured data to be used for training AI models. The quality and quantity of annotated unstructured data directly impact the performance of AI systems.
