What is data annotation?
Data annotation is the process of labeling data – text, 2D images, 3D renderings, audio, or video – so that machines can understand it.
Let’s say you are an AI programmer archivist for National Geographic, working with an extensive catalog of cat images. An AI algorithm will need training to help understand the difference between domestic cats, like calicos and tabbies, and wild cats like lions, tigers, and jaguars. AI models don’t have the natural bias or experience to understand the differences between furry felines.
When the bot or algorithm is fed a quality diet of labeled training images of every conceivable cat breed, with an appropriate amount of human intervention, they will be locked in their memory for fast, highly accurate recall.
What are the differences between automated data annotation and labeling for deep learning?
Data labeling and annotation are generally used interchangeably to describe the process of tagging or enriching data with metadata tags. This process makes the training data more meaningful to a machine. Data annotation sometimes goes beyond strictly labeling images for computer vision and identifies patterns and relationships among data sets.
Using the cat image classification example above, a bot may find an article about a Jaguar vehicle traveling faster than any land animal ever could. If that article or video is appropriately labeled as being about luxury cars, a properly-trained search algorithm that is programmed with a bias for jaguar cats would skip it and move on. Otherwise, the bot could include the Jaguar car video or article in the search results. Or the anomaly might cause data processing to stop until a supervising human knowledge worker identifies the article as not relevant.
The Jaguar article or video might be problematic if the cat algorithm was strictly trained on reading metadata labels. More advanced data annotation could identify relationships between the four-wheeled Jaguar vehicles that don’t align with four-legged mammal jaguars.
Types of data annotations
For every kind of media, there are specialized forms and priorities for data annotation.
Text annotation – Due to the sheer volume of textual data and how frequently errors like typos or duplicates occur, there are three kinds of text labeling:
- Sentiment annotations such as in e-commerce product reviews.
- Intent annotations, such as online chat topics.
- Semantic annotations can personalize product listings for regional or cultural vocabularies.
Examples of text annotation can include paragraph highlighting, circled sentences, crossed-out phrases, or notes in the margin.
Image annotations –
- Polygons can identify irregularly-shaped objects such as articles of clothing, parts of buildings, or cars in traffic. Cuboids can do the same for 3D renderings
- Bounding boxes frame objects within an image
- In a CT scan, semantic segmentation can identify specific parts of an image, such as parts of a brain.
Video annotation services like those listed above for image annotations, with added spatial object tracking.
Audio annotation – involves transcription and time stamping of presentations, keynote speeches and the like.
What is a Data Annotation Platform?
For decades, developers have used the expression ‘garbage in, garbage out’ (GIGO) to explain how poor-quality, nonsense data input produces nonsense data output. If you flip that idea on its head, it makes sense that high-quality data input will deliver quality, sensible data output (QIQO). That is especially true for machine learning (ML) data for bots and algorithms.
Sama research has found that on average, 80% of the artificial intelligence (AI) value chain effort consists of data training activities from curation, preparation and analysis to annotation and enrichment.
The remaining work entails AI model development, training, tuning and deployment. Manual data annotation and labeling are time-consuming, monotonous tasks that are prone to human error. Automating annotations frees up humans to work on more complex, abstract, creative tasks which are more engaging and mentally stimulating.
Why do you need a data annotation platform?
Once the best data annotation platforms are properly trained on textual or visual data with high-quality tags or labels, they can be tasked with labeling future data sets. These data annotation tools process text and images at higher speeds and accuracy than humans could ever achieve. Data points can then be ingested by a properly trained and tested computer vision or text-processing engine on a go-forward basis, with minimal supervision or quality risk.
In 2021, there were 79 zettabytes of data worldwide created. To put that in perspective, one zettabyte is equal to a trillion gigabytes. They expect 97 zettabytes of data will be created in 2022, or 2.5 quintillion bytes per day/1.7 MB per second per person. Only a select few data annotation services will be able to process any data amount close to the volumes described above.
Between 80% and 90% of created data is unstructured (information without a predetermined data model or schema) including emails, videos, and audio files. Without AI-augmented data annotation and classification, much of your organization’s valuable information gets lost in the chaos.
Creating Order from Chaos
Oceans of text, image, audio, and video data are created by organizations, bots, and humans every day. Data annotation platforms help users to discover and work with this data securely, effectively and ethically.
Better AI training data helps bots and algorithms to recognize and remember data points, understand relationships between them, and to be able to make decisions based on their programming rules.
To see how Sama’s modern, AI-augmented data annotation has progressed from the old, inefficient manual ways, visit our website.