Annotation Services

Efficient data labelling: A guide to AI annotation

The Artificial Intelligence (AI) and Machine Learning (ML) landscape is evolving rapidly as machines learn to interact with humans and new applications emerge. However, AI tools, like chatbots, often grapple with a negative reputation of being unable to understand or misunderstanding user requests. At the heart of transforming such rigid script-bound tools into dynamic communication tools is AI data annotation.

Data annotation is the cornerstone of how machines are learning to understand and interpret the world around them. It is a foundational building block of a successful, representative, and unbiased AI model. Let us delve deeper into data annotation, understanding different data annotation tools and techniques, and the best practices to overcome major AI annotation challenges.

Understanding AI annotation

An integral link between raw data and actionable intelligence, data annotation systematically tags or labels data to train AI models and help them interpret and understand the human world around them. Using these contextual tags, AI models can understand the nuances of human interactions, navigate complex environments, and interact with users with accuracy and sophistication previously unattainable. This precision can be transformative in many industries, from early diseas e detection in healthcare to the precision of self-driving cars, highlighting the central role of AI data annotation in evolving human-machine interactions.

Types of data annotations

Data annotation is not a singular technique but rather a spectrum of techniques unique to different data formats and AI functionalities. Here are some key types of annotation playing a crucial role in harnessing the full potential of different AI tools:

Text annotation:

The cornerstone of natural language processing, text annotation encompasses semantic annotation, intent annotation, and sentiment annotation, helping AI tools understand the meaning, goal, and emotions within the text.




Image labelling

A foundational building block of computer vision, image labelling encompasses identifying, classifying, and labelling different visual elements within the image.




Video annotation

An extension of image labelling, video annotation involves labelling objects, actions, or events in moving footage for applications like traffic monitoring or sports analysis.




Object detection

Object detection is a specialised form of image and video annotation that focuses on locating and identifying specific objects within an image or a video. It is essential for applications involving inventory tracking or autonomous vehicle navigation.




Audio annotation

The basis for voice recognition technologies, audio annotation involves transcribing and tagging different elements within audio files.

Data annotation tools and techniques

Various factors, like data type, project requirements, AI functionality, and resource constraints, dictate the choice of the AI annotation tool. Some of the common AI data annotation tools and techniques you can choose from include:

Manual annotation

This involves human annotators going through the data and labelling different elements manually. Although this offers greater control over the precision and accuracy of the labelling process, it is time-consuming and prone to human errors.




Automated annotation

These techniques use ML algorithms to automatically label the data, offering enhanced scalability and efficiency. These may be more expensive and less adaptable when handling complex datasets.




Annotation tools

These include software applications and AI annotation services that can help you streamline the overall annotation process. They offer features like collaborative workflows, customisable interfaces, and quality assurance tools for precision, scalability, and efficiency.




Data labelling platforms

These are end-to-end solutions that combine data annotation with project and workforce management features. They are ideal for managing diverse AI data annotation needs at scale.

AI data annotation challenges and best practices

AI data annotation is a novel and dynamic field, evolving rapidly with technological advancements. Despite its importance, data annotation is a complex process, presenting challenges like:

• Limited resource availability
• Ensuring annotation quality and accuracy
• Balancing quality and quantity
• Eliminating subjectivity to ensure consistent tagging.
• Handling sensitive data

A well-structured approach to data annotation can help overcome these challenges and ensure accuracy and consistency in AI data annotation. Here are some best practices you can follow to ensure high-quality data annotation:

• Establish clear annotation guidelines.
• Implement rigorous quality assurance protocols.
• Adapt to domain-specific annotation requirements.
• Promote collaboration and communication.
• Leverage automated AI annotation tools.
• Ensure regulatory compliance.
• Review and update guidelines.

Advancements in automation tools and ML algorithms are fundamental driving forces shaping the future of AI data annotation. AI annotation services will play a key role in improving the interactions between humans and machines. But human expertise will be the guiding force behind the scenes, ensuring AI tools understand the intricacies of the human world.

How can Infosys BPM help with AI Annotation?

AI data annotation services are an integral part of the transformative wave of innovation AI and ML technologies are bringing about. Infosys BPM annotation services can help you build training data at scale. Our platform plus human-in-loop service model can help you annotate text, image, audio, video, and sensor data for applications across CPG, retail, media, oil and gas, insurance, healthcare, transportation, and financial services. Leverage Infosys BPM flexible and scalable managed annotation services to achieve 98% accuracy and 30% faster speeds for your AI data annotation needs.