Data annotation & labeling blog.

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Data annotation

Data annotation

Data annotation is one of the important parts of machine learning, especially in AI example. To know more about this and get better knowledge on Data Annotation, we need to know ALL the basics! So, we will be putting up a blog on "Data Annotation" [AIN-103] with A - Z coverage which will help you in understanding different parts related to Annotation of data.

Data Annotation

Red-Teaming Abstract: Competitive Testing Data Selection

A competitive testing abstract is a systematic stress testing of systems against real-world threats. Modern language models face risks of misinformation leaks and malicious actions by fraudsters. Therefore, a Red-Teaming abstract is needed, where teams simulate attackers to reveal vulnerabilities in an AI model. Leading companies like Microsoft and IBM

Computer Vision

Preference-Based Annotation: Labeling Data to Reflect Human Values

Traditional data annotation methods often rely on objective, predefined labels that may fail to capture the nuances of human values and subjective judgments. Preference-based annotation offers a more human-centric approach, allowing systems to learn from comparative preferences rather than rigid labels. This method is beneficial in tasks where absolute correctness

Data Annotation

Designing Microtasks: Breaking Down Annotation Jobs for Faster Completion

Breaking down complex tasks into smaller ones, known as microtasking, can improve efficiency and accuracy in creating AI training data. We'll cover strategies for optimizing workflows, applying cognitive personalization, and implementing quality control mechanisms. We'll also discuss methods for optimizing the user interface for worker productivity.

Data Annotation

Clustering for Pseudo-Labels: Grouping Data Before Manual Annotation

Clustering and pseudo-labels have streamlined the data annotation by providing unlabeled data for AI models. Unsupervised methods generate pseudo-labels to train AI models on unannotated data. This approach is used in semi-supervised learning, where pseudo-labels improve the performance of AI models. However, generating noiseless labels remains a challenge that requires

Data Labeling

Embedding-Based Annotation: Leveraging Vector Spaces for Automatic Labeling

The basic idea behind embedding-based annotation is to represent data (e.g., text, images, or other complex objects) as vectors in a continuous, multidimensional vector space. These vectors capture the semantic meaning or relevant characteristics of the data, and their relationships in this space are structured so that similar data

Computer Vision

Gamifying Annotation: Increasing Labeler Engagement and Accuracy

Gamification involves adding game-like elements, such as points, rewards, levels, leaderboards, and challenges, to encourage participation and enhance motivation. Integrating these features may make the process more enjoyable and rewarding for labelers, potentially leading to higher engagement and better focus. This can result in improved annotation quality and faster processing

Data Annotation

Evidence Linking Annotation: Connecting Sources to Claims

When we talk about AI-based computer models, an essential component of their work is correctly connecting data with their sources. Evidence-based annotation is a process that allows you to automatically or semi-automatically link the information presented in a study to reliable sources. This approach adds confidence in the accuracy of