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

Iterative Dataset Refinement: Constantly Improving Quality Over Time

In machine learning, data rarely remains unchanged. Data is constantly reviewed, errors are corrected, and refinements are added to make the models more accurate. For example, if the algorithm often confuses objects in images, experts review the markup and make adjustments. This approach allows us to gradually improve data quality

Machine learning

Creating Prompt Templates: Standardizing Annotation Instructions for LLMs

Prompt templates allow you to standardize the format and structure of instructions for annotators working with texts, images, or other types of data. Each template has clearly defined steps and requirements for performing annotations. To annotate texts, you can create a template that includes tasks for highlighting key terms, analyzing

Machine Learning

Annotating Features for Interpretability: Mapping Inputs to Model Decisions

As machine learning models evolve, the need for feature annotation is more pressing than ever. By mapping the input and output of a model, we can better understand the AI decisions that will lead to sophisticated AI systems. Identifying key features helps us understand how factors influence the model'

Data Annotation

Curating Alignment Test Sets: Evaluating Whether Models Follow Instructions

Alignment test sets are essential for verifying that models understand and execute tasks as intended. These instructions and evaluations are a cornerstone of reliable AI systems. As we explore the significance of alignment test sets, we'll see how they enhance AI model instructions and NLP model evaluation. Key

Data Annotation

Integrating Human Feedback Loops into LLM Training Data

Reinforcement Learning from Human Feedback (RLHF) is essential for training large language models (LLMs). This method combines machine learning and human feedback. With RLHF, models generate helpful responses and reduce bias. We will look at how RLHF changes the LLM training process, improves data quality, and impacts model performance. Quick

Machine Learning

Curating Alignment Test Sets: Evaluating Whether Models Follow Instructions

Alignment test sets are essential for verifying that models understand and execute tasks as intended. These instructions and evaluations are a cornerstone of reliable AI systems. As we explore the significance of alignment test sets, we'll see how they enhance AI model instructions and NLP model evaluation. Key

Computer vision

Annotating Underwater Imagery: Techniques for Marine Life and Environments

We are at the cusp of a technological revolution in underwater image labeling, essential for practical marine life annotation, environmental monitoring, and aquatic scene understanding. Dynamic underwater ecosystems present complex challenges that require innovative solutions through deep learning and crowdsourcing methodologies. Key Takeaways * Oceanographic monitoring advancements elevate observation scales, yet