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Featured Articles
How We Develop Standardized Protocols for Data Labeling Projects
Character.AI Joins Forces with Pareto.AI to Improve Conversational AI Models
Should You Pay Per Task or By Hour? Optimizing Worker Productivity for High-Quality Data
Reinforcement Learning from Human Feedback: Everything You Need to Know
Equitable Pay for All: Understanding Geographic Wage Disparity in Remote Work
The Future of Crowd Work: Q&A with Dr. Mark Whiting
Discover how semantic segmentation transforms image analysis by classifying each pixel for detailed scene understanding. Explore tools and techniques for enhancing AI models and achieving precise object recognition.
Let's break down AI bias: what it is, why it happens, and how we help companies fine-tune their language models to eliminate it.
Uncover the powerful technique of adversarial training, crucial for shielding AI models from deceptive attacks. This in-depth blog delves into its workings, applications, and ethical considerations, empowering you to build robust and secure AI systems.
Diffusion models are a category of latent variable generative models that includes diffusion probabilistic models and score-based generative models. Understand their structure, limitations, and applications.
Dive deep into an important conversation around challenges in the current crowd work ecosystem, worker incentive design, and the future of crowd work with Dr. Mark Whiting.
Expert labelers favor payment per task over hourly wages for high-quality data annotation despite published research. Gain insights into the contrasting influence of pay-per-task and hourly wage compensation structures for data labeler productivity.
Human-in-the-loop (HITL) is an approach that combines human expertise with AI capabilities at various stages of development. It's a collaborative approach where humans provide input, validate outputs, and refine AI models. This collaboration ensures better accuracy, adaptability, and ethical considerations in AI systems.
Learn all about data labeling in AI development. Uncover its significance, techniques, and pivotal role in training machine learning models. Discover how you can contribute towards the advancement of AI by being a data labeler.
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