Crowdsourcing and human computation are two related concepts that involve the use of human intelligence to perform tasks that are difficult or impossible for computers to do alone. They are used in many applications, from image recognition and natural language processing to data annotation and content moderation.
Here are some key concepts and techniques used in crowdsourcing and human
computation:
- Microtasks: Microtasks are small, discrete tasks that can be completed quickly by a human worker. They are typically simple and require minimal training.
- Crowdsourcing platforms: Crowdsourcing platforms, such as Amazon
Mechanical Turk, Upwork, and CrowdFlower are online marketplaces that
connect businesses and individuals with workers who can perform microtasks. - Gamification: Gamification is the use of game design elements, such as points, badges, and leaderboards, to make tasks more engaging and motivating for workers.
- Quality control: Quality control is essential in crowdsourcing and human
computation to ensure that the work is accurate and reliable. This can be done through techniques such as peer review, consensus-based judgments, and worker reputation systems. - Human-in-the-loop machine learning: Human-in-the-loop machine learning involves using human intelligence to improve the accuracy of machine learning models. This is done by having human workers annotate or label data to train the model, or by having them provide feedback on the model’s output. Crowdsourcing and human computation have many advantages, including their scalability, flexibility, and low cost. They can be used to perform tasks that are too complex or expensive for computers to do alone, such as natural language processing and sentiment analysis. However, they also have some challenges, such as maintaining quality control and managing worker incentives
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