📌 Crowdsourced Data Labeling Summary

Crowdsourced data labelling is a process where many individuals, often recruited online, help categorise or annotate large sets of data such as images, text, or audio. This approach makes it possible to process vast amounts of information quickly and at a lower cost compared to hiring a small group of experts. It is commonly used in training machine learning models that require labelled examples to learn from.

🙋🏻‍♂️ Explain Crowdsourced Data Labeling Simply

Imagine you have a huge pile of photos and you need to sort them into categories like cats, dogs, and birds. Instead of doing it all yourself, you ask lots of friends to each help with a few photos. By sharing the work, the sorting gets done much faster and everyone only needs to do a little bit.

📅 How Can it be used?

A company can use crowdsourced data labelling to quickly tag thousands of customer support emails for training an automated response system.

🗺️ Real World Examples

A tech company developing a self-driving car system uses crowdsourced workers to label objects in millions of street images. The workers draw boxes around cars, pedestrians, and traffic signs so the system can learn to recognise them during real-world driving.

A mobile phone manufacturer uses crowdsourced data labelling to transcribe and categorise voice commands recorded by users. This helps improve the accuracy of their voice assistant by providing better training data.

✅ FAQ

Crowdsourced data labelling is when many people, often working online from around the world, help to sort or tag large sets of data like photos, text, or sounds. This method is helpful because it allows companies and researchers to process huge amounts of information quickly and cheaply, which would be difficult if only a few experts did the work. It is especially important for training computer programmes to recognise patterns, like teaching an app to spot animals in pictures.

To make sure the data is labelled correctly, companies often ask several people to label the same item and then compare their answers. If most people agree, it is likely to be right. Sometimes they add test questions with obvious answers to check if workers are paying attention. They also use quality checks and review the work regularly to catch mistakes or spot anyone who is not doing the job properly.

Yes, most crowdsourced data labelling platforms are open to people from many backgrounds, and you usually do not need special skills to get started. The tasks are often simple, like choosing the right category for a photo or highlighting words in a sentence. However, some projects might need people who speak certain languages or have specific knowledge. It can be a flexible way to earn some money or contribute to interesting projects online.

📚 Categories

• Artificial Intelligence
• Data Science
• Model Training & Tuning

🔗 External Reference Links

Crowdsourced Data Labeling link

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