The successful implementation of Data artificial intelligence and its subsets (machine learning, deep learning, computer vision, natural language processing, speech recognition, etc.) relies on large amounts of high-quality labeled data. Work such as data collection, labeling, and validation is critical in the AI development lifecycle, so why is data annotation important?
What is Data Labeling?
Data labeling is the process of creating datasets for machine learning algorithm training. Data annotation for AI training includes various data processing activities such as labeling, classification, review, transcription, translation, and validation. High-quality data annotation is a critical step in all AI developments, as it teaches the system how to produce more accurate results.
Taking self-driving cars as an example, due to high-quality and fully labeled datasets, often containing complex information, including video data, 3D sensor data, images and audio, they recognize and avoid obstacles, adjust speed and make split-second decisions capabilities are developing rapidly.
Why is data labeling important?
Properly labeled data is important for the development of self-driving cars, computer vision for aerial drones, and many other AI and robotics applications. Self-driving cars must be able to recognize everything they might encounter on the road, so data annotators need to label pedestrians, traffic signs, other vehicles, and many other items in millions of images in order for such cars to function safely and properly.
In precision agriculture, drones can help farmers identify poorly growing crops so they can adjust fertilizer, water or pesticide applications before an entire harvest is lost. Computer vision must be trained to recognize fruits and vegetables, which can vary widely in shape and orientation and function under different conditions. Because data labeling is time-consuming, many companies outsource the task to competent data labeling service providers.
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