Data labeling will fuel the AI revolution

“To give another example, to train an algorithm to analyze medical images for signs of cancer, you would need to have a large dataset of medical images labeled with the presence or absence of cancer. This task is commonly referred to as image segmentation and requires labeling tens of thousands of samples in each image. The more data you have, the better your model will be at making accurate predictions.

Sure, it’s possible to use unlabeled data for AI training algorithms, but this can lead to biased results, which could have serious implications in many real-world cases.”

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