Automation: The Antidote to Overcoming Labeling Inefficiencies

As more and more technologies are emerging with AI foundations, the global datasphere is continuously expanding. This improved data accessibility is a definitive advantage for developing cutting-edge autonomous systems. However, the paucity of accurate, labeled data will substantially slow down AI advancement. 31% of IT decision makers reported that slow data performance was a big obstacle to their data strategy; this shows how the inadequacy of high-quality labeled data can substantially slow down development. Most companies dealing with large datasets have problems achieving desired levels of accuracy with data labeling. And one of the most obvious reasons for this problem is increased reliance on human intelligence to execute tedious and complex annotation tasks.

Automation has always been the answer to by-passing the inefficiencies involved in fully-manual operations. Therefore, when it comes to data labeling, with semi-to-sometimes-fully automated tools, acquiring large swathes of diverse, high-quality ground truth datasets can be executed faster, at reduced costs, and with improved accuracies.

At Playment, we believe ML-assisted labeling tools can help overcome these barriers of scale, quality, and accuracy, facing AI’s most underrated workforce. Dive into a few illustrations of how Playment’s ML-assisted innovations can simplify the execution of complex and tedious annotation tasks.

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