Artificial Intelligence (AI) and machine learning (ML) are fast becoming integrated into everyday life. Today, applications of AI and ML are appearing in familiar places, such as grocery stores that employ cleaning robots or even homes with shoppable television sets.
However, these technologies remain limited in terms of correctly identifying products or hazards by themselves alone. There remains a need to fine-tune and retrain machine learning models by labeling data generated in real-world production settings to ensure accuracy of service.
“Humans are still an important part of the equation,” says Shoma Kimura, Senior Director of AI Community Operations at TaskUs, in an article published in KDnuggets, a leading site on topics such as AI, analytics, big data, and machine learning. In his article, Kimura discusses how human intervention is still necessary after an AI model has been deployed to ensure continuous optimization and improvement in the face of evolving data.
Real-time verification is also important especially for mission-critical operations. For instance, real-time data labelers can confirm the decision of the AI if it identifies sensitive content. In this case, having a person behind the technology ensures speed and accuracy.
“Not only is real-time data labeling or active learning necessary today, but it will remain necessary for as long as AI models have new data to assess and act on appropriately. In other words, real-time data labeling will always be needed, and humans will always be a critical component of operable AI,” Kimura says.
Read more here: Why we will always need humans to train AI — sometimes in real-time
