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FebAs machine learning, aka ML applications, become advanced and spread worldwide, from facial recognition software to self-driving cars, ethical considerations in their development become more important. But, unlike common knowledge, the journey to ethics begins long before algorithms crunch data – it starts with the very foundation: data annotation. Data annotation, the process of labeling and classifying […]
17
JanIn the age of machine learning, data reigns supreme. But not just any data; high-quality, accurately labeled data is necessary for effective ML models. However, striking the right balance between the quantity of data and its quality can be a difficult feat, especially when juggling resource constraints and project deadlines. This article delves into the art of[…]
06
JanIn the bustling world of machine learning, the race for the most powerful, most insightful algorithms is always on. In this pursuit, the data annotators play the most crucial work behind the curtains. Their meticulous labeling and classification form the very foundation of ML models. However, this essential role comes with its own side of difficulties, which[…]
03
JanWith the development of technology, the biggest threat to a person’s personal life was the advancing nature of the tech, making keeping things private difficult. To avoid this phenomenon, a new kind of security was released, which we know today as facial recognition. Facial recognition technology, with its promises of enhanced security and convenience, is rapidly weaving[…]
28
NovNatural Language Generation aka NLG models are designed to generate human-like text and are trained on vast datasets. They have become integral to various applications, from chatbots and virtual assistants to content generation and data summarization. Data annotation in the context of NLG involves labeling or marking data to provide context, structure, and meaning to the training[…]
