Published on Jul 25, 2019
Updated on Apr 3, 2020
The machines are learning. Slowly, sure, but they are learning and we (humans) are the ones teaching them. We tell the machines how they should learn through the algorithms we write, and then feed them an enormous amount of data, so that it trains endlessly. Data labeling (the process of augmenting unlabelled data with meaningful and informative tags), is a necessary part of machine learning and sadly there’s a simple reason behind the use of a lower-wage workforce to train ML (Machine Learning) models — you only pay them half as much. The market for AI data preparation is projected to leap from $500M in 2018 to $1.2B by 2023.
Data is the only real fodder for any type of AI system. The more it trains on large amounts of ‘good data’, the faster it learns. Behind every piece of machine learning code intended to solve real issues, is a network of digital construction workers bearing the burden of building the foundation for AI — preparing data. For example, AI systems are trained to recognize objects. Data Labelers upload, categorize and cluster millions of images — just about everything from people, animals, buildings, plants, cars, signs, shapes, and things. In doing so, you now have an AI system