But you require a very specific skill set to recognize a tumor on an X-ray. Any human being (and even our toddlers) can tell what a dog looks like. But there is an inherent difference between the labeling jobs that go into both of these tasks. For a machine, both are just a selection of pixels on an image or a scan. Curiously, the task of recognizing a tumor on an X-ray is, in essence, the same as recognizing a dog on a photo you just uploaded to Facebook. Reading CT and MR scans and deciphering X-rays are among them. There's a wide array of medical tasks that need AI for automated activities and the facilitation of human work. Healthcare is a great example to demonstrate this challenge of a data labeler's job. This is usually true for very technical, highly-specialized industries. SpecializationĪlthough most data annotation jobs do not require any in-depth knowledge in other spheres, there are still projects that cannot be done by people without special training. Still, there are a few crucial challenges of the jobs for data labelers that we'd like to discuss in detail. It's a comparatively lower-skill job that you can even do online, as a freelancer, which is great both in the wake of the self-isolation mode brought by COVID-19 and the growing trend for workforce independence within the digital industrialism of the Information Age. The job requirements for a data labeler don't seem like much, with no specific training in development or knowledge of data science needed. There's no magic and no flare to the process of annotation, and data labelers know this best of all. 80% of the time of an AI project is dedicated to data processing and annotation. While it doesn't seem complicated, this is where the majority of the work is done. What we must not forget is that data labelers need to do a lot of tedious, boring work to annotate the photos, in other words, to properly label every photo with the tag that says 'cat'. In a nutshell, a data labeler has to manually assign meaningful labels to the separate data pieces that will be later fed into the machine learning model. You need a human, a data labeler to tell the model that this is a cat on the image. That where the term " supervised machine learning" comes from. You need supervision in order for the computer to make correct predictions. However, it's not enough to show the photos to the machine. That way, a computer will figure out what a cat is on its own. In order for a machine to understand that and be able to tell if there's a cat on a new photo you upload, you have to show it thousands of other photos with cats on it. They lack the understanding that would allow explaining to them that a small furry creature with four legs and long whiskers is a cat. How do you proceed? Computers don't see things as humans do. Let's say you've built a model that uses image recognition to classify photos. What Does a Data Labeler Do?įirst, let's briefly talk about data annotation. And there's a good chance that data labelers could become one of the most in-demand jobs on the market given the constant need for data annotation and the time spent on this hidden task essential for nearly any AI project. The experts agree that data scientists, AI engineers, and data labelers will flourish in this era of AI. Still, AI and ML don't only "kill" the jobs but also create new opportunities. The occupations most affected by this trend include physical labor, office support, and customer interaction jobs. At the same time, a third of the 60% of the jobs will be automated, leading the way of the transformations. 5% of the jobs will be completely eliminated by automation. McKinsey reported that, by 2030, significant workplace and job requirement changes will be in place. As the technology of ML algorithms becomes more advanced, they naturally take on the jobs that were previously done by people. The variety of tasks that machines can do to entertain, enforce, and help humans do their work are nearly unlimited. There's not a field that wasn't transformed by artificial intelligence. How to Get Your Data Labeled: Outsource or Keep It In-House?
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