Best Remotasks Promo Code · Remotasks Discounts & History
Hey, Remotasker! If you are new in Remotasks, it is likely that you have seen that we have several task categories or groups to choose from. We understand that having to choose a project type to train on these task groups can be very complicated, so we want to make the process much easier for you. In this article, we'll look at the main groups of tasks so that you can get a better idea of what type of tasks one of them would cover. Remotaskers can do a wide variety of tasks across multiple project types. Although we assign different names and specific needs to each task, they all generally fall into basic task groups.
When we say 2D Tasks, we mean tasks that need remote tasks to analyze 2D environments. They range from images to video or footage in a series of frames. In 2D Tasks, remoteskers need to draw bounding boxes and label various objects to help AI to identify or exist within this image. For example, remoteskers can look at images from traffic cameras to help AI identify elements of traffic. Unlike 2D Tasks, 3D Tasks requires remote kers to analyze 3D environments. It is generally a form of LiDAR (Light Detection And Ranging), which uses light to 'see' images around a focal point. Remotaskers handling 3D tasks generally precisely check the 3D footage of an autonomous car prototype, or that means placing bounding cuboids and identifying various objects around our prototype. We use 3D Tasks to help autonomous cars learn about the traffic elements around them.
Hey, Remotasker! If you've been hanging around Remotasks for a while, chances are you've heard the term 'data labeling' used in the context of a Task. Of course, literally speaking, 'data labeling' might have to do with labeling data, right? This is correct at some level, but there's a deeper meaning behind the concept that enables us Remotaskers to help improve smart technology today.
In the context of artificial intelligence and machine learning, Data Labelling is the process of identifying and attaching informative and meaningful labels to raw data like videos, images, and text. That way, AI and machine learning can use the labels and the raw data to learn certain functions and mechanisms. For instance, data labeling can pave the way to help a self-driving car separate other cars from the road, pedestrians, and traffic elements.
Data labeling can help artificial intelligence and machine learning models make more insightful insights towards elements interacting in various media. However, since there are different kinds of raw data available, different kinds of data labeling use exist for AI and machine learning models to use. Here are some of the mechanisms used for data labeling:
Natural language processing: One of the most common ways of data labeling in terms of AI and machine learning has to do with natural language processing. In essence, this involves tagging or segmenting sections of a text with specific labels to identify and label their uses in the context of the text. These can be parts of speech, noun classification, blurbs, and other categories such as places or people. NLP can help in terms of optical character recognition, entity name recognition, or even sentiment analysis.
Computer vision: Data labeling in images and video usually comes with the help of computer vision. In these kinds of labeling processes, identifiers such as key points, borders, pixels, or parts of an image are used to create datasets. In turn, these can help classify images depending on the content, quality, or even pixels. Data labeling in this manner can create a computer vision model that can automatically categorize images as soon as they locate these key points.
Audio processing: Contrary to computer vision, audio processing is data labeling that makes use of sounds such as wildlife noises or speech. In this way, sounds are converted into a proper format that can be used in machine learning. Systems that can categorize these kinds of audio according to content, quality, or information can help systems make use of images to gather accurate data.
If you're interested in helping AI identify objects more accurately, then you might want to become a part of the Remotasks team! In our earnings platform, we pay Remotaskers to do a variety of Tasks and project types related to AI - including Data Labelling! In fact, Data Labelling is one of the most popular project types among our repertoire of task categories. Just sign up on our website for free, get trained without charge, and start getting paid for doing Data Labelling tasks!
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OCR, which stands for Optical Character Recognition, is a technology that uses technical data to understand or printed text in menu images and documents. When remote remotes handle OCR transcription tasks, they usually receive an image and need to confirm what the text in the image says. When we look at too many things, we tend to categorize them based on obvious things like shape and color. We also sometimes categorize things based on more complex things, like what they're made of or the type of object. That's why we can differentiate clothes or other objects. When we do categorization tasks, we identify objects based on specific keywords. With this data, AI and other smart assistants can use our knowledge to learn how we recognize things the way we do. While categorization makes us look at a specific object, comparison makes us look and compare two objects belonging to the same category. In turn, Comparison Tasks require Remotaskers to identify the crucial difference between two objects so that the AI and other intelligent assistants can understand the thought processes we use. In data collection tasks, remotes need to search the web and collect data about certain things. With the information we collect, AI and smart assistants can learn to better navigate the Internet to get useful results.
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