Introduction to Data Annotation

February 9, 2023

From phones, hospitals, planes, and roads, people have become irrevocably reliant on artificial intelligence. Transforming countless industries, AI models adjust and identify trends in data to perform given tasks. This process utilizes data annotation, the process of labeling and attributing information with relevant tags in order to augment a machine’s understanding of a dataset. In order for an AI model to differentiate between thousands of distinct objects, it needs to be provided with immense volumes of training data. With more labeled information, a machine can identify patterns more accurately, and the absence of organized information inhibits rapid technological growth. With the ability for vast impact, data annotation, which is used as an umbrella term, includes different processes depending on what information and which medium an AI model is trying to extract from. The most common include:

  • Text classification: Extracting and sorting key words from a written work or document in order to summarize it
  • Image annotation: Recognizing annotated areas of images, distinguishing placement of pixels, and utilizing semantic annotation to identify an entity in an image
  • Semantic annotation: Labeling people, places, and key words within a text for augmented comprehension
  • Intent extraction: Identifying placement and context of verbiage in order to determine the true meaning or intent of a statement or question
Types of Data Annotation

Because of its broad usability, data annotation can reconstruct the basis of multiple industries. With utility in nearly every aspect of daily life, data annotation has various applications with the ability to reimagine vehicle safety, medical processes, and scientific research.

Safety industries, specifically vehicle and road safety, concern entire populations. Staying safe while on the road is top priority for all drivers, and technology companies are driven to ensure safety by using artificial intelligence in vehicles as both reinforcement and backup. Image annotation, specifically object detection, enables a machine the ability to recognize obstacles, such as pedestrians, vehicles, and fallen debris, within split seconds, trumping the reaction time of a driver. In turn, drivers and passengers avoid potentially dangerous situations because a machine has the ability to act and control its vehicle before a driver might have noticed and reacted to the situation. Dozens of cameras and LIDAR, light detection and ranging sensors, collect a minimum of ten frames per second. Given that a drive lasts one hour and each frame may have approximately fifteen different obstacles, a car’s software system has to annotate at least 500,000 objects within that period of time. In the absence of a trained and sophisticated artificial intelligence system, camera systems in vehicles become unreliable, posing a larger problem with the rapid rise of autonomous driving systems. Without data annotation, an AI model in a vehicle might not be able to distinguish objects, allowing for myriads of potentially risky situations for drivers to be in. Not only does safety apply to driving, but it also can be applied to a variety of industries.

Vehicle detection via image annotation in autonomous driving Image source

By using similar image annotation techniques as vehicles, data annotation can be used to reinvent the medical industry. Detecting and diagnosing every day patients with trained AI ensures a more efficient process and alleviates stress of both patients and physicians. Medical Annotation uses image annotation of medical scans from x-rays, MRIs, CTs, and even ultrasound scans. If a patient has a fractured bone or even a cancerous tumor, instead of waiting and relying on radiologists to examine scans, an AI model with input data can analyze and compare the scans, providing an immediate diagnosis and results. Because of the popularization of AI models, companies have started to release apps and online services that are trained to make remote diagnoses, providing low-income and underserved communities access to medical aid. Furthermore, scientists use data annotation to identify patterns and diagnoses in epidemiology and pathology, research areas that have been brought under keen public surveillance since the spread of COVID-19 in 2020.

Organ identification via ultrasound video annotation Image source
Teeth classification via surgery video annotation Image source

Just like the booming medical industry, scientific innovation changes by the day. Scientists use a variety of subsections of data annotation to label trends within their areas of research and, with the help of AI, can identify patterns that could lead to breakthroughs. Panoptic segmentation, which combines semantic and instance segmentation, object detection, image classification, and semantic text annotation all contribute towards increased understanding of aspects of the scientific realm. By analyzing videos, images, and documents with AI, researchers can determine behavioral habits within a species. Along with discovering the mannerism of living species, data annotation benefits society on a global scale. The climate’s rising temperatures pose problems not on people from particular regions, but people internationally and all future generations of humankind. By utilizing data annotation, environmental scientists can identify common sources of emissions, as well as solutions to countless problems. From finding safer alternatives to rechargeable batteries, to identifying discrepancy for water consumption, data annotation applied in scientific research can improve individual and planet health. A machine's ability to process information drastically outperforms the work of thousands of humans, so with the help of AI models that are trained to identify and adapt, scientists can expedite the process of discovering solutions to large-scale problems. The variability with data annotation allows for endless revolutionary applications.

With artificial intelligence at the forefront of the future, the process of data annotation will only augment an AI model's intelligence. Technology companies focus on advancing their artificial intelligence models with data annotation processes due to its efficiency and vast usability. Data annotation is the lucrative solution companies scramble to find, with the ability to enhance and expedite a machine’s artificial intelligence.

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