Data is integral for successful machine learning (ML) projects. The ML model must undergo training and learn to use the data to prepare for deployment. However, raw data is useless for ML projects. This is where data annotation and data labeling come into the picture. Every shred of raw data must be annotated so that it is ready for consumption by ML models. It is time-consuming and tedious but crucial for ML projects. Accurate data annotation is vital to ensure that the ML implementation works successfully. Data annotation adds attributes, tags, and labels to raw data to help machines understand it. Whether artificial intelligence (AI) is used for speech recognition, chatbots, or automation, data annotation is essential for developing a fool-proof model.
Data annotation significance
Supervised ML projects require accurate data annotation, and data scientists will help the machine learn. Unsupervised ML projects learn on their own. This is feasible if the ML model uses only a few objects with the standard structure. As the project expands and the scope of ML increases, unsupervised ML projects too need data annotation. Almost 80% of the development time for AI projects is spent preparing data. Accuracy is paramount in data annotation because a single mistake can result in cascading inefficiency in the whole AI project. So even though ML projects are designed to replace manual tasks done by humans, humans are integral for data annotation, without which ML projects can’t be deployed.
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Types of data annotation
Data annotation requirements vary according to the data used in the ML model. The data can be text, image, audio, video, or a combination of all.
Text annotation
Even though text data seems to be straightforward, it is inherently complicated due to the semantics and dialects used widely in human communication. For efficient communication, the AI tool should understand what the human on the other end is trying to communicate through his texts. There are several abstract elements in textual data that require detailed annotation. Semantic annotation, intent annotation, entity annotation, and text categorization are some of the methods of text annotation. Text annotation is vital for all-natural language processing (NLP) models. The efficiency of AI chatbots and virtual assistant devices depends on the accuracy of text annotation.
Image annotation
Image annotation annotates objects and tags those objects with multiple elements to allow AI machines to detect and recognize the visual perception of the concerned objects. 3D cuboid annotations, landmark annotation, bounding box, and 3D point annotation are widely used image annotation methods. Image annotation is essential in projects involving facial recognition, robotic vision, computer vision, etc. Image annotation is also significant in healthcare ML projects. Automated ML algorithms can be trained to diagnose diseases based on ultrasound, CT scans, and X-rays with accurate annotation of medical images.
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Audio annotation
Audio annotation is inherently complex because the final ML model should be able to recognize the speaker’s language, mood, demographics, dialects, intent, and emotion based on the audio input. Timestamping, audio labeling, etc., are some of the methods used to annotate verbal and non-verbal cues in the audio data. Even the background noise must be comprehensively annotated for successful and efficient audio annotation. Any AI project involving the user giving audio input, for example, SIRI, Alexa, etc., requires accurate audio annotation.
Video annotation
Video annotation can be considered as an expanded version of image annotation. Images are still while videos have moving images. Every frame in the video compilation can be considered an image. Besides image annotation techniques, video annotation includes adding key points, bound boxes, and polygons to annotate each object in any given frame. ML projects that require recognizing video data should understand motion blur, object tracking, localization, and more through proper annotations.
Benefits of data annotation in supervised ML models
Compared to unsupervised ML models, the supervised ML models offer a significant advantage because the trained model will be accurate when deployed. Data annotation services providers will use various tools to annotate data in multiple formats to make the data ready to be consumed by the ML models. Accurate data management services and annotation offers the following significant benefits:
- Immersive user experience – ML model trained on accurately annotated data will guarantee an immersive user experience for end-users. Chatbots, search engines, and virtual assistants can offer accurate results to user queries with well-annotated data.
- Ability to crack the Turing test – The modern ML models are close to cracking the Turing test proposed by Alan Turing for AI machines. AI systems indistinguishable from the human mind are believed to have cracked the Turing test. As a result, the end-user will not know whether he is chatting with an Autobot or an actual human agent on the other end. Virtual assistants like Siri and Alexa have improved leaps and bounds as they can now offer quirky and sarcastic responses similar to humans.
- Effective AI model results – The ROI on AI models will improve significantly with accurate data annotation. Efficient ML projects deliver expected results precisely and effectively when they are trained on accurate data. It allows the ML projects to learn dynamically according to the situations.