Recent industry surveys show that data issues are the main reasons behind the lack of adoption of AI in industrial enterprises. Data generation and quality data are the keys to AI and data analytics because without data there is no AI. Unprocessed data adds no intelligence to the automation and allied systems. As most of the data generated in various formats are unstructured, the system cannot derive any decision. We will see why data annotation and labeling are necessary to make data useful in AI.
Data – the Backbone of ML and AI
Data is fuel for training AI models designed for machine learning needs is data. We need to feed information into the algorithm for ML models to process and deliver outputs and findings. The process can happen only when the algorithm understands and classifies the data. However, data sought from multiple sources is unstructured. Thus, the need for data annotation arises.
Data Annotation and its Importance
Data annotation is the process of adding attributes by labeling data. Labeling or tagging relevant information/metadata in a dataset allows machines to understand what it is. This is necessary so that machines can understand it. In supervised machine learning, data annotation is essential because ML models must understand input patterns to process them and provide accurate results.
By annotating the data in the dataset, the AI model will know whether the data it receives is in the format of audio, video, text, graphics, or mixed. Based on the functionality and parameters assigned to the data, the model classifies the data and proceeds to perform its tasks. By having data annotation implemented, the data helps train the model accurately. Data annotation is indispensable as ML models need to be trained consistently. This is imperative for the ML and AI models to be more efficient and effective in delivering the output required. Therefore, when the model is deployed for automation, speech recognition, bots, or other processes gives optimal results.
Let’s see why data annotation is required in the machine learning system.
Why is Data Annotation Necessary?
Only through the process of data annotation can models distinguish between birds and animals, nouns and verbs, or mountains in the sea. Images without any data annotation would be the same factors for machines because they have no inherent information or knowledge about any object depicted in the image. For models with machine-driven decision-making systems, data annotation is essential. Proper annotations ensure decisions are accurate and relevant. This process helps ML models, such as computer vision, NLP (Natural Language Processing), sensors, and speech, identify entities to train recognition models.
Building AL and AI Models
To build a truly reliable AI model, we must provide the algorithm with properly structured data. While developing the ML model, a volume of AI training data is provided to help the model learn. Algorithms make them better at making decisions and recognizing objects or entities. Therefore, data annotation and labeling are prerequisites for a model to learn how to perform given tasks. To train such models, AI experts classify data by adding machine-recognizable attributes. They attach captions, identifiers, and keywords to their data elements, such as images. The algorithm then recognizes and understands these parameters and learns autonomously according to the rules set in the algorithm.
Building a high-performance AI/ML model as a solution is determined by how accurately the data is annotated. Realizing the need for resources, time, and expertise to build such capabilities is a complex business challenge. Most fast-moving businesses, thus, prefer hiring data annotation services. Many companies rely on external vendors to perform complex annotations. In addition to time and cost optimization, data annotation experts accelerate AI capabilities and conceptualize ML solutions to meet market needs and customer experience.
Types of Data Annotation
Data annotation is a widespread practice for machine learning. There exists a labeling process associated with each type of data. The commonly used types of data annotations include images, text, audio, and video.
- Image Annotation: The essence of many data analysis processes is to supply images with metadata information to annotate image data. The increasing use of images in business operations has made the process of annotating images almost mandatory. Being an important part of operations that demands dedicated resources and time, many organizations hire image annotation services to facilitate their tasks.
- Text Annotation: Search engines extensively use text annotations, where words and phrases are tagged. Text tagging helps match keywords with URLs in the database and allows search engines to produce the desired results. Annotations enable search engine algorithms to fetch pages containing search keywords.
- Audio Annotation: Various attributes associated with audio data include, for example, language, speaker demographics, dialect, intent, mood, emotion, and behavior. Parameter techniques, such as time stamping, audio labeling, etc., tag these annotations. Verbal cues and non-verbal cues are used comprehensively in the annotation. These are silence, breathing, background noise, etc. Algorithms are developed to understand and efficiently process audio annotations.
- Video Annotation: A video is a collection of images that create the effect of moving objects, called frames. Video Annotation adds key points, polygons, or bounding boxes to different objects in the field in each frame. AI models learn from the frames, movements, behavior, patterns, and more. Video annotation allows the application of concepts such as localization, motion blur, and object tracking. A practical example of video annotations is the one that provides tremendous visibility into road traffic patterns, drivers’ actions in the cabin, accident-prone locations, etc., and thereby significantly enhances road safety.
The Conclusion
The AI models function well and produce the most effective and accurate output when data is annotated and tagged correctly. Organizations must build robust data annotation capabilities to support AI and ML model building and prevent failure.
