We do talk to machines every day. They speak to us too. Does it seem like a fairy tale? It is nothing but a fact. Asking for route maps, clearing doubts with shipment and delivery online, and many more has become part of our daily lives. But do you know how they respond to you just like human beings? What makes them bring the desired results that we search? What helps them in over performing human intelligence?Machine learning solutions like this will never work on their own but needs human-led training. AI data annotation is a technique that makes a machine learn how to do its job. Let us see what it is and how it helps businesses.
What is Data Annotation?
Data annotation is the process of labelling contents such as video, audio, text, and images to recognize by machines. Through this process, we can develop machine learning models that improve efficiency in time. It is the job that makes computers connect various contexts and train them to respond to each instance. GM Insights predicts that the global data annotation tools market will grow nearly 30% annually over the next six years. It has enormous applications in various business sectors as retail, healthcare, and automotive. It is the best way to interact with consumers when the consumer requirements are rigorously changing. Reports say that almost 70% of customer interactions will filter through technologies like machine learning applications, chatbots, and mobile messaging in the upcoming years. So, let us see the types of annotations that help to train machines.
Text annotation is the process of highlighting keywords, phrases, sentences, and other words that are commonly used by consumers. With this AI data annotationtechnique, machines can recognize human emotions by analyzing words. It enables them to respond to humans by understanding their needs. We can categories them further as,
- Sentiment Annotation- Help to identify opinions, attitudes, and emotions to identify their requirements. Human annotators verify e-commerce sites and social media platforms to identify the commonly used phrases and queries. Also, it helps machines to overcome the barriers of slang variations.
- Intent Annotation- Every interaction made by a consumer with a machine requires getting something. The failure in identifying the user's intends will make the machine ask for a repeated inquiry. Intend annotation resolves this by multi-intent data collection and categorization. It differentiates intents into categories like request, command, booking, recommendation, and confirmation.
- Semantic Annotation- Semantic annotation helps to tag specific documents to concepts that are most relevant to the information. It is a type of metadata indexing that groups documents with descriptive words or concepts. It helps the machine to address in-depth problems analyzing words.
- Named Entity Annotation- It is used to get the desired information from a large set of data. Named Entity Recognition identifies some specific entities such as names, dates, places, brands, etc. It requires huge amounts of data for manually training with annotation. In fact, business transformation servicescan find help here to handle it.
Data in audio format is becoming more popular, especially when more companies use the facilities of voice searches. Audio annotation assigns meanings to sounds. It separates sound files as meaningful and not. Machines are trained to identify pronunciation differences, intonations, and intentions too. Also, it enables to handle different languages and keep a user demographic database. Also, identifying alarming noises like glass breaking, screaming, etc will help machines to give alerts and act in emergencies more wisely. In fact, these types of machine learning solutions gain more popularity in the automobile sector to improve safety measures.
Image Annotation and Video Annotation
With increased popularity for visual lenses and image recognition tools, Image annotation has big relevance. It has a wide range of applications like computer vision, facial recognition, etc. Through these, machines can acquire the knowledge to interpret images. For this, metadata tags are assigned to images with captions, keywords, and other identifiers. It enables machines to recognize an annotated area in the image like bouncing boxes. A bouncing box is an imaginary box drawn to an image in order to specify something.
It has applications is guiding automated vehicles. Also, it has enormous usage in face recognition. Image annotation increases precision and accuracy by effectively training these systems. At the same time, video annotation is an extension of image annotation. Here also, bouncing boxes are used to predict movement. But it acts as frames and needs more time to train them. Companies use it in tracking shipment products and personalizing operations.
As per the studies of The Visual Capitalist, an estimated 463 exabytes of data will be created globally daily, By 2025. After the pandemic, consumer patterns have changed a lot, and the requirements of creating more customized business options have hiked. Here, finding smart solutions to meet the consumer requirements are a must, to stay relevant in the market race. Artificial intelligence development servicesare something that companies rely upon the most, to minimize human effort and bring more operational efficiency. Also, these smart solutions will avail human intellect to channelize into more creative business functions. As an experienced BPO agency with a higher reputation assisting companies with business transformation services, Allianze Infosoft can help you. You can know more about us by dropping a mail to [email protected]