Data annotation labels relevant data in texts, videos, and images for the machine to understand. It plays a crucial role in supervised Machine Learning, where the system requires labelled datasets to learn and understand the fed data to produce the desired output.
Annotated data is fed into the machine learning algorithms for the machine to understand when and why a particular text or image was given each label to make correct decisions independently shortly. The higher the quality of the annotated data fed into the training model, the better the result.
Various types of data annotation:
- Text annotation
- Image Annotation
- Video and audio annotation
- Key–point annotation.
All the above data annotation types have various applications in the real world.
Various Use Cases of Data Annotation
- Sentiment Annotation
Sentiment Annotation is the assessment and the labelling of emotions and opinions within a given text. Since language can be quite subjective and can assume various meanings in different contexts, it remains a challenging field in the domain of Machine Learning. For example, a human with a good grasp of the English language would be able to understand the meaning of the phrase “Break a leg” as “best of luck,” but a machine would only be able to grasp the literal meaning of the phrase.
A sentiment analysis model, when fed with accurate data, can help business models detect the sentiments of –
- Customer Testimonials
- Product Reviews
- Social Media Posts
- Emails
- Automated Cars:
Data Annotation has been instrumental in the development of semi-autonomous and autonomous vehicles. While the latter is still a futuristic concept, semi-autonomous vehicles sure have revolutionised the driving experience. These vehicles contain multiple cameras and sensors that produce many data about the surroundings. Through image annotation and computer vision by implementing techniques such as bounding boxes, 3D cuboids, and semantic segregation, vehicles can traverse busy streets safely by detecting objects such as cars, streetlights, potholes, pedestrians, etc. Elon Musk’s Tesla is actively working towards building self-driven cars and making automated driving a reality.
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- Finance
According to a report from Grand View Research, the global Artificial Intelligence in the fintech market is expected to grow at a Compound Annual Growth Rate of 16.5% from 2022 to 2030 as more companies adopt AI and ML modules to automate their financial processes. Compiling your company’s financial data followed by its meticulous annotation can empower your machine learning models to do wonders. There are multiple data annotation services that will help you to annotate your data to train your ML models precisely.
Some of the use cases are:
- a) Chatbots – AI-powered chatbots enable you to provide exceptional customer service 24×7 and minimise the time to resolve conflicts.
- b) Fraud Detection – With AI, you can detect instances of cheque tampering, predict threats, and identify the accounts most vulnerable to fraudulent transactions.
- Advancements in the Medical Field
Computer Vision and AI have made significant strides in the medical industry. Medical annotation has paved the way for improved health care, better diagnostics, and speedy drug recovery.
Medical annotation involves labelling medical images to enable the Machine Learning models to provide insightful information on new unlabelled images. This helps healthcare professionals verify and provide accurate diagnoses of diseases.
Some other medical annotation use cases include –
- a) Cancer Detection: Artificial Intelligence models trained with medical annotations can detect tiny cancerous cells, which otherwise would have been difficult to identify visually. The model is trained with images labelled with cancer data and hence can help in the early detection of this deadly disease.
- b) Detecting Bone fractures: X-Ray allows for the visualisation of bone structures to facilitate the annotation of fractured areas. This annotated data is then fed to the model to detect bone fractures accurately.
You can take assistance from various image annotation services that can help you streamline the process.
- Agriculture
The farming sector has been no exception in adopting data annotation to incorporate AI-enabled farming. Integrating agriculture image annotation in drones, robots, and other automated machines can help streamline monitoring crop health, livestock management, and unwanted crop detection.
- Facial Recognition Software
Facial recognition Software uses landmark annotation to detect various facial pointers to detect a person’s face. Since all faces are unique and contain multiple features, they annotate the faces with dots to detect various facial attributes such as nose, the shape of eyes, the width, and the height of the face, etc. For accurate detection, these dots are joined together to form an outline. These pointers are stored in the database for future reference. If a particular face matches the current information, they will be granted/denied permission.
Multiple tech companies like Apple have integrated facial recognition to enhance their security features.
Conclusion
Data annotation is instrumental in the development of Artificial Intelligence and Machine Learning. With the rapid growth in technological advancement, it would become necessary for all sectors to incorporate annotations and keep up with the latest trends.
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