Data annotation is the basis for any automated technology you see around you in this modern world of digitalization. All these things are carefully designed to execute complex tasks at different times. The enormous digital environment is made possible by vital information processed correctly using algorithms and codes which give logic to these technologies. Such schemes are done either by pre-fed training of technology or real-time online information updates.
Artificial intelligence in any technology or machine is only as intelligent as the training the algorithms have received. Training of such algorithms is a time taking activity as raw data such as images, videos, and audio datasets need to be converted into meaningful information that the technology can understand, also known as the process of computer vision machine learning.
To an advanced learning algorithm, unpatterned data is merely noise, and we’re still a long way from developing artificial intelligence that can interpret any random algorithm. Hence data annotation and labeling becomes vital to turn raw data into usable information for algorithms.
What is data annotation, and why is it important?
Technology works according to our will, but how do we make them understand what we want? Data annotation is simply training the machine (or the algorithm in the device) to perform a specific desired task. In technical language, labeling information so that the algorithms can understand the data is called Data Annotation.
In the Artificial Intelligence AI system, the constant annotation of patterned and labeled datasets is crucial for the technology to learn and get smart with time. The more annotated and readable the device gets, the more it can make complex decisions.
For many organizations, it remains a challenge because of the following reasons:
- Complexity involved with annotating data;
- Resources and time required for the most accurate and authentic annotated data;
- Finding the correct expertise of a data annotator.
Annotation Lab’s advanced labeling and data annotation services for AI technologies & Computer Vision solves all of the above problems.
How does data annotation work?
The AI development & ML process prerequisite is a high-quality dataset that can be annotated or labeled. As a rule of thumb, the richer the quality of raw data, the more accurate the annotation and thus better initial training of the computer vision machine learning models.
The process of labeling is a complex one. It needs highly skilled annotators, data scientists, and technical tools and software to prepare the data with accurate information. The human input required is costly and a time-consuming series of tasks in which Annotation Labs excels. Learn about our methodical and meticulous approach to each annotation project. As ironic as it may sound, the fundamental development of any autonomous machine starts with complex human processes.
To better understand what data annotation is, consider an example of an MRI report. For an ordinary person, it is just unreadable data. To understand the MRI report, you need a doctor who can interpret and extract the relevant information. Similarly, data annotation extracts information from raw datasets, such as tagging metadata and highlighting the specific points. The process of understanding and extraction of data is not possible without expert help. Similarly, machines also require human assistance to understand what is being done; a process called data annotation and labeling.
Artificial intelligence can not be made practical without access to the correct data at the right time. With the constant feeding of understandable data into the form of inputs, the machine eventually learns what type of data is proper and what type of data is noise, which is called the Machine Learning process.
What are the types of data annotation?
There is not one single language spoken across the world. Similarly, the types of data that AI-enabled Technologies understand can be various; some of the common types of data annotation are given below.
Image annotating tools use bounding boxes, polygonal, and semantic annotations to assist ML & AI Algorithms in effectively interpreting the different types and shapes of things and objects in an Image. Image Annotation service is used to train data sets for models ranging from autonomous vehicles to face detection and image search recognition models.
Video Data Labeling extracts information from video clips and footage by annotating objects in each video frame. The annotated training video data are fed into ML & AI models to identify and train on various parameters in live videos. Its dynamic applications extend to automated surveillance, drone imagery, and more.
Read how Annotation Labs use video annotation to track movement of objects
Words in the text dataset need to be identified, labeled, and linked with other words in the texts for AI & ML models to interpret the meaning of the whole text, a process called Text Annotation & Text Classification. The linkage of the different words is done basis on parameters such as language, tone, and other complex lexicon or word libraries. Text Labeling is essential for designing auto-replies chatbots, conversational AI models, and translation tools.
Annotation Labs use these Text Annotation tools for text labeling
Audio to text transcription & annotation
Speech recordings, interviews, and voice commands need to be converted to text before Audio AI models can interpret the information and produce an output. Such audio transcription services include analyzing different accents and shortening long messages before Voice Assistants and Voice Command Engines can comprehend the audio datasets.
Learn about expert Audio Transcribers from Annotation Labs
What is data annotation used for?
Automation of processes and machines will find use in almost all industries in 2022. Some use cases are mentioned below.
Automobile autonomous driving
A substantial, high-quality annotated dataset is needed to train Computer Vision-based ML to develop self-driving capabilities, ADAS features, and autonomous functionalities before achieving safety clearance.
Learn more on how Annotation Labs is using annotation services in automobiles
Drone image & satellite surveillance
AI & ML algorithms and computer vision models for city & urban planning and construction activities need precise annotated Satellites, Drones, and UAV images.
Understand how Annotations are used in Drone & Satellite Imagery
Retail and e-commerce
E-Commerce Brands & Marketers are always on top of ever-changing customer preferences, tastes, reviews, and feedback about their products and services. Interestingly, such data points can be captured online and offline with customer-centric automation models, requiring accurately annotated training datasets.
Annotation Labs uses these Automation tools to understand consumer preferences
Medical and healthcare
Healthcare AI such as Autonomous surgery, radiology, pathology diagnosis, and healthcare chatbots need faultless and error-free annotated healthcare datasets to train AI & ML models in the healthcare sector.
Learn more about AI & ML in Healthcare & Medical industry
Agriculture and farming
Modern farming techniques, aerial soil and crop monitoring, automated vegetable and fruit quality and ripeness, and autonomous livestock management improve output quality and quantity while reducing production costs. AI and ML in agriculture need distinct datasets to train the Computer Vision Machine Learning Models.
Read more about Annotation & Labeling for Agriculture Automations
Operations, warehousing & supply chain
Reducing human error by adopting computer vision in the supply chain, automated warehousing, and AI inventory management needs specialized annotated datasets to train AI & ML models.
Annotation Labs uses these tools to annotate datasets for supply chain
Computer Vision Robots, AI prediction of downtime and maintenance, are installed by modern-day manufacturing units for higher quality and quantity of production. Such AI Robotics need distinct datasets to train the Automation Models.
Learn more about annotation tools for robotics
Online and digital
The e-commerce search box is optimized with highly complex annotated data inputs to give the user the most accurate search results. Virtual Assistant engines such as Siri and Alexa are trained to understand the various languages, dialects, and accents. Content moderation algorithms and chatbots filter and moderate content in real-time.
Digital have widespread uses of labeled data using these annotation tools
How to know that you are annotating in the right direction?
It is better to know that you are annotating in the right direction before you start to annotate. To achieve this, you should first understand the industry of technology, the format of annotated datasets needed for ML Algorithms, and the various characteristics that you are trying to train the algorithm on. This process is commonly known as ONTOLOGY.
Ontology plays an essential role in the machine learning process, and by definition, it is the naming of the process and things. An Artificial intelligence algorithm must work similarly to how humans make decisions. For the algorithm, it is crucial to identify the type of information you are trying to feed and understand how the AI Models can interpret this information into a meaningful result.
How much data is required to annotate an AI & Computer Vision Algorithm?
The answer is infinite. To make the technologies understand complex logics, tasks and structures on its own, you need to annotate as much data as possible. Which, as you can understand, is an impossible task. Therefore a specific datum line is set. The clear datum line is more likely decided by a Data Scientist & Data Annotation Specialist with Industry expertise who can handle the extreme annotation and evaluate the accuracy of annotation based on machine learning algorithms.
Is it necessary for an annotator to be a specialist?
It depends on the complexity of the data and the type of annotation that is being done. The simple answer to that question is crucial to have the right expert and experienced data annotators who can also bring in industry-specific expertise. Several companies use inexperienced and ill-trained staff for primary and straightforward annotation. At the same time, the complex and more creative annotation requires special skills and abilities to ensure efficiency and accuracy.
This process can be easily understood by example. Everybody knows that paracetamol is used to treat mild fevers and pain, and this process does not require a piece of expert medical advice. Still, if you want to cure a tumor or have heart surgery, you will concern a person with the most information about it, and the slightest error in the treatment may lead to extreme consequences. Similarly, if there is a slight error made in data entry or interpretation, it may lead to the failure of the whole structure of the AI & ML Computer Vision models.
Can data annotation & labeling be outsourced?
Generally speaking, companies spend about five times the resources and time on data annotation when they do it internally in-house than outsourcing data annotation to a 3rd party specialist like Annotation Labs.
Data annotation is a costly process. The accuracy of manual annotations cannot be matched by nascent automated annotation tools, and higher accuracy of data results in faster and more robust learning of Computer Vision models. Worse than that, it is time-consuming and demands the valuable time of talented minds invested in other productive work. Moreover, building the pathway for the annotation requires more work and skills than ordinary machine learning and automation projects.
The easy answer for all these problems is outsourcing annotation to an expert organization, Annotation Labs, which provides secure, accurate, and expert data annotation services. But many companies hesitate to do so because of security issues and data leaks, and for Annotation Labs, security is non-negotiable. Our clients constantly outsource their long and painful annotation process without the risk of security issues, thanks to our privacy protocols and security procedures.