Unless you are jetting back from life on another planet, artificial intelligence (AI) and machine learning (ML) are all around us. Both AI and ML have revolutionized the way we live and work, and life has been made easier and convenient. From self-driving cars, smart and nudge replies to emails, and smart conversations in social media networks using emojis – all these breathtaking developments are AI-powered. Needless to say, smart equipment and smart life have become an integral part of our daily routines. What is more amusing is that AI and ML are so ingrained in simple things that we do not notice them on a whim and only take note of their presence in the grander scheme of things.
Having that in mind, did you know both AI and ML depend on well-annotated data? In every successful ML project, the model must pass through training. To train the premium quality ML model, data annotators need to feed their ML algorithms with accurately labelled data. Thus, data labeling identifies objects in raw data in various formats aforesaid and tagging labels on them helping your ML model make accurate predictions and estimations.
In this blog, we are taking you on a journey to fully understand why data annotation is important for ML and AI, and key takeaways will include:
- What data annotation means in AI and ML
- The key techniques and types of data labeling done depending on the project’s requirements
- The benefits of data annotation
- Use cases on data annotation in AI or ML
Let’s take the plunge.
What is Data Annotation in AI or ML?
To clear the air, the terms data annotation and data labelling are used interchangeably to refer to the technique of tagging labels in contents available in a range of formats. The process uses a data annotation tool that makes objects of interest within a text, image, or video recognizable to machines through computer vision, NLP or Audio Processing.
Data annotation plays a key role in making sure AI or ML projects are scalable. Training an ML model requires the model to understand and detect all objects of interest in algorithm inputs for accurate outputs. Depending on the project’s requirements, various techniques and types of data labelling can be applied. In addition, human effort is required to identify and label specific data to make it easier for machines to identify and classify information. If data labelling is not done, ML algorithms will not compute the essential attributes with ease.
Text Annotation for NLP
Text annotation for NLP or speech recognition by machines is carried out to create a communication mechanism among humans communicating in their local dialects or languages. In this case, text annotation is done using virtual assistant devices and AI chatbots to respond to various questions put across by individuals in their native speech styles.
Although different text annotation types exist, a common feature is a metadata added to create recognizable keywords for machines to make critical decisions.
Video Annotation for High-Quality Visualized Training
Similar to text annotation, video annotation is done with the sole objective of making machines recognize moving objects through computer vision. Precision is key in video annotation, such as annotating frame-by-frame objects, and different objects are also annotated to estimate their movements.
Video annotation is applicable in creating training data for autonomous vehicles or visual perception models for driverless cars.
Image Annotation for Recognizable Objects
Image annotation is done with one goal in mind: make the objects of interest detectable and recognizable to visual perception based ML models. With image annotation, the object is annotated and tagged with different elements that make it easier for AI-enabled machines to perceive ranging projects.
Different types of image annotation are used in developing training data sets for AI businesses. Among the leading methods commonly used in ML, projects include 3D cuboid annotation, bounding box, landmark annotation, and 3D point annotation.
Annotation for Medical Imaging
Data scientists annotate medical images to create healthcare training data for ML. Images from Radiology departments like CT Scan, Ultrasound, and X-rays are annotated medical images training ML models to automatically diagnose different diseases with a high level of accuracy.
It’s the medical experts in Radiology annotate medical images manually through appropriate annotation tools, making diseases recognizable to AI machines in order for the latter to detect the diseases in real-life situations.
Benefits of Data Annotation in ML
In a nutshell:
- With supervised learning, ML models receive accurate training to make correct prediction and estimation.
- ML automated systems can give various stellar experiences for end-users. For example, digital assistant devices and chatbots respond to users’ queries according to the speed of their demands.
- Web search engines are using ML technology like Google in improving the accuracy of their results based on the history of search behaviour of end-users.
- Similarly, ML in speech recognition has come in handy, offering virtual assistance in human speech with the help of NLP.
- Properly labelled data guarantees success in all ML projects because the smallest error in preparing the data for training ML models can be detrimental and disastrous.
- Data annotation enables AI to reach its full potential. Numerous benefits are coming from AI, and with correct data labelling, we can get the best and most value from it.
Data Annotation for ML Use Cases
Adobe Stock for Asset Profile: Adobe’s Stock is one of Adobe’s flagship offerings, which is a curated collection of superior quality stock imagery. The library has over 200 million assets (including millions of videos, photos, 3D assets, and editorial assets). Each of these assets were made discoverable with a model created from accurate training data.
HERE Technologies: HERE has a history of providing businesses and companies with accurate and detailed location data and insights. In their ambitious ML project, the company wanted to annotate tens of thousands of miles of driven roads for ground truth data to power their sign-detection models. A Video Object Tracking technology was presented as the ultimate solution to the problem.
Now you understand why data annotation is highly significant in ML or AI. There is no imagination for an AI-powered project without high-quality training data sets on the table. As a matter of fact, training data available in various forms such as texts, images or videos is the “fuel” for ML algorithms capable of creating any imaginable autonomous models.