What Is Data Labeling? Everything You Need To Know
Let us begin with a definition of data labelling by Amazon which defines it as “the process of identifying raw data (images, text files, videos, etc.) and adding one or more meaningful and informative labels to provide context so that a machine learning model can learn from it. For example, labels might indicate whether a photo contains a bird or car, which words were uttered in an audio recording, or if an x-ray contains a tumor. Data labeling is required for a variety of use cases including computer vision, natural language processing, and speech recognition.”
Like most definitions, it says a lot but perhaps leaves room for more explanation.
The labelling of data highlights properties in the data that can be understood by a computer and used to establish patterns that enable it to predict what is known as the ‘target.’ In a data set for training autonomous vehicles, for example, these ‘targets’ could be traffic lights, pedestrians or lanes on the road. It allows the software program to assign meaning to raw data and establish patterns.
For example, an AI model being trained for identifying facial expressions and emotions may need to be trained by enabling it to first identify a human face and thereafter connect it with human emotions through the complex interplay of facial features. For example, drooping lips could be an identifier of sadness.
Context is important. Labeling varies based on the requirement and objective of the AI model which it is being created for.
oWorkers has been providing data entry and labelling services to power the AI ambitions of its clients. Its partnership with leading technology providers provides it access to the latest technologies for the task. The fact that 75% of its clients are technology companies, while a challenge in terms of high technology expectations, also ensures it stays ahead of the curve in leveraging technology solutions for its work.
The need for data labelling
An understanding of ‘what is data labeling’ cannot be complete without an understanding of the need for data labelling.
While data labelling and data annotation are sometimes used interchangeably, data annotation is also usually referred to as the process through which data labeling is achieved, or we produce labeled data.
A research by Global Market Insights put the market for data annotation at $700 million in 2019, and projected to grow to $5.5 billion by 2026.
What is driving this growth?
Artificial Intelligence (AI) and Machine Learning (ML).
Is it surprising?
Perhaps no. Analysts say that almost every piece of technology now has an element of AI embedded in it. In your pocket. In your car. In your home. The search engine recommendations that are tailored to our preferences, the expected time you will take to reach a particular place, the chatbot response to your query, the identification of a weapon in a video grab, there is AI everywhere, though we may not recognize it at the point of our interface.
And Machine Learning (ML) is the handmaiden of AI, working in the background to produce training data sets that will make the AI models smarter and smarter.
Training data sets produced for training AI models use labelled data, that make raw data understandable to a computer. It is estimated that 80% of the time spent on AI projects is in the process of creation of training data-sets and labelling them.
An AI model being only as good as the training data, it is a task of great responsibility. After all, we don’t want an autonomous vehicle not running over pedestrians 8 out of 10 times. The model needs to ensure that it does so 10 out of 10 times. There is no scope for error. 8 in 10 is just not good enough.
Operating as locally registered units in the three geographies it has centers located in, oWorkers leverages its position as an aspirational employer for local jobseekers to access deep talent pools to handle all kinds of labelling requirements. It also has the flexibility of seasonal or other ramps to the tune of a hundred people in 48 hours. With its preference for employed staff over freelancers for working on client projects and stable, transparent employment policies, oWorkers experiences best in class attrition and provides stable solutions to clients for all data labelling needs.
What is data labeling – Key Concepts
A label is the tag or additional information added in the process of annotation to trigger the development of associations with identified features of the data. It is the basic unit of information on which training models are built. It needs to be remembered that labelling is contextual. Labels added to an image of a roadside for building AI for an autonomous vehicle may be very different from labels that need to be added to build AI to detect depletion of greenery in a particular location, even though the image may be the same.
For an image, a label might identify buildings or shops. In case of an audio, a label might associate the noise/ sound with some part of the language, like words or phrases. Understanding a ‘label’ also provides a good understanding of ‘what is data labeling.’
Visual data is much richer than textual data. Unfortunately, software coding has no place for visual cues to be given or received. Through AI, we are keen to teach computers to see and understand visual data in the same manner that humans do.
Computer vision is a broad term used to refer to the ingestion of visual data by a computer and its interpretation.
Typically, a large number of labels put together will constitute training data. The collected information that enables a software program or computer to make sense out of raw or unstructured data.
Humans in the loop
This is a term used to refer to the process through which human beings are allowed to add inputs into the model and provide insights that purely statistical data may not have been able to provide.
While one could argue that the training data used should have been of adequate quality and quantity that such feedback loops should not have been needed, in reality building training data is a tedious and expensive task. Therefore, for some applications that may not present a risk of injury or death, limited data-sets with a human-in-the-loop feedback cycle are used to refine models.
Ground truth refers to the reality check. The point where ‘the rubber meets the road.’ Often used at the initial stages after the AI model has been trained and unleashed on an unsuspecting world. At the initial stages it is important to keep track of its results and ensure that the results delivered are in line with human expectations.
With hands-on experience of over 20 years, the leadership team of oWorkers is well aware of the answer to the ‘what is data labeling’ question and well placed to provide guidance on data labelling projects to all its projects and team members. With support for 22 languages, a GDPR compliant business and operating from ISO certified facilities, oWorkers offers a compelling proposition for data labelling services.
Labeling common data types
Creating structured text and having a computer interpret it and act on the interpretation is a science mastered by humans and computers many decades back. That is called software programming.
When we talk about text in the context of AI, the reference is to unstructured text. How do we get a computer to understand and interpret text that was not created for the specific purpose of being interpreted by the computer. A computer would need training to even comprehend the phrase ‘what is data labeling.’
There are concerns these days about the destructive force that some of the social media platforms can be when they propagate falsehoods and hatefulness. A small, tiny, pathetic human being might enjoy his moment in the sun through such a message, but the cost and implication for society could be high. AI models trained to read and interpret text can be used to head off the potential damage by suppressing or deleting such messages and identifying and apprehending the perpetrators.
Labeling textual data is also useful in applications that use Natural Language Processing (NLP) like Voice Assistants and Speech Recognition. Audio converted to text through speech recognition technologies and used as training data-sets can also provide a variety of applications. Chatbots that are increasingly becoming popular for responding to customer queries have been trained with labeled textual data.
Making sense of unstructured data being the key objective of AO models, working with images has become an increasingly important requirement. Videos are also often handled as a sequence of images in rapid succession.
What is an image for a computer? It is an image. That is all. At best, in the digital world, a computer may be able to identify an image as a collection of pixels.
Labeling an image is the process of making the image, or certain parts of it, meaningful to a computer, so that it can create associations and patterns out of it.
A hundred years back estimating the level of summer ice on the North Pole may have been a manual exercise done by accessing each floe and measuring it. Today, it can be done with the help of AI models. By training the model to recognize sea ice by feeding it millions of images where the ‘target’ is made distinguishable to the computer by its features being called out, it becomes capable of identifying sea ice on an image where it has not been marked, thus doing in an instant what it took probably hundreds of mandays to achieve earlier.
Some common techniques for labelling images:
Semantic Segmentation – Pixel level labelling, used for more precise recognition of objects in a single class to differentiate them from each other.
2D Bounding Box – To facilitate the detection of certain objects, rectangular, close-fitting boxes are drawn around the target objects.
Polygonal Annotation – Similar to a 2D Bounding Box, but the figure drawn around the object to be identified is not rectangular, but polygonal instead.
Cuboid Annotation – Also called 3D cuboid annotation, cuboidal annotation is used where the third dimension of depth is relevant for the AI model. A case in point could be autonomous vehicles here the model needs to know how long it might take for a truck to pass
Software programming has developed along textual pathways, with programs coded in textual formats being read and understood by machines. Audio remained a ‘bridge too far’ for computers. But that is changing with AI. With the creation of training data sets created to train AI models, this field is developing rapidly along with developments in what is known as NLP or Natural Language Processing.
The most obvious use of audio capability in AI appears to be to convert speech to text. Being the most precise method of communication, with a finite set of characters and words and symbols in each language, text is the preferred language of communication for computer systems. Therefore, the path to any operation on an audio file lies through text. If one needs to search for a certain string in an audio file, it would be searched as a text string and not as an audio string. If it is searched as an audio string, the computer, with the use of AI, will perhaps convert it to a text string and match it with the original audio file which it is searching against, which presumably is also stored as a text file. Development of AI models has greatly speeded up the growth of NLP.
Examples of audio capability application:
- Conversion of speech to text, automating transcription
- Voice response units for customer service
- Emotion and sentiment identification and management of potential danger signals
With its combination of visual and audio content, video remains the richest and densest media that is handled by AI models. As discussed elsewhere, videos are generally handled as a sequence of images, with the additional element of changes taking place in the identified variables from one frame to the next further enriching the information contained.
Autonomous vehicles, security surveillance and virtual examination proctoring are some of the applications of AI that is trained through video labelling.
oWorkers excels in its chosen area of specialization, having consistently been identified as one of the top three providers of data services in the world. Its rankings on Glassdoor have always been above 4.6 out of a possible 5. Though not specific to data labelling, our spread of centers also provides clients the capability of contingency planning, with capacity being made available in more than one location for the same service. Your work enables us to bring a few more people from disadvantaged backgrounds into the digital economy and change not only their own, but lives of their families as well.
We know the answer to ‘what is data labeling’ as well as ‘how it can be done efficiently,’ ‘what are the right tools to be used’ and ‘how to create value for client AI models.’