It might come as a surprise to many, but the idea of robot doctors, self-driving cars and other similar advancements is still very much a fantasy. In other words, the full capability of artificial intelligence (AI) is far from being realized. The reason? To propel many of the AI-based initiatives, large volumes of data is essential to accelerate the progress and turn ideas into reality.
AI needs large volumes of data to continuously study and detect patterns. It can, however, not be trained with any raw data. Artificial intelligence, it is said, can be just as intelligent as the data it is fed.
Smart data is one that provides key information to what is otherwise raw data. It gives structure to data that would be nothing more than mere noise to a supervised learning algorithm.
Data annotation is the process that helps add essential nuggets of information to transform raw data into smart data.
- Data Annotation
- Challenges In Data Annotation
- 1. What Needs To Be Annotated?
- 2. How much of data is required for an AI/ML project?
- 3. Is it necessary that annotators must be subject matter experts?
- 4. Should data annotation be outsourced or performed in-house?
- 5. Is the annotation accurately representing a specific industry?
Also known as data labelling, data annotation plays a key role in machine learning and artificial intelligence projects being trained with the right, essential data. Data labeling and annotation are the first step in providing machine learning models with what they need to identify and differentiate against the different inputs to produce accurate outputs.
By means of feeding annotated and tagged datasets frequently with the help of algorithms, it is possible to refine a model to get smarter with time. Models get smarter and intelligent as more of the annotated data is fed to train them.
Challenges In Data Annotation
Generating the required annotation from a given asset can be challenging, which is largely because of the complexity associated with annotation. Also, getting highly accurate labels requires expertise and time.
To ensure machines learn to classify and identify information, humans must annotate and verify data. In the absence of labels being tagged and verified by humans, machine learning algorithms would have difficulty computing the essential attributes. In terms of annotation, machines cannot function properly without human assistance. It is also being said that for data labeling and AI quality control, human-in-the-loop concept will not be going away any time soon.
Let us take the example of legal documents, which are largely made of unstructured data. To understand any of the legal information and the context in which it is delivered, the expertise of legal experts is paramount. It might be necessary to tag any essential clauses and refer to cases that are pertinent to the judgment. The extraction and tagging process provides machine learning algorithms with information that they do not obtain on their own.
It is impossible to achieve success with AI if the right, essential information is not accessible. Feeding AI with the right data, with learnable signals frequently provided at scale, will enable it to improve over time. Therein lies the significance of data annotation.
But, before anyone gets started with a data annotation project, they must consider at least five key questions.
1. What Needs To Be Annotated?
Various forms of annotations exist based on the format of the data. It can vary from video to image annotation, semantic annotation, content categorization, text categorization and so on.
It is important to identify the most important one to help achieve specific business goals. It is also important to ask which format of data may help speed up a project’s progress more than its alternative.
Ultimately, it is about what needs to be a success.
2. How much of data is required for an AI/ML project?
The answer to the question would be: as much as possible.
However, in certain cases, benchmarks may be established depending on a particular requirement. The data requirement must be handled by a domain/subject matter expert who handles annotations and frequently helps measure the accuracy in order to create ‘ground truth’ data which will be applied to train the algorithm.
3. Is it necessary that annotators must be subject matter experts?
Based on the complexity of data that needs to be annotated, it is essential to have the best set of hands handling annotations.
While it is common for companies to entrust the crowd when it comes to basic annotation tasks, it is necessary to have annotators with specialized skill sets to annotate complex data.
Similar to having the requisite subject matter experts to decode the information provided in legal documents, it is essential to acquire the service of experts in annotation. People with an in-depth understanding of complex data will help ensure the data and the training sets do not carry even the minute errors that can throw a spanner in the works when it comes to creating predictive models.
4. Should data annotation be outsourced or performed in-house?
As per a report, organizations spend 5x more on their internal data labelling efforts than they spend on third-party data labeling. This way of working is not only expensive but also time-consuming for teams that could otherwise be focusing on other tasks.
Also, designing the requisite annotation tools typically requires way more work compared to certain machine learning projects. Not to mention that for a lot of companies, security can be an issue, which leads to hesitation in releasing data. However, this is unlikely to be of concern to companies that have the necessary security and privacy protocols already in place.
5. Is the annotation accurately representing a specific industry?
Before someone starts with data labeling, it is essential for them to understand the format and category of the data and the domain vocabulary they plan to use. This is known as ontology, which is an integral part of machine learning. Financial services, healthcare and legal industries have unique rules and regulations for data.
Ontologies lend meaning and help AI to communicate through a common language. It is also necessary to understand the problem statement and identify how AI would interpret the data to semantically address a use case.