Artificial intelligence (AI) and machine learning (ML) are constantly evolving technologies that allow us to create amazing things. Think about a self-driving car or your smartphone’s face ID unlock feature. Did you ever consider how that truly works?

To be able to decide not to drive into the next tree, a machine must first be educated to understand precise information. To create such automated devices and applications, massive amounts of training data are required. Companies can either buy training data or hire a team of experts known as data annotators who can deal with raw data.

In general, data annotation is a sophisticated and expensive procedure that should be performed by experts to ensure a satisfactory outcome.

Many firms dealing with AI struggle with data annotation and are unsure where to begin. As a result, in this blog, we will define data annotation, describe the various types of data annotation methodologies that are accessible, and highlight why data annotation is so important these days.

What is Data Annotation?

Data annotation is the process of annotating data from a video, image, or text. The data is labeled so that models can quickly understand a given data source and recognize specific formats, items, information, or patterns in the future.

Computer vision is used by the model to analyze and interpret the photos, videos, and other formats offered. Computer vision is a subfield of artificial intelligence (AI) that enables software and computers to perceive and interpret digital visual data. But how does this affect data annotation?

To educate computer vision to recognize objects, patterns, or other information, data must be carefully annotated, or in more technical terms, provided with a well-established machine learning model. This is achieved by using adequate methods and tools.

In general, no matter if data annotation is done manually or automatically, the procedure consists of two steps:

• Labeling of data
• Quality checks and audits

The first step in data annotation is to label images, videos, and text. In this context, data labeling refers to adding one or more useful labels to raw data (pictures, text, video, etc.) in order to offer context and allow a machine learning model to learn from it. Traffic lights, pedestrians, automobiles, and buildings are labeled to teach the model what they are, as shown in the diagram below.

The second stage begins once the data has been labeled. The annotated dataset is validated for authenticity and precision. This is a really crucial phase. Otherwise, the constructed model is trained on inaccurate data, which can result in costly retraining operations.

Types of Data Annotation

As previously stated, the data annotation technique can be used to a variety of layouts. This means that several data annotation methods are used. To make the blog more readable, we will focus on three different data annotation strategies. Please keep in mind that this list is not exhaustive.

Image annotation

For a wide range of applications such as computer vision, robotic vision, facial recognition, and solutions that rely on AI, image annotation makes it possible to interpret images. Before this is applicable though, the AI model has to be trained with thousands of labeled images. 

The training can be conducted by assigning metadata, such as identifiers, captions, and keywords to hundreds of images. With effective training, the accuracy of the AI-model increases and allows you to use it for numerous purposes (e.g., self-driving vehicles, auto-identified medical conditions).

Image annotation itself has again different types of annotation methods, such as:

• Bounding boxes → Drawing a rectangle around the object you want to annotate in a given image. The edges of the bounding box should touch the outermost pixels of the labeled image to ensure the highest possible accuracy.

• 3D cuboids → This method is very similar to bounding boxes annotation. The only difference is that the user has to take the depth factor into account. It can be used to annotate planes or cars on an image. 

• Polygons → While making use of bounding boxes or 3D cuboids, various objects might be unintentionally included in the annotated area. With the polygon tool, a line can be drawn around the specific object in the image that needs to be annotated. 

• Keypoint tool → An object can be annotated by a series of points. This is often used for gesture detection or motion tracking. 

With this in mind, let’s now have a look at video annotation. 

Video annotation

Video annotation is conducted on a frame-by-frame basis to make annotated objects recognizable for machine learning models. In general, it uses the same techniques as image annotation (e.g., bounding boxes) to detect or identify the desired objects. 

This annotation method is an essential technique for computer vision tasks such as localization and object tracking, in which an algorithm can track the movement of an object. Therefore, video annotation is helpful for several industries such as the medical sector, manufacturing, and traffic management. 

Read Also: How Data Cleansing Can Revive Your Marketing Efforts

As the last type of data annotation, we want to talk about text annotation. Text is the most commonly used data category, as most companies depend greatly on text in various business processes. 

Text annotation

Text annotation refers to adding metadata or labels to pieces of text. Let’s have a closer look at what that means.

• Adding metadata

Adding metadata means providing relevant information for the learning algorithm. That way, it can prioritize and focus on certain words.

Example: “Here is the invoice (document_type) for the new computer (order) you ordered yesterday (time).” 

The metadata added in the brackets provides the relevant information to the learning algorithm, ensuring that in future it can detect the information it has been trained for.   

• Assigning labels

By adding labels, words can be assigned to a sentence that describes its type. A sentence can for example be described with sentiments or technicality. 

Example: “The product doesn’t satisfy my needs, I want to return it.” Here, the label “unhappy” could be assigned.

This helps the algorithm to understand the sentiment and intent of a text and is closely related to named entity recognition. Let’s have a look at why that is. 

• Named Entity Recognition (NER)

Named entity recognition is used to search for words based on their meaning. It aims to detect predefined named entities and expressions in a sentence. In general, NER is useful when extracting, classifying and categorizing information. 

Let’s take the example of an invoice. By training the model to know the word “Invoice number” plus the characteristics of an invoice number (e.g., amount of digits) it can classify the document as an invoice. The same works for different words on different documents as well, which means you can use the Named Entity Recognition method to classify various documents based on data fields.

On top of that, an AI algorithm can be specifically trained to understand the sentiment and intent of a sentence, as this is very important to understand human behavior. Let’s see what that means.

Sentiment Annotation

As previously mentioned, sentiment annotation is performed by assigning labels to text that represent human emotions. For that purpose, labels such as sad, happy, frustrated, or angry are used. Then it can be used for sentiment analysis in, for example, the retail industry to understand customer satisfaction. 

Intent Annotation

Intent annotation basically means that labels are assigned to sentences that express a certain intent or need. This can be very helpful for, e.g., customer service.

Let’s take a chatbot as an example. When a customer submits the sentence “I have trouble paying with my credit card” the person can immediately be directed to the financial team. 

This is possible because the algorithm has been trained with hundreds of sentences that state a similar need. 

Words like “trouble” express an emotion (sentiment) of the customer. On top of that, words like “credit card” were previously labeled as “paying method” or similar labels, allowing the algorithm to direct the customer to the financial department.  

What is Human-Annotated Data?

Human-annotated data refers to information that has been reviewed, labeled, or classified by humans. This procedure requires human annotators to understand the context, nuances, and intricacies of the data, whether it is text, photos, audio, or video. The human factor in annotation adds a depth of cognitive knowledge and interpretation that totally automated methods may not capture. This human touch adds richness and accuracy to the data, making it ideal for training and refining AI and ML models.

The importance of human intuition and knowledge in data annotation cannot be understated. Humans can interpret confusing or complex events and add annotations indicating a deeper knowledge of the content. This human perspective is critical for training AI systems to execute tasks such as sentiment analysis, object recognition, and decision making in a manner that is more consistent with human judgment and behavior.

Furthermore, human annotators are more adaptable to new and emerging types of data than automated systems. The combination of human intuition and computational capacity paves the door for increasingly sophisticated, nuanced, and dependable AI applications.

The Crucial Role of Humans in AI Oversight

In an era of rapidly evolving artificial intelligence (AI) technology, there is often anxiety that humans may be rendered obsolete and replaced by intelligent computers. However, humans are still as important as ever in controlling and directing AI systems. Integrating AI technologies with human expertise and oversight is critical for making ethical decisions, maintaining accountability, and mitigating potential hazards.

Let’s look at why people continue to play an important role in AI monitoring, even as technology advances.

1. Ethical decision-making

AI systems process vast amounts of data and make decisions based on predefined algorithms. While AI algorithms can be highly efficient, they cannot assess and prioritize ethical considerations. But people possess the moral compass to ensure that AI decisions align with societal values.

Humans can define ethical guidelines, establish boundaries and review AI outputs to avoid biases, discrimination and unethical behavior. By exercising ethical decision-making, humans can harness AI’s potential for positive impact while minimizing the risks associated with unchecked automation.

2. Accountability

Accountability is a fundamental aspect of any decision-making process. In the context of AI, accountability is necessary to ensure that AI systems and their outcomes are transparent, fair and justifiable. Humans oversee AI systems’ development, deployment and maintenance, holding the technology accountable for its actions.

Additionally, human oversight enables identifying and rectifying errors or biases that may arise during AI operations. By assuming responsibility and providing transparency, humans help build trust between AI systems and the society they serve.

3. Adaptability and contextual understanding

AI systems rely on patterns, algorithms, and data analysis. However, they frequently struggle to adapt to changing circumstances or comprehend the complex nuances of human interactions. Humans can adapt to new situations, use contextual knowledge, and make informed decisions. This adaptability allows humans to negotiate complex decision-making scenarios, assess the impact of AI-driven recommendations, and consider alternative viewpoints. By integrating AI’s analytical power with human contextual awareness, we can get the best of both worlds.

4. Continual learning and improvement

AI systems use training data to learn and generate predictions. However, these methods have the potential to reduce data quality and bias. Humans, on the other hand, have the ability to learn on a constant basis, think critically, and be creative.

Humans can uncover AI models’ flaws, biases, or unforeseen consequences and make the necessary modifications or changes. AI systems can improve and become more accurate, dependable, and responsive to human requirements and expectations with continuous human engagement.

While AI technologies evolve at an unprecedented rate, the value of human monitoring cannot be understated. Humans contribute ethical decision-making, accountability, adaptability, and constant development to the table.

By combining human experience with AI systems, we can fully realize the potential of technology while reducing risks and guaranteeing a responsible and sustainable future. And, as we move forward, it’s critical to acknowledge that humans and AI may collaborate, resulting in a tremendous synergy that benefits individuals, organizations, and society as a whole.

Final Words

Next to saving time and money, data annotation has a couple more benefits. These benefits are as follows:

• Higher efficiency → Data labeling allows machine learning systems to be better trained, which makes them more efficient in recognizing objects, words, sentiment, intent, etc.
• A higher degree of precision → Correct data labeling leads to more accurate data to train an algorithm. And this will lead to a higher degree of data extraction accuracy in the future. 
• Reduced amount of human intervention → The better the data annotation, the better the output of the AI model. Accurate output of the algorithm means that less human intervention is needed, which reduces costs and saves time.
• Scalability → This applies to automated data annotation, which allows you to scale data annotation projects to improve AI and ML models.

Well, next to benefits, every solution comes with its limitations. Therefore, to understand the bigger picture, it is important to discuss those next.