Computer Vision – Data Annotation, The Human Element

Whether ordering food or consulting a doctor, there isn’t much we can do these days without technology to aid us. Top it off with the raging pandemic that began in 2020, and we’ve seen people across the world become more reliant on their hand-held devices for the smallest things.

But, despite the onslaught of wearable tech and the Internet of Things, it is impossible to eliminate or replace human efforts. The simple fact is that Artificial intelligence still isn’t smart enough to take over jobs without the help of people.

Computer vision is a leading trend in AI that might just change all that.

What Is Computer Vision?

Computer vision is a field of artificial intelligence that deals with the computer interpretation and comprehension of the visual world through videos and pictures, all in an effort to replicate the human optical system.

It enables computers to take actions and recommendations based on visual inputs. It allows the computers to see and observe, while artificial intelligence enables thinking and comprehension.

Computer vision is designed to work similarly to the human visual system. It trains a machine to perform the same functions as a human eye but with much more speed. However, with age, humans learn to interpret and pick out the flaws in the objects around them; computer vision cannot do that independently. Instead, it needs to be programmed to tell different things apart based on their specifications, characteristics, and appearance.

This is the point where humans come into the picture. As computer vision cannot automatically learn the information it needs, humans provide the necessary support by helping to input and program the data.

Moreover, this programming needs to be thorough as computer vision machines analyze thousands of products or processes in a minute. So, for them to notice and capture any defect, their programing must be completely flawless. That perfection is achieved with the process of data annotation.

What Is Data Annotation?

Building artificial intelligence features that perform human tasks requires an extensive amount of data training. This training is impossible without human resources.

AI models that perform human tasks are trained using data annotation. It is the categorization and labeling of data for artificial intelligence applications. It is detailed work to organize and label every piece of related content such as videos, texts, audio, and images for the machine. So later, machines can recognize the material and make correct predictions.

Types of Data Annotation

Data annotation simply refers to the act of characterizing and differentiating data in unique ways.

For computer vision to be effective and efficient, it should be able to do the following  types of data labeling:

Semantic Annotation

Semantic annotation is the process of naming or labeling concepts. So, it would include the ability to specify companies, names, places, and people and even categorize entire nations. This type of data annotation improves the quality of AI-enabled chatbots and search relevancy by leading to new concepts for future texts.

Image Annotation

Image annotation helps machines visually recognize an area, an object, or a person with precision and accuracy. The available data should consist of images labeled with captions, identifiers, and keywords for this type of annotation. Image annotation is used for self-driving vehicles, sorting and packing machines, facial recognition software, and healthcare applications.

Audio Annotation

Audio annotation is made possible by the analysis of speech data. This type of annotation includes speech recognition and transcript of different accents. With audio annotations, AI machinery and applications recognize various languages spoken in different accents. Audio annotation is also used in security applications that require an in-depth analysis of violent, aggressive sounds to assess the situation and dispatch help if needed accurately.

Video Annotation

Video annotation is similar to audio annotation. Therefore, it uses similar techniques for gathering data. However, data collected for video annotation is utilized in localization and object tracking systems.

Intent Annotation

The primary purpose of the intent annotation is to acquaint the AI applications with the standard verbiage and language of an area. Intent annotation is the process of labeling common phrases and sentences people use in a culture or a location. This annotation allows the robots to help find a solution instead of forwarding the issue to a human representative. This annotation saves time, allowing AI to discern requests that need immediate assistance by figuring out the intent and resolving the matter independently.

Named Entity Annotation

This one finds and identifies the relationship between two places, objects, or companies. It enables the machine to understand the context of the concepts.

Text Categorization Annotation

Text categorization annotation is the process of assigning correct categories to different phrases or sentences in a document. These annotations help businesses save time and resources spent on deconstructing a file.

Sentiment Annotation

Sentiment annotation plays a significant role in assessing attitudes, emotions, and opinions in business decisions. This annotation provides an emotional insight that helps businesses make a correct choice.

But the fact of the matter is that predicting feelings and attitudes is still very tricky for human beings. And since programming accurate sentiment annotation data from the start is a human task, a machine’s ability to discern feelings essentially depends on the people that work with it.

Human annotators evaluate business content on social media and ecommerce sites, etc. Then the paired emotions are assessed with the help of sentences and phrases associated with the content. This job is quite risky as only human annotators can detect modern trends, and if the message is misinterpreted, it can damage an organization’s reputation.

Phrase Chunking Annotation

In phrase chunking annotation, different phrases and words are programmed with their grammatical definitions. This helps AI applications or software know the context of the sentence and not misinterpret it.

The Importance of Data Annotation in Various Fields

AI cannot accomplish its goals without data annotation. Here are several advantages of getting it right:

Enhanced Customer Experience

Data annotation is the backbone of experience quality. Brands collect a vast amount of data from their customers to measure the quality of their experience and how fruitful the interactions were at different touchpoints.

But without data annotation, it can all go to waste. That’s because it enhances the understanding of AI analytics systems, resulting in useful information for elevating the experience quality.

In addition, as customers’ experiences will mainly filter through AI and machine learning technology in the future, the importance of data annotation keeps rising for businesses.

Faster Medical Diagnoses

Most medical diagnoses require MRIs, CT scans, ultrasounds, x-ray photographs, etc., to collect data about a person’s health. In modern medicine, computer vision is used to interpret these visual tests and to make a diagnosis.

Therefore, correct data annotation is necessary to prevent false diagnoses and save the cost of the wrong treatment plan. However, this technology doesn’t replace human professionals but only aids and speeds up the diagnosis and treatment process.

Positive Impact on Outsourcing

Data annotation has also left a positive impact on outsourcing. With the readily available data, companies reduce costs and time in choosing a suitable third-party service. It also saves resources spent on training sessions by making a suitable outsourcing match.

Improvement In Agricultural Output

Before AI, farmers spent quite a lot of time checking their crops for pests and plant diseases, and often they couldn’t get to them all on time. This limitation often resulted in a massive loss of healthy and expensive crops. However, now with the help of image annotation, humans code all the information collected through images, drones, and satellites. This information helps early disease detection. Moreover, AI also informs farmers if the soil and temperature are perfect for planting specific vegetation.

Advanced Automotive Solutions

Self-driving cars are no longer a vision of the future but a genuine and present reality. And it’s all thanks to AI and data annotation. These self-driving vehicles scan their surroundings in real-time while you drive and compare the resulting data with images, maps, and directions annotated in them. This helps keep them safe, providing valid suggestions and preventing accidents. With the help of this feature, even handicapped people have a chance of living an independent life.

Role of Humans in Data Annotation

During data annotation, humans’ task is to add metadata tags with the correct text, images, video, and audio clips. These metadata tags guide the computer program in selecting the proper function.

The primary role of annotators is to manually label the collected media so the machine algorithm has no problem accessing and understanding the installed sample. Without humans or annotators, machines cannot run their algorithms to detect patterns, make predictions, and correct false assumptions.

Annotators need to have communication and collaboration skills to code the correct information while maintaining teamwork. Also, they should work in a team to avoid biases during data annotation.

Moreover, a lot of data lacks accuracy and quality, which a machine cannot detect. So, it is the responsibility of human annotators to sort and select reliable data. This process is tiring, extensive, and time-consuming as there is a considerable amount of data, and there is no room for compromising the quality.

Therefore, human annotators are necessary for data labeling, maintaining, and updating the sample data.

As artificial intelligence and the field of computer vision are rapidly growing with time and are predicted to keep growing, so is the demand for data annotators. However, even though technology and artificial intelligence are about to rule the world, this can’t be made possible without human input.