Despite being a concept introduced in the 1950s, Artificial Intelligence (AI) did not become a household name until a couple of years back. The evolution of AI has been gradual and it has taken almost 6 decades to offer the insane features and functionalities it does today. All this has been immensely possible due to the simultaneous evolution of hardware peripherals, tech infrastructures, allied concepts like cloud computing, data storage and processing systems (Big Data and analytics), the penetration and commercialization of the internet, and more. Everything together has led to this amazing phase of tech timeline, where AI and Machine Learning (ML) are not just powering innovations but becoming inevitable concepts to live without as well.
Almost every solution today is powered by an AI-driven module or an algorithm. From suggesting what you should watch next on Netflix and recommending groceries at the right time on your on-demand apps, to powering autonomous cars and oncology studies, AI lies at the foundation of it all.
However, whenever we talk about the stunning capabilities of AI, we often overlook the fundamental element that makes everything possible. This one-step, without which AI would not just be weird but probably dysfunctional, too.
We call this crucial phase data annotation.
Honestly, AI modules are systems that follow the Garbage In Garbage Out protocol. If there’s one element that makes AI as effective it really is, it is data annotation. This is a process where the raw data that is fed to an AI module for training and testing is tagged, labeled, or annotated to make sure a system understands the different elements contained in a dataset.
In this post, we are going to talk about the less-spoken topic on the rise of data annotation, specifically image labeling and annotation and decode some reasons for the surging demand.
Let’s get started.
The Rise Of Computer Vision
With concepts like self-driving cars, advanced drones, satellites, IoT devices, and more rolling out one after the other, computer vision is increasingly becoming an integral part of a system’s tech stack. To give you a quick idea of how computer vision is deployed across diverse industries, here’s a quick sample list:
- Computer vision plays a crucial role during natural disasters and crises such as floods, earthquakes, and more. It is used to assess damage areas, predicting vulnerable regions, and help disaster teams to assist with plans of action.
- It is being increasingly used in agriculture to allow farmers to make informed decisions about their crops, soil, and harvesting techniques. With computer vision, some of the insights farmers get include crop health, pest penetration, soil health, yield quality and more.
- In healthcare, computer vision is used to detect anomalies, minute tumors, and conditions that often go unnoticed to the naked eye.
- Facial recognition is used by some popular banks across the globe to assist people in opening bank accounts with minimal documents and biometric details.
- Sentiment and behavioral analyses in classrooms are now possible with computer vision as well.
- Law enforcement uses facial recognition and computer vision to track serious offenders, fugitives and more and keep national and public security at check.
Apart from these, the technology is also implemented in sectors like waste management, advertising, retail and more.
The Growth Of Image Labelling And Annotation
For systems reliant on computer vision, what is fundamental is image labeling or image annotation. It is because of this process that an autonomous car can differentiate between a mailbox and a pedestrian, the red light and the green light, and more; in order to make appropriate driving decisions. For an image recognition system to be powerful, it has to process millions of images to precisely understand different objects in a segment it is intended to be implemented for.
With the rise of commercial and industrial AI applications, the need for expert image labelling and annotation services is simultaneously increasing as well. This is exactly why there is a surging demand for image annotation tools and services.
To throw in some numbers, the data annotation tools market was valued at around 700mn in the year 2019. However, with the increase in the number of use cases and applications, the industry is expected to grow at a CAGR of 30% (to 5,500mn) within the year 2026.
With that said, the demand for image labeling and annotation is not just because of the rise of use cases or applications stemming out of computer vision and AI. It is, in fact, because of the quality assurance that is needed for AI systems to work. In industries like healthcare, automobile, security and finance, error rates have to be either minute or none at all. Any slight increase in error rates or decrease in efficiency rates of AI systems could prove expensive or lethal to stakeholders.
That’s why behind every successful result of computer vision, there is a team of image annotators meticulously tagging multiple elements in an image, and making sure systems are working without any bias, and are able to deliver results that could be effective in the development of applications.
So, if you’re building an AI system, having ambition alone isn’t adequate. The backend that powers your ambitions (and AI systems) should be as powerful as your goals. That’s why it is highly recommended that you have the best team to work on your image labelling and annotation processes for the most accurate results. Quality at the time of build has always translated to quality during results.
Co-founder and CEO
As Co-Founder and CEO of Shaip, Vatsal Ghiya has 20+ years of experience in healthcare software and services. Besides Shaip, he also co-founded ezDI – a one-of-a-kind cloud-based software solution company that provides a Natural Language Processing (NLP) engine and a comprehensive medical knowledge base with products such as ezCAC and ezCDI which are computer-assisted coding and clinical documentation improvement products called. In addition, Vatsal co-founded Mediscribes, a company that provides medical transcription-based offerings in the healthcare domain.