Whenever we talk about Artificial Intelligence (AI) and Machine Learning (ML), what we instantly imagine are powerful tech companies, convenient and futuristic solutions, fancy self-driving cars, and basically everything that is aesthetically, creatively, and intellectually pleasing. What hardly gets projected to people is the real world behind all the conveniences and lifestyle experiences offered by AI.
For your device to set an alarm clock just by listening to your voice, hundreds of hours of work would have gone through at the back end — right from the time of ideation to developing prototypes and testing. And that’s just for one specific function of a feature. Now, imagine the scale of operations and efforts behind your Netflix recommendation engines, eCommerce personalizations, home automation systems, on-demand transport, and food delivery solutions, and basically anything powered by a smartphone or an app.
Today’s spectrum of artificial intelligence is just like a fancy restaurant that gets marketed among people. What people see are concierge services, well-dressed butlers, exotic dishes and beverages, amazing ambiance, and sumptuous food and interiors. But what has been functioning non-stop to deliver these experiences and bring all elements together is the chaotic kitchen at the backend.
For great experiences to be delivered, the kitchen has to be perpetually functional and this is exactly what we are going to expand on today.
In this post, we will step aside from all the fanciful offerings of the technology and explore the real job that goes behind the curtains — aspects like data generation, data annotation or data labeling, data processing, and more. So, let’s get started with understanding why artificial intelligence is incomplete without data annotation.
Data annotation in simple words involves labeling the content within the data and turning it into a format that is understandable by machines and your ML models. Any algorithm that you build, requires data to be in a specific format for it to understand and identify what it should process and how it should process. Data annotation is simply making the machines understand what they should do.
There are different types of data annotation such as –
- Image annotation — where elements in an image are annotated or tagged individually. Objects, animals, background locations, and even distortions and noises can be tagged for machines to learn. Some of the annotation techniques for images include bounding boxes, 3D cuboid annotation, polygon annotation, landmark annotation, and more.
- Text annotation — where sentences, phrases, and texts from messages, social media comments and posts, descriptions, and more are tagged based on requirements for processing. Text annotation involves the identification of sentence structures, tagging intent, emotion, urgency, and more for sentiment analysis, chatbot responses, and diverse purposes
- Audio annotation — where it is powered by NLP and speech recognition processes to annotate or tag sentences and phrases with adequate metadata and keywords for optimized processing. From sentiment analysis and virtual assistant responses to voice search optimization, audio annotation is put to a myriad of uses.
- Video annotation — is similar to image annotation in terms of purposes but differs in the fact that video annotation relies on computer vision to recognize moving objects. Elements are identified and boxed in similar ways as in image annotation but happen per-frame basis. Video annotation is immensely crucial and is used for facial recognition, surveillance, self-driving cars, and more.
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Data annotation lays the foundation for AI-based processes to happen. Without data annotation, artificial intelligence and machine learning models would undoubtedly fail because the models wouldn’t know what to do with the data being fed. They will either show no results or throw results that don’t make sense at all.
If there were no data annotation processes, it would only appear like words coming out of a baby’s mouth. Data annotation ensures every single byte of data is tagged with adequate information that a system needs to process, in the most seamless ways possible.
It trains AI models and makes them scalable in the longer run. A simple example of what would happen if data annotation is poor is you get an email with your name replaced with your email address. The machine learning algorithm responsible for automated email triggers would have wrongly recognized your email address as your name. With wrong tags, the names would be interchanged causing confusion.
Like you understand by now, data annotation is as complex as the processes and purposes it supports. Despite the advancements in technologies we talk about on a daily basis, most of the data annotation work is still manual. Human intervention is inevitable in tagging elements for AI models and this makes the entire process not just time-consuming but tedious as well.
That’s why companies across the world prefer getting data from external sources as they are already tagged. Otherwise, they choose to get their annotation done by third-parties because they can’t afford to dedicate their existing talent pool to work on data labeling. By collaborating with data annotation companies, they keep their AI training pipelines perpetually active.
With over 80% of the time on AI development being spent on data annotation, it only makes sense to bring in experts to do the job. The fact is that this 80% of the time requires 100% of the attention and focus as even a single minor mistake could stall the entire AI model and skew results. So, if you intend to avoid instances like these and get your AI models optimized for performance, get in touch with an expert data annotation company.