AI is increasingly becoming an essential aspect of doing business. Already, over 77% of all devices we use feature AI in one form or another. By 2025 the global AI market is expected to reach $60 billion. And one of the most-used personal assistants has already accumulated over 100,000 skills globally, with more than 66,000 accessible in the US.
Over 80% of all businesses expect to need AI operations in the next year. While AI offers huge efficiencies, it relies on human intelligence and input to train its models. In this post, we go over the types of data labeling that businesses need and how to implement them.
What is Data Labeling, and Why Is It Important?
Data labeling is the process of taking raw data and adding one or more meaningful information labels to provide context. Accurately labeling data is critically important in all AI applications, especially things like self-driving cars and facial-recognition technology.
The challenge is that AI projects are complex, and often 80% of the time it takes to complete a project is spent gathering, organizing, and correctly labeling data.
Engineers know that the best way for an AI to identify objects correctly is not through code but with a good deep machine learning model. Data labeling makes objects recognizable and understandable for machine learning which saves a considerable amount of time and vastly improves the efficiency of AI projects.
The challenge is that machine learning requires human involvement. Machine learning requires regular adjustments and tweaks to the algorithm both initially and throughout the entire process.
5 Types of Human Data Labeling for AI
There are five main types of human data labeling that companies utilize. Each one is a little different but they can work together to create efficient machine learning.
Image processing means extracting helpful information from an image. Search engines do it all the time with reverse image search, and it's beneficial across many industries. Image processing includes things like object detection, facial recognition, and the recognition of text on images.
One of the best examples of image processing is Google Photos, which uses tags to quickly create collages and videos of images of specific occasions like birthdays and holidays. Other applications of image processing are used in agriculture, the military, the environment, and the medical industries.
Video annotation is the process of labeling or tagging video clips used in machine learning to help computer vision models identify objects. It's a more complicated process than image processing since the objects are in motion. Video annotation can include bounding, scanning polygons, landmarks, labeling/tagging, classification, and event tracking.
One great example of this is virtual reality (VR) applications, such as when smart vehicle technologies (like self-driving) use accurate object detection and tracking across video frames to steer the vehicle in the right direction and avoid collisions. Other applications include sports analytics technology, retail, and video surveillance.
Data tagging is the process of organizing information by tagging it with specific tags or keywords. It's commonly used on eCommerce marketplace and social media platforms where product images are tagged and displayed in search results.
For example, today, some companies are creating technology that helps eCommerce brands offer camera search features. A user can take a picture of a clothing item and then find similar items on the brand's website. On their own, eCommerce brands wouldn't have the resources to tag each product image manually. However, by using AI, they can incorporate thousands of tags and automate the whole process.
Data digitization is the process of converting documents from analog to digital format. It can include things like scanning a document or photograph into a PDF. The process doesn't change the data itself. It simply encodes it into a digital format.
Data digitization is important for a lot of legacy organizations that have high volumes of documents stored in an analog format. It's important for businesses that require a lot of paperwork, such as the finance sector, accounting, and insurance. AI helps automate the process and safely store all documents in a digital format.
Natural Language Processing
Natural language processing (NLP) is a type of AI that helps computers understand the text and spoken communication. This technology has been around for decades, but it's often taken for granted. Good examples of NLP are the spellcheck and autocomplete functions that many of us use in our emails and documents every single day. Natural language processing can include things like entity recognition, sentiment analysis, text summarization, aspect mining, topic modeling, and machine translation.
One of the well-known examples of NLP is smart assistants. In fact, industry watchers predict that smart assistants are on the way to becoming the third great consumer computing platform of the decade. As of the beginning of 2021, over 90.7 million adults in the US own smart speakers, when the technology wasn't even on anyone's radar in 2015.
NLP is also used in digital phone calls where it enables computer-generated language that is close to the voice of a human. Another area in which NLP plays a role is language translation, where it can detect different sentence structures and slang that straight translation services often miss.
Unlock the Power of AI with Human Data Labeling
Human data labeling is critical for the success of AI. It's also a massive undertaking that requires input from teams of people to identify objects correctly, including Image Processing, Video Annotation, Data Tagging, Data Digitization, and Natural Language Processing (NLP).
Most businesses that need to deploy AI can't rely on internal resources to engage in large-scale AI data labeling projects. Outsourcing data labeling makes economic sense and allows companies to meet their AI goals quickly and more efficiently.
Outsourcing human data labeling with a solution like Helpware is an excellent way for businesses to implement AI without all the upfront costs involved. Connect with us to learn how to make your next AI project a success.