AI and machine learning are some of the fastest-growing technologies that offer incredible innovations that benefit various sectors of the global economy.
However, in order to create such systems, a lot of training data is required to allow the machines to recognize things we want them to find. This training data needs to be annotated by human workers to prepare the raw data to be consumed by machines. This, in essence, is what data annotation is.
As Magellan Solutions show in this article, data annotation is required for AI projects in any industry. It is an essential aspect of any machine learning project.
For a model to make decisions and take action, it must be trained to understand specific information.
Data annotation is the categorization and labeling of data for AI applications. Training data must be properly categorized and annotated for a specific use case.
With high-quality, human-powered data annotation, companies can build and improve AI implementations. The result is an enhanced customer experience solution such as product recommendations, relevant search engine results, computer vision, speech recognition, chatbots, and more.
There are several primary types of data:
The most commonly used data type is text.
According to the 2020 State of AI and Machine Learning report, 70% of companies rely on text.
Assesses attitudes, emotions, and opinions, making it important to have the right training data.
To obtain that data, human annotators are often leveraged as they can evaluate sentiment and moderate content on all web platforms. This includes social media and eCommerce sites, with the ability to tag and report on keywords that are profane, sensitive, or neologistic, for example.
As people converse more with human-machine interfaces, machines must be able to understand both natural language and user intent.
Multi-intent data collection and categorization can differentiate intent into key categories including request, command, booking, recommendation, and confirmation.
Semantic annotation both improves product listings and ensures customers can find the products they’re looking for. This helps turn browsers into buyers.
By tagging the various components within product titles and search queries, semantic annotation services help train your algorithm to recognize those individual parts and improve overall search relevance.
Named Entity Recognition (NER) systems require a large amount of manually annotated training data. Organizations like Appen apply named entity annotation capabilities across a wide range of use cases.
This includes helping eCommerce clients identify and tag a range of key descriptors, or aiding social media companies in tagging entities such as people, places, companies, organizations, and titles to assist with better-targeted advertising content.
Audio annotation is the transcription and time-stamping of speech data. This covers the transcription of specific pronunciation and intonation, along with the identification of language, dialect, and speaker demographics.
Every use case is different, and some require a very specific approach: for example, the tagging of aggressive speech indicators and non-speech sounds like glass breaking for use in security and emergency hotline technology applications.
Image annotation is vital for a wide range of applications. It includes computer vision, robotic vision, facial recognition, and solutions that rely on machine learning to interpret images.
To train these solutions, metadata must be assigned to the images in the form of identifiers, captions, or keywords.
From computer vision systems used by self-driving vehicles and machines that pick and sort produce, to healthcare applications that auto-identify medical conditions, there are many use cases that require high volumes of annotated images.
Image annotation increases precision and accuracy by effectively training these systems.
Human-annotated data is the key to successful machine learning. Humans are simply better than computers at managing subjectivity, understanding intent, and coping with ambiguity.
For example, when determining whether a search engine result is relevant, input from many people is needed for consensus. When training a computer vision or pattern recognition solution, humans are needed to identify and annotate specific data, such as outlining all the pixels containing trees or traffic signs in an image.
Using this structured data, machines can learn to recognize these relationships in testing and production.
When you are looking at possible data annotation companies to outsource your work to, it is important that they have a rigorous QA process in place.
Here is how we can ensures the accuracy of all the data annotation work performed:
Even though data annotation is very tedious and time-consuming work, it is necessary to the overall success of the project.
In fact, the accuracy of the data annotation will play a big role in whether or not the system will function correctly if any biases exist if it is able to recognize the needed items in its surroundings and a lot of other important outcomes.
Companies developing AI and machine learning projects understand the importance of data annotation. But they do not have time to do such work internally.
The following are the common industries that outsource their annotation services to BPO companies in the Philippines
One of the most popular applications of AI is in the automotive industry with autonomous vehicles.
You have most likely heard about companies like Tesla, Waymo, and many other developing cars that can drive by themselves. In order to train the machine learning algorithms that power self-driving cars, a lot of video and image annotation is required. It allows the system to recognize things like other cars, street signs, pedestrians, and many other things. This is usually done via labeling, 2D/3D boxes, semantic segmentation, LiDAR, and other types of annotations.
The healthcare industry is also actively relying on AI especially given the disruptions caused by the recent pandemic. AI systems can take a lot of work off the shoulders of human doctors allowing them to devote more time to patients.
A lot of companies are developing AI products that can analyze medical images like X-rays, CT scans, mammograms, and many others and provide a diagnosis.
There is still a big role human doctors need to play in providing quality healthcare since their expertise is required to annotate the medical images that train AI systems. Also, they still need to confirm the diagnosis provided by the machines and they are the ones working directly with patients.
The agriculture industry relies on various robotics and drones to grow greater amounts of healthier crops. This includes robots that can harvest ripe crops by themselves, fertilize the soil, provide aerial surveillance of the field, and analyze crop growth, and many other applications.
Although robotics is a separate industry in its own right, robots are allowing farmers to save a lot of money since they can replace human labor in performing routine tasks.
Such robots use LiDAR technology that produces a 3D Point Cloud, which is a representation of how they see the physical world. This 3D Point Cloud needs to be annotated to allow the robot to recognize all of the objects in their surroundings and their proximity to those objects.
We all know that training data preparation is one of the least enjoyable chores in the machine learning process.
While having humans in the loop to execute tasks like labeling unstructured data is often an essential step in preparing training data for your model, its tedious and time-consuming nature makes it a task not ideally suited for small teams of highly skilled & well-paid data scientists or engineers. This is why many organizations choose to outsource their data annotation projects in order to leverage lower-cost labor at scale.
Although working with these external teams comes with its own set of challenges, there are a few steps all organizations can take to optimize their annotation partnerships.
Here at Magellan Solutions, we work with organizations on a daily basis who are facing the challenges associated with finding and working with the ideal data labeling teams.
Contact us today and outsource the rest of your data entry services with us!
Contact us today for more information.