Data, in general, is the lifeblood of assisted machine learning projects. The more data you have, the more accurate the end-product will be. However, it is not simply enough to have raw data. You need to have this data annotated so that the machine learning algorithm can properly identify the objects in a given image, understand human speech, and many other functionalities. Before we dive into the importance of quality annotated data, let’s first take a brief overview of the differences between supervised and unsupervised machine learning.
Supervised vs. Unsupervised Machine Learning: What’s the Difference?
It would be best to compare supervised machine learning with a teacher and a pupil. Just like the teacher supervises the pupil to make sure they are correctly learning the material, the same principles apply to supervised machine learning. The only difference is that each individual data scientist is the teacher and the computer or the AI system is the pupil. When human beings annotate the data, they help the data scientist to teach the ML algorithms on how to properly identify objects in their immediate surroundings.
With unsupervised machine learning, the system has to connect the dots and learn all by itself and try to identify the objects in the image as best as they can. If your project is fairly simple and you only need to identify a handful of objects, then the accuracy rate could be fairly decent. However, sometimes the objects and people in the images could be difficult to identify or the system could simply be tasked with labeling a lot more things in the image. If this is the case, then the accuracy level will decrease since the degree of difficulty will increase.
What is the Importance of Data Annotation in Such Projects?
Even on the surface, we can see the correlation between correctly annotated data and the success of the project. However, this is also supported by research since according to some estimates, 80% of AI project development time is spent on preparing the data. The reason data annotation is so important is that even the slightest error could prove to be disastrous. As humans, this is one of the areas where we have a leg up on the computers since we can better deal with ambiguity, decipher the intent, and many other factors that go into data annotation.
If you are working on an unsupervised machine learning project, sooner or later, you might need to have data annotation work done if you want to reach better performance of the algorithms. Prior to deploying your product, you would want to increase the accuracy rate. In other words, human data annotations will have to manually go through each image and determine whether the quality of annotation is high enough to teach the algorithms.
While there are many publicly available datasets that have already been annotated, it is not a good idea to resume them. First of all, according to the McKinsey Global Institute, about three-quarters of AI projects require monthly data refresh while a third of them require weekly ones. Since a lot of the datasets need to be frequently refreshed, simply reusing open-sourced data may not be an option.
Data annotation allows AI to live up to its full potential. According to research by McKinsey, AI has the potential to deliver additional global economic activity of around $13 trillion by 2030. With so many benefits we could be receiving from AI, it is very important that all of the data is annotated correctly to make sure that we get the most value out of it.
Trust Mindy Support With All of Your Data Annotation Needs
Since data annotation is very important for the overall success of your AI projects, you should carefully choose your service provider. Mindy Support is the largest BPO provider in Ukraine with extensive experience actualizing data annotation projects for SMEs, Fortune 500, and some of the GAFAM companies. We have more than 2,000 employees in six locations across Ukraine and we can assemble even the most sizable team quickly. We offer a free trial for you to see how we get the job done right the first time and the overall quality of our process.
Posted by Il’ya Dudkin