Machine learning and artificial intelligence (AI) have certainly come a long way in recent times. Towards Data Science published an article on some of the biggest developments in machine learning over the past century. One of the most remarkable changes has been the Named Entity Recognition tool, which can differentiate between the capitalization rules of German and English.
A number of new applications are making machine learning technology more robust than ever. Not only are these applications capable of processing vast amounts of data per second, but they enjoy many practical applications. These have likewise impacted the average consumer as well as the business community. One extremely innovative and far-reaching advancement can be seen in the form of an annotation tool platform (such as that which is offered by Kili Technology). As the majority of readers might not be familiar with this concept, it warrants a closer look.
What is the relation of data and image annotation to machine learning? What are its primary applications and what specific benefits does this approach have to offer? Let us examine each of these questions in greater detail immediately below.
Data Labeling and Annotation at a Glance
As the term may already suggest, this type of data annotation involves the process of assigning a value to specific portions of a piece of data. It is a great way to use data for quality control purposes. These metrics are then used to aid machine learning processes. In other words, the system in question is able to better understand what it is being presented with. Note that annotation can interpret various file types. These include (but are not necessarily limited to):
- Images
- Audio files
- Videos
- Text
- PDF documents
There are several ways in which this labeling can be performed. In-house methods through the use of proprietary software systems may be used. Automatic scripts could also be used (although these normally require additional human intervention). Alternatively, data annotation can be outsourced to trust third-party platforms.
What Advantages Does this Technique Offer?
There are several reasons why data annotation is becoming commonplace. As this article observes, some primary benefits include:
- Data labeling allows machine learning systems to be better trained, and therefore more efficient
- Accurate data helps to “train” algorithms, ensuring greater degrees of precision in the future.
- Cutting-edge annotation packages drastically reduce the amount of human intervention required. This helps to streamline many processes.
We can therefore see that data annotation is capable of reducing the costs associated with past techniques that would have required manual intervention.
A Host of Interesting Applications
We have already taken a decidedly abstract look at data annotation. It is now prudent to examine some of its numerous real-world applications. Perhaps the most prominent involves business automation. As many CRM (customer relationship management) systems now rely upon artificial intelligence, accurate data and image labeling make it much easier to identify important documents and files. However, the benefits are not limited to the business community. Labeling and annotation services can be used to recognize specific images (such as the license plate of a vehicle or even individual faces). These are some of the more tangible applications.
While data annotation will often occur “behind the scenes”, its impacts upon AI and machine learning cannot be denied. It will therefore be quite interesting to see what the future may have in store.
Make the Most of Data Annotations
Data technology is changing significantly significantly. One of the biggest trends has been in the realm of data annotations. Companies need to know how annotations work and how to use them.