Scaling Machine Learning with Automatic Data Labeling

What do astronomy, Instagram and kilometers of shelves have in common with files in companies? At first glance, not much. But if you take a closer look, all three consist of a sea of rapidly growing volumes of data. Humans can only process these with a great deal of time. If I search for pictures with dogs and cats, they have to be identified beforehand (get a tag or label). Manually, with the daily flood of images, this has become impossible. The way out of this dilemma is offered by Automatic Data Labeling. The work is delegated to computers, which can automatically annotate the large amounts of data. This information then helps AI algorithms to correctly analyze even unknown documents. But it doesn't work entirely without human intervention.

What is automatic data labeling anyway

Let's start with the question: what is data labeling? Even before the digital age, we used labels, even if we didn't always call them that. Family photos are not just collected. We add notes, dates, location information to give them semantic context. Our file folders have categories, are sorted by document type or sender, so we can always find the insurance data or bank statements quickly.

Automatic Data Labeling, on the other hand, is the process of labeling this data using Artificial Intelligence (AI) automatically instead of manually with tags or categories. These labels are then used to support machines in analyzing and processing data and to improve their accuracy and efficiency.

Relevant use cases

This technique works for a variety of data sources, resulting in a wide range of possible applications.

1. Image sources: Galaxies, lines of cars, or diseased forests-once properly understood, an AI alogithm can quickly recognize them in images with a high degree of certainty.
2. Video and sound sources: Music tracks, TV reports or feature films automatically receive additional information such as genre, mood or cast through algorithms with significantly reduced editorial effort.
3. Text documentsHere, image information from scans or PDFs is first converted into text (OCR) before the relevant information can be understood and evaluated by the AI algorithm.

Documents are particularly complex because they can contain image and text information that the algorithm must reliably recognize. Within the document, it must then make statements about the semantics through the relative positioning of the information, the format, and the sequence. Supervised learning means that after manual training, the results get better and better through continuous checking and correction.

When is automatic data labeling worthwhile, and when is it not?

Automatic Data Labeling is worthwhile when fast and accurate labeling results are needed. For example, employees can use Automatic Data Labeling in situations where large amounts of data need to be labeled, or when manually labeling data would be too time-consuming or error-prone. For smaller amounts of data, on the other hand, the effort is rather not worth it, as it requires a certain amount of effort to use the system successfully.

AI can also be very helpful in Automatic Data Labeling. Machine learning and AI algorithms allow data to be annotated automatically, making the process faster and more efficient. In particular, AI helps improve the accuracy of the labeling process by detecting patterns in the data and taking them into account when labeling.

However, automatic data labeling is not always worthwhile. If the data is incomplete or skewed, automatic labeling may produce inaccurate or irrelevant results. In such cases, manual labeling may be more appropriate to ensure that the labels are accurate and relevant.

Furthermore, it is important that users regularly review and validate the data labeling process to ensure that the labels are correct. If this step is not performed, errors can accumulate in the labeling process and affect the result.

Why does the process need an AI model?

The overall goal is to reduce the effort for humans as much as possible. However, in order to automatically assign the annotations for data that is still unknown, the artificial intelligence of the Automatic Data Labeling System needs a so-called AI model. AI models are a collection of algorithms and rules that are executed by a computer to solve specific tasks. Employees initially train these models using known data until the model itself can make confident statements about new data and employees can focus on monitoring the quality and updating the learning data

Automatic Data Labeling, only as good as its data

One of the problems with Automatic Data Labeling is accuracy. Even though AI algorithms are very powerful, they can still make mistakes. Therefore, it is important that system administrators regularly check and validate the Automatic Data Labeling process to ensure that the labels are correct.

Another problem with automatic data labeling is the quality of the data itself. If the data to be collected is incomplete or distorted, this can affect the result of the labeling process. Therefore, it is important that the data used is of high quality and relevance.

With Konfuzio there is the possibility to improve the labeling process by supporting multiple users. Also, multiple tagging tools (auto-tagging tools) can be used simultaneously to make efficient use of the different strengths of the tools. For example, one tool may be better for labeling images, another better for text information. Some applications also specialize in specific subject areas. For example, the learning curve for financial documents can be abbreviated. Konfuzio is able to use these different tools for its own classification of data.

Conclusion

Overall, Automatic Data Labeling is an important step in the analysis and preparation of data. Through its use, unknown documents can be analyzed faster and more accurately. Especially with large amounts of data, think again of Youtube or Instagram, the automated process is indispensable. However, it is important to monitor the accuracy and quality of the data in the Automatic Data Labeling process. This is how you make sure that the annotations or labels are correct and relevant. Overall, the use of AI in data labeling significantly improves the efficiency and accuracy of AI systems. As a reward, you get better results in the long run when analyzing and processing the data.

1. More on artificial intelligence: https://de.wikipedia.org/wiki/K%C3%BCnstliche_Intelligenzhttps://de.wikipedia.org/wiki/K%C3%BCnstliche_Intelligenz
2. Supervised Learning Background: https://en.wikipedia.org/wiki/Supervised_learning