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How important are data labeling tasks for building AI products?
As we know, most AI applications require large amounts of data. Fueled by these huge amounts of data, machine learning algorithms are incredibly good at learning and detecting patterns in the data and making useful predictions... without requiring hours of programming.
Exploiting raw data is therefore a priority for the Data Scientist, who will resort to Data Labeling to add a semantic layer to his or her data. Quite simply, this involves assigning labels or categories to data of all types, both structured and unstructured (text, image, video), in order to make it comprehensible to a supervised Machine Learning or Deep Learning model.
Data Labeling for Computer Vision (and NLP) models
Supervised machine learning algorithms leverage large amounts of labeled data to train neural networks to recognize patterns in the data that are useful for an application. Data labelers define annotations on the data that have "ground truth" value, and engineers feed this data into a machine learning algorithm.
Let's take the example of a"Computer Vision" model of dog and cat recognition. To train this model, it is necessary to have a large amount of animal pictures labeled as either dogs or cats. The model will then use this labeled data to learn to differentiate between dogs and cats, and will be able to predict which category a new unlabeled image belongs to. Data Labeling is therefore essential to train Machine Learning models accurately and efficiently. However, it can be tedious and expensive to do manually, especially when there are large amounts of data to process. For this reason, many automated tools and platforms have been developed to facilitate this process.
What types of data can be exploited to feed AI models?
Almost any data can be exploited:
- Structured data, organized in a relational database.
- Unstructured data, such as images, videos, LiDAR or Radar data, plain text and audio files.
While structured data has been widely exploited over the last 40 years since the rise of database management systems (Oracle, Sybase, SQL Server, etc.), unstructured data is largely unexploited and represents a wealth of information in all business sectors.
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Supervised learning and unsupervised learning
In applied AI,supervised learning is at the heart of innovative AI applications that are becoming part of our daily lives (ChatGPT, obstacle detection for automatic cars, facial recognition, etc.). Supervised learning requires a massive volume of data, precisely labeled, to train the models and obtain quality results or predictions.
In contrast,unsupervised learning does not rely on quantities of data but analyzes a limited data set to learn and improve. While there are proven applications of these techniques, the trend is toward building AI products with a data-centric approach for a good reason: the results are generally more accurate and faster to obtain. Fewer and fewer commercial applications of Machine Learning rely on complex "code". The work of Data Scientists and Data Engineers becomes more and more important: the role of these data specialists will be more and more focused on the efficient management of a Data Pipeline, from data collection to labeling, qualification of annotated data and release to production.
Labeling data: the importance of accuracy for AI models
Data Labeling must be done in a rigorous and accurate manner to avoid errors and biases in the data. These errors can have a negative impact on the performance of the Machine Learning model and it is therefore necessary to ensure that the data is labeled in a consistent manner.
Data Labeling is a laborious job, which requires patience, efficiency and consistency. It is also a job that is sometimes considered thankless, because it is repetitive if one simply processes data in series without applying a labeling strategy or a dedicated methodology, or without using appropriate tools (ergonomic and efficient platform) or assisted annotation technologies (e.g.Active Learning).
Companies tend to outsource data labelling tasks to :
- Internal" teams (interns, temporary workers, beginners, etc.) assuming that the task is accessible to all because it is considered simple. A problem: this tends to frustrate these profiles, which are nevertheless expensive!
- Crowdsourced teams via online platforms, which gives access to a large pool of Data Labelers, generally from low-income countries with a negative human impact (dilution and very low salaries) and little control over the production chain of the labelled data.
- Teams of specialized Data Labelers, experts in a functional area (health, fashion, automotive, etc.) and with a knowledge of the labeling tools on the market as well as a pragmatic and critical view of the labeled data and the labeling process
In summary, Data Labeling is a key process in the field of Machine Learning and Artificial Intelligence. It consists of assigning labels to data in order to make it usable and intelligible for a Machine Learning model. Although tedious and costly, it is essential to give importance to this process in order to avoid errors and biases in the data, to build the AI products of tomorrow!
