.jpg)
What importance should be given to data labeling tasks to build AI products?
We know it: most AI applications require a significant amount of data. Powered by these huge amounts of data, machine learning algorithms are incredibly good at learn and detect trends (”Patterns“) in the data and make useful predictions... without requiring hours of programming.
Exploiting raw data is therefore a priority for the Data Scientist, which will use Data Labeling, or data labeling in French, to add a semantic layer to its data. It is simply a matter of assigning labels, that is to say labels or categories, to data of all types, structured and unstructured (text, image, video) in order to make understandable for a Machine Learning or Supervised Deep Learning model.
Data Labeling for Computer Vision (and NLP) models
Supervised machine learning algorithms exploit large amounts of labeled data to train neural networks to recognize trends in data that are useful for an application. Data Labelers define data annotations Who have value of “truth” (“ground truth”), and engineers feed that data into a machine learning algorithm.
Let's take the example of a model”Computer Vision“dog and cat recognition. To train this model, it is necessary to have a large quantity of photos of animals labeled as either dogs or cats. The model will then use this labeled data to learn how to differentiate dogs from cats, and will be able to predict which category a new, unlabeled image belongs to. Data Labeling is therefore essential for training Machine Learning models. accurately and effectively. However, it can be tedious and expensive to do this manually, especially when there are large amounts of data to process. For this reason, numerous automated tools and platforms have been developed to facilitate this process.
What types of data can be used to feed AI models?
Almost all data can be used:
- Of structured data, organized in a relational database.
- Of unstructured data, like images, videos, LiDAR or Radar data, plain text, and audio files.
Although structured data has been widely used over the past 40 years since the rise of database management systems (Oracle, Sybase, SQL Server, ... ), Unstructured data, on the other hand, is largely unexploited. and represent a wealth of information in all sectors of activity.
-hand-innv.png)
Supervised learning and unsupervised learning
In applied AI, the supervised learning is at the heart of innovative AI applications that are introduced into our daily lives (ChatGPT, obstacle detection for automatic cars, facial recognition, etc.). Supervised learning requires a massive volume of data, accurately labeled, to train models and obtain quality results or predictions.
Conversely, the unsupervised learning does not rely on quantities of data but analyzes a limited set of data to learn and improve. While there are proven applications of these techniques, there is a trend towards building AI products with a data-centric approach for good reason: results are generally more accurate and quicker to obtain. Fewer and fewer commercial machine learning applications rely on complex “code.” The work of Data Scientists and Data Engineers then makes perfect sense: the role of these data specialists will be increasingly focused on effective management of a Data Pipeline, ranging from data collection, to labelling, qualification of annotated data and production.
Labeling data: the importance of precision for AI models
The Data Labeling must be done rigorously and accurately, in order to avoid errors and biases in the data. These errors can in fact have a negative impact on the performance of the Machine Learning model and it is therefore necessary to ensure that the data is labeled consistently.
Data Labeling is a painstaking job, which requires patience, efficiency and consistency. It is also a job that is sometimes considered thankless, because it is repetitive if we simply process serial data without applying a labeling strategy or a dedicated methodology, or without using appropriate tools (ergonomic and efficient platform) or assisted annotation technologies (for example, the Active Learning).
Businesses tend to entrust Data Labeling tasks to:
- “Internal” teams (Data Scientist intern, interim, beginner profile, etc.) assuming that the task is accessible to everyone because it is considered simple. One problem: this tends to frustrate these profiles, which are nevertheless expensive!
- “Crowdsourced” teams via online platforms, which gives access to a Pool large number of Data Labelers, generally from low-income countries with a negative human impact (dilution and very low salaries) and poor control of the labelled data production chain.
- Teams of specialized Data Labelers, experts in a functional field (health, fashion, car,...) and with a knowledge of market labelling tools as well as a pragmatic and critical look at labelled data and the labelling process.
In summary, Data Labeling is a key process in the field of machine learning and artificial intelligence. It consists in assigning labels to data in order to make them usable and intelligible for a Machine Learning model. Although tedious and expensive, 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!
