Automated Data Labeling with Machine Learning

Automated data labeling has greatly reduced the workload of machine learning practitioners. They are the ones who truly understand that the manual labeling process is among the most tedious and labor-intensive processes in creating AI products. Collecting, preparing, and labeling generally takes up to 80 percent of the whole project.

An automated data labeling pipeline permits human labelers to drastically minimize the time it takes to label data. Although its principal benefit is speed, auto-labeling is often not compatible with all kinds of tasks.

Frequently it is essential to rely on human beings to achieve the finest outcome. For instance, crowdsourcing may be an effective and relatively inexpensive alternative. Below we will discuss reasons why automatic labeling is beneficial, and when it is more appropriate to turn to another type of labeling, such as human labeling via crowdsourcing.

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Labeled data

Labeled data is the cornerstone of all machine learning. Today, high-quality ML models cannot be generated without training data, which is comprised of datasets with labels. An important note is that labels or tags are most often assigned to the data by humans, but there are exceptions, for instance in some techniques of automated labeling, which will be additionally discussed further on.

Data labeling process

The necessity for people labeling data arose as ML technology became ubiquitous. Algorithms are supposed to learn from the training dataset, and it is often impossible to obtain it until a human creates it.

How does data labeling work? Commonly the data labeling process consists of the following:

• Data collection and data preparation
• Data labeling, acquiring ground truth – the target variables that the model is supposed to foresee
• Quality control. The data has to be of high quality, precise, and coherent

Manual labeling

Automated labeling is commonly different from manual labeling. Such a way of labeling assumes that the labeling specialist makes all the necessary labels manually. For instance, outlining an object in a bounding box on a photo or video, or assigning text to an audio recording when designing a natural language processing application.

Manual labeling allows for better control of the data labeling process. Human labelers are more likely to have a deeper comprehension of the objective and skills to interpret the data presented to them. Human-processed data is apt to propose higher quality labels and more accurate data than those produced by automated tools. Such quality is essential in complex projects such as processing medical images or videos for the development of autonomous vehicles, which will determine the safety of the people on the road.

How Does Automated Data Labeling Work? ‍

Automatic labeling refers to a feature of data annotation mechanisms that employ artificial intelligence to refine, annotate, or auto-label datasets. Having this characteristic supports manual labeling, thus saving time and costs on data annotation for ML.

Artificial intelligence training relies on huge quantities of high-quality data. More data can certainly be beneficial in many situations, as it can provide a more complete and accurate understanding of a particular subject. The data quality is also decisive since not all data is equally valuable.

Auto labeling algorithm assumes that the pre-trained program will accomplish everything on its own, without human input. However, auto-labeling is not about having a machine doing all the work for you. Different types of automated labeling involve humans in one way or another. Humans come up with algorithms that could teach machines to identify and label the required items. They also collect the data or place data labels on the data to automate data labeling or perform QA checks of the system, or a combination of some of the above.

Automatic data annotation provides a straightforward, fast, and up-to-date approach to processing data using AI itself. The software tools possessing this feature are backing up human efforts while saving time and money on data labeling. However, it still is unable to completely replace manual labeling and it has its drawbacks, which we will discuss further on.

ML approaches that enable auto labeling

Deciding which type of ML that enables auto-labeling is most efficient for your particular project varies depending on your objectives, project requirements, the field of work you are in, and the type of data you are handling, among several other important aspects. Similarly, there is no single classification of approaches to automated data labeling. Here are a few of the most notable ML methods that allow the creation of auto labeling tools:

Supervised Learning

Supervised learning is an ML technique, where a model is learning from labeled data. In other words, the algorithm memorizes the examples given to it. One of the techniques for a data set to be annotated automatically and evolve into training data involves data labeling professional uploading the relevant information to an ML tool, which already can qualitatively label the data. Supervised learning is employed to develop such applications.

This method for automated labeling may be called extremely reliable since supervised learning requires vast amounts of human pre-labeled data to start the learning process in the first place. As a result, specialists create software that can automatically assign labels to new data.

Frequently, to automatically label photos, video, or audio for a particular area of use, such as autonomous vehicles, highly specialized professionals are brought in to help with the labeling, which makes the software based on such data even more reliable. But again, creating such a platform to auto-label data requires a tremendous amount of resources, including trained professionals and hours of work. Not every team that builds ML tools can afford that.

Reinforcement learning

In RL, experts typically train a model called an agent. This agent cooperates with the environment through actions in it. The method is founded on the concept that a functioning algorithm has to be rewarded for its achievements and punished for its failures. As a response, the environment communicates some reward in exchange for the right agent's action, as well as its new state. Thus, the agent accumulates knowledge about the rewards received for their actions in certain states. The agent's goal is to maximize the total reward received over a period of time. The agent starts to forecast outcomes that are appropriate to the specific context through trial and error.

RL algorithms, in contrast to the majority of widely known machine learning algorithms that make predictions, require interaction with the outside world they are put in, which requires analyzing data over some period of time. Doing so helps such models meet their main goals. RL algorithm's purpose consists in maximizing their gain during a given period of time. These types of algorithms most closely resemble the mechanisms of conditioned response in people.

Unsupervised Learning

In supervised learning, a computer has a specific manager telling it how to do the right thing and what to choose. That is, the supervisor has already labeled all raw data in advance, and the machine learns from the specific cases.

Unsupervised Learning is the complete opposite of supervised learning, first of all, due to the fact that the learning system has no supervisor or labeled data. Unsupervised learning algorithms work autonomously, without any manual intervention. It evolves its own mechanisms to produce the correct results. A human doesn't need to label the data sets in this type of machine learning. It figures it out on its own.

In unsupervised learning, the algorithm is not informed of the end goal or given any templates, just presented with data sets. Shared characteristics of the data items are recognized automatically. However, these systems still require a bit of a human workforce to validate the output variables.

In addition, labeled datasets generated in this manner are often unreliable and difficult to interpret. In unsupervised learning, it is harder to deduce the precision of the algorithms since there are no true answers or labels in the given data.

Active learning

Active learning is an area of ML where a program collaborates with a data provider, which is capable of annotating the requested data. There are occasions when there is a great deal of unlabeled data and getting manually labeled data is costly. ML algorithms in this case may actively request labels from the user (data provider). Typically, a person or even a group of individuals represent a data provider. Active learning aims at achieving the best attainable quality of the model, employing as few additional examples as possible.

Active learning involves the data labelers first submitting a small subset of labeled data to the system for the model to learn from this training set. Then it questions a human by determining unlabeled data regarding which it has no certainty so that a person could respond by creating labels for those instances. The model then updates itself once again by repeating the process until a fairly high degree of accuracy has been achieved. By ensuring that the human trains the model in an iterative manner, the model may be refined in less time, with much less labeled data.

However, the most significant impediment of active learning comes from the fact that sometimes it is difficult to predict the reliability of the algorithm's results, even despite the fact of using only the most high-quality training assets.

When do we require automated data labeling?

The applications of automatic data labeling may vary widely, but commonly they are in one of the following categories:

1. Automated labeling for Natural Language Processing (NLP). NLP refers to a subfield of computer science and AI that is concerned with ways in which machines analyze natural or human languages. NLP allows ML algorithms to be applied to word processing, voice recognition, and speech transcription;
2. Audio labeling process for the purposes such as recognizing not only human speech but also sounds such as dogs barking or vehicle sounds;
3. Image/video labeling for computer vision (CV) purposes. The goal of computer vision models is to make computers recognize patterns in visual data similar to how people do it. For example, CV technology makes it possible to create and improve self-driving cars.

Why you can't always use auto labeling

Automated data labeling is still not appropriate for the vast majority of machine learning projects. For instance, it is not suitable for collecting ground truth data. Since ground truth is the ideal expected result, auto labeling cannot always provide 100% correct results. The output of such a system requires constant human review to evaluate model performance and quality.

The labeling team follows the automated process by monitoring, fixing, and adding labels, which may increase the time required to do labeling projects, as compared to manual data labeling. There are exceptions and edge cases where the system will not be able to assign an auto label and only humans can help it do so.

It is never absolutely clear how the automatic labeling system will perform. In some cases, it provides a solid baseline for getting the job done and decreases the time it takes to accomplish projects. But in other cases, it produces low-quality results, particularly when there are edge cases, which increases the time required to complete machine learning projects.

Why crowdsourcing is a good alternative to automated data labeling?

Automated data labeling is clearly an advanced and functional method of labeling, but as already mentioned earlier, it is still at such a stage of development that it is not always able to completely replace humans.

Crowdsourcing on the other hand allows separating a large-scale task into many smaller tasks and assigning them to different people. This makes it convenient to collect and label vast amounts of data.

Involving a large number of people in data labeling is especially crucial when the output values require certain knowledge that is available only to human beings and which is most likely to be the most complicated for automatic algorithms to grasp.

The labor-intensive process of qualitative data labeling can be accelerated by crowdsourcing. Due to the fact that a large talent pool carries out labeling tasks all at once, it requires much less time than the process completed by internal staff.

Labeling data automatically also provides a relatively quick way of labeling, but as previously mentioned, it does not perform well on all types of data and not in all situations. Occasionally, not only may it not speed up the labeling process, but it may also slow it down.

The human approach to labeling that crowdsourcing enables is not only more accurate but also quicker, requiring fewer or no subsequent revisions, since contributors who are unable to complete tasks properly are more likely to be eliminated from the project. Moreover, hiring experts via crowdsourcing platforms is likely to be much cheaper than preparing your own automated labeling systems.

Types of jobs on crowdsourcing platforms

Crowdsourcing offers several basic ways to optimize the creation of high-quality datasets. Thousands of users on crowdsourcing platforms assist in improving existing algorithms, developing new ones, keeping the data up-to-date, and collecting new real data. Essentially, they are engaged in manual data labeling, solving these and many other tasks:

• evaluation of the site's relevance
• object detection on photos
• semantic segmentation of images
• image classification
• image segmentation and so on

Summary

Data labeling and preparation take a lot of energy and resources when developing ML projects. However, automated data labeling exists for this purpose, which allows labeling experts to reduce human error and the time prescribed to process data sets.

But this type of data labeling does not always work correctly and often cannot solve the task with 100% accuracy, or may even slow down the project implementation. A great alternative to this type of labeling is employing crowdsourcing platforms that offer a huge selection of labeling tools, as well as a large selection of human labelers with various skills that can accurately and quickly.