Artificial Intelligence is a wonderful tool that should be in the possession of every self-respecting retailer and help companies and customers find the easiest and most convenient way to get to each other.
AI is already attracting enormous investments today, and if you believe the predictions, the sums will be even more incredible in the next 20 years.
Very few people understand the practical benefits of AI technology yet, but thousands of startups have already entered this field, ready to lay down their bones to find effective applications for it.
Every big data training initiative hinges on the amount of data that can be analyzed as part of that project.
An algorithm needs data to have something to “learn” from. Therefore, machine learning models need certain labels associated with the data.
Here data labeling AI services come in handy.
A new segment of machine training operations is data labeling.
Despite the fact that the size of this market is still relatively small – analysts at Cognica Research estimate it will reach $1.2 billion in 2023 – data labeling work takes up to a quarter of all ML project time.
So what is data labeling? The main task of machine learning is to train an algorithm, whether traditional statistical models or new neural networks, to look for and use patterns from a training set of questions and answers.
These questions and answers can be in the form of text, in the form of an image, in the form of an audio or video file.
For example, we want to teach the model to determine the tone of the voice of the person who calls the call center.
Then the question will be: What is the tone of the caller? And the answers will be positive or negative.
The need for human data labeling emerged when the industrial application of machine learning technologies began: algorithms need to be trained on data, and it is often impossible to get it until a human creates it.
How Exactly Does Data Labeling Technology Perform?
The computer training simulations in use today are based on a technique called “teacher-assisted learning”.
The technique relies on a training approach to extract different patterns from the labeled data. To maintain the previously mentioned procedures, the data must first all be tagged.
It is when a person intervenes in the process and utilizes computer software to label all of the relevant input data.
For instance, to develop a machine learning model capable of recognizing pictures with vehicles, the cars must first be labeled as such.
In a more general tagging process, the algorithm simply postulates if a car is in the picture. To enhance the automatic training experience, you can implement pixel-based marking.
Yes, this is quite a complex and routine task, but as a result, machine learning models begin to produce far superior projections from input data encountered soon.
Current requests for the implementation of automated systems are most often related to the analysis of photo or video files.
This is due not only to the ease of obtaining raw data (almost all modern industries and companies have video surveillance systems) but also to the fact that such a format allows collecting the maximum amount of information – visual means reflect much more specific properties and characteristics of an object.
However, for proper functioning each neural network must go through the process of initial training, that is learning to recognize images with pre-fixed values.
This is one of the key stages of creating an effective system for image analysis, where a concept such as data labeling plays an important role.
Markup in itself is a preprocessing of, for example, images that make the information accessible to the neural network.
In the process of markup, metadata – certain tags that carry information about specific properties of an object – are attached to the original image or video file.
The complexity of this process is that to create a quality set of raw data (dataset), it is necessary to mark up thousands of images on a given subject, to expand the visual variability and avoid problems with “blindness” of the neural network in the course of work in the real world.
Currently, there are a large number of open datasets – images with selected objects on them as an additional file with annotation, which contain, as a rule, a label (name) of a class and coordinates, which the object contour occupies in the given image.
To train convolutional neural networks, the labeled data is fed in the required format for the specific task being solved in different ways.
These labels allow the algorithms to remember the outlines of objects, colors, shapes, and subsequently to find them in new images that will be fed to the system from the operating objects.
Correctness and accuracy of data markup are some of the key elements in the process of neural network training – highlighting a number of certain objects allows focusing “vision” on a particular task.
Partitioning is performed with different levels of accuracy depending on the complexity and class of the problem to be solved.
For one case, it may be enough to select markings with rectangles (bounding boxes), but for another more complex task, such precision of markings may be insufficient, which will affect the result.
In this case, you may use path selection – segmentation of each object to transfer information about this or that fragment in each image more precisely.
It is also important to note that sorting data into different folders without selecting specific objects or any additional labels may be enough for pilot project development.
The creation of datasets for photo and video analytics tasks can be performed in several ways using different technologies depending on the tasks at hand and the available resources.
It is possible to distinguish several standard methods of partitioning, the results of which are most often given to development teams:
1. Rectangular Labeling
This type of image processing is one of the simplest ways of selecting objects in photos for attributing them to this or that class.
Such markup is the quickest method and allows one to significantly reduce time spent on processing.
This method allows the creation of an algorithm capable of selecting several properties of an object according to the multi-tags affixed to it.
Such tagging of images allows to create a faster process of data preparation for algorithm’s work, in which depending on conditions the object will or will not be taken into account, but it has one of the biggest errors in the quality of the final system’s work.
3. Polygon Extraction
When you use this type of partitioning, the exact boundaries of the objects in the image are highlighted completely, which allows the neural networks and algorithms to get the “cleanest” data.
Partitioning for the task of segmentation is quite a complex and lengthy process, but in turn, it significantly increases the teachability of the neural network and expands its value through the use of less noisy data.
4. Break Lines (Polylines)
A separate class of markup, which is allocated in the standard means of services for the selection of objects, is the most convenient for the selection of road markup, which can be seen on the image with polygons, there this approach is also applied.
5. Point Sets
This markup is useful for solving problems of finding key points of those or other objects, it is usually used to solve problems of faces classification: to distinguish one person from another.
Benefits And Use
The source for machine learning is large amounts of data partitioned. Partitioning acceleration is a major benefit that businesses get from data partitioning software.
Moreover, these software products work collaboratively, which means that entire departments are able to cooperate on data labeling.
Due to these advantages, such instruments are vital for optimizing work processes in enterprises in various sectors.
The use of machine learning algorithms is becoming an increasingly relevant method for solving both scientific problems and business needs.
The range of tasks for remote AI and ML in today’s IT market is quite wide and includes both quality control at production facilities and security at facilities, tracking employee fatigue and attentiveness, collecting and processing a large volume of statistical data to identify errors in technical facilities and prevent incidents.
Such tasks can be solved using a wide variety of methods, including machine learning.
Quite often companies that implement ML (machine learning) have to deal with computer vision tasks, which are solved with the help of convolutional neural networks.
Creating and training a neural network is quite a complex process, which involves a large annotation staff of specialists of different profiles (starting from DevOps-engineers to deploy servers and environment, data engineers who prepare datasets for training, and ending with data scientists who turn all the previous work into magic).
The principle of neural network operation is essentially the transformation of the sum of input information into the value of a single output result.
After algorithm initiation, it is possible to conclude, whether the neural network is correctly converting received signals.
That is why the basis for any network is, first of all, qualitatively selected and processed input data.
How to Choose a Data Annotator?
Considering the importance of data labeling instruments, it is not surprising that there are already numerous articles that explore the instruments in detail.
Many companies choose software based on some factors:
- Capability – Which capabilities does the instrument offer?
- Difficulty – To what extent is the application difficult to use?
- Productivity – How smoothly is the labeling program working?
- Cooperative work – Are collaborative functions provided? If possible, multiple people can do labeling at the same time.
- Price – Does the instrument have an acceptable price tag?
The responses to such queries are fundamental to determining the outcome of every data-marking instrument test.
Data labeling in machine learning is an important part without which such learning would not be possible.
In fact, any Internet user can touch machine learning and become a “teacher” of artificial intelligence, because so far this process cannot be automated.
And maybe this is a good thing because the “human” partitioning of data assumes that at the top of all learning is a human, and it is he who determines the knowledge benchmark of any artificial intelligence.