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You may be wondering; how can an AI machine recognize a cat or a mouse or cars or even people?

Teaching a machine to recognize and understand certain things requires intense preparation and gathering amounts of data sets.

As Artificial Intelligence evolves through time, Data Labelling is vital in feeding the machine recognition for it to perform and interact with human beings. Its system needs to understand what is shown to it, said to it, even the written texts given to it and other things.

So what is data labelling?

Data Labelling is a part of natural language processing that helps an AI machine to understand and recognize actions and reactions from the outside perspective. Putting electronic markings on image files, placing marks on significant areas of photos, tagging pictures with relevant keywords or rephrasing texts based on the perspective of the person interacting are the most common phase of this kind of technique.

 

 

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Categorizing texts, audios, or videos is also one of the important phases of data labelling. This is what data scientist and AI people call “sentiment analysis.” Sentiment Analysis lets your system or machine recognize the feeling and meaning of certain command or sentence that humans said towards your machine.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          

Understanding and recognizing might be difficult for the AI machine since Natural Language and Recognition are unlikely constructed therefore machines cannot easily identify specific sentiments that it may encounter. Most people use idioms, repetition, figures of speech to express themselves without conscious planning. And it takes human understanding of these kind of approach for a machine to learn from it.

How does data labelling works?

As stated, putting markings on images is an important part of data labelling, regardless of forms. For example, bounding boxes are used to mark recurring elements in an image, like various vehicles. This gives the algorithm of the machine to recognize different shapes in numerous placements, positions and sizes whereas placing it to one category which is vehicle. Data Labelling also teaches the AI machine what is being shown in every image by tagging elements that could help it recognize objects.

Labels are being applied to every part of the image represented which will make it easier to be analyzed and recognized.

Face markings on the other hand is used to optimize facial recognition software. These markings are placed to indicate the shape of the face, lips, eyebrows and others. By learning from these markings, algorithms can easily identify faces.

Data Labelling highlights features that can be analyzed for patterns that can help predict the target.

Granted that innovations on data labelling seems boundless, feeding a quality dataset is one of the most shortfalls it is facing.  There are two prevailing considerations in this situation. First is observing and maintaining HIGH QUALITY DATA LABELS, remember that your machine will only be good if the data being fed to it is good. Second is INVESTING ON DATA SCIENTISTS, having experts take care of your data could help you produce a better AI machine model.

 

Understanding the tradeoffs helps pave the way for a clear strategic data labelling roadmap. It is imperative in considering your data label workforce.

1. Knowledge and context – this will allow the machine to learn to differentiate the genuine products to specific ones. Let’s take tissue for example, when you feed the machine a data labeled recognition of tissue, the machine wouldn’t be able what kind of tissue will it be or what brand. Remember that having the highest quality data must have the key details about the particular subject matter you are referring to.

 

2. Agility and Flexibility– sharpness on updating the knowledge of your data set. This will require agile preparation on preparing new datasets and enriching existing data sets to improve the learning of your machine. Flexibility to incorporate changes that adjust to your end user’s needs. As you develop algorithms and train your models, collating flexible data is much needed.

Elite Worldrgroup Inc. is one of the leading multilingual technology solutions providers in Asia that offers innovative models for machine learning.

We have mastered to combine people, process and technology to optimize data labelling quality.

 

Would you like to find out how? Contact us now!