Ground Truth Intelligence: What It Is And Why You Need It

Artificial Intelligence or AI as we’ve come to know it, is inescapable.  From language processing to generative AI, each progression has stretched the parameters of our limited minds to the outer edge of what’s possible. 

That said, the machine learning capabilities of AI are only as good as the models used to train them. For this reason, it’s vital to have impeccable ground-truth intelligence for feeding machine learning programs. 

This post explores the importance of ground truth intelligence, the problems that may impact its effectiveness, and the solution to improving it. 

What is Ground Truth Intelligence?

The term “ground truth intelligence” describes the intention, humanly provable observation of knowledge treated as facts.  In recent years, various machine learning and deep learning approaches have popularised the term.

Another way to define ground truth is by referring to the nature of a problem as the target of a machine learning model, reflected by data sets similar to the use case under consideration. 

Ground truth allows for the interpretation of data as it relates to actual real-world features and materials on the ground.  It permits remote-sensing data calibration and assists in interpreting and analyzing what’s sensed. 

When done correctly, ground truth intelligence has the potential to add value to every industry by reducing potential human error and increasing productivity within machine learning. 

Why Is Ground Truth Data Important in Machine Learning?

The success of any AI model or project relies on a quality ground truth dataset.

Training a model with poor-quality data could result in severe consequences. For example, imagine a smart car that can’t recognize the difference between a green or red traffic light or a machine built to match the skills of human doctors that fails to differentiate between organs. 

The consequences of poor ground-truth datasets are not only dangerous but potentially fatal. 

To get proper and accurate results, you need a combination of quality annotated and labeled data. Models may require thousands of input and output examples to learn from to perform effectively.  

For an algorithm to assimilate a variety of edge cases and produce models that handle them, larger datasets must incorporate great numbers of historic examples to learn from. 

Although ground truth data is the key to the successful facilitation of ML, problems arise from time to time.

What Problems Could Impact Your Ground Truth Intelligence?

It all boils down to creating high-quality labels. Missing or inconsistent annotations and lack of expert knowledge are common problems when attempting to create labels. Let’s take a closer look at some of these common challenges.

Missing Annotations

Data scientists have the tedious task of manually creating annotations. It’s repetitive, mundane, and laborious.  Missing some objects for the label due to occlusion, mental fatigue or small object size isn’t uncommon. 

Of course, nothing is without consequences. A bad learning signal during training is one such consequence. The model is penalized even when correctly identifying the missed object based on incomplete or inaccurate ground truth data.

Inconsistent Annotations

Picture in your mind’s eye, large amounts of data that need annotating, paralleled against many annotators. As one person’s interpretation differs from the others, inconsistencies in the labeling can occur. 

A person’s gaze, for example, can vary based on the annotator’s judgment. And something as little as 10 degrees results in an error with no consistent learning signal.

Expert Knowledge

Some instances, like in the case of a medical machine, would need pre-existing specialized knowledge. Expert knowledge is also needed when an unforeseen example is encountered and no labeling guidelines are provided. 

How to Improve Your Ground Truth Data

Incorrect labels slow down the machine learning process opening the door to the time-consuming task of re-labeling. 

To prevent manpower from business core processes, outsourcing to an external team of data scientists who can do the job for you is worth it. 

Werkit’s managed teams and managed crowds services are experienced in data labeling and semantic segmentation. 

Our workforce helps you build useful training datasets so that your AI services offer higher accuracy. Start your journey towards transforming the quality of your ground truth data by contacting us.

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