Data acquired from real-world settings to train algorithms like human gestures and actions, natural language text, vocal speech, and geographical orientation is known as ground truth. The phrase “ground truth” was used in geology to describe the process of validating data while going out and analyzing the field. This phrase has been applied in numerous disciplines to describe the concept of “knowing” accurate facts.
The interface is evolving from mouse and keyboards to voice commands, touchscreens, facial recognition, gestures, and beyond, completely changing how we interact with computers.
AI and ML algorithms, which rely on reliable ground truth to provide a favorable acknowledgment of the actual world, are driving this change.
Smart home technologies, self-driving automobiles, and virtual reality headsets, and are just a few examples of products that use AI and ML technology. Energy, financial services, retail, manufacturing, and healthcare are among the industries that are reaping the benefits. And AI and machine learning are still in their infancy.
Why is it crucial?
Ground truth is critical for organizations developing AI and machine learning solutions that may eventually require human engagement. The efficiency of AI and ML algorithms is dependent on the quality, which drives the performance of such solutions. The algorithms will fail if data is not maintained effectively.
Obstacles in the ground truth
How much is sufficient: While it is not realistic to scan all seven billion individuals on the planet, enough data is required to guarantee that the algorithms perform properly. A gadget like a mobile phone, for example, will not be able to discriminate between various faces if there is not enough data.
Data collection is difficult: It is a prevalent misconception that gathering ground truth is simple therefore not enough effort is spent designing the procedure. Before executing, the most important step is to completely comprehend what type of data is required and all of the relevant factors. If you don’t follow this approach step by step, you’ll waste effort and money trying to gather the proper data.
Set of rules: Another misunderstanding is that there should be a data-collecting standard. However, every single project is distinct, depending on the scenarios required to optimize it. The execution component may be standardized, after rigorous planning and design of the data collecting process.
One isn’t finished: Some may believe that by just tweaking the algorithms, the data acquired may be utilized for future scenarios. However, depending on the situations you’ve created, the data collection will certainly fall short of filling all gaps.
It’s possible that the ground truth is incorrect. It’s a measurement, therefore there’s a chance it’ll be off. In some machine learning contexts, it may also be a subjective measurement when defining an underlying objective reality is challenging – for example, expert opinion or analysis that you want to automate. Any machine learning model you train will be restricted by the quality of the data you use to train and test it, as explained in the Wikipedia quotation. It’s also why data collection methods should be fully described in published studies concerning machine learning.
Machines, which are becoming increasingly intelligent in terms of making judgments or providing suggestions, will become more prevalent in the world. Self-driving vehicles, computer interaction, cameras, home automation, and a slew of other applications of AI and machine learning algorithms demonstrate this trend. These products are built on the foundation of ground truth.