How Data Labeling facilitates AI models

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It was 5 minutes past 7:00 in the morning. I had been working on this very piece that you’re reading for close to two hours comfortably in my sweatpants. I was so indulged in the thought process that it worked up my appetite. I headed straight to the kitchen to make some coffee and breakfast.

I poured some hot coffee and scooped the batter onto the griddle. As I served the pancake one on top of the other on my plate, I was all set to gobble it up.

*Spoiler AlertPlot twist ahead.

The first bite cringed my taste buds that raised the tempo of my growling stomach. The salt overdose completely ruined my desire for those yummy pancakes.

I had generously splurged salt into the batter instead of sugar.

A simple recoverable human error, right?

I just had to do the process again with sugar. Nice.

But, had I labeled my look-alike glass jars as “sugar” and “salt”, this could’ve been avoided. The inability to comprehend an ingredient or should I say overlooking has completely changed the concept of my intro piece.

We humans have powerful senses. We have the ability to see, comprehend, analyze, react, interpret, and judge. The mistakes we make are either due to negligence or lack of awareness. 

But,  not the case for machines or rather, AI-based models.

Why Data Labeling is the trigger in the gun for AI-based models 

AI-based models are highly dependent on accurate, clean, well-labeled, and prepared data in order to produce the desired output and cognition. These models are fed with bulky datasets covering an array of probabilities and computations to make its functioning as smart and gifted as human intelligence.

To substantiate the power of data labeling, I’m going to now walk you through some interesting scenarios.

To continue reading, head to KDnuggets where this article was originally published.

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