Nowadays, everyone seems to be building artificial intelligence, but no one talks about one of
the most important aspects of the work: data annotation.
The people who are undertaking this time-consuming, boring task without the flare that usually
encircles A.I.
Without their dedicated work, it is impossible to develop algorithms, so I thought it is
time to sing an ode to the superheroes of A.I. development: data annotators.
Data annotation is the process of labeling data to make it usable for machine and deep
learning.
Without humans labeling what’s in a picture, how to recognize music or how to read text
in different languages, even the most advanced A.I. algorithms are useless.
Human data annotators provide hard work for machines to successfully interact with our
human world.
Let’s say you want to have an algorithm to spot cats on pictures.
It sounds easy, right?
You describe it as a furry animal with two eyes.
You describe its size, color and how its cheek looks.
But a computer doesn’t understand legs, eyes or what fur is in the first place.
That’s where annotators come in.
You will need millions of photos where those photos that have a cat in them are appropriately
labeled as having a cat.
That way a neural network can be trained through supervised learning to recognize pictures
with cats in them.
So, basically, you won’t tell the algorithm what’s a cat, but you feed it with millions
of examples to help it figure it out by itself.
Do you see how difficult it is?
Let’s see how it works in healthcare.
Let’s say you want to have an algorithm to spot cancer.
You will need to use existing databases of CT or MR scans.
Data annotators will have to draw lines around tumors.
Thousands of them.
No magic, no self-conscious computers.
Hard human labor.
And the practice is even more complicated than this.
There’s a lack of experts for data annotation, and it’s hard to find databases of scale.
Usually, the precision of an algorithm depends on the size of the sample – the bigger,
the better.
However, hospitals or medical centers usually don’t have enough data or enough annotations.
And even if they do, medical data archives were obviously not created with mathematical
algorithms in mind.
So it’s another gargantuan work to try to standardize existing samples.
In the future, these annotated databases will fuel life-saving algorithms, but we need more
experts and tech giants like Google, Amazon, and Tencent to come in and bring their big
data expertise to the table.
A machine is only going to be as good as the humans behind it, and the unsung heroes of
the upcoming digital health revolution are those experts who, even right now are sitting
in dark hospital rooms, in front of computers, and annotate radiology or ophthalmology images.
Without them, we will never have artificial intelligence.
So kudos to all of them!