Artificial intelligence is taking the world by storm.
It is already manifesting in a plethora of industries and organizations more reluctant to adopt it than ever.
Data is the fundamental unit of a ML model, and theres nothing that it can do without it.
Machine learning works by learning a set of features and implementing them to produce new instances of information.
However, the process is more difficult than it sounds.
ML needs large amounts of datasets to learn any particular thing.
This is the way they learn.
Ever since machine learning kick-started in the 1980s, several algorithms were developed.
However, there is one of the areas where theres hardly been any progress.
And thats feeding the models with data.
It is also one of the reasons why practitioners limit themselves from performing quality assurance of machine learning models.
Until they receive what they want as the output, they find no reason to test the model.
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Neural web connection models in machine learning are designed to imitate the functioning of neurons in the brain.
But the real question lies in asking ourselves, do we learn the way a machine does?
Take legos for an example.
They might pause to think about what the problem is at hand.
All they do is tell them one or two times and the rest is automatically figured out.
Similarly, when teaching how to build legos, parents illustrate the technique a limited number of times.
How often have you seen a child confuse a chair with a cow?
Machines on the other hand train on thousands and even millions of samples.
You cant ask a machine to apply its existing training on an entirely different model.
They get confused in instances where two things look identical.
It is nothing but the way we are programmed to grow up and learn.
And thats what we call common sense.
The way machines learn is by finding statistical patterns in a piece of data.
For example, takeGoogles Deep Mind Alpha Zero.
It can be trained to play a game of chess from scratch.
If youre wondering how is it able to do this, the logic is simple.
When Alpha Zero plays a game, it gets a score.
Overtime when it plays a million sets of the game, it learns to maximize the score.
In other words, machines dont even understand the mechanics of a game.
All they do is find patterns that help them reach a desirable outcome.
These objects might be a ball, dog, parent, etc.
These objects move haphazardly at odd angles in a childs vision.
This is the exact opposite of how machines learn.
They need to be explicitly told of their objective.
The data for a child, on the other hand, is largely unsupervised.
Parents do tell their children notions such as Danger or Good Job.
But it is mostly because they want to keep them safe.
Most of a childs learning is unsupervised.
Machine learnings unsupervised algorithms like clustering are prone to even more errors.
They are extremely sensitive to their initialization and need large amounts of data with multiple iterations to function appropriately.
The way babies learn is on account of an innate ability.
Until now we havent been able to replicate this, even in complex machines.
Ultimately, machines must take cues from children and learn to become curious.
