In this post, I am hoping we will shed some light on these topical
concepts, that although are usually used interchangeably, they do not quite
refer to the same things.
The main idea behind these three concepts, lie in the image below:
As you can see, Deep Learning (DL) is a subset of Machine Learning (ML),
which is also a subset of Artificial Intelligence (AI).
Let’s dig deeper so that we can understand better what each of them
three concepts encompass.
Artificial Intelligence:
As the name suggests, artificial intelligence can be interpreted as
incorporating human intelligence to machines.
Whenever a machine completes tasks based on a set of stipulated rules
that solve problems (algorithms), that behaviour is what is called artificial
intelligence.
We classify AI-powered machines into two groups; general and narrow.
The general artificial intelligence machines can intelligently solve
problems, for example, moving or manipulating objects, recognizing whether someone
has raised the hands, or solving a mathematic problem.
The narrow intelligence AI machines can perform specific tasks very
well, sometimes better than humans can; however, they are limited in scope. The
technology used for classifying images on Pinterest is an example of this.
Machine Learning:
As we already saw, ML is a subset of AI (in fact it is just a technique
for realizing AI) and can be loosely described as ability of the computer
systems to learn.
The intention of ML is to train algorithms to enable machines to learn
by themselves how to make decisions.
Training in machine learning entails giving a lot of data to the
algorithm and allowing it to learn more about the processed information.
For example, below is a table that identifies the type of fruit based on
certain characteristics:
As you can see on the table above, the fruits are differentiated based
on their weight and texture.
However, the last row gives only the weight and texture, without the
type of fruit. A machine-learning algorithm can be developed to try to identify
whether the fruit is an orange or an apple.
After the algorithm is fed with the training data, it will learn the
differing characteristics between an orange and an apple and predict accurately
the type of fruit with those characteristics in the future.
Deep Learning:
As earlier mentioned, deep learning is a subset of ML; in fact, it’s
simply a technique for realizing machine learning; DL is the next evolution of
machine learning.
DL algorithms are inspired by the information processing patterns found
in the human brain. Just like we use our brains to identify patterns and
classify various types of information, deep learning algorithms can be taught
to accomplish the same tasks for machines.
The brain usually tries to decipher the information it receives. It
achieves this through labelling and assigning the items into various
categories.
Whenever we receive a new information, the brain tries to compare it to
a known item before making sense of it which is the same concept deep learning
algorithms employ.
Comparing deep learning vs machine learning can help to understand their
differences. While DL can automatically discover the features to be used for
classification, ML requires these features to be provided manually.
Hopefully you are now slightly more clear about what each of these
concepts mean and how they are interlinked!
