Original article was published on Artificial Intelligence on Medium
What’s the difference between Artificial Intelligence, Machine Learning, and Deep Learning?
People often use these three terms interchangeably but they do not quite refer to the same things.
Refer to the diagram shown below for this.
As per the diagram, AI seems to be the superset of ML, and ML seems to be the superset of DL. Let’s dive into these terms one by one.
So What is Artificial Intelligence?
As the name suggests, artificial intelligence can be loosely interpreted to mean incorporating human intelligence to machines.
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.
Some of the activities computers with artificial intelligence are designed for include: Speech recognition, Learning, Planning.
For example, such machines can move and manipulate objects, recognize whether someone has raised the hands, or solve other problems. These are the examples of General AI.
There’s Narrow AI as well. The narrow intelligence AI machines can perform specific tasks very well, sometimes better than humans — though they are limited in scope. The technology used for classifying images on Pinterest is an example of narrow AI.
What is Machine Learning?
As the name suggests, machine learning can be loosely interpreted to mean empowering computer systems with the ability to “learn”. ML Intends to enable machines to learn by themselves using the provided data and make accurate predictions.
ML is a subset of artificial intelligence. It is a method of training algorithms such that they can learn how to make decisions.
For example, here is a table that identifies the type of fruit-based on its characteristics:
As you can see in 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. Therefore, 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 different characteristics between an orange and an apple. Therefore, if provided with data of weight and texture, it can predict accurately the type of fruit with those characteristics.
Finally, What is Deep Learning?
As earlier mentioned, deep learning is a subset of ML; in fact, it’s simply a technique for realizing machine learning. In other words, DL is the next evolution of machine learning.
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 labeling and assigning the items into various categories. Whenever we receive 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.
Deep learning networks can be successfully applied to big data for knowledge discovery, knowledge application, and knowledge-based prediction.
For example, Automated Driving: Automotive researchers are using Deep Learning to automatically detect objects such as stop signs and traffic lights. Besides, Deep Learning is used to detect pedestrians, which helps decrease accidents.
I hope you get a clear reference of these terms. Here I’ve added some references that I’ve referred to while writing this blog.
Thank you for reading and Happy Learning!