We’re all familiar with the term “Artificial Intelligence.” After all, it’s been a popular focus in movies such as The Terminator, The Matrix, and Ex Machina (a personal favorite of mine).

AI

As the name suggests, artificial intelligence can be loosely interpreted to mean incorporating human intelligence to machines.
Artificial intelligence is the broader concept that consists of everything from Good Old-Fashioned AI (GOFAI) all the way to futuristic technologies such as deep learning.
Whenever a machine completes tasks based on a set of stipulated rules that solve problems (algorithms), such an “intelligent” behavior is what is called artificial intelligence.

It was termed in 1956 by John McCarthy, AI involves machines that can perform tasks that are characteristic of human intelligence. While this is rather general, it includes things like planning, understanding language, recognizing objects and sounds, learning, and problem-solving.

For example, such machines can move and manipulate objects, recognize whether someone has raised the hands, or solve other problems.

ML

As the name suggests, machine learning can be loosely interpreted to mean empowering computer systems with the ability to “learn”. The intention of ML is to enable machines to learn by themselves using the provided data and make accurate predictions. ML is a subset of artificial intelligence. In fact, it’s simply a technique for realizing AI and DL is a subset of ML

The ultimate goal of machine learning is simply a way of achieving AI.

Arthur Samuel coined the phrase not too long after AI in 1959, defining it as, “the ability to learn without being explicitly programmed”. You see, you can get AI without using machine learning, but this would require building millions of lines of codes with complex rules and decision-trees.

So instead of hard-coding software routines with specific instructions to accomplish a particular task, machine learning is a way of “training” an algorithm so that it can learn how. “Training” involves feeding huge amounts of data to the algorithm and allowing the algorithm to adjust itself and improve.

To give an example, machine learning has been used to make drastic improvements to computer vision (the ability of a machine to recognize an object in an image or video). You gather hundreds of thousands or even millions of pictures and then have humans tag them. For example, the humans might tag pictures that have a cat in them versus those that do not. Then, the algorithm tries to build a model that can accurately tag a picture as containing a cat or not as well as a human. Once the accuracy level is high enough, the machine has now “learned” what a cat looks like. that’s quite a lot don’t you think, anyway moving on!

DL

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. DL algorithms are roughly 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.

Deep learning is one of many approaches to machine learning. Other approaches include decision tree learning, inductive logic programming, clustering, reinforcement learning, and Bayesian networks, among others. Deep learning was inspired by the structure and function of the brain, namely the interconnecting of many neurons. Artificial Neural Networks (ANNs) are algorithms that mimic the biological structure of the brain. In ANNs, there are “neurons” which have discrete layers and connections to other “neurons”. Each layer picks out a specific feature to learn, such as curves/edges in image recognition. It’s this layering that gives deep learning its name, depth is created by using multiple layers as opposed to a single layer.