Artificial Intelligence, Machine Learning and Deep Learning are terms that are often used interchangeably. But are they really the same? Let’s find out.
The easiest way to think of the relationship between the above terms is to visualize them as concentric circles using the concept of sets with AI — the idea that came first — the largest, then machine learning — which blossomed later, and the most recent being deep learning — which is driving today’s AI explosion — fitting inside both.
Graphically this relation can be explained as in the picture below.
As you can see in the above image consisting of three concentric circles, Deep Learning is a subset of ML, which is also a subset of AI. This gives an idea that AI is the all-encompassing concept that initially erupted, which was then followed by ML that thrived later, and lastly, Deep Learning that is promising to escalate the advances of AI to another level.
Starting with AI, let us have a more in-depth insight into the following terms.
Intelligence, as defined by Wikipedia, is “Perceiving the information through various sources, followed by retaining them as knowledge and applying them with real-life challenges.”
Machines built on AI are of two types – General AI and Narrow AI
General AI refers to the machines capable of using all our senses. We’ve seen these General AI in Sci-Fi movies like The Terminator. In real life, a lot of work has been done on the development of these machines; however, more research is yet to be done to bring them into existence.
What we CAN do falls in the hands of “Narrow AI”. These refer to the technologies that can perform specific tasks as well as, or better than, we humans can. Some examples are – classifying emails as spam and not spam and facial recognition on Facebook. These technologies exhibit some facets of human intelligence.
Where does that intelligence come from? That brings us to our next term -> Machine Learning.
Learning, as defined by Wikipedia, is referred to as “acquiring information and finding a pattern between the outcome and the inputs from the set of examples given.” ML intends to enable artificial machines to learn by themselves using the provided data and make accurate predictions. Machine Learning is a subset of AI. More importantly, it is a method of training algorithms such that they can learn to make decisions.
Training in machine learning requires a lot of data to be fed to the machine which then allows the machine (models) to learn more about the processed information.
Deep Learning is an algorithmic approach for the early machine-learning crowd. Neural Networks form the base for Deep Learning and is inspired by our understanding of the biology of the human brain. However, unlike a biological brain where any neuron unit can connect to any other neuron unit within a certain physical distance, these artificial neural networks (ANN) have discrete layers, connections, and directions of data propagation.
For a system designed to recognize a STOP sign, a neural Network model can come up with a “probability score”, which is a highly educated guess, based on the algorithm. In this example, the system might be 86% confident the image is a stop sign, 7% convinced it’s a speed limit sign, and 5% it’s a kite stuck in a tree, and so on.
A trained Neural Networks is one that has been analyzed on millions of samples until it is sampled so well that it gets the answer right practically every time.
Deep Learning can automatically discover new features to be used for classification. Machine Learning on the other hand, requires to be provided these features manually. Also, in contrast to Machine Learning, Deep Learning requires high-end machines and considerably significant amounts of training data to deliver accurate results.
Wrapping up, AI has a bright future, considering the development of deep learning. At the current pace, we can expect driverless vehicles, better recommender systems and more in the forthcoming time. AI, ML, and Deep Learning (DL) are not very different from each other; but are not the same.