As the co-founder of a machine learning startup, many people have asked me to explain the difference between Machine Learning, Artificial intelligence and Deep Learning. Usually, I provide a reasonably concise explanation of the three areas. I then point them to this great article by Michael Copeland on the Nvidia Blog. However, that article is probably a bit more in-depth than people are expecting; most people want at maximum a 1–2-minute answer. So, I thought it was about time I have a go at writing a sub-2-minute article on the topic. Here goes….
Artificial intelligence is an area in computer science to describe when computer systems perform tasks that would usually require human intelligence. Great examples of this are object detection (apples vs oranges), classification of images (fruit vs vegetables) or speech recognition. These are all tasks that come second nature to humans, but something computers have historically found to be very tough.
Machine Learning is the building of Artificial Intelligence algorithms that learns from a series of inputs and outputs generating a final algorithm that can predict the answer when provided the data input (e.g., predict whether an image is of an apple or an orange.)
Aren’t they the same thing? Well, no — Artificial Intelligence is the broad concept of machines being able to do human tasks, and Machine Learning is one application of that broad concept into applications such as recognizing objects.
So, what is Deep Learning?
Deep Learning is a subset of Machine Learning which tries to replicate the way a human brain works, automating much of the learning process. As humans, we ingest vast amounts of data without even knowing it. Ever wondered how we can tell an apple from an orange even though no two apples look exactly alike? Well, we have seen tens of thousands of apples and oranges in our lifetimes — some have been labelled for us (like in a supermarket), others have not. However, we can identify them because of their color, shape, and how others peel and eat them.
Deep Learning is similar, using a very large dataset (that may be labelled like the apples in the supermarket — supervised learning) or not (like all the other apples you have seen and had to determine for yourself what they are — unsupervised).
Hopefully, this short article helps to demystify these much-discussed phases, which are often interchangeably used.