Simple explications (for all audiences) about the difference between Artificial Intelligence, Machine Learning and Deep Learning
Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are some of the hottest topics right now.
The term “AI” is thrown around every day. Actually you can read many articles about the benefits of AI to retail or how AI is transforming the fashion industry; and you also hear executives saying they want to implement AI in the customer journey to improve the customer experience or internal processes.
While we’re all familiar with the term “Artificial Intelligence”, quite often, many people don’t understand what AI is and what’s the difference between Machine Learning and Deep Learning.
If you are one of them, don’t worry. Once you’ve read this article, you will understand the basics of AI, ML and DL and you will talk with the confidence about them.
What’s the difference between AI, ML and DL?
This ‘techopedia’ is prepared for everyone, ordinary people just like you or me…
Artificial intelligence (AI) is a multidisciplinary field of science whose goal is to create intelligent machines. In other words, AI is the replication of human intelligence in computers. While this is rather general, it includes things like planning, understanding language, recognizing objects and sounds, learning, and problem solving.
Machine Learning (ML), at its core, is simply a way of achieving AI. It is the ability of a machine to “learn” by itself using large data sets without being explicitly given the instructions for how to do so. This process is known as “training” a model using a learning algorithm that progressively improves model performance on a specific task. Machine learning model is trained to solve a task by learning from examples.
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 person with jacket on them versus those that do not. Then, the algorithm tries to build a model that can accurately tag a picture containing a person with jacket or not as well as a human. Once the accuracy level is high enough, the machine has now “learned” what a person with a jacket looks like.
Deep Learning (DL) is the most popular and sophisticated type of Machine Learning method. It uses a Neural Network to imitate 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 to learn how to recognize complex patterns in data. The ‘deep’ in deep learning refers to the large number of layers of neurons in ML models. It faces far greater types of problems than just rudimentary sorting. It works in the realm of vast amount of data and comes to its conclusion with absolutely no previous knowledge.
For example, if it was to differentiate between two different shoes, it would distinguish them in a different way compared to regular machine learning. First, all pictures of the shoes would be scanned, pixel by pixel. Once that was completed, it would then parse through the different shapes, colors and patterns, ranking them in a different order to determine the difference.
Artificial intelligence in all of its many forms combined together will take us to our next technological leap forward. And of all the AI disciplines, Deep Learning is the most promising for creating a generalized artificial intelligence.