Differentiating Artificial Intelligence, Machine Learning, and Deep Learning

Artificial intelligence (AI), machines learning and deep learning are the buzz words in the analytical space these days. There has been a lot of confusion in the terminology and people mix up and cluster them into the same basket with almost no difference. Although they all are part of business intelligence and related to one another, their application in the real-time world varies significantly.

Let's go through them one by one and understand how they work.

Artificial Intelligence dates back to 1950s
To put it simply, artificial intelligence is a sub-field of computer science (which covers a set of methods, algorithms, and techniques) to take actions that maximize the chance of success for a specific goal. The term "Artificial Intelligence" was coined in 1956 at a conference in Dartmouth College by John McCarthy.

Since then the field has evolved rapidly and we are not surprised to see a lot of real world problems are solved through artificial intelligence. Starting from self-driving cars to robots, all use artificial intelligence to make our lives easier. Interestingly, artificial intelligence has given rise to all the other fields which blend machines and human interaction.

Machine Learning is part of Artificial Intelligence
Machine learning is a subset of artificial intelligence which uses algorithms to test the data, learn from it and make predictions. The word "learning" implies that the machine analyses different actions and selects the correct one without any human interference. In other words, programmers just need to feed the input and the algorithms will come up with the best possible output. Machine learning is performed through three methods- Supervised, Unsupervised, and Reinforcement Learning. You may visit my previous post to understand these methods deeply.

Today, machine learning is used to solve a variety of business problems, some of which may not have been solved by statistical methods alone. These include forecasting, classification and pattern recognition. In fact, all of these are not new and existed before the 1980s.

Deep Learning is a new area of Machine Learning
Deep learning is a relatively new and popular area of machine learning. The most common and highly useful application of deep learning is neural network. The name neural network is termed after the functioning of the brain which contains billions of cells called neurons.

A network of neurons is interconnected within the brain to learn things, recognize patterns and make decisions. In the same way, a computer contains millions of transistors connected with logic gates to different nodes, similar to neurons. The nodes are arranged in multiple layers including an input layer where data is fed, an output layer which answers the problem, and hidden layers where the learning takes place. The deep learning software effectively recognizes patterns in images, sound, and data. This has led to remarkable advances in image and speech recognition technology.

The way ahead
Artificial intelligence, machine learning, and deep learning are becoming part of our everyday lives. They have all evolved from the interaction of computers with human programming. The easiest way to differentiate them is to think of artificial intelligence as a bigger circle which includes machine learning and deep learning inside it. Machine learning and deep learning is the present and future of smart decision-making. And the time is not long away when we would be able to see the amazing stuff shown in science-fiction movies becoming reality. Thanks to companies including Google, Microsoft, Facebook and Amazon which are making our imaginations come true!