Artificial Intelligence (AI) has entered our daily lives with a bang. From marketing to medicine, every business and industry seems to be affected. Technology companies are competing for dominance in the race to lead the market and acquire the most innovative and promising AI businesses.
You may already be using AI in everyday life, with applications such as speech recognition, virtual assistance on your smartphone, the recommendation algorithms of shopping websites and music or video streaming services, or even when you visit the doctor and he compares an X-Ray or other medical images with other medical data.
And then there are the terms machine learning and deep learning, which seem to confuse many people. Too often they are used interchangeably, but although they are closely related to each other, they have different meanings. So, what is the difference between AI, machine learning, and deep learning?
In the broadest sense, according to its founders, AI is the science and engineering of making intelligent machines, in particular intelligent computer programs. It is a way of making a computer, a computer-controlled robot, or a software think intelligently in a way similar to the way humans think while exploiting the vastly greater speed and power of the computer.
Knowledge engineering is a core part of AI research. Machines can act like humans only if they have abundant information relating to the world. An autonomous car can only drive safely with sufficient data about its environment. Decision-making algorithms are only as good as the input data.
In other words, artificial intelligence must have access to objects, categories, properties, and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious approach. We are nowhere near really intelligent machines.
Whereas Artificial Intelligence covers the entire spectrum of machine learning, the term machine learning has a much narrower meaning, namely “the ability to learn without being explicitly programmed.” Incidentally, this is where the biggest changing are happening right now: feeding massive data sets into computers and waiting for them to come with results.
Machine learning has truly evolved over the years by its ability to sift through a complex data set. These are often referred to as “big data”. Many may be surprised to know that they encounter machine learning applications in their everyday lives through streaming services like Netflix and social media algorithms that alert to trending topics or hashtags. Feature extraction in machine learning requires a programmer to tell the computer what kinds of things it should be looking for that will be formative in making a decision, which can be a time-consuming process. This also results in machine learning having decreased accuracy due to the element of human error during the programming process.
Deep Learning is the youngest area of machine learning research, which has been introduced with the objective of moving machine learning closer to artificial intelligence.
Under the deep learning paradigm, essentially the machine is ‘trained’ using large amounts of data and algorithms to give it the ability to learn how to perform the task. This data is fed through neural networks which ask a series of binary true/false questions or numerical values, of every bit of data which pass through them and classify it according to the answers received. Today, image recognition by machines trained via deep learning are used in training autonomous robots and vehicles, in medicine to identify disease markers and all kinds of images.
Some time ago Google’s AlphaGo learned the game in hours by playing against itself over and over and over. This unsupervised, increasingly faster ability to learn is the key to the current hype over deep learning. But the next revolutionary technology is not far away.