Artificial Intelligence (AI) has entered our daily lives like never before and we are yet to unravel the many other ways in which it could flourish. All tech giants such as Microsoft, Uber, Google, Facebook, Apple, Amazon, Oracle, Intel, IBM or Twitter are competing in the race to lead the market and acquire the most innovative and promising AI businesses. AI is already being used in everyday life with applications including speech recognition, smart cars, fraud detection, security surveillance, music recommendations and AI-powered personal virtual assistant such as Cortana (Microsoft), Siri (Apple) or Alexa (Amazon).
Discussions on AI are generally dappled with the terms, ‘Machine Learning’ and ‘Deep Learning’. Moreover, they are often interchangeably used. Though they are closely related to each other, they have different meanings. This article will go through such differences and illustrate what is artificial intelligence machine learning and deep learning and also point out how they are interconnected to each other.
According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs.” Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think. While exploiting the power of the computer systems, the curiosity of human, lead him to wonder, “Can a machine think and behave like humans do?”
Thus, the development of AI started with the intention of creating similar intelligence in machines that we find and regard high in humans.
Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. 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.
The phrase ‘machine learning’ dates back to the middle of the last century where Arthur Samuel in 1959 defined machine learning as “the ability to learn without being explicitly programmed.”
Machine learning is a type of AI that facilitates a computer’s ability to learn and essentially teach itself to evolve as it becomes exposed to new and ever-changing data. For example, Facebook’s news feed uses machine learning in an effort to personalize each individual’s feed based on what they like. The main elements of traditional machine learning software are statistical analysis and predictive analysis used to spot patterns and find hidden insights based on observed data from previous computations without being programmed on where to look.
Machine learning has truly evolved over the years by its ability to sift through complex and 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 a new area of machine learning research, which has been introduced with the objective of moving machine learning closer to artificial intelligence.
It relates to study of ‘deep neural networks’ in the human brain and, under this perspective, the deep learning tries to emulate the functions of inner layers of the human brain, creating knowledge from multiple layers of information processing. Since the deep learning technology is modelled after the human brain, each time new data is poured in, its capabilities get better.
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, in some scenarios is better than humans, and that ranges from cats to identifying indicators for cancer in blood and tumors in MRI scans. Back to some lines ago, Google’s AlphaGo learned the game, and trained for its Go match — it tuned its neural network — by playing against itself over and over and over.