Over the last few decades, machine learning has become one of the most sought-after computer science technologies. Though the implications of machine learning are seemingly virtual, it’s all around us. Take autonomous tools for example; smart cars that drive autonomously, Facebook’s News Feed that personalizes each user’s wall, search engines that show us result based on a few keywords, sales automation software that tracks and validates leads – machine learning can be found in all these fields.
What is Machine Learning?
Simply put, machine learning is a sub-field of Artificial Intelligence (AI) that deals with training computers using algorithms to generate results without human intervention or assistance. But despite being a sub-field of AI, machine learning intersects other fields such as mathematics, physics, and theoretical computer science among many.
Some Popular Definitions on Machine Learning
Machine learning is an Artificial Intelligence (AI) discipline geared toward the technological development of human knowledge. Machine Learning allows computers to handle new situations via analysis, self-training, observation, and experience.
The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.
– Tom Mitchell
Machine learning is a type of Artificial Intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.
Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.
What Were the Early Foundations of Machine Learning?
The early foundations of machine learning were laid out during the early 1950s when pioneers such as Alan Turing, Arthur Samuel, and Frank Rosenblatt presented their respective theories on intelligent machines. In 1950, Turing devised the “Turing Test” to determine if a computer has real intelligence to fool human beings into believing it is a human. Arthur Samuel, in 1952, wrote a computer learning checkers game that learned from its experiences and moved on to predict winning combinations. Similarly, Frank Rosenblatt, in 1957, designed the first neural network for computers that was able to simulate the thought process of the human brain.
One of the major breakthroughs came in the 1990s when machine learning shifted from a knowledge-driven approach to a data-driven approach. Scientists begin creating programs for computers to analyze large amounts of data and draw conclusions from the results. Lately, due to the rise of new digital technologies and the big data produced by them, machine learning has become a popular technique in tasks that require information extraction from large sets of data.
Why is Machine Learning Gaining Prominence?
Many data scientists have realized the usefulness of machine learning for various reasons. However, there are two major reasons for its prominence: The enormous amount of data & the massive computational power. Due to the rise of smart devices and Internet of Things (IoT) , the amount of digital data being produced is humongous. On the other hand, machine learning technology is helping analyze these data and making intelligent decisions based on the patterns.
According to Moore’s law, the current hardware has the capability to store huge amounts of data and perform massive amounts of computations within a given timeframe. In other words, machine learning can extract patterns from humongous data in a way humans cannot or are not capable of. Thus, machine learning is gaining a lot of prominence and reputation with its capability to extract a large chunk of data and automated process.