Throughout 2018, you have probably consistently heard and seen these buzzwords thrown around in conversation, in the comments of social media posts, from Youtube, or as think pieces from writers.
Researchers have made tremendous headway in the field of AI and this progress has already influenced your daily lives whether it is for novelty or for practical reasons.
Though this article will break it down in further detail, in short, AI is any technology that showcases anything that resembles human intelligence. Think of any of your favorite sci-fi films.
However, ML or Machine Learning is a subset of AI that uses mathematical models from data to make decisions.
Source: Medium/Seema Singh
Before diving any deeper into the world of machine learning and artificial intelligence, one should look at the brief history of the subjects.
There has been a fascination with AI that goes all the way back to the Greeks, with them describing mechanical people who could walk and think like men.
However, the first stop on the historic timeline for Artificial Intelligence is the Second World War.
During WWII, genius, computer scientist Alan Turing worked to crack the impossible German forces Enigma Code, a form of communication used to send messages securely and plan attacks.
To decipher the code, Turing created the Bombe machine. This machine was “intelligent” and able to learn an eventually crack the code.
Turing’s machine has laid the foundations of what ML and AI are today. Over the decades to follow, researchers were eager to push the boundaries of computer intelligence for the military and for scientific research.
From the creation of the AI programming language, LISP, in the 60s to the eventual creation of IBM’s Deep Blue in the 90s, all of these events have laid the framework for the AI you know today.
So, what exactly is machine learning? For starters, ML is not as far away as you think.
Tools you use everyday incorporate ML to create better experiences for you. Google even uses your data to optimize advertising. Even your beloved Netflix uses ML to make recommendations of what you should watch.
ML learns from large amounts of data to make predictions. “Machine learning algorithms are widely employed and are encountered daily."
"Examples are automatic recommendations when buying a product or voice recognition software that adapts to your voice,” says researchers from the University of Maastricht.
Machine Learning “learns” using, from a term that you have probably heard thrown around a lot, "neural networks". Neural Networks is where Machine Learning “learns and trains” from a large set of data to determine the probable outcome of a situation.
Without getting overly complicated, neural networks are where a computer would learn for thousands of hours to identify a person or animal in an image or even learn how to translate a language.
Read the full article on Interesting Engineering