If you're keeping up to date on technology news, you're probably seeing references to machine learning everywhere, and for good reason: machine learning is an integral component of the way that computers process information.
Machine learning is all around us, informing our day to day lives from the way we navigate Google maps right down to the way we check our inboxes.
But what is it exactly, and when did it start being such a big deal?
Here's a quick explainer to get you up to date:
There have been two especially important developments in the history of machine learning: the first began with artificial intelligence pioneer Arthur Samuel, who coined the term "machine learning" back in 1959.
In 1959, MIT engineer Arthur Samuel described machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed." Samuel was busy creating his own computing machine: an autonomous checker program that he envisioned would someday beat the top world checker player champion.
The other important development in machine learning? The internet.
The advent of the internet presented a trove of accumulated data. With so much information readily available, there seemed but one thing to do: figure out a way to organize it into meaningful patterns — one of machine learning's most integral roles.
Big data is the fundamental building block of machine learning.
Big data, is, essentially, exactly what it's called: a ton of data. It's all of the information accrued by social media companies, search engines, and even microphones and cameras that are constantly collecting information.
Algorithms sort through this vast collection of information so that technology can predict what we'll do next.
Vast amounts of data inform machine learning algorithms, equipping technology with methods of predicting future patterns. These algorithms provide a way to forecast future behavior and anticipate forthcoming problems.
One of the best-known examples of this is Amazon's suggested product feature. It reads your preferences and the buying habits of other people, and then recommends other products you might be interested in.
Machine learning is all about sorting through those troves of collected information to discern patterns and predict new ones.
Machine learning differs from human learning, insofar as the machine only knows what you tell it: A computer can't express curiosity or make inferences.
For example, if you watch a lot of scifi movies on Netflix, you might find Stranger Things in your suggestion queue. But it's only a matter of math, based on Netflix's data alone — if you've never told Netflix that you love documentaries, or rated one highly, the system will likely never show you one.
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Examples of machine learning surround and inform our day-to-day lives.
Machine learning is all around us: it informs everything from our Facebook feed, our suggested traffic routes in Google Maps, our autopilot email spam filters, and even the security of our banking information.
But current day iterations of machine learning have radically evolved since the 1600s. Today, machines can learn with only minimal human intervention.
Through machine learning, technologists have mimicked the way the human brain works by producing sophisticated systems called neural networks. In turn, neural networks enable deep learning, an outcome that has produced computer systems superseding human intelligence.
Machine learning plays a key role in the development of artificial intelligence.
A.I. and machine learning are often conflated, but they're not the same thing. Artificial intelligence refers to a machine's ability to perform intelligent tasks, whereas machine learning refers to the automated process by which machines weed out meaningful patterns in data. Without machine learning, artificial intelligence as we know it wouldn't be possible.
There's plenty in store for the future of machine learning. Expect to see machine learning informing the development of virtual reality, the driverless car industry, and artificial intelligence.