Machine learning. This term seems to be a buzzword right now, and for good reason. Machine learning impacts us every day, from the way we check our inboxes to the way we navigate Google maps.
You could be forgiven for being a little uncertain about exactly what machine learning is, and why it’s such a big deal for both businesses and individuals. Here’s everything you need to know:
Machine learning is an integral part of the way our computers are processing information. Arthur Samuel first coined the term in 1959. An engineer from MIT, Samuel taught a computer to play checkers, and by the mid-1970s, it was able to challenge respectable amateurs.
Samuel described machine learning as the ability for computers to learn without needing to be explicitly programmed.
In the 1990s, computer science laid much of the theoretical background for programmers to develop neural networks for machine learning. This is similar to how our neutrons connect in our brains.
While Samuel’s approach was to program computers to solve a problem in the most efficient way (continually searching for the best sequence of moves), this is now much more complex. Machine learning systems are increasingly built around letting the computer figure out the rules from scratch while learning along the way.
For machine learning to work effectively, it needs a ton of data. The rise of Big data has made this possible. Search engines, social media channels, websites, and even cameras and microphones are continually collecting information for computers to use.
Machine learning algorithms use this data to predict future patterns. That way, businesses can forecast future behavior while anticipating any potential problems. If you’ve ever used Amazon, only to see a list of recommended products, this is an example of how machine learning can read your preferences and other people’s buying habits to find similar products.
You’ll also find machine learning in your Facebook feed, in your traffic route, in your email spam filters, and even in your banking security. Now, machine learning is mimicking the way human brains work. Those neural networks mentioned earlier? They enable deep learning, which allows computer systems to supersede human intelligence.
Supervised machine learning is when systems classify objects in specific categories. Basically, that’s when computers can make predictions based on data. Unsupervised machine learning is when systems can identify patterns within streams of input. This means that the systems have a sense of autonomy as they learn.
While artificial intelligence and machine learning are often confused as the same thing, they’re actually different. They both belong under the same artificially-intelligent “umbrella), but while AI refers to a machine performing intelligent tasks, machine learning is the automated processes machines use to find meaningful patterns in data.
If we’re going to build machines that are artificially intelligent, they need to be able to learn. When we talk about artificial intelligence, we’re often talking about the broad concept of machines carrying out certain tasks in a way that makes them “smart.”
Machine learning, on the other hand, is based on the idea of giving machines access to huge amounts of data and letting them learn from themselves.
Machine learning is being used for a variety of different business applications. If you’d like to learn how machine learning can help your business, get in touch today and let’s chat.