If you have your eye on the tech industry, then you’re sure to have seen the term “machine learning” thrown around a lot in recent years. In fact, this phrase is becoming increasingly common in almost every sector, from the automotive industry designing self-driving cars to the telecommunication space hoping to deliver better customer experiences. The question is, what does “machine learning” really mean?
Simply put, machine learning is a method of data analysis in which analytical model building can be automated. Computers are taught to use algorithms that learn from the data they encounter, finding hidden insights in information without being told where to look.
The process of machine learning begins with getting technology to observe data to define patterns and make informed decisions in the future. Ultimately, the aim is to allow computers to learn naturally, like a person, without any human intervention.
What’s the Difference Between Machine Learning, and AI?
The terms “machine learning” and “artificial intelligence” are frequently used interchangeably by people outside of the technology sphere. While both concepts are connected, they’re not exactly the same thing. So, what separates machine learning from the over-arching theme of artificial intelligence?
The simple answer is the “artificial intelligence” refers to the broader concept that machines will one day be able to carry out tasks that we consider to be “smart”. Machine learning is simply an aspect of what makes these devices intelligent. It’s about teaching computers and innovative technology to perform in unique and world-changing ways.
The Rise of Machine Learning
The concept of machine learning isn’t new – it’s been around for decades, ever since a man called Arthur Samuel made a discovery in 1959. Arthur realised that instead of teaching complicated lessons so that they can complete tasks, there could be a way to teach a machine to learn for itself.
Recently, machine learning has grown more popular thanks to the emergence of the internet and the rise of neural networks. These two factors mean that machines not only have more data than ever before to inform their education, but they can also access structured learning platforms that make it easier for develops to design a bot for a specific purpose.
As new innovations in the AI space continue to arise, engineers are discovering ever-improving ways to teach computers how to perform more efficiently. Some experts even suggest that we’re well on our way towards an era when computers become more intelligent than people.
The Resurgence of Machine Learning and the Future of AI
Although machine learning algorithms have been in the works for decades now, the ability to automatically apply complicated mathematical processes to big data is a very recent development. In the last few years, we’ve seen dozens of amazing updates that demonstrate the possibilities of educated machines, such as:
- Fraud detection systems that can determine when a fraudulent purchase is taking place by listening to someone’s voice.
- Online recommendation offers like those provided by Netflix and Amazon, designed to tell people what to buy.
- The possibility of the self-driving car.
As we surge into a new era of digital transformation, both artificial intelligence and its counterpart machine learning have a lot to offer the world of the future. With the ability to automate mundane tasks, deliver useful insights and simplify complex data work, machine learning could transform the world as we know it today.