A look at the possibilities of machine learning — and how you can take advantage…
It used to be really straightforward to boost computing power. You could add more RAM, upgrade the server, install a new processor.
But now with machine learning, the rules have changed. Because when you give a machine more data you increase its intelligence. Unlike humans, there’s no “information overload” or “analysis paralysis”. Machine learning means that decisions don’t become harder.
They become smarter.
Machines can process far more data sets and analyse variables. And the more data sets there are, the more insights can be found, or “mined”. Machine learning then uses this to improve its own processes and “understanding” of patterns. Which delivers better results to its end-users. And gives data scientists the power to forecast trends and anticipate problems — on an industrial scale.
But it’s not just the ability to crunch unprecedented volumes of data that makes machine learning so important. In the era of Big Data, machine learning gives businesses the power to make business-critical decisions, faster. As long as you have historical data. So that the machine can analyse it, and predict future activity. So the sooner you invest in data, the sooner you can start taking advantage of machine learning.
Facebook uses machine learning to analyse historical behaviour. The platform learns from the behaviour of its users, to better serve up relevant content. The more you “Like” or reply to comments from certain people, the more Facebook understands which updates you want to see in your News Feed. It then adjusts its algorithm in real-time. There are 1.44 billion active Facebook users. So it would be impossible to do this manually. It’s only possible with machine learning.
Google’s self-driving cars use machine learning to make driving decisions. From when to stop at a junction, when to go at roundabouts, to when to take a different route home. All the while gaining more knowledge and information about roads and traffic. Machine learning how to drive. In six years of running tests, Google’s cars have driven over 1.9 million miles. During that time there have been 11 accidents. How many of those were attributed to the car or its software failing? None. How many of those accidents were other (human) drivers to blame? All of them.
Amazon uses machine learning to personalise its customers’ shopping experiences. You probably already know that if you search for a particular item online, it won’t be long before you start seeing related ads pop up on other websites you browse later on. That’s machine learning in e-commerce. Understanding what you’re looking for, and trying to give you what you want.
You probably already knew that internet giants like Facebook, Google, and Amazon would be using machine learning. And yes, you probably already knew that storing, crunching and delivering this data requires major investment. But you might not have realised that this level of computational power is available in a Digital Centre. Or that new machine learning platforms are in development. These will soon be accessible to businesses. The Digital Centre can ensure you’re ready to take full advantage.