Machine learning, a sub-category of Artificial Intelligence, has been gaining popularity in the tech world. You’ve come across the product of ML every day by interfacing with various apps and websites. For instance, whenever an application or technology supplies you with a recommendation of any sort – that’s machine learning at work! So what exactly is it, what does it do, and how does it function for technology and our day-to-day lives? Below is a description of ML and some examples of how it functions today.
What is machine learning (ML)?
The formal definition of machine learning is a subset of artificial intelligence (AI) that provides technological systems the ability to automatically learn and improve the system as a whole based on experience. This means that the tasks performed through ML are not explicitly coded – but instead, on a more interactive level, are learned based on experience over time and whatever data accumulates in that time. Programs that utilize ML are able to essentially advise users with unmatched accuracy – whether that be what show they would prefer to watch next, what house on the market they should look at, or whether or not that’s their face recognized by Facebook in a photo.
Machine learning at work
Data and probability are at the core of machine learning – and the important thing to understand in this respect is that is exactly what is at work when human beings in workplaces are doing their jobs and completing tasks. It takes a human some amount of experience to know how much time it will take for them to achieve a similar task again, or what sort of outcome to expect from a tiff with a co-worker based on prior experiences in the office. This is similar to how ML works, and why it works so well and is so helpful – because really, it predicts and analyzes situations the way that a human does, except much, much faster and more accurately.
This is sometimes referred to as statistical learning and could be considered the way all learning occurs in humans when you really get down to it. The reality is computers are better at computing probabilities than humans are, so machine learning takes that learning process in our own brains, where we automatically asses situations based on statistics from past experiences and can utilize it through many iterations at once. That’s some serious power!
This gives human beings the power to use machine learning in a way that benefits their functionality, consistency, and performance. A program that utilizes machine learning can simply be provided parameters – and send then send the program on its way to learn, and keep learning and refining its information (just like a human brain does).
As an example, the real estate industry – machine learning can be used and is starting to be used, to help effectively advise the real estate industry on the housing market. Some specific parameters about properties, like how long it’s been listed, where it has been listed, and the price point, could be enough to give a machine learning program the information it needs to be well on its way to constantly advising realtors and buyers with real-time, accurate advice on when to check out a property or when to list their home – without having to do any effort whatsoever, other than providing the initial parameters.
The best part about machine learning is that the longer the program takes the initial parameters and runs through the data, the more accurate its insights become – just like the human mind, machine learning capabilities stretch far beyond a rigid set of information but rather learns dynamically from experience.
More broadly, machine learning is at work when we use Siri on our smartphones – “she” learns to recognize your voice and vocal patterns based on your interactions. Like already mentioned, machine learning is at work when image recognition is at work on Facebook or Instagram (or even pictures of food on Yelp).
Machine learning and disruption
Since ML is able to more effectively learn based on parameters and experience to a certain extent than a human being, it has the ability to disrupt industries. Some of the industries machine learning is beginning to disrupt is the medical industry, the customer service industry, the automobile industry, and the real estate industry.
Every time you use an application on your phone that is able to give you an example of an address you may be looking to get picked up from, or a restaurant you would like, or something you may want to purchase based on something you’ve purchased before – that’s machine learning, and that has the power to disrupt various markets as it displaces the need for a human being to provide these insights for you.
Machine learning limitations
It’s important to understand the limitations of machine learning, however. It really is only a tool to provide us with information that we know as valuable – the programs do not know what is valuable information or not, instead, machine learning simply gives you whatever information you ask it to learn for you, so at the root of it, it has become a tool for companies that need to learn valuable information quickly – the information that is valuable to each company varies, and the parameters surrounding a program varies as well.
The interesting, and exciting thing about this, though, is that the sky is the limit – whatever is deemed as necessary to be learned by experience and probability, is based on certain parameters, and needs to be learned in a time-sensitive manner, ML can handle.
The value
Machine learning provides companies and industries with the ability to take in large amounts of data and process it effectively, instantly, and not only data-driven insights but results-driven insights. The outcome of ML provides companies with the tools they need to advise customers, take their next successful step financially, or create content consistency that would otherwise be impossible to complete in the same amount of time by a human employee. It’s truly the magic of AI at work and is why ML is easily one of the most applied subsets of artificial intelligence in the modern tech world today.
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