Machine learning refers to a system’s ability to obtain new data without actually having that exact data programmed into it. Quite simply, it is when a machine learns something on its own as opposed to having a computer programmer manually enter the code into a system.
Systems that use machine learning, learn through data that can be gathered from observations of activity, patterns or experiences, much like we learn as humans.
This is one of many applications of Artificial Intelligence (AI), where computers learn and adjust their actions automatically without the need for any human intervention. But is that a good or bad thing? That’s the million dollar question.
While there’s no arguing that machine learning has revolutionized the world of computer programming, it’s still too early to determine its long time impact.
Examples Of Machine Learning
Machine learning systems can be found in more places than you may think, especially in today’s increasingly computerized world. The best way to understand its concept and how it works is by taking a look at real world applications of this ground-breaking technology.
GPS tracking predictions: When we need to be somewhere on time, we often use a GPS to decide when we need to leave home and which route will get us there in the shortest time. This is machine learning in action.
A GPS uses the concept of machine learning to track traffic trends over time. It then uses this knowledge to plot the best route for us and calculate how long it will take, based on which route tends to have the least amount of traffic at that specific time.
‘People you may know’ predictions: Social media utilizes machine learning in multiple facets but a prominent example of this is when an app suggests a person you may know. If you’re on Facebook or LinkedIn, you’ve probably received several ‘people you may know’ notifications from these social media networks.
Facebook and LinkedIn do not hire people to keep track of your actions and send you these messages. These are automated notifications sent out by their systems. Their system suggests people that you are likely to actually know through the analysis of probability and cross-referencing mutual friends or other things you have in common, such as geographical location.
Maybe you visit the same sites, purchase the same items, or follow the same people. These systems use machine learning to track your digital journey and make these suggestions.
Spam filters: Most software and applications now offer you the ability to block spam emails or messages that contain malware. They do this by constantly learning and updating their databases on new methods and formats of spam messages.
They also detect the patterns of code present in malware and check their databases to find similarities with known malware and then learn to block the new malware they were just presented.
Pros Of Using Machine Learning
Machine learning was built for a system to make informed and personal decisions in the future based on the experiences it has had and patterns it has recognized with its users. It’s inevitable that these systems are only going to get stronger with time as they receive more fresh data that they can draw from to make their decisions.
Machine learning can make our life so much easier in several different ways, particularly when it comes to analyzing data. These systems are capable of analyzing large amounts of data at lightning fast speeds.
Take Amazon’s recommended section as an example. Amazon’s machine learning software analyses your search and purchase history to find common trends to help suggest more items that you may be interested in. It also compares your searches with other users that have a similar search history and recommends new products for you based on the products that those other users looked at.
With the huge number of users on Amazon, each with their own search history and trends, and the various combinations of searches that users may perform, there would simply be too much information for data analysts to analyze. Machine learning software on the other hand does this almost instantaneously.
This eliminates the need for analysts to spend hours manually analyzing data and then programming the systems to respond accordingly.
Also, given that trends are organic and change constantly, it wouldn’t be feasible to have someone program the systems every time there is a change in a trend. With machine learning, companies no longer have to invest as much as they did in labor as their software can now adapt and make changes without explicit human intervention.
Cons Of Machine Learning
Well yes, you can’t possibly overlook the cons of machine learning. We’re still a long way to go before we can eliminate all its drawbacks.
Sometimes trends can be misinterpreted by systems with machine learning. Two items may follow a similar buying pattern but may not be related at all. Let’s say 100 cat owners buy cat food once a month and they also buy soap along with it. A machine learning system may relate the two and start recommending cat food to all people who buy soap even though they don’t own a cat.
This is where human analysts come in. Human analysts have the ability to make a clear distinction between the two products and recognize that they aren’t related. If they could just analyze the data as quickly as the system, their predictions would be more accurate.
If the hardware that inputs data into the system such as a data logger is faulty, it will enter incorrect data into the machine learning system. The system will continue to analyze the data and in turn produce inaccurate data never realizing that the data that was entered was incorrect in the first place. This is something a human analyst could potentially pick up on before the data was inputted in the first place.
These systems can also take time to reach their full potential. Computers use machine learning to play chess against humans. Computers build their database based on the opponents that they have faced earlier and then use that data to perform their next move.
However, when the machine is installed, unless programmed into it, the machine will not have any historical data to refer to while playing, making it a fairly weak opponent to play against.
And as the popular discussion goes, whilst automation is favorable for companies and employers, it’s not good news for analysts and employees who are likely to lose their jobs because their skills aren’t needed anymore.
Is Machine Learning Worth It?
In today’s age, it is becoming increasingly more feasible for businesses to make their operations automated, and machine learning is undeniably a step in that direction. Implementing machine learning into systems can cut costs for businesses in the long run by reducing their spending on labor where machine learning systems can provide a similar service and result.
However, machine learning does have its limitations and while it may be fantastic at handling mountains of complex data, it is prone to making mistakes such as relating two pieces of data that have absolutely nothing to do with each other.
This makes it crucial for businesses to learn how to maintain that balance between machine learning systems and a strong team of analysts, instead of depending on one or the other.
While machine can go through the data at amazing speed, you need a team of professionals to take a second look at the data and make sure that the machine learning systems are functioning correctly and that the data being generated seems logical. Human data analysts are needed to monitor the machine and make sure that the machine learning system isn’t skewing the data.
That being said, with leaps and bounds being made in the world of technology, machine learning systems are becoming considerably more secure, reliable and sophisticated. This will not only make implementing machine learning systems into business operations more desirable but also more accessible due to the increased support for and capabilities of such systems.
At the speed at which we are improving, the future is closer than ever and so is the business world that will function around machine learning.