It seems that talks about machine learning are all over the internet, creating hype from out of nowhere. However, what exactly is it? In its entity, Machine learning (ML) is an application of artificial intelligence that enables systems to instinctively learn from the sets of data without being explicitly programmed for this purpose. ML aims to automate the learning process while adjusting the actions of the computer in the needed direction. Therefore, a multitude of industries already exploit all the benefits of machine learning, both in the lab and commercial settings. For in order to understand the specific tasks that ML can solve, there is an obvious need to first focus on successful lab-concentrated research.
Impressive solutions in Lab
The creation of the human-like robot Sophia, the AI that trained itself from observing humans, as well as the real-time generation of the photorealistic fake celebrities images — all is possible with the help of machine learning. In the context of a lab, such solutions proved to be fully functioning, even though the practicality and dangers of such projects are widely debated.
Increasing revenue with Machine Learning
While Machine Learning, undoubtedly, has the potential to not only create the cool things in the context of lab, but also help a wide array of businesses to increase their revenue. In fact, according to McKinsey, machine learning is about to positively revolutionize twelve sectors of the economy and fade away multiple jobs as the automatization process continues.
As can be seen on the statistics above, a lot of the existing industries may undergo a technical feasibility. This claim ultimately means that the time spent on the multiple activities can be automated with the help of machine learning among others, thereby shaping the nature of businesses. While some are reluctant to forecast the potential impact of ML, it’s apparent that the outlook of various enterprises will change. How so? To answer this question, there is a need to delve into some of the spheres where ML is already making an enormous impact.
The sphere of recommendation is the purest example of how ML helps companies to make money. One of the examples, Amazon, has reported 29% percent sales increase, the majority of which due to the service’s integrated recommendations: after gaining data from the purchase baskets, pages viewed, as well as items added to wishlist, Amazon personalizes its advices to force the users into instinctively spending more money.
Netflix, likewise, relies heavily on ML for forming the artwork-based recommendations as they constituted over 82% of customer focus. Thus, Netflix created new image algorithm that would change the image, accordingly to the watching preferences of the user, which eventually helped the company to gather the 100-million subscribers.
The infographic below may assist you in seeing the reasons behind implementing recommendation systems:
In the context of marketing, ML helps to analyze and predict the behavior of the targeted group, while simultaneously assisting with the sales in the fast and cohesive way. For instance, ML can teach the system to recognize the difference between cat and dog, thus leading to the “correct” ads for the pet owner. ML takes all the data clues, cross-channel marketing campaigns, personalization, and sales into careful consideration, thereby producing the needed customization for boosting the required products. So the next time, you keep seeing the location-based ads or seeing your favorite pizza from Domino’s at every web-page, you would know how it got there.
The finance industry is actively using ML for conducting a significant overview of the clients as well ensuring the absence of risks. If there is a fraud in place, a bank could prevent the loss of funds by quickly performing a robust analysis due to the implementation of ML systems. With the careful analysis of multiple variables, banks can also adept the needed finance strategies such as evaluating a mortgage request and assessing the credit score in a matter of seconds (the accuracy improves 8 to 10 percent due to ML).
Health Care is undergoing a constant change in the use of Machine Learning. Among them are making diagnoses with medical imaging, seeing treatment queries and suggestions, crowdsourcing medical data, discovering drugs, as well as performing robotic surgery. In some cases, ML also helps with designing health care plans and policies as can be seen by the 87% predictive accuracy in risk-prediction tool. In fact, not only is it getting possible to make conclusions out of substantial means of data but also eliminating the human error, something that is much needed in the medical world. If a machine is already suitable for performing some close-knit surgeries, then merely imagine what it can possibly do after lengthy and precise training.
By focusing on the real-life examples of ML’s impact, it’s possible to note that the technology is on the edge of revolutionizing the industries. Whether it is increasing revenue in production, improving precision, or eliminating the human errors, one can be confident that machine learning is playing a major part. Thus, as the new changes in the industries are everlasting, so is the potential that ML carries with.