Machine Learning All Around Us
Machine learning is no longer a novel concept for students, researchers and companies to experiment with and dream about. It is now a requirement in today’s world to make sense of the large amounts of data that is being collected using machine learning systems. Machine learning is actually a well-defined area of study that has been around for over sixty years. The constraints of slow computers and expensive memory years ago were the bottleneck in this field of study, but that is no longer the case. Computers are much faster and cheaper now than they have ever been, and the bottleneck is now finding software engineers to build these new learning systems.
Some of these machine learning systems have already been built by companies and we use the systems on a daily basis without even knowing it. Google’s search engine, which is trying to optimize its search results with the words we type into the Google search bar, is an everyday example. Google saves each and every query we type into the search bar and is trying to refine the results that we see so that we only encounter the most relevant results.
As more people search for a particular query on Google — for instance “Michael Jordan Nike shoes” — the results that pop up are an estimate from millions of other similar searches. The websites that don’t get clicked on receive a lower score and won’t be displayed on the top ten results. The websites that do consistently get clicked on a viewed receive a higher ranking and stay amongst the top results.
Many companies such as Netflix and Amazon have developed similar machine learning systems to suggest movies based on the categories of ones that have previously been viewed by a registered user. Have you ever wondered how Amazon comes up with the “suggested viewing” material that you see after you search for a TV show or movie?
For Netflix customers, there is a similar machine learning system that suggests movies based on previously viewed material, and the movie content that you have searched for. In a less trivial application, health care and medical diagnosis can take advantage of machine learning. On average, the best doctors in the world can remember approximately 150 patients and the treatments that they have given to them. A computer can remember tens of thousands of patients and their information, including their illnesses, symptoms and treatments that were given to successfully bring them back to full health.
Much like Google improving its search results based on past searches, doctors can now quickly find past results of successful treatments of their patients and get more accurate suggestions on medications and the corrects measures of diagnosis for new patients.
Machine Learning Improves the Shopping Experience
To date, retailers such as Amazon and Facebook have been leading the way in the development of machine learning systems. Let’s think about another example of how this could help companies deliver successful ad campaigns and marketing that would ultimately increase sales. Imagine talking to ten people and asking them basic questions about their shopping experience at a particular retail store. You would have a short list of questions that you would ask each shopper. You would then talk to each shopper individually and write down their responses to each question. The next step would be to collect all of the responses and manually type the entries into some kind of text document or spreadsheet to save the answers, which is essentially a database.
Systems like Facebook can now gather the same information, but it’s all collected from what a user “likes.” This is determined by the links they click on and the content or text they have posted. They can build an entire retail profile on a user and sell specific ads to that person based on their Facebook page. This is exponentially faster than previous forms of customer profiling and advertising, but future applications will be able to help in ways we can’t imagine.
Machine Learning Does the Hard Part
In a general sense, machine learning can be thought of as the automation of tasks like these. The goal of machine learning is to use “learning” algorithms that will take data as input, internalize it and output valuable results. Ultimately, another program could be generated from the data set. No longer do we manually have to generate the spreadsheet and other artifacts to save the data that we recorded. Many of these algorithms aren’t very complicated. The hard part is preparing the large amounts of data that is now being gathered by many companies.
Students who study computer science at Grand Canyon University will be introduced to these algorithms and data preparation techniques as part of the big data analytics emphasis. This is a very exciting field of study that provides students with a skillset that many top companies are looking for around the world.
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The views and opinions expressed in this article are those of the author’s and do not necessarily reflect the official policy or position of Grand Canyon University.