How Machine Learning Works

Written by: Kevin Gardner

One of the latest technologies that we see in our everyday lives is artificial intelligence, or AI. Whether it happens when an ad for a product you have been researching pops up on your social media or if you use a virtual assistant in your home, AI helps you by staying one step ahead of your needs. This is possible because the devices are programmed with machine learning, which allows them to adapt each time you make a request of it. Here are a few ways that this works and how it uses data science to make it happen. 

What Is Machine Learning

This method of programming takes various forms of data and compiles them in a way that a computer can predict what a person wants next. They search out patterns to point them in the correct direction while discarding ones that are irrelevant to the search. One formula that plays a large role in this is algorithms. The information that is gathered on the behavioral habits of a consumer or on a product of interest is run through the algorithms. Artificial intelligence then presents the results before they are requested. While in some cases, the buyer might disregard the advertisement and move on, there are just as many chances that they will click on it to learn more. It can also be used to shuffle spam out of your inbox before it gets to you, to detect fraud on your bank account and stop it, or to make your voice recognition on your cell phone more efficient. As the advancements in machine learning grow, it has great potential in the areas of medicine, transportation, and internet security. 

Supervised Learning

When artificial intelligence is directed to deliver the solution to a question that we give it, this is called supervised learning. It can do this with either structured data, which are tables and spreadsheets, and unstructured data, which are documents, photos, and videos. The algorithm is told the outcome that we desire then computes the information to find it. It can utilize an optimizer to attach the discarded data to what still works to arrive at a better answer. With structured data, it can calculate difficult problems that a human might have difficulty with on their own. With unstructured data, it increases the quality of facial recognition software. The computer is trained to pick out similar images down to the minutest detail and gather those pictures that are similar to each other. This can be a great asset to the law enforcement and banking industries. 

Unsupervised Learning

During unsupervised learning, patterns are searched for instead of the structured data found in spreadsheets. It analyzes the database to look for clusters that are related then compiles the results for the user. This allows you to find answers that you may have been unaware of but can utilize. This is what banks use to find fraud in the accounts they service. Their systems scan the thousands of transactions that happen during the day. It studies the location of the deposits and purchases that each member of the institution may have. If one seems out of place, the system alerts the banker that there could be a problem so action can be taken.

Reinforcement Learning

This method is basically trial and error. When an artificial intelligence device attempts to predict something and succeeds, it is rewarded for doing so. If it tries a different action and fails to complete it, it is reprimanded. The algorithm performs its tasks in a dynamic situation and is allowed to act on its own with consequences for what it does. This form of machine learning is dealt with mostly in research arenas and is rarely seen in the consumer world. The artificial intelligence found in today’s devices make our lives easier, taking the burden of searching for information by predicting what we need and providing it to us. Machine learning gives these programs the ability to adapt to the situation they are in. This continual advancement provides a huge benefit to many facets of our lives. 

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