Artificial Intelligence Explained

With artificial intelligence, what once was only science fiction is now a reality. When thinking of artificial intelligence, images of the beloved robot couple Wall-E and Eva may come to mind, or the infamous Ultron that tried to take over the world in the second Avengers movie, or maybe the little voice that responds when you say, “Hey Siri.” All of these are examples of artificial intelligence, and the applications of AI are broad and seemingly endless. New developments in AI research are occurring every day. The field is evolving at an exponential rate. In 2017, tech companies began investing billions of dollars in AI development and research; today, we are seeing the results of those dollars. 

AI encompasses any programming that makes computers “think” like people. There are varying levels of AI, from Alexa telling you what time it is to self-driving cars stopping for pedestrians in the street. AI aims to enable computers to process data in the same way humans do, but quickly and much more complexly, such as by recognizing patterns, making decisions, and remembering information.

You may have heard the term “Machine Learning” used interchangeably with AI. While related, machine learning is not the equivalent of artificial intelligence; it is the foundation. With machine learning, programmers can input giant data sets, train the AI on what to think about the data, and then let AI take the wheel. Machine learning gives programmers more freedom in how they can program computers. In the past, programmers had to give computers inputs corresponding to exact outputs to operate correctly. If a variable not in the original instructions occurred, the entire system would crash because the computer could not function outside of its set program. With machine learning, the programmers give the computer some input data and some output data. Then, the computer learns the correlations and can make decisions based on old information to make new decisions about new information.