As a part of Generation Z, technology and computers have been integrated into my everyday life since I was little. However, when my parents were my age, computers didn’t even exist. Technology and the computing industry are growing at an exponential speed, because computers hold the promise and capability to be able to compute and understand data or patterns that even humans cannot. The study of how to harness and make use of a computer’s full potential is artificial intelligence.

What is the first word that comes to mind when you hear “artificial intelligence?”

Is it Siri or Alexa? These are called weak artificial intelligence, because although they can perform a few human-like functions, such as responding to conversations, they do not have a formal consciousness, and they have one narrow area of focus.

Or maybe you think of movies like The Matrix. Robots with human-like features and traits, with the ability to reason and to feel emotions. These types of robots are strong artificial intelligence, meaning that they have the ability to think, feel, and make decisions for themselves. Although these robots are kept behind the movie screens for now, strong artificial intelligence is closer to present day than you’d think.

One way scientists are trying to improve artificial intelligence is through machine learning. Machine learning is where scientists use a set of data that is pre-categorized to feed to the computer to teach it to recognize and identify patterns within data sets without known labels accurately.

One example would be to task a computer to identify a cat from a dog.

In machine learning, a human would label the left picture as cat, and the right as a dog. There are a few distinguishing factors to cat and the dog, such as their fur pattern, the point of their ears, and the shape of their mouths.

However, what if the pictures the computer is trying to sort look like this instead?

The distinguishing factors we previously listed—fur color, nose and ears—can no longer tell the two apart. It’s the complexity of the data sets used that makes the designing of artificial intelligence so difficult.

One approach scientists are taking to improve both accuracy and human efficiency of the process is using deep learning, where instead of having a human create the labels for all the data sets, which can be time consuming and inaccurate, the computer labels the data set itself. Its other name is neural networking, because it simulates the work of neurons in the human brain, actively making the computer act more human-like. The computers take large amounts of data to interpret on a trial-and-error basis. The implications of this project are being able to understand patterns in data that humans cannot find, which artificial intelligence is already starting to do.

This is relevant in today’s society not only to optimize the efficiency of everyday life such as in autonomous cars, but it will also play an instrumental role in the medical field, especially pertaining to life threatening diseases, where identifying the illness early is crucial. Being able to diagnose and monitor a patient accurately will revolutionize today’s healthcare. Deep learning can even be used to detect brain cancer early on.

Approximately one in two men and one in three women will be diagnosed with cancer in their lifetime. On a personal level, struggling through cancer or seeing someone else battle cancer is one of the most painful experiences. Deep learning and artificial intelligence promise to change that for the future, to be able to eliminate the process by detecting the signs at early stages. Deep learning has the potential to touch so many lives and revolutionize medicine, along with our world.

Now when I ask you what you think of when I say “artificial intelligence,” will your answer be different?


Megan is a gender-tech activist immensely passionate about bridging the gender gap in technology, fostering underserved girls’ interests in tech through her organization, GEARup4Youth (

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