Mr. Besner is a major in computer science and English teacher. He used to love watching YouTube. During his time as a student, it was his favorite thing to do. His favorite videos to watch were the “How It’s Made” series. This series would show you all about how products are made. The insight that those videos provided fascinated Mr. Besner. Every night before school he would watch these videos before he went to sleep. It would be 1 a.m. and he would be sat up in his bed intensely watching these videos.
“I just couldn’t click off of them” - Mr. Besner
YouTube kept feeding and feeding these videos into his recommended section. They even knew what specific episodes he would like. For example, they knew he would like to learn about how a basketball is made, but that he wouldn’t be interested in how a sailboat is made. The habit of watching these videos grew and kept him up until his eyes were forced closed. He lost sleep, got behind on his homework, and didn’t pay attention in class all because of his addiction to YouTube.
This is due to the algorithms set in place by these apps and websites. YouTube, TikTok, Facebook, and more all use machine learning algorithms to track your clicks. But what is machine learning?
“Machine learning is the study of computer algorithms that allow computer programs to automatically improve itself based on experience.” - Tom Mitchell (American computer scientist)
The machine learning technology that these apps use records what videos you click on, what videos you enjoy, if you commented on the video, how long you watched a video. Then based on all of these things, they start recommending what they think you’ll like. As you spend more and more time on these sites, they narrow down what they think you’ll like and start to know what you like.
The companies that employ these algorithms have one incentive: money. The more time you spend on their site, the more they earn. This could be from advertisements, monetization of videos, or micro-transactions.
But, this goes further. Websites and apps can track your search history. They can delve into your personal information and data to profit themselves. Say you googled “Star Wars t-shirt”; other websites would start advertising Star Wars t-shirts so that you would click to make them money.
But just how advanced can this AI get? We already established that machine learning AI can learn on its own, but what if this AI became more advanced than humans? This is called ASI (Artificial Super Intelligence). Artificial super intelligence is the crazy but all-so-real concept of AI that is incredibly smarter than humans across the board. AI that would be better than humans in every single way possible. Technology is advancing ever so quickly, and we need to monitor it. Don’t get me wrong, technology is a wonderful thing. We live in an era of technology that surpasses any belief of people 50 years ago. Still, our humanity needs to be real. We can’t let artificial intelligence take over everything in our life.
*But can algorithms have a positive impact?* “Absolutely” - Mr. Besner
Despite these negatives aforementioned, Mr. Besner explained one positive. Apps like Spotify can use machine-learning AI to benefit the user. It gathers the music you listen to and creates a “discover weekly” playlist based on what you like. It’s not forcing you to listen, it’s just a way to find new music that you might like.
Other instances of machine learning can be good as well. A subsection of machine learning called supervised learning is a good example. Supervised learning is where algorithms are given datasets, then are trained to accurately predict the progression or regression of the dataset. This is beneficial because it can be used in informational fields such as stocks or science, rather than on social media apps.
I feel as if machine learning and artificial intelligence has a mixed impact. Machine learning AI that is used in social media apps is unproductive and has negative effects on the people that use them. These algorithms cause the apps to become addictive. However, machine learning can be used for constructive purposes. It comes down to the incentive of the person making the apps and algorithms. The motivation of the creator can cause the AI to be either helpful or ill-natured. Nevertheless, we need to prevent people all across the globe from becoming addicted to their screen. We need to not only prevent personal issues such as sleep loss or mental health drainage, but also societal issues. The detachment of us from one another and attachment to our screens is a major problem. The compulsion to watch TikTok or YouTube all night and day set in place by the algorithms is utterly awful for us not only as individuals, but as a whole society.