It all started when I was little. I wasn’t a big fan of mathematics, not because I was not good at it — I was — but because all those numbers and operations didn’t seem to hold any meaning to me.

During math classes, the questions that ran through my mind where things like;

‘Why on earth do I need to learn trigonometry or Pythagoras theorem?’

‘Do I need to calculate the area of a circle before I figure out which birthday cake at the store is larger?’

‘It is annoying enough that x is always missing, but what I don’t understand is why it is my responsibility to find it?’

I understood what each formula was used for, but I couldn’t see any use for them in the real-life situations I encountered, and each calculation felt like an utter waste of time and brainpower. I couldn’t see the story and the magic behind the numbers and so I always felt deflated solving math.

But as I grew older, I fell in love with science subjects like Physics and Biology.

I was intrigued by the stories of nature biology told, stories of the cells that made breathing possible, the functions each biological entity performed, and the consequences that were inevitable when an organ failed to complete its assigned task. I was excited to learn that the laws of energy conservation in Physics weren’t just some useless bunch of rules made up by a grumpy bored old man, but they were unchanging stories told by physical phenomena that occur all around us — like the swinging of a pendulum and the snapping of a stretched rubber band. It was amidst all this fascination in the matters of science that I began to appreciate mathematics, as it never failed to make its presence known in every science experiment I saw, or each scientific concept that was explained.

I studied Electronic Engineering in the university, and I took courses like Digital Signal Processing and learned some more annoying mathematical theorems like Principle Component Analysis and Lagrange’s theorem. It was like déjà vu all over again. I started asking myself questions similar to the ones I asked when I was younger.

‘Does this mean we have to keep learning things we don’t need at the moment in preparation for future situations that demand them?’

The answer to that question is yes, but it is easier said than done. The brain loves to expend less energy, and it effortlessly pursues a solution whose problem it is well acquainted with. So, isn’t it wasteful to learn solutions to problems that we do not know or care about?

Well, that is how I felt until I got to know about Artificial Intelligence. Apart from the occasional sci-fi drama that features cool all-powerful robots performing mind-blowing tasks, I was first captivated by the field of artificial intelligence when I saw the YouTube video of Dr. Fei Fei Li’s Ted talk on computer vision. I can’t find the right words to explain the way I felt when she told the story about teaching computer machines to ‘see’ our world the way you and I can. The images, the numbers and the mathematical equations involved were connected firmly to each other with the purpose and intent of describing the contents of an image to a computer in the only language it understands — the language of numbers.

So, I dived into the field of AI, excited to learn more, and each new concept I learned opened my eyes to the stories behind those annoying mathematical formulas that were constantly shoved down my throat since my childhood.

I got to know that Artificial Intelligence is a field that allows us to program ‘intelligent’ machines. By intelligent machines, I mean machines that think, learn, recognize, and remember. I think of AI as a tool that allows me to create a child whose capabilities I get to decide. I might not be a medical practitioner, but I can use AI to program an expert machine for cancer diagnosis. Most importantly, I can train computers to do the things I can do, but do not have the time, interest, or resources to do, and to achieve this I must train these systems to understand how human beings communicate with each other. The sub-fields; computer vision, and natural language processing are dedicated to teaching computers to make sense of images, written text, and language. The techniques used to achieve these, although full of mathematical theorems and concepts, do not freak me out as they used to. Because they are not just a bunch of intimidating formulas, they are connecting threads leading from one problem to its solution.

This is why I love AI. It can get difficult, rigorous, and confusing. But it is a field where anything is possible, and everything is relevant. It is a world where numbers are converted to magic, and solutions for all kinds of problems are born.