One of my most joyful moments was while working on a high school assignment. The task was to create a flowchart detailing the possible decisions a character makes throughout a game. Given that this was a strict, all-girls Catholic school – where true happiness was confined to sewing the patterns of your husband’s boxers – the submission of my assignment resulted in an afternoon detention.

These afternoons were like any other day for me, so it did not bother me. What bothered me was the sense of completeness during the assignment. It was probably the only time where my chaos didn’t result in an unfavourable outcome. At that time, I asked my teacher, “Is there a place in this world where people construct these cobwebs of probabilities?” She suggested coding.

To me, coding algorithms is not just about following pseudocode; the challenge lies in ensuring computational efficiency. It’s like following a baking recipe. As you bake, you try to use fewer cooking utensils to save the cleanup time. This was fun, but it didn’t feel quite right. With this pursuit on pause, I shifted my focus towards mathematics.

My favourite mathematical concept has always been Algebra, but I couldn’t love it enough to enter the realms of modern mathematical concepts like Category theory, Number theory etc. Then there is differential equations, the backbone of statistics and time. I am also a huge fan of Geometry. Reading a book on Geometry is like reading a history book but just with more numbers, funny looking polygons and less evil people. The theorems in this topic are among the oldest in mathematics, dating back to Pythagoras and even before him.

I can’t love these concepts enough to lose myself in them, but I also cannot dislike them enough to ignore them.

There must be a point where they all meet. I started my search by firstly, studying what I love most, Algebra. In my spare time, I played around with matrix operations and at my resting state, my mind often wandered off to thoughts of projection matrices. After some time of self-doubts, my thoughts were reassured upon finding a gem on page 2 of YouTube.

I extended this by trying to explore the role of eigenvalues and eigenvectors. It became apparent that the Linear regression curve was composed of data points’ projections or shadows. From here, I entered the world of AI where I experienced the complications of the lack of data resources. That wasn’t the main problem but that predicting even the most simplest thing like tomorrow’s weather, can never rely on one machine learning model but many, like a spider web within a bigger spider web – a cobweb of probabilities. But for ideas to blossom, you need a playground and an immense amount of patience.

I still have many things to learn, especially within the areas of Geometric analysis. Perhaps, I need to revisit some books first to grasp the full potential of AI. After that, there won’t be any barriers like mother Theresa, can stop me.