I remember one day, when I was about 15, my little cousin had come over. Being the good elder sister that I was, I spent time with her outside in the garden, while all the adults were inside having a hearty conversation.
I soon found myself chasing after this active little 4 year old as she bustled around, touching every little flower and inspecting every little creature.
At first, she carried this out as a silent activity, the only noise being her feet as she ran across the grass. After a while, however, she could no longer contain herself, and she began questioning me about each and every object and phenomenon within her radius of sight. For a while, I felt thrilled that I was old enough to answer these questions satisfactorily. This thrill was short-lived, however, as she began delving deeper in her thirst to know more.
This lasted until my mom came outside and called us for dinner. As I gratefully made my way back into the house, I came to two conclusions:
1. The human mind is brilliantly inquisitive
2. I’m not as smart as I thought I was
Now when we think about it, it’s quite interesting to note that all that we know to do, from counting the number of toes we have, to singing the national anthem on key, to naming the planets in the Solar System, are all skills that we have developed over time.
Were we born with these abilities?
No, of course not.
But we do have the ability to learn how to do all these things, with the
help of our brain which continuously learns and processes information. The more we learn, the greater our knowledge. The greater our knowledge, the more intelligent we are.
Nikita Silaparasetty is a data scientist and an AI/deep-learning enthusiast specializing in statistics and mathematics. She is currently pursuing her Masters in Data Science at Liverpool Hope University. She is the head
of the India-based “AI For Women” initiative, which aims to empower women in the field of artificial intelligence. She has strong experience programming using Jupyter
Notebook and a deep enthusiasm for TensorFlow and the potential of machine learning. Through the book, she hopes to help readers become better at Python programming using TensorFlow 2.0 with the help of Jupyter Notebook, which can benefit them immensely in their machine learning journey.