Skip to main content

Time Travel

So what happened in the movie, pa? Sorry I slept midway through it.” My five year old son Abram asked me as we were returning home after the late evening show.

Rupa, my wife, threw an accusing glance at me. The idea of her only son following the path of her movie crazy husband did not go very well with her. Being a self-confessed adorer of the moving pictures, I believed that nothing matched the pleasure of having watched a really good movie.  And today I was happy. But I may have made a mistake when I took Abram to watch Interstellar with me when children of his age preferred Tom and Jerry.

“Well to put it short, men of the future went back in time and built a device to help us out.”

Abram’s eyes widened. “How can men of the future do that, pa? Travel back in time!”

“All that is a bit too much for your little head, sweet. You shall figure it out someday. Now it’s the time to say goodnight and get sleeping. Don’t you have classes tomorrow?”

After he went to bed, Rupa and I sat down for supper.

“What was the name of that Christian director, Abhijith?”

“Christopher Nolan.” I corrected her. “One of the absolute geniuses in the industry.”

We both laughed out loud. My devotion to movies and her complete disinterest in them were one of the many differences that we shared. Yet it was as if those differences defined us, complimenting each other perfectly. Like the Chinese Yin and Yang. After a quite supper, we decided to retire for the day.

As we passed Abram’s room, I noticed that he had not slept yet. I went near him. His eyes were tightly shut alright, but he was stirring constantly. And stirring meant that he was disturbed. I had to figure out what disturbed my little son.

“What’s it, dear?”

Abram opened his eyes, slowly.

“Nothing, pa. Goodnight.”

“Are you gonna keep secrets between us, honey? You know I cannot sleep with that.”

He said nothing, but sat up. After a pause,

“Haven’t I told about you Prithvi, my best friend at school?”

“He’s the one with the Ben Ten watch, right? What about him?”

“We had a small fight the last day. Some silly thing it was. He has not spoken to me since.”

“Is that it? Don’t worry, my kid. Go to him tomorrow and tell him that you are sorry.”

“But it was he who started it, pa.”

“Do you want to be friends again?”

“mmm yeah…”

“Then do as I say. Trust me you’d feel better. Now you better get some sleep.”

As I switched the light off, his room filled with darkness while the colorful world of dreams filled his mind.


***



The day that followed was a particularly tiring day for me at work. Two of my clients had not turned up, and there were some delays involved in processing of the funds as well. I reached home half hour late than usual. As I reached, Abram ran to me excitedly.

“Thank You, pa.”

“How did it go, dear?”

“Great. I went to him and before I got to say sorry, we were both laughing. We talked a lot, and shared a chocolate from the canteen. I love you, pa.”

I smiled. All the worries from work seemed to have vanished.

“Congrats, champ. See how you time traveled?”

“What! When?”

“Today you went back and corrected a mistake from the past, right? That’s pretty much it.”

His little eyes danced with wonder, and joy. No more words were spoken. We hugged each other for some time. Then I knew that no pleasure matched the love of your family. 

Comments

Popular posts from this blog

Machine Unlearning #0 (Intro)

You might be familiar with the term Machine Learning. Worry not if you have not, cause I have tried to give a gist of the concept here. The term has been in the limelight of late and has been tossed around rather liberally to denote anything related to artificial intelligence, robotics, and data mining. Machine Learning, as the name suggests, could simply mean the field of study of enabling the “machines” (computers) to “learn” from past experiences and make informed decisions in the future.   Wait a minute! Learning from past experiences is something humans do, right? Exactly! The computer folks want computers to behave more and more like us. As if there aren't enough of us already. As the machines are becoming more like us, we are becoming more like them. Introspection time! Most of us wake up every morning like clockwork! Then we rush through the morning routines - get dressed, wade through the traffic, and reach our offices or schools or wherever people expect us to be. We spe

The High State

 Before The Judgement I believe I must begin by addressing the pressing question - Was planning a vacation in the midst of a pandemic a recommended move?  No. Yet we went ahead with it. Here is why.  We (Nithya & I) were newly married, and our vividly planned vacation at the island of Langkawi was stolen away from us by the virus. Our stay in Delhi was coming to an end due to job-related moves, and we felt it would be a waste not to utilize this opportunity in exploring at least one of the tourist hot spots easily accessible from the national capital region. Let us end this section by answering another question - Are the reasons listed above good enough to risk a vacation during a pandemic? No. We had taken a calculated risk. Arrival at Manali There are two phases to this - planning and execution. We had not started planning with Manali in mind. There were numerous choices - starting from Jaipur and Amritsar to Nainital, Shimla, and Manali. After a bit of reading and deliberations,

Machine Unlearning #1 (Classification)

You can’t conclude a discussion on Machine Learning without mentioning classification. Classification is a machine learning technique where the machine is trained to predict the label of the given input data. Alright, let’s cut the jargon and get some real-world examples. Oranges and Bananas. Let’s assume that we have a box of fruits that contain some oranges and some bananas. You are asked to pick one fruit at random and tell if it is an orange or a banana. Pretty basic, right? For us, it is straightforward. We would know the answer at first sight. But, how would a computer be able to tell the difference? In classification, the machine would first be trained on some pre-labeled data. It would be shown an orange and we would tell it that the fruit is an orange. The machine would study the orange and remember its features - orange color and round shape. Then it would be shown a banana and the process is repeated. What are these features? A feature is anything that helps us uniquely labe