Skip to main content

Jab KJo met Imtiaz

(Last year, at Imtiaz Ali's place)

Karan Johar: Hey Imtiaz!

Ali: Hello Karan.

KJo: my new movie got released. Did you watch?

Ali: No Karan I'm allergic to bullshit.

KJo: Come on it's not that bad. Ranbir is the hero.

Ali: Why do you spoil him yaar? I got him up and running with Rockstar and Tamasha.

KJo: Don't be so judgmental. Watch and see..

*Ali watches the movie*

KJo: How was it?

Ali: Basically it's a stupid movie where Anushka plays a girl, whose wedding is due in weeks, roaming carefree around Europe and falling in love with a charming man she met. Why did you go for Ranbir? Your buddy SRK would've nailed this role.

KJo: Khan Sahib is good, but hasn't he aged enough to stop playing such roles? But it hurts me to shoot a movie without him. That's why I added the pointless cameo. How was that?

Ali: Sorry yaar the screenplay was so boring that I dozed off a bit and missed the whole Khan scene. But do you seriously think SRK is too old to play the lover role?

KJo: I don't know yaar.

Ali: Interesting.

KJo: What are you thinking?

Ali: Nothing. Achcha chalta hoon! Got to direct a movie.

(One yaar later)

KJo: Hey Ali! Heard that your new movie is out, and that too with Khan Sahib.

Ali: Yes. Since you took my boy Ranbir, I thought why not cast your buddy Khan.

KJo: What's the plot like?

Ali: This is a never before told story where Anushka plays a girl, whose wedding is due in weeks, roaming carefree around Europe and falling in love with a charming man she met.

KJo: Hmm. Are you sure it is never told before?

Ali: Of course!

KJo: Interesting.

Ali: What? The movie?

KJo: Nope.


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