Wednesday, October 20, 2021

g-f(2)585 THE BIG PICTURE OF THE DIGITAL AGE (10/20/2021), MIT SMR, Democratizing Data in Hollywood: Jumpcut’s Kartik Hosanagar

ULTRA-condensed knowledge

"g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital ageArtificial Intelligence, AI-powered startup (10/20/2021)  g-f(2)426 

Opportunity, MIT SMR 

  • Learn how an AI-powered startup is helping new entertainment industry talent get discovered.
  • Kartik Hosanagar wasn’t your typical Hollywood hopeful. He didn’t pack his life into a sedan, drive to Los Angeles, and work a series of part-time jobs while trying to make it big in the film industry. Kartik is a professor of business and marketing at the University of Pennsylvania’s Wharton School who penned a screenplay while on sabbatical. When he started pitching it to potential producers, he quickly discovered that the film industry can be hesitant to take risks on new writers and directors — which often means that diverse talent is overlooked. So, to help unknown talent to break into the entertainment industry, he got to work founding Jumpcut, a venture-funded startup that aims to uncover new voices.
  • In the first episode of Season 3 of Me, Myself, and AI, our hosts talk with Kartik about how Jumpcut uses AI to identify creative individuals and help them develop their ideas into studio-ready productions.

Genioux knowledge fact condensed as an image


Me, Myself, and AI is a collaborative podcast from MIT Sloan Management Review and Boston Consulting Group and is hosted by Sam Ransbotham and Shervin Khodabandeh. Our engineer is David Lishansky, and the coordinating producers are Allison Ryder and Sophie Rüdinger.

Extra-condensed knowledge

The story of JumpcutMIT SMR 

  • Kartik Hosanagar: My new startup is called Jumpcut, as you mentioned. What we are doing is essentially trying to create a new data-driven studio that’s reimagining the way films and TV shows are developed, with the specific goal of elevating fresh new voices. The context here that got me interested in this is, Hollywood has historically been an old boys’ club — a few execs making decisions on what movies get made, who’s in those movies, [and] at what budgets. All of these [are] just pretty much based on gut and relationships — who knows who. There are costs [to] this kind of decision-making. There’s the economic cost; Hollywood has historically had a very poor batting average. There’s the social cost; by just about any measure, Hollywood has not been a particularly inclusive industry. And then there’s the cost to audiences. So, what we’re trying to do is break that mold and use data to make more objective, better decisions. But ultimately, by doing that, we can assess storytellers and stories on their merit as opposed to who is connected to whom or just [the] gut feelings of a few people. So that’s how we’re trying to democratize Hollywood using data. 

Condensed knowledge


How do we discover stories and storytellers?, MIT SMR 

  • Kartik Hosanagar: The classic Hollywood approach is that studios get submissions of scripts from agents, and agents are representing talent and people who are outside of the system; they don’t have a godfather who’d connect them or don’t already have the connections. So the first thing we had to solve for is how do we find stories and storytellers that are outside of the system? So for that, one of the things we do is, our algorithms are assessing content as well as creators on various platforms like YouTube or Reddit, or even storytelling platforms like Wattpad and others. And what we’re doing in those platforms — I’ll use YouTube as an example. If a screenwriter or if a director has created short films and has posted those short films that are already resonating with audiences, then we try and discover them. So the idea is to find people where they are. That’s whatever country, whatever platform you’re in, and go discover them there.
  • And essentially, the algorithms and analytics are trying to analyze the content to look for high production value, which you can infer from the frames and images in the videos, for example. You’re looking for strong storytelling and strong audience response, which you can infer from the kinds of comments that are elicited in response to these videos or even tech stories. 

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  • Category 2: The Big Picture of the Digital Age
  • [genioux fact deduced or extracted from MIT SMR]
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“genioux facts”: The online programme on MASTERING “THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)585, Fernando Machuca, October 20, 2021, Corporation.


PhD with awarded honors in computer science in France

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