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The Jay Leno Show has been widely discussed.  Is it a fundamental shift in TV? does it change the economics?  Will it flop? Will others follow?

Pre-launch reactions weren’t really positive nor negative.  However, tonight’s airing didn’t really seem to knock people’s socks off.

The ratings will have to be the final verdict. BUT…. (it wouldn’t be a fun blog post if I didn’t speculate without sufficient data, right 🙂 )

My initial take: this will be a mediocre success in the short term and eventually make for a hard decision at NBC.  The huge amount of internal media thrown at it by NBC ensures that people know about the show.

The show’s content long term challenge will come from the Internet.  A topical comedy show that aims to be on top of the day’s events is really the specialty of Internet media.  The fact is TV content needs to be of a certain quality to succeed long term and trying to churn out decent comedy in this new form is going to be very difficult.

The business of the show will struggle long term as well.  They have to make big bucks on TV ads and I don’t think they can make the same cashflow with this show AND 2 late night shows. Here is also another issue… how will the other shows and the local affiliates react.  Let’s say this does work a little bit.  There’s a high likelihood that the Tonight Show and Jimmy Fallon will suffer from lack of a strong lead in and ad dollar competition.  The local affiliates might hate it to as for decades viewing behavior has been news then comedy.  If others are like me then as soon as these monologues finish you start to fall asleep…. uh oh!

Oh, yes, let’s discuss Kayne and his impact on Leno’s ratings. This is not going to be a long term boost to ratings.  When Hugh Grant happened, we didn’t have youtube and twitter.  Kayne’s moment has already peaked.  What I mean is that the consumer attention for this Kayne moment on Leno has already been exhausted by the Internet.

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I propose one hard test for the progress of comp sci.  I’ve laid the ground work for a computational engine that can write late night talk show monologues as well as the human writers.

Do you think it’s possible?

Here’s my basic idea…code forth coming.

—GENERATIVE JOKE ENGINE——

Some Basic Info
http://en.wikipedia.org/wiki/Computational_humor

Mathematicas and Humor, a book by John Allen Paulos

Philosophy of Humor/Theories of Humor
http://en.wikipedia.org/wiki/Philosophy_of_humor
http://www.iep.utm.edu/h/humor.htm

Some useful mathematical theory
http://en.wikipedia.org/wiki/Catastrophe_theory

Liguistics
http://www.tomveatch.com/else/humor/paper/humor.html

Joke Generator
http://grok-code.com/12/how-to-write-original-jokes-or-have-a-computer-do-it-for-you/

Potential Ideas
Simple Program based on Replacement rules of Subjects, Relationships, Events

Simple Program of puns, word combinations, definition crossing

Simple programs and then an rich interface that uses and avatar or on screen talent to “tell” the selected jokes.  Would prefer it to all be computer based as we want to find out whether the “telling” of a joke contains a lot (most?) of the humor.
How to do this:

Prep: Create a database of common objects, slang terms, relationship descriptions

a) parse the news each night for subjects, relationships, objects, events

b) enumerate all jokes (basically sentence combinations) using replacement of subjects, objects, relationships with objects in the prep database.

c) run training algo against real monologues (what jokes are likely to be used based on past jokes)

d) tune it

e) create inflection and pausing algorithm that “tells the joke better”

We can exclude the use of existing monologues to train the algorithms and instead use an audience (internet visitors) to rate the jokes and monologues.  The algo can then learn what replacements, what structures, and what styles work best.  Though i think using existing monologues is realistic as most writers and comedians borrow from successful previous work to save a long, boring training period.

Exhaust all possibilities of jokes using replacement rules.  Then run this model against actual jokes used on late night television.

Analyze how many of the actual jokes we found.  Push this analysis to back in to give weighting to the generated jokes to predict late night monologues.

Can we ever replace monologue writers?

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