Episode 4 - Codebreaking, Bizarro Harry Potter, and the Proper Way to Gerrymander
My official description is this, but you need to listen in order to understand what this is: IN WAS NDE BESN OF NIZES IN WAS NDE WORSN OF NIZES IN WAS NDE ACE OF WISTOZ IN WAS NDE ACE OF FOOLISDGESS IN WAS NDE EPOUD OF BELIEF IN WAS NDE EPOUD OF IGURETMLINY IN WAS NDE SEASOG OF LICDN IN WAS NDE SEASOG OF TARQGESS IN WAS NDE SPRIGC OF DOPE IN WAS NDE WIGNER OF TESPAIR WE DAT EVERYNDIGC BEFORE MS WE DAT GONDIGC BEFORE MS WE WERE ALL COIGC TIREUN NO DEAVEG WE WERE ALL COIGC TIREUN NDE ONDER WAY (I'll explain!)
In this episode, I talk to Aaron again about our mutual project. I bet that I can use Bayesian Inference and a few Machine Learning techniques to crack the Substitution Cypher, but I hadn't ever done it in practice. So we gave it a try, and Aaron gave me some encoded messages that I haven't seen. The system worked, except there was one message I couldn't encode. Maybe you'll have better luck: BYPPCVACMZQVYAP
Now for the code that I used to break Aaron's messages, go to my Github repository for the show. Also check out Aaron's original messages, and try to run the python code yourself to decode them! I know I mixed up Pride and Prejudice with Tale of Two Cities - but note they were both on the list. I mentioned the movie Patterson with regard to the William Carlos Williams poem. We also have a Trump Tweet, quotes from Lord of the Rings, Moby Dick, Huckleberry Finn, and of course the Declaration of Independence.
Click here to read about the Bot from Botnik Studios that wrote a fake Harry Potter Book.
Click Here to read about how Markov Chain Monte Carlo (MCMC) was used for Gerrymandering in this New York Times article by Jordan Ellenberg.
For more on Bayes Rule cracking the enigma code at Bletchley Park, read The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy, and also check out the (fictionalized but very good) Imitation Game. Also this refers back to Episode 0 on Bayesian Inference.
