What’s the Problem: Panda Romantic Pairing

“Well if you were listening, I went over that on slide 38, but okay, it is Monday,” the general begins scrolling back slide by slide until he gets to one with an image of a sad panda and a graph. “America has become too dependent on Chinese imports, Pandas being chief among them. We have been trying to increase our domestic Pandas production for years to fill our domestic zoo needs but they are just too hard to breed in captivity. We’ve been collecting data and developing an AI algorithm for years with the hopes of closing this Panda gap but it keeps pairing up the wrong pandas with each other who don’t want to have sex with each other. After decades of DARPA research, we can’t seem to come up with a successful model on what makes one Panda attracted to another and would like to design an AI to solve this impossible problem.” “But doesn’t China own all pandas born around the world because they lease all pandas and own any of their offspring because they’re not gifted?” “Shut up” he replies. What do you do?

Look at the data, whatever it is, you’ll be able to figure it out. You might have dozed off during the meeting and missed it or you are about to be treated to a dozen more hidden backup slides.

Just get into the Algorithm, data is Data!

Don’t accept the Job, if decades of DARPA research and their AI can’t do it, what makes you think you’re year or so experience can?

Published by B McGraw

B McGraw has lived a long and successful professional life as a software developer and researcher. After completing his BS in spaghetti coding at the department of the dark arts at Cranberry Lemon in 2005 he wasted no time in getting a masters in debugging by print statement in 2008 and obtaining his PhD with research in screwing up repos on Github in 2014. That's when he could finally get paid. In 2018 B McGraw finally made the big step of defaulting on his student loans and began advancing his career by adding his name on other people's research papers after finding one grammatical mistake in the Peer Review process.

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