Can entropy be reversed?

### Platonic Payments

Paying is strange. Consider the following exchange:

Me: Hello unfamiliar person. I would like to give you some fiat currency in exchange for the goods and / or services you provide.

Clerk: Splendid! Let me just whip out this large, obnoxiously bright, unwieldy machine and get the banks involved in our newfound relationship.

Me: That sounds reasonable. Surely Jamie Dimon did not make enough money last fiscal year and deserves a cut of your vanishingly low margin retail business. Please take this cold, hard, magnetized piece of plastic. The design reflects my personality and pecuniary worth.

Clerk: I am obliged to thank you for handing me this trinket. Allow me to swipe it, the same way humans have read information off of cards for decades. You will also be required to sign a piece of paper because I honestly believe you may call your bank and vehemently deny purchasing this cup of black coffee.

Me: Perfectly understandable. I love the feeling of generating potential evidence for a court case every time I want to buy something. I also demand a paper record of this transaction because I do my taxes with a quill pen and slide rule.

Clerk: Yes quite common. Well, have a wonderful day, stranger I have interacted with but not touched. This large, black, computerized point of sale system symbolizes the technology-driven separation that grows between humans even when we interact in pers..

Next Customer: Hello unfamiliar person!

Paying isn’t too hard. Swiping plastic takes seconds. It’s just too weird. Here I propose the way I would prefer to pay, optimized for low friction and high humanity.

The handshake, like currency, has been around for millennia as a symbol of trust. Unlike currency though, the handshake requires no additional hardware, central banking system, or card. It does not allow for a precise and safe transference of wealth, but it could, with a little help from our friend the bitcoin.

This is an idea I call “platonic payments”.

To accept payments you wear a watch, or ideally it is integrated in to an existing watch like the Pebble. To pay you wear a ring, which contains a short-range rewritable RFID tag storing a single bitcoin address. The payee specifies the amount with two dials on the watch, one for dollars, one for cents. Both parties then shake hands, bringing the RFID ring within reading range of the detector, which uses the information to charge the payer’s bitcoin wallet. No fees. No slow hardware exchange. No cold plastic or large, expensive POS systems blaring light in an otherwise relaxing environment. I also think service establishments should just charge 20% tip by default.

Bitcoin addresses are nearly infinite and can be created, online or off, and disposed of at will. Thus an NFC chip in a smartphone could passively rewrite the tag with a fresh address every time you pick up your phone. The merchant software, either run on the watch or connected to a smartphone, would request the appropriate amount from the user’s bitcoin wallet with the public key. This is not built in to the bitcoin protocol but could be managed by a service like Coinbase, which could then verify the account is valid and securely transfer the private key.

This does face the problem of a change by both parties. However, if it started being built in to smartwatches and all users had to do was buy a cheap, stylish ring that barrier may lower. A payment method that didn’t force me to take anything out of my pockets or even carry a wallet, while increasing the humanity of a transaction sounds like a dream come true. I would even get my tag implanted so I wouldn’t have to wear the ring.

The other problem is the system still involves trust. Someone could skim your ring, grab the address, and post a request if they got close enough. Since each transaction involves a new address the hacker could only make one request, but it could be big. This could additionally be mitigated by only allowing transactions of a certain size or only keeping a small amount of money in this wallet, making skimming uneconomical.

Alternatively, Coinbase could ask the user to verify the transactions they made after the fact, but this would allow the payer to deny legitimate transactions. And that, is something I would love to test. If you knew you would get away with it, would you rob someone after shaking their hand?

### High Frequency Dating

The other day I realized there was something missing in my life, so I set out to find a solution. Online dating is in vogue, which makes sense. The internet already has no small part in satisfying most of my other needs. I was pleased to learn that the latest popular dating app, Tinder, now has an Android client. Besides forcing me to reactivate my Facebook account it seems simple enough. Unfortunately, I quickly realized that this was going to turn in to a massive time sink. Perhaps, I thought, I could optimize it?

I did some simple man in the middle packet sniffing to reverse engineer the Tinder API. It’s pretty simple. Send your location, grab a handful of images and user ids, and tell the server which ones you liked. I wrote a minimal python client in Ubuntu and began designing an algorithm to speed up the process a bit. The algorithm first segments the main image by finding all of the faces via OpenCV. If none are found the candidate is discarded. If multiple faces are found the end score will be the average of all of them. This seems to work since people tend to associate with those of similar levels of attractiveness. Facial attractiveness is surprisingly uncomplicated to quantify. Essentially, evolution has us seeking partners that are as “normal” as possible. Anything that is unusually big or small, any ratio that differs from $\phi$, or about 1.618, hurts the score. After the face(s) are identified in the image, a mask of 25 anthropometric proportion indices is overlaid and mean compliance is measured. This is also done with a custom OpenCV routine.

The client also has messaging capabilities. After a match is identified the algorithm sends a simple message “Can I have a dance?” inspired by Mos Def’s success with Ms. Fat Booty. If no response is received the candidate is discarded. If any response is received, it is ignored and a follow up message is sent ”Haha okay then how about we go to a fancy seafood restaurant?”, inspired by the classic meat-for-sex exchange that is common in the animal kingdom as well as among humans. The client uses NLTK to judge an affirmative or negative response. From here an Odesk virtual assistant coordinates dates. This also handles rescheduling but conflicts are not an issue as you will soon see.

Come date night a Double Robot loaded with over 10 hours of pre-recorded content of me rolls up to a restaurant automatically chosen from Yelp based on reviews, distance, cost, and whether or not another double of me has a date there at the time (awkward). Reservations are made via OpenTable’s API. Everything from witty, non-offensive stories to mildly embarrassing personal traits to compliments are recorded. According to the logs candidates are often taken aback at a robot showing up, but a sincere recording complimenting their shoes immediately puts them at ease. Mostly, though, it asks questions and listens. The algorithm aims for a 4:1 ratio of listening to presenting. Based on tone of voice computed by DSP, the system knows which topics to go deeper on and which to avoid, organized in a tree structure in memory. If things are going poorly the emergency “tell me about your cat” routine is run and the microphone is muted to prevent the Speech to Text processor from running useless cycles.

The check is paid via E La Carte and a car is called with Uber’s API. If the algorithm has not been meeting its heuristics the candidate is driven home and the robot self-destructs after uploading its data to the cloud so future iterations can learn from its mistakes. If it has been going well an AirBnB room is insta-booked and the Uber drives there. Once in the room a music playlist is algorithmically generated with Spotify and the candidate’s musical taste gleaned from their Facebook likes. At this moment an Instacart driver should be arriving with a \$10 bottle of wine and fresh strawberries and an Exec delivers a NeuroSky Mindwave 3 and a Vibease Smart Vibrator.

Both devices connect to the Double’s iPad via Bluetooth wirelessly and to the female directly. The female’s brainwaves are fed in to a Learning Vector Quantization Artificial Neural Network (FABIO). At first FABIO adjusts the parameters of the Vibease mostly at random, but partially based on previous experience. Based on feedback generated by the headset the system learns and adaptively adjusts the output parameters in order to maximize EEG amplitude. Unfortunately, the complex mathematical operations required by FABIO typically exhaust the Double’s battery in around 01:57-02:03 minutes, depending on the female. At this point the Double gruffly requests the female retrieve his charger. The robot records the candidate’s experience with a Go Pro 3 and securely uploads the video to a private Amazon S3 bucket.

At 09:07 in the morning an Uber is automatically called for the female and 3 days later she will receive a heartfelt e-card / receipt. The algorithm will also wish her happy birthday on Facebook and like the top 20% of her Instagram photos as they are posted and start getting a lot of other likes. This continues until her Facebook relationship status switches away from single.

Ahhh modern romance. Turing would be proud. Unfortunately I haven’t found the time to watch any of the videos since I’m too busy optimizing the algorithm.