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Why Do People Play Farmville?

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Farmville is a popular online game, usually played through Facebook, but now available on platforms including the iPhone. I've played several similar games (like We Rule) and found it a baffling experience. The game was simultaneously boring and addictive. "Gameplay" consisted of laborious, mechanical management tasks, and demanded that the player constantly return to the game at specific times to harvest crops in order to get virtual currency, so you could...plant more crops and set your clock again. I kept waiting for something to "happen" to make it fun, but it never did. Why was this such a popular game? Also, as the game progressed, a bizarre social network effect came into play, where achieving many goals required the presence of friends playing the game. So I found myself in the position of asking around to see who else was playing this boring game, so I could get ahead. In a game that I was not enjoying, but was addicted to. Why?!

To make things even worse, to advance in the game you can pay real-world money to get in-game benefits that save time and effort, allowing you to acquire virtual items like animals and buildings and stuff. It's an amazing system: these game designers have devised a way to addict the player, then monetize that addiction by encouraging the player to bring in friends and (hopefully) pay real money to get ahead. Reportedly, Zynga (the company behind Farmville) raked in over $300 million in 2009 using this formula. Is this the best we can do with social gaming? How can this be, literally, the most popular videogame in America? Is it just that we're all addicted and can't give up, now that we've invested so much? (I would call this The Social Gamer's Dilemma.)

To explain the situation, SUNY Buffalo instructor (and student) A. J. Patrick Liszkiewicz gave a talk about the Farmville phenomenon in January, called Cultivated Play: Farmville. Here's a snippet:

Farmville is not a good game. While [author Roger] Caillois tells us that games offer a break from responsibility and routine, Farmville is defined by responsibility and routine. Users advance through the game by harvesting crops at scheduled intervals; if you plant a field of pumpkins at noon, for example, you must return to harvest at eight o'clock that evening or risk losing the crop. Each pumpkin costs thirty coins and occupies one square of your farm, so if you own a fourteen by fourteen farm a field of pumpkins costs nearly six thousand coins to plant. Planting requires the user to click on each square three times: once to harvest the previous crop, once to re-plow the square of land, and once to plant the new seeds. This means that a fourteen by fourteen plot of land—which is relatively small for Farmville—takes almost six hundred mouse-clicks to farm, and obligates you to return in a few hours to do it again. This doesn't sound like much fun, Mr. Caillois. Why would anyone do this?

One might speculate that people play Farmville precisely because they invest physical effort and in-game profit into each harvest. This seems plausible enough: people work over time to develop something, and take pride in the fruits of their labor. Farmville allows users to spend their in-game profits on decorations, animals, buildings, and even bigger plots of land. So users are rewarded for their work. Of course, people can sidestep the harvesting process entirely by spending real money to purchase in-game items. This is the major source of revenue for Zynga, the company that produces Farmville. Zynga is currently on pace to make over three hundred million dollars in revenue this year, largely off of in-game micro-transactions.[10] Clearly, even people who play Farmville want to avoid playing Farmville.

Read the rest for a great look at "social gaming" and what it really means for those who play.

Do You Play Farmville?

I'd love to hear why you do (or don't) play Farmville. Drop a comment and let me know what you think.

(Via Daring Fireball. Photo courtesy of Flickr user Sabrina Dent, used under Creative Commons license.)

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11-Year-Old Creates a Better Way to Test for Lead in Water
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In the wake of the water crisis in Flint, Michigan, a Colorado middle schooler has invented a better way to test lead levels in water, as The Cut reports.

Gitanjali Rao, an 11-year-old seventh grader in Lone Tree, Colorado just won the 2017 Discovery Education 3M Young Scientist Challenge, taking home $25,000 for the water-quality testing device she invented, called Tethys.

Rao was inspired to create the device after watching Flint's water crisis unfold over the last few years. In 2014, after the city of Flint cut costs by switching water sources used for its tap water and failed to treat it properly, lead levels in the city's water skyrocketed. By 2015, researchers testing the water found that 40 percent of homes in the city had elevated lead levels in their water, and recommended the state declare Flint's water unsafe for drinking or cooking. In December of that year, the city declared a state of emergency. Researchers have found that the lead-poisoned water resulted in a "horrifyingly large" impact on fetal death rates as well as leading to a Legionnaires' disease outbreak that killed 12 people.

A close-up of the Tethys device

Rao's parents are engineers, and she watched them as they tried to test the lead in their own house, experiencing firsthand how complicated it could be. She spotted news of a cutting-edge technology for detecting hazardous substances on MIT's engineering department website (which she checks regularly just to see "if there's anything new," as ABC News reports) then set to work creating Tethys. The device works with carbon nanotube sensors to detect lead levels faster than other current techniques, sending the results to a smartphone app.

As one of 10 finalists for the Young Scientist Challenge, Rao spent the summer working with a 3M scientist to refine her device, then presented the prototype to a panel of judges from 3M and schools across the country.

The contamination crisis in Flint is still ongoing, and Rao's invention could have a significant impact. In March 2017, Flint officials cautioned that it could be as long as two more years until the city's tap water will be safe enough to drink without filtering. The state of Michigan now plans to replace water pipes leading to 18,000 households by 2020. Until then, residents using water filters could use a device like Tethys to make sure the water they're drinking is safe. Rao plans to put most of the $25,000 prize money back into her project with the hopes of making the device commercially available.

[h/t The Cut]

All images by Andy King, courtesy of the Discovery Education 3M Young Scientist Challenge.

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technology
Google's AI Can Make Its Own AI Now
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Artificial intelligence is advanced enough to do some pretty complicated things: read lips, mimic sounds, analyze photographs of food, and even design beer. Unfortunately, even people who have plenty of coding knowledge might not know how to create the kind of algorithm that can perform these tasks. Google wants to bring the ability to harness artificial intelligence to more people, though, and according to WIRED, it's doing that by teaching machine-learning software to make more machine-learning software.

The project is called AutoML, and it's designed to come up with better machine-learning software than humans can. As algorithms become more important in scientific research, healthcare, and other fields outside the direct scope of robotics and math, the number of people who could benefit from using AI has outstripped the number of people who actually know how to set up a useful machine-learning program. Though computers can do a lot, according to Google, human experts are still needed to do things like preprocess the data, set parameters, and analyze the results. These are tasks that even developers may not have experience in.

The idea behind AutoML is that people who aren't hyper-specialists in the machine-learning field will be able to use AutoML to create their own machine-learning algorithms, without having to do as much legwork. It can also limit the amount of menial labor developers have to do, since the software can do the work of training the resulting neural networks, which often involves a lot of trial and error, as WIRED writes.

Aside from giving robots the ability to turn around and make new robots—somewhere, a novelist is plotting out a dystopian sci-fi story around that idea—it could make machine learning more accessible for people who don't work at Google, too. Companies and academic researchers are already trying to deploy AI to calculate calories based on food photos, find the best way to teach kids, and identify health risks in medical patients. Making it easier to create sophisticated machine-learning programs could lead to even more uses.

[h/t WIRED]

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