This paper introduces the Beer2Vec model that allows the most popular alcoholic beverage in the world to be encoded into vectors enabling flavorful recommendations. We present our algorithm using a unique dataset focused on the analysis of craft beers. We thoroughly explain how we encode the flavors and how useful, from an empirical point of view, the beer vectors are to generate meaningful recommendations. We also present three different ways to use Beer2Vec in a real-world environment to enlighten the pool of craft beer consumers. Finally, we make our model and functionalities available to everybody through a web application.

Beer is delicious but it is not one thing. If you disagree with the former part of the previous sentence please keep the latter in mind[1]. Think of sports, for instance. Many would agree with the blanket statement "sports are fun" but depending on what you have in mind two people can easily have opposite reactions to being presented the opportunity to play ping-pong. Sports are not one thing, music is not one thing, and neither is beer. Presented with a finely crafted brew in a style of your preference it is difficult to have a more pleasurable gastronomical experience.

As a person living in the 21st century, it's almost inevitable that you've had the seamless, fast, and hassle-free experience of shopping online: a few clicks and you're done without ever needing to interact with anyone, and then your items can show up at your door in as little as a day. But as the holiday season ramps up, it's a good time to remember that there's actually a whole lot of human labor behind that fast and easy click. While we at Mother Jones recently reported on how robots will one day take these jobs, they haven't taken over just yet. Just consider a great story last week from Gizmodo's Bryan Menegus shedding light on a mysterious program known as Amazon Flex: a "nearly invisible workforce" of independent contractors charged with delivering the "last mile" of Amazon orders from a local storage facility to the customer's door. As Menegus explains, "It's a network of supposedly self-employed, utterly expendable couriers enrolled in an app-based program which some believe may violate labor laws."

In the spring of 1960, just after he turned 16, Chuck Stout went to work as a "garbage boy" at a McDonald's in Toledo, Ohio. For 85 cents an hour, he swept and mopped the floors, kept the drive-in lot tidy, filled the shake machine, and washed dishes. It was an escape--somewhere to go that wasn't the Weiler Homes public housing complex, where he lived with his mother and sister. They were barely scraping by. "My mom drank so much," he says, "she didn't know what I was doing." Not only did Chuck love his job, the job loved him. He went from garbage boy to french fry maker to burger cook to cashier. He became a manager, then a supervisor, then a field consultant, then a professor at Hamburger University, where McDonald's trains new franchise owners and managers. By 1976, Chuck was serving as a director of product development for the entire corporation. The next year, he was on the team that brought ice cream sundaes to the chain's menu. For the effort, Chuck was rewarded with a handsome bonus and a personal letter from founder Ray Kroc, whose wisdom Chuck was fond of quoting from memory. Chuck eventually got fed up with corporate culture and told his superiors he wanted to go back out "in the field." When two planes hit the World Trade Center in 2001, he was 57 and running his own McDonald's franchise in Columbia, Pennsylvania. He rushed to Manhattan, where for three days he loaded up Egg McMuffins, hash browns, and coffee, first onto a luggage trolley, then a golf cart, and hauled them down to the debris pit to feed rescuers.

To the throngs of those who've put down their complicated digital SLR cameras in favor of easy-to-use smartphones, Ryan Stout has a message: Not so fast. Stout is the founder, CEO and lead developer of Arsenal, a six-person, Montana-based startup that's using computer vision to intelligently automate the abundant capabilities of DSLR cameras. Stout admits he himself had become a smartphone-dependent photographer. But several years ago, he decided to pull his camera out and start taking night photos. From setting shutter speeds, apertures and ISOs to choosing just the right filter, he was quickly reminded that photography is an intensely technical undertaking.

Craft brewers are running out of beer names. NPR reports that companies are having to compromise over shared a name, or getting in Twitter fights over them. Even lawyers are settling spats over imagery, or hop puns like Hopscotch and Bitter End. So, rather than relying on our boring human brains, why not use artificial intelligence to come up with fresh new names? That's what scientist Janelle Shane (who uses artificial intelligence for this purpose frequently) decided to do.

Now, she's turned her AI naming capabilities towards beer. While writing about her neural network-generated paint color names, Gizmodo's Ryan Mandelbaum mentioned the issues craft brewers were having coming up with names for their beers -- issues that have at times led to legal action. Not long after that, with the help of a Gizmodo reader who put together a dataset of beer names culled from BeerAdvocate.com, Shane plugged the names into a neural network and out popped a bunch of new names for brewers to use. "For these names I turned the neural network's creativity variable higher and got results that can be described mainly as ... interesting," she said on the blog.

Veronica Rivera signed up for the introduction to computer science class at Harvey Mudd College mostly because she had no choice: It was mandatory. Programming was intimidating and not for her, she thought. She expected the class to be full of guys who loved video games and grew up obsessing over how they were made. There were plenty of those guys but, to her surprise, she found the class fascinating. She learned how to program a computer to play "Connect Four" and wrote algorithms that could recognize lines of Shakespeare and generate new text with similar sentence patterns. When that first class ended, she signed up for the next level, then another and eventually declared a joint major of computer science and math.