This is a project I've been working on for a few months, and I recently got a job offer to do similar work so all progress on the project is about to halt. Figured this would be the best time if ever to submit it here, so I'd love to hear any feedback/suggestions. In addition to being a project exploring conversation models in TensorFlow, it is also a good reference for anyone looking to get a simple app deployed on a website using their TensorFlow models. I experimented with both Heroku and Google AppEngine but stuck with GAE. For anyone looking to play with sequence to sequence models and/or write your own, this project would be a good reference (imo).

When the Netflix series House of Cards premiered in 2013, it quickly became the most downloaded content in the company's history – a statistic that came as no surprise to Netflix executives. They had previously examined a vast pool of Netflix data on subscribers' viewing habits and determined that the show was likely to become a hit even before they purchased it. The wisdom behind Netflix's sure-fire choice came from machine learning, which, loosely defined, is the ability of computers to learn on their own (without being programmed) by using algorithms that churn through large quantities of data. Machine learning's talents aren't limited to picking the next TV blockbuster, either. Consider some of the more down-to-earth uses that we already take for granted today.

In the last two years, the number of machine-learning (ML) startups has skyrocketed, and companies (in truth or in hype) increasingly predicate their value proposition on artificial intelligence (AI). Although ML and AI in healthcare and finance have garnered a tremendous amount of venture investment and press, other areas, such as marketing and business intelligence, have the potential to more quickly impact profitability, and are less fettered by regulation. As of last year, there wasn't serious consensus or research into current and future AI marketing trends, so we decided to poll and interview over 50 ML and AI marketing executives. The goal: to determine the industries and applications with the most promise. The full ML-in-marketing survey, conducted over three months, sheds light on various noteworthy trends and patterns; below, after defining some terms, I've highlighted three major findings.

Just a few months after GoPro threatens to close its entertainment doors, DJI is ready to step in. The drone-cum-camera company has just announced that it's launching a Smart TV app that'll stream plenty of aerial content and 4k videos captured from DJI drones and cameras. The app will initially be made available on Samsung's Tizen TVs and Apple TV. This is yet another instance in which DJI and GoPro have overlapping businesses. GoPro started with cameras and moved into drones, while DJI went from making drones and then moved into the camera business (The DJI Phantom Vision 2 was the first to ditch GoPro cameras for its own). GoPro has also been trying to make inroads as an entertainment company for a few years now -- indeed, there's a GoPro channel on Xbox, PS4, Roku, Samsung and LG TVs, as well as Virgin America's in-flight entertainment center.

At the Think About! conference staged in Warsaw by Ringier Axel Springer, the eastern Europe joint venture of Ringier and Axel Springer, I talked about the changes that AI, artificial intelligence, will bring to the profession of journalism in the future. You can already find AI in a huge amount of online products, for example in thematic pages that are almost completely automatically generated. The articles appear in a narrow thematic context, such as political or cultural content, but the possibilities here are almost endless. And with personalized content from online content providers, such as Axel Springer's news platform for Samsung smartphones, UPDAY, an algorithm learns from the user which issues and format he or she prefers, and constantly improves the personal selection of this content.

In spite of all the current hype, AI is not a new field of study, but it has its ground in the fifties. If we exclude the pure philosophical reasoning path that goes from the Ancient Greek to Hobbes, Leibniz, and Pascal, AI as we know it has been officially started in 1956 at Dartmouth College, where the most eminent experts gathered to brainstorm on intelligence simulation. It went then through two main'winter' periods, in which investments and interested drastically declined, and back in the nineties it looked like pursuing the creation of an artificial intelligence system was a public shame and a waste of energy. However, as in the movie "The Curious Case of Benjamin Button", the more time it passed the more AI became actual and relevant. Luckily enough, in 1993 this period ended with the MIT Cog project to build a humanoid robot, and with the Dynamic Analysis and Replanning Tool (DART) -- that paid back the US government of the entire funding since 1950 -- and when in 1997 DeepBlue defeated Kasparov at chess, it was clear that AI was back to the top.

I would've thought to tie both networks together but allowing the two to be trained independently makes much more sense given the training data.

This image released by Twentieth Century Fox shows Michael Fassbender who portrays android David in a scene from "Alien: Covenant." Filmmakers have long projected that artificial intelligence could spell the end of humanity. "Prometheus" director Ridley Scott, who further explores the cunning side of artificial intelligence in his new "Alien: Covenant," says, "If you're going to use something that's smarter than you are, that's when it starts to get dangerous."

Silicon Valley is Mike Judge and Alec Berg's biting comedy about the American tech industry, now in its fourth season. Every week, we'll be taking one idea, scene, or joke and explain how it ties to the real Silicon Valley and speaks to an issue at the heart of the industry and its ever-lasting goal to change the world -- and make boatloads of money in the process. In the fictional tech industry of HBO's Silicon Valley, the soul-crushing mundanity of grunt work is often treated as a punchline. Like many sitcoms about careerism and the slog of American professional life, it's considered an insult to have to do boring, seemingly meaningless tasks just because some higher power demands it. But on last night's episode, Silicon Valley highlighted a rather pernicious aspect of the tech industry that's currently serving as the foundation of modern artificial intelligence, shining a spotlight on a type of human labor often overlooked when we discuss the marvels of automation.

In the last two years, the number of machine-learning (ML) startups has skyrocketed, and companies (in truth or in hype) increasingly predicate their value proposition on artificial intelligence (AI). Although ML and AI in healthcare and finance have garnered a tremendous amount of venture investment and press, other areas, such as marketing and business intelligence, have the potential to more quickly impact profitability, and are less fettered by regulation. As of last year, there wasn't serious consensus or research into current and future AI marketing trends, so we decided to poll and interview over 50 ML and AI marketing executives. The goal: to determine the industries and applications with the most promise. The full ML-in-marketing survey, conducted over three months, sheds light on various noteworthy trends and patterns; below, after defining some terms, I've highlighted three major findings.