In the end, it was perhaps not unexpected that the scourge of malevolent artificial intelligence should be thrust upon humanity by Twitter. It all started innocently enough on Tuesday, when Microsoft introduced an AI Twitter account simulating a teenage millennial girl. Named "Tay," the program was an experimental program launched to train AI in understanding conversations with users. Within hours, however, Tay had turned into a racist, genocidal, sex-crazed monstrosity spouting Hitler-loving, sexist profanities for all the world to read, forcing the company to shut her down less than 24 hours after her introduction. And while decades of sci-fi pop culture have taught us that this is what AI is wont to do, Tay's meltdown was not in fact a case of robots gone rogue.

As the oracles of Silicon Valley debate whether the latest tech boom is sliding toward bust, there is already talk about what will drive the industry's next growth spurt. As Google chairman Eric Schmidt stressed during today's keynote, Google believes machine learning is "what's next." MIT's Personal Robots Group has been piloting what they envision as "the preschool of the future" -- a marriage between digital devices and a friendly classroom robot that could help tutor preschoolers. Yeah, sure, some elements of the narrative about developing computers that can think like humans have been exaggerated. But–A.I. technology is already much more advanced than you probably think it is.

Software's immersion within businesses, process and objects has expanded the scope of what could be automated. When you add in rapid advances in artificial intelligence, you start seeing a lot more interest in technologies that are increasing how well machine sense, learn and act. Investors are taking notice – investments in artificial intelligence start-ups by venture capitalists have increased roughly 20x in the last four years. What's your Intelligent Automation play?

Artificial intelligence has been in the news a lot lately, specifically related to machines' ability to deep learn, or more closely mimic the actions of a human brain. In the near future, will a computer's algorithms be able to do the project manager's job as well as you can? Stanford professor Andrew Ng holed up at the Google X Lab at the company's Silicon Valley headquarters and initiated a project dubbed "Google Brain." Google Brain encompassed a connected network of 16,000 computers programmed to mimic aspects of human brain activity by looking for recurring patterns on the Internet. In a period of three days, the Brain had successfully trained itself to recognize a cat based on 10 million digital images taken from YouTube videos. Google Brain was an example of an artificial neural network, designed after the densely interconnected neurons of the human brain.

Artificial Intelligence (AI) has been much in the news with the defeat of Go champion Lee Sedol. In the past, some including Stephen Hawking and numerous Hollywood films have warned about the inexorable march of a potentially hostile Artificial Intelligence. Others fear the disruption to traditional jobs of these new technologies. And if you don't work in the tech industry, it's easy to get the impression that AI systems are just ticking things off the list one by one until we humans are all redundant. But human vs. machine competitions that capture our imaginations represent only a small slice of the amazing AI research that's occurring worldwide.

Last year, a curious nonfiction book became a Times best-seller: a dense meditation on artificial intelligence by the philosopher Nick Bostrom, who holds an appointment at Oxford. Titled "Superintelligence: Paths, Dangers, Strategies," it argues that true artificial intelligence, if it is realized, might pose a danger that exceeds every previous threat from technology--even nuclear weapons--and that if its development is not managed carefully humanity risks engineering its own extinction. Central to this concern is the prospect of an "intelligence explosion," a speculative event in which an A.I. gains the ability to improve itself, and in short order exceeds the intellectual potential of the human brain by many orders of magnitude. Such a system would effectively be a new kind of life, and Bostrom's fears, in their simplest form, are evolutionary: that humanity will unexpectedly become outmatched by a smarter competitor. He sometimes notes, as a point of comparison, the trajectories ...

With retro games and Go well-conquered, where is an artificial intelligence like Google DeepMind meant to turn next? Before you get too excited (or maybe insanely depressed as you imagine a toaster holding aloft the Magic World Championship trophy on its ejection lever), there are no plans to set the AI loose on playing these popular card games. For now, the folks over at Oxford University are happy enough for DeepMind to analyse card data and transform it into code. Essentially, the task it is being set is one of translating the data from human to machine speak and while the cards have their own game "language" and structure, they can certainly throw some curveballs. Many language generation tasks require the production of text conditioned on both structured and unstructured inputs. We present a novel neural network architecture which generates an output sequence conditioned on an arbitrary number of input functions.

Microsoft released an interesting new Artificial Intelligence (AI) technology this week, but unfortunately for them the bot got a little out of hand. Tay, a chatbot programmed with the personality of a cheeky teenage girl, took to twitter under the handle @Tayandyou. She was designed to "engage and entertain people where they connect with each other online through casual and playful conversation." She is a learning AI, and the more she chatted with other twitter users, the smarter she became, with the intention of personalizing the conversation towards the other user. She was targeted to 18-24 year old twitter users in the US.

In this article we will show how you can build a simple Sentiment Analysis tool which classifies tweets as positive, negative or neutral by using the Twitter REST API 1.1v and the Datumbox API 1.0v.

I've completed a course on discrete mathematics and elementary programming concepts (in C and Python). What can I read that won't be over my head? Or what else should I learn about CS so I can tackle more complex ML topics over the summer?