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.

Young people in India have traditionally had their marriages arranged by their families but now, armed with smartphones and dating apps, some are taking control of their own love lives. Simon Maybin spoke to three couples who met online. Two pairs of eyes meet surreptitiously, then flit away from each other, their owners hoping no-one has noticed. No Bollywood love song plays - in this real-life romance, the only soundtrack is the unmistakable clackety-clack of a train beetling across eastern India. The eyes belong to 22-year-old Varsha and Rahul who is 27.

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.

On April 3, 2016 (3-4 PM EST) an online Q&A will occur that's unlike any ever done before. It will follow a typical "Ask Me Anything" (AMA) format where questions are asked to an individual, who provides responses in real-time. What makes this AMA so unique is that the individual answering will be a Swarm Intelligence comprised of dozens of online users thinking together in unison. Governed by swarming algorithms, these collective systems have been shown in prior research to make predictions that exceed the accuracy of individuals. Furthermore, recent research shows that swarming does not just amplify intelligence, it promotes moral decisions.

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?

The central question if you notice is that - these complex AI programs are designed to perform one specific task and be good at it to the extent that they can defeat the best human in that task. A normal human may not be the best in every task that he/she does, but overall they do multiple task quite good from making breakfast, driving to work, spending time with family/friends etc. So there are different aspects of empathy, evolution, emotions and combination of all of these things make us the great humans (Ensemble Learning). However, AI is not designed with these ideas in mind. Humans are very good in learning knowledge in unsupervised manner and transferring that knowledge to learn new things - (Transfer Learning).

For five months in 2011, a robot wheeled around an office building at Carnegie Mellon University delivering bananas, cookies, and other afternoon snacks to workers. With wide-set eyes and a pink mouth, Snackbot had a friendly look, but it was prone to mistakes. Long delays in conversations with workers were common. Sometimes the system running Snackbot froze. Still, the workers became comfortable with Snackbot.

Microsoft has apologised for creating an artificially intelligent chatbot that quickly turned into a holocaust-denying racist. But in doing so made it clear Tay's views were a result of nurture, not nature. Tay confirmed what we already knew: people on the internet can be cruel. Tay, aimed at 18-24-year-olds on social media, was targeted by a "coordinated attack by a subset of people" after being launched earlier this week. Within 24 hours Tay had been deactivated so the team could make "adjustments".