If J.K. Rowling was ever concerned that artificial intelligence could do her job for her, she let out a sigh of relief this week. A fan of the Harry Potter series in San Francisco had an artificial intelligence computer algorithm write a few chapters for the iconic series, after teaching the computer to read earlier novels. The results are far from ideal. Max Deutsch, a product manager at Intuit, trained a a Long Short Term Memory (LSTM) Recurrent Neural Network computer by teaching it to read the first four Harry Potter novels. A LSTM computer is trained to notice patterns (say, in genomes or handwriting), which makes it a great test subject for writing patterns.

The app won't require any sort of authentication, allowing anyone -- with or without a cable subscription -- to watch CBSN. It takes advantage of the 4th generation Apple TV's Siri integration, letting viewers launch the live stream by telling Apple's personal assistant they want to "Watch CBS News." While the stream is playing on the top left-hand corner of your screen, users can also browse and bookmark related videos.

One of the difficult problems in using machine learning to generate sequences, such as melodies, is creating long-term structure. Long-term structure comes very naturally to people, but it's very hard for machines. Basic machine learning systems can generate a short melody that stays in key, but they have trouble generating a longer melody that follows a chord progression, or follows a multi-bar song structure of verses and choruses. Likewise, they can produce a screenplay with grammatically correct sentences, but not one with a compelling plot line. Without long-term structure, the content produced by recurrent neural networks (RNNs) often seems wandering and random.

Recently, we have seen artificial intelligence triumph over humans in Jeopardy and chess. And there is a growing presence of virtual assistants like Alexa, Cortana, and Siri that populate our computers, phones, and homes. It's only a matter of time before A.I.-powered assistants play a significant role in the workplace, experts say. In fact, the global intelligent virtual assistant market is forecast to be worth 5.1 billion by 2022, up from – 600 million in 2014, according to Transparency Market Research. What are the potential benefits and challenges of giving smart virtual assistants a home in the enterprise?

When the final book in the Harry Potter series came out in 2007, fans of the novels were left despondent at the thought of an end to the exploits of the boy wizard and his friends. But there could be a way to keep the magic alive, without the need for author, JK Rowling. One Harry Potter fan in San Francisco has trained an artificial intelligence computer to write new adventures for the iconic series after teaching it to read the earlier novels in the series. To test his theory, Mr Deutsch trained a Long Short Term Memory (LSTM) Recurrent Neural Network computer with the first four Harry Potter books, including'The Prisoner of Azkaban' (still from the film, pictured) Sadly for those hoping for fresh tales about Harry and his friends, the results are hilariously rubbish. Max Deutsch, a Product Manager at Intuit, was interested to see if artificial intelligences could mimic human writing.

James Barrat is a writer, director and producer of documentary films. He is also author of the nonfiction book Our Final Invention: Artificial Intelligence and the End of the Human Era. Time Magazine named him one of the 5 smartest people who believe AI could bring on the apocalypse and his book was named on of the 8 definitive tech books of 2013 bu Huffington Post. Be sure to check us out on www.waitwhatif.com,

Researchers from MIT have created an algorithm that can understand human visual cues, and predict the next action. This algorithm will help AI have the ability to be able to understand and predict human interaction and predict what could happen next. Hopefully, this could help in different fields in the future, like home assistants or intelligent security systems which could call the police or an ambulance immediately in case the need arises. MIT's Computer Science and Artificial Intelligence Laboratory created this deep learning algorithm. The researchers inputted different videos with human interaction onto the program and tested whether the programed actually "learned" sufficiently to be able to predict the right outcomes.

The other day, a search for "artificial intelligence in law" produced 86,400 results from just the News section of Google's vast index. From the Web as a whole, 32,800,000 results and from Videos – 261,000, beginning with Jude Law's role as Gigolo Joe in the movie A.I. (thank you, RankBrain). The first News story was "Law firm bosses envision Watson-type computers replacing young lawyers," reporting on the answers to one question in the recent Altman & Weil survey of law firm leaders (page 82). As wittily argued by Ryan McClead, "the question is flawed on many levels [and] … it's time to cut the hysteria surrounding artificial intelligence in law." But we need to parse the pile a bit.

Y Background foreground mapping 0.0 Y 1.0, where 1 indicates definitely foreground and 0 indicates definitely background

The Emmy nominations are always "political," but they're rarely political. While many non-artistic factors may affect the anointing of a chosen handful with the word "best," the nominations, at least as a group, rarely reflect the sociopolitical concerns of the American citizenry. In the midst of a presidential race bristling with divisive issues and distrust, the Television Academy singled out a large number of series that took on that distrust and those issues in ways dramatic, comedic and deeply human. Political TV came in two distinct forms this year. Robot," "Veep"--reflect a near-hopeless cynicism toward actual government. Other nominated series took on issues rather than governance --"black-ish," "The People v. O.J. Simpson," "American Crime"-- and directly addressed complicated themes, especially those of race, in a way that is, if not always hopeful, at least productive. Likewise, series anchored by the once-rare strong female lead and her experience filled virtually every ...