It's hard to imagine one Death Eater kissing another one on the cheek while more of Voldemort's supporters gather around them and applaud -- unless you're a computer. Botnik Studios, a company that uses algorithms to train computers to behave in certain ways, has produced a brand new chapter from a new Harry Potter book. While a computer can certainly mimic elements of the famous series, though, it can hardly capture J.K. Rowling's magic -- which is why the result, called Harry Potter and the Portrait of What Looked Like a Large Pile of Ash, is so strange and funny that fans are begging for more. Too funny: Botnik Studios used predictive keyboards to write a new chapter in a new Harry Potter book. It's called Harry Potter and the Portrait of What Looked Like a Large Pile of Ash Has that computer been Confunded?

Do you remember that memorable scene in the Harry Potter books when a person seeking revenge on Ron turns out to be Dumbledore hiding behind a cream cake? If you don't, that's probably because you read the version of Harry Potter written by J.K. Rowling instead of an LSTM recurrent neural network -- trained to generate new Hogwarts-related stories using a data set consisting of the series' first four books. "I've been experimenting with deep learning over the past few weeks, and the Harry Potter story is the result of one of those experiments," creator Max Deutsch tells Digital Trends. "Beyond just looking for a fun way to practice what I've been learning, the Harry Potter project was an attempt to make something enjoyable to read." Since the results are more surreal mash-up than anything likely to give Rowling sleepless nights, "enjoyable" may not be exactly the word.

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.

The goal of my work is to detect implicit social ties or closely-linked entities within a data set. In data consisting of people (or other entities) and their affiliations or discrete attributes, we identify unusually similar pairs of people, and we pose the question: Can their similarity be explained by chance, or it is due to a direct (“copying”) relationship between the people? The thesis will explore how to assess this question, and in particular how one’s judgments and confidence depend not only on the two people in question but also on properties of the entire data set. I will provide a framework for solving this problem and experiment with it across multiple synthetic and real-world data sets. My approach requires a model of the copying relationship, a model of independent people, and a method for distinguishing between them. I will focus on two aspects of the problem: (1) choosing background models to fit arbitrary, correlated affiliation data, and (2) understanding how the ability to detect copies is affected by factors like data sparsity and the numbers of people and affiliations, independent of the fit of the models.