The Library of Congress has restored the first film adaptation of Mary Shelley's Frankenstein, an Edison production from 1910 directed by J. Searle Dawley. We tend to think of effects-driven spectacles as a product of the modern era, but decades before that checkerboard floor in Terminator 2: Judgement Day started moving, studios were selling films on the basis of single FX shots. Here's how the Edison company described their Frankenstein: To those who are not familiar with the story, we can only say that the film tells an intensely dramatic story by the aid of some of the most remarkable photographic effects that have yet been attempted. The formation of the hideous monster from the blazing chemicals of a huge caldron in Frankenstein's laboratory is probably the most weird, mystifying, and fascinating scene ever shown on a film. Frankenstein's creation is no longer the most weird, mystifying, and fascinating scene ever shown on film, but it's a fun trick-shot, using reversed footage of a dummy that has been set on fire to give the impression of a body knitting itself together from nothing.

This work describes a novel recurrent model for music composition, which accounts for the rich statistical structure of polyphonic music. There are many ways to factor the probability distribution over musical scores; we consider the merits of various approaches and propose a new factorization that decomposes a score into a collection of concurrent, coupled time series: 'parts.' The model we propose borrows ideas from both convolutional neural models and recurrent neural models; we argue that these ideas are natural for capturing music's pitch invariances, temporal structure, and polyphony. We train generative models for homophonic and polyphonic composition on the KernScores dataset (Sapp, 2005) a collection of 2,300 musical scores comprised of around 2.8 million notes spanning time from the Renaissance to the early 20th century. While evaluation of generative models is known to be hard (Theis et al., 2016), we present careful quantitative results using a unit-adjusted cross entropy metric that is independent of how we factor the distribution over scores. We also present qualitative results using a blind discrimination test.

Tech, media, entertainment, and telecommunications enterprises are earning an average 20% or greater ROI on their AI investments today. Enterprises competing in these industries are in a race to create and license as much intellectual property (IP) as possible. With the global market for cognitive technologies predicted to reach $19.1B globally, patents are key to creating new markets. In addition to the billions of dollars Apple, Google, IBM, Microsoft, and others are investing in AI and cognitive technologies this year, Netflix is using AI to improve customer experiences. Using AI, Netflix found that if customers search for a movie for more than 90 seconds, they give up.

The movie studio 20th Century Fox developed a machine learning algorithm that it's using to predict who will like new movies. The system, described in a paper published on the preprint server ArXiv in late October, analyzes and categorizes movie trailers based on the objects and people it detects in them. Other films with similar trailers, according to the algorithm, will likely appeal to similar audiences. For instance, a blog post about the algorithm details how it interpreted the X-Men film "Logan." After watching the trailer, the top four labels picked up by the algorithm were "tree," "facial hair," "car," and "man," which led the algorithm to recommend the visually-similar film "The Revenant," perhaps because of all the beards and forests.

This undated image provided by Amazon.com, Inc. shows an Amazon Fire TV Cube. Watch whatever you want, whenever you want. All you have to buy is one little box, and the monthly subscriptions are up to you. After decades of flipping through TV channels, many of us find the promise of internet-based television too miraculous to pass up.

Alan Turing's famous test of whether machines could fool us into believing they were human – "the imitation game" – has become a mundane, daily question for all of us. We are surrounded by machine voices, and think nothing of conversing with them – though each time I hear my car tell me where to turn left I am reminded of my grandmother, who having installed a telephone late in life used to routinely say goodnight to the speaking clock. We find ourselves locked into interminable text chats with breezy automated bank tellers and offer our mother's maiden name to a variety of robotic speakers that sound plausibly alive. I've resisted the domestic spies of Apple and Amazon, but one or two friends jokingly describe the rapport they and their kids have built up with Amazon's Alexa or Google's Home Hub – and they are right about that: the more you tell your virtual valet, the more you disclose of wants and desires, the more speedily it can learn and commit to memory those last few fragments of your inner life you had kept to yourself. As the line between human and digital voices blurs, our suspicions are raised: who exactly are we talking to? No online conversation or message-board spat is complete without its doubters: "Are you a bot?" Or, the contemporary door-slam: "Bot: blocked!" Those doubts will only increase. The ability of bots – a term which can describe any automated process present in a computer network – to mimic human online behaviour and language has developed sharply in the past three years.

We demonstrate a conditional autoregressive pipeline for efficient music recomposition, based on methods presented in van den Oord et al.(2017). Recomposition (Casal & Casey, 2010) focuses on reworking existing musical pieces, adhering to structure at a high level while also re-imagining other aspects of the work. This can involve reuse of pre-existing themes or parts of the original piece, while also requiring the flexibility to generate new content at different levels of granularity. Applying the aforementioned modeling pipeline to recomposition, we show diverse and structured generation conditioned on chord sequence annotations.

Thomson Reuters has a series, AI experts, where they interview thought leaders from different areas - including technology executives, researchers, robotics experts and policymakers - on what we might expect as we move towards AI. As part of that series I recently spoke to Paul Thies of Thomson Reuters, and here are the excerpts from the interview: Anticipating the next move in data science Thomson Reuters: For timely information concerning developments in data science, data mining and business analytics, KDnuggets is widely regarded as a leading outlet in the field. Created in 1993 by founder, editor and president Gregory Piatetsky-Shapiro, it is frequently cited as one of the top sources of data science news and influence by various industry watchers. Thomson Reuters: What are some use cases of data science that you find to be particularly valuable to organizations in this age of Big Data? GREGORY: Where people typically apply data science, probably not surprisingly, are in the areas of customer relationship management (CRM) and consumer analytics. Data science allows you to predict consumer behavior better and usually make incremental improvements and predictions, but those incremental improvements could translate to significant revenue.

A eureka moment struck, recalls Mr. Rippel. "Hey, hold on, we could do this for real." The two Ph.D.s quickly decided to try in reality what HBO's fictitious startup was trying on screen. Ever since, the pair of Facebook Inc. alums-turned-startup founders have been living a case of life imitating art imitating life. HBO's comedy series, which began its run in 2014, tells the story of scrappy startup Pied Piper, which hits on a brilliant algorithm for compressing information so it can zip through the pipes of the internet more quickly.

So far, we've seen a continued uptick in augmented reality, video content, and influencer marketing. But now that 2018 is one-third of the way over, you may find yourself wondering: "what does 2019 have in store for us?" We can't be sure yet, but we have a few predictions for the upcoming New Year. We'll discuss our predictions in this article and give you a look at how the ever-changing digital marketing landscape may look in just a few months. This one may be strange to see on a digital marketing trends list, but hear (pun intended) us out.