USA TODAY's Jefferson Graham takes a look at three new connected speakers that all have video features. Amazon has enlarged and enhanced its Echo Show device, which brings video to its line of smart speaker, Google counters with the Home Hub, adding video to the Home line of speakers, and Facebook is in there as well, with its controversial Portal video speaker. We've taken all for intensive test drives. Which one is for you? The Google speaker, ($149, on sale for $99 during Black Friday sales) is our all-around favorite of the trio because it does more than the others.

Computers aren't going to replace creative pros -- but machine learning and artificial intelligence can be powerful tools in the storytelling process. The 60-second spot was directed by Oscar-winner Kevin Macdonald, working from a script that was developed by IBM's Watson AI system. To produce the spot for the Lexus ES executive sedan launching in Europe, the automaker enlisted its creative agency, The&Partnership London, along with technical partner Visual Voice. The agencies collaborated with the IBM Watson team to use AI to analyze 15 years' worth of footage, text and audio for car and luxury brand campaigns that have won Cannes Lions awards for creativity, as well as a range of other external data. Watson identified elements common to award-worthy commercials that were "both emotionally intelligent and entertaining," according to IBM.

Mark Zuckerberg fiercely defended Facebook in a question-and-answer session with employees on Friday afternoon, pushing back against criticism of the company in the wake of a New York Times investigation into how it reacted to Russian influence operations. In an hour-long video-conference broadcast to Facebook offices around the world, Mr Zuckerberg responded to questions from employees on a range of topics, from Facebook's behaviour over the past 18 months to how it should handle leaks to the media, according to three people familiar with the discussion but not willing to discuss it publicly because it was a private meeting. The idea that Facebook tried to "cover up anything" was wrong, an impassioned Mr Zuckerberg said, using an expletive in his response, according to these people. Some employees responded with muted applause and cheers. The session came at a fraught time for the social network, as executives mobilised to deal with a torrent of criticism of the company.

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.