Given how easy it is to discover new music on Spotify, it's just as easy to forget about all the equally delicious tracks you found last month. Thankfully, Spotify won't let that happen, today releasing Your 2017 Wrapped. This clever feature, initially rolled out last year to replace its previous'Year In Music' recap, lets you effortlessly relive all the gems you discovered in 2017 and set some goals for the new year ahead. By heading here and signing into your account, Spotify swiftly analyses your year's worth of valuable listening and comes back with some pretty remarkable stats. Check out how long our CEO Spotify-binged this year! I listened to 230,639 Minutes of music this year: https://t.co/ko6UNvC6wL

Most salespeople will tell you the same thing – no amount of preparation, banter, or efforts can ever match the absolute luck involved in catching a customer at the precise point that they are looking to make a purchase. Finding a person who is a potential customer is simply one part of a company's acquisition process; like the salespeople, companies want to talk to the person, precisely when the time is right and get the right deal at the right time. However, this is very rare – those very salesmen will tell you many stories about the many hundreds and thousands of customers whom they've spoken to and ended up wasting ridiculous amounts of time on, but just never made a purchase. Companies have long known that each customer goes through different stages before actually buying a product and different stages post-purchase as well. Knowing which stage a customer is in helps companies phrase and frame their communications to them, and this customer life-cycle is a very important part of any company's sales strategy.

Virtual advisors are helping meet the demands of investors who want data-driven insight and a rich, personalized experience akin to Netflix. Cognitive software platforms, which provide the tools to analyze, organize, access, and provide advisory services based on a range of structured and unstructured data, are set to attract investment of nearly US$2.5 billion in this year alone. Additionally, spending on intelligent and cognitive applications is forecast by IDC to grow at an impressive 69 percent compound annual growth rate from 2017 to 2020. Driving this explosion is the fact that more data will be created in 2017 than in the last 5,000 years. And it's expected to increase three-fold from this year until 2021.

The Echo Show is the first of Amazon's Alexa-enabled devices to feature a screen, and to be honest, it's pretty darn cool. While the Show may be the most expensive* of the Echo lineup, ringing in at $229.99, it includes a slew of features and skills you can't get from the other Echo devices, thanks to the 7-inch touchscreen and camera. So why would you want to choose the more expensive Show over the new Echo ($99.99) or the more affordable Dot (on sale for $29.99)? There are several Show-specific skills that are both fun and functional. From watching HD movie trailers to making video calls, these are our picks for the top 10 skills you definitely need to try with your Amazon Echo Show.

Now I am going to use CNN or RNN to extract features in a time sequence, for example, a sequence related to user clicks, (Oct 10 19:20:30 click page 10, Oct 10 19:20:35 click page 22, etc). Simply, I can represent the two clicks as 000...10.....22...000 (0 for no click). As you can see, if use one number for the action in one second, that will generate a quite long sequence with a lot of blank fragments, which is not good for RNN or CNN. But we remove all zeros, only with 10-22 we don't know the time interval between two clicks. So, can anyone give a suggestion on how to express this kind of sequence properly so that we can combine it with neural networks easily?

Artificial Intelligence (AI) has generated enormous hype in recent months, but opinions about the real impact of technologies like machine translation, autonomous vehicles, natural language processing, and computer vision vary widely. Optimists envision a world in which "driver" becomes a superfluous word; pessimists worry about the millions of truckers at risk of unemployment. But while often described with the futuristic fancy of Skynet in the "Terminator" movies, AI has very real near-term applications for the entertainment industry. Here are some of the most exciting examples. AI is poised to touch every facet of entertainment, even that most time-honored of parental traditions: reading stories to children before bed.

Over the past year, almost every conversation on artificial intelligence involved a discussion on ethics, humanity, and policy. Sometimes a picture is worth a thousand words. In this case, one can look to Hollywood for a few good suggestions to ponder. The Matrix raises many philosophical points from perceptions of reality. For instance, has a general artificial intelligence fully manipulated humanity?

We investigate the association between musical chords and lyrics by analyzing a large dataset of user-contributed guitar tablatures. Motivated by the idea that the emotional content of chords is reflected in the words used in corresponding lyrics, we analyze associations between lyrics and chord categories. We also examine the usage patterns of chords and lyrics in different musical genres, historical eras, and geographical regions. Our overall results confirms a previously known association between Major chords and positive valence. We also report a wide variation in this association across regions, genres, and eras. Our results suggest possible existence of different emotional associations for other types of chords.

FusionGAN is a novel genre fusion framework for music generation that integrates the strengths of generative adversarial networks and dual learning. In particular, the proposed method offers a dual learning extension that can effectively integrate the styles of the given domains. To efficiently quantify the difference among diverse domains and avoid the vanishing gradient issue, FusionGAN provides a Wasserstein based metric to approximate the distance between the target domain and the existing domains. Adopting the Wasserstein distance, a new domain is created by combining the patterns of the existing domains using adversarial learning. Experimental results on public music datasets demonstrated that our approach could effectively merge two genres.

Coditany of Timeness seems like an average entry in the black metal genre, until you consider its source. It was created by a neural network algorithm without a single guitar, without any drums, without even a vocalist. Instead, it's a product of machine learning, the result of a project by Zack Zukowski and CJ Carr called "Dadabots." As their inspiration, the duo used the New York death metal band Kralice and their 2011 album Diotima to teach their algorithm how to create its own music. After breaking the audio into small pieces, they fed it all into the algorithm to let the artificial intelligence learn what death metal was and how to create it.