Style transfer and image generation are fun but are ends unto themselves if you want to use them as artistic tools--this looks like it could be deployed and replace basic character locomotion animation.

Both Google and Sony have projects underway to advance how computers write music. The inherent issue with computers writing music, however, is trying to figure out what the math is for inspiration. Because creativity can't really be quantified, it's difficult to develop an algorithm for it. Douglas Eck of Magenta, an offshoot of a Google Brain artificial intelligence project, says that many of the issues in figuring out how to enable computers to write music are human, rather than machine-based, and revolve around discovering the right questions to ask. Since writing music is not linear, creating a way for machines to write music cannot be linear either.

You are free to share this article under the Attribution 4.0 International license. A four-armed, marimba-playing robot can now write and play its own compositions with aid from artificial intelligence and deep learning. Researchers fed the robot nearly 5,000 complete songs--from Beethoven to the Beatles to Lady Gaga to Miles Davis--and more than 2 million motifs, riffs, and licks of music. Aside from giving the machine a seed, or the first four measures to use as a starting point, no humans are involved in either the composition or the performance of the music. The first two compositions are roughly 30 seconds in length.

As literally every sci-fi movie has predicted, we're becoming increasingly reliant on artificial intelligence. AI can already compose music, play Ms. Pac-Man--like a pro, nonetheless--and even manage a hotel. But it's never been used solely for the purpose of naming small, fluffy guinea pigs--until now. Earlier this week, research scientist Janelle Shane got a fantastically unusual request from the Portland Guinea Pig Rescue, asking if she could build a neural network for guinea pig names. The rescue facility needs to generate a large number of names quickly, as they frequently take in animals from hoarding situations.

After discovering the amazing power of convolutional neural networks for image recognition in part five of this series, I decided to dive head first into Natural language Processing or NLP. This hotbed of machine learning research teaches computers to understand how people talk. When you ask Siri or the Google Assistant a question, it's NLP that drives the conversation. Of course, as an author of novels and articles, working with language seemed like the obvious next step for me. I may suck at math but words are my domain! So I set out to uncover what insights NLP could give me about my own area of mastery. I had so many questions. Had NLP uncovered the hidden keys to writing heart-wrenching poems? Could AIs turn phrases better than the Bard? Luckily, I had just the right project in mind to test the limits of NLP. I was in the midst of naming the second book in my epic sci-fi saga The Jasmine Wars but I'd struggled to find the perfect title. What if I could feed a neural net with the greatest titles of all time and have it deliver a title for the ages? This isn't my first foray into computer assisted title generation. There are a number of random title generators out on the interwebs that I've tried from time to time. They're the type of toy you play with for a few minutes and then move on.

I followed the tensorflow transfer learning tutorial and tested it on the sample flower dataset and a custom cats vs dogs dataset. This is a documentation of the same process with the errors that I faced and their solution. This is my first post on Reddit, suggestions are welcome.

I'm trying to classify data into two classes and my loss is less than 0.01 under both MSE and BCE. This seems contradictory to me that my performance on the training set is still so low - the ratio of true positives to false positives is at least 1:5 even when sweeping the threshold. Does this behavior mean my net is still not learning?

This paper introduces DeepBach, a graphical model aimed at modeling polyphonic music and specifically hymn-like pieces. We claim that, after being trained on the chorale harmonizations by Johann Sebastian Bach, our model is capable of generating highly convincing chorales in the style of Bach. DeepBach's strength comes from the use of pseudo-Gibbs sampling coupled with an adapted representation of musical data. This is in contrast with many automatic music composition approaches which tend to compose music sequentially. Our model is also steerable in the sense that a user can constrain the generation by imposing positional constraints such as notes, rhythms or cadences in the generated score. We also provide a plugin on top of the MuseScore music editor making the interaction with Deep-Bach easy to use.

New York – Mumbai: Tata Consultancy Services (TCS) today said that it has launched ignio for SAP ERP to help customers run their operations in SAP more efficiently and effectively. As such, ignio becomes an "intelligent virtual expert," in understanding how customers use SAP ERP for business operations and significantly enhancing the value of customers' investments in the SAP platform. In particular, the continuous focus that ignio demonstrates to tackle IT challenges, such as support for SAP, will help to further advance the field of artificial intelligence in businesses." "ignio is built to learn, resolve and protect a company's enterprise IT infrastructure," said Akhilesh Tripathi, Head of Global Sales for Software Products - TCS. "Many of our customers currently operate in an SAP environment. It builds a blueprint that binds together knowledge from the various SAP modules, establishing important intelligence that allows it to rapidly identify and address potential issues.

Annalee Newitz is the Tech Culture Editor at Ars Technica. Her work focuses on cultural impact of science and technology. She founded the science and science fiction blog io9.com, and is the author of Scatter, Adapt, and Remember: How Humans Will Survive a Mass Extinction. Her first novel, Autonomous, comes out in September 2017. She has a Ph.D. in English and American Studies from UC Berkeley, and was the recipient of a Knight Science Journalism Fellowship at MIT.