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In a first, scientists have developed a marimba-playing robot that uses artificial intelligence to create its own music inspired by the works of musicians like Beethoven and Mozart. The robot with four arms and eight sticks writes and plays its own compositions on a marimba, using a database of well-known pop, classical and jazz artists. Researchers at Georgia Institute of Technology in the US fed the robot nearly 5,000 complete songs – from Beethoven to the Beatles to Lady Gaga to Miles Davis. They worked with the robot named'Shimon' for seven years, enabling it to listen to music played by humans and improvise over pre-composed chord progressions. Shimon is now a solo composer generating the melody and harmonic structure on its own, researchers said.

According to Aristotle, while living things moved themselves at will, inanimate things moved according to their natures: heavy things, made of earth or water, descended, while light things, made of air or fire, ascended. Twenty years later, the French King Henri IV hired the Italian engineer Tomaso Francini to build him some waterworks for the royal palace at Saint Germain en Laye. In 1650, the German polymath Athanasius Kircher offered an early design of a hydraulic organ with automata, governed by a pinned cylinder and including a dancing skeleton. The designers of the automatic loom used automata and automatic musical instruments as their model; then Charles Babbage -- the English mathematician who designed the first mechanical computers during the 1830s, the Analytical and Difference Engines -- in turn used the automatic loom as his model.

Check out Crab if you haven't already. SVD will probably not work well off the bat, unless you have a way to mark "unmeasure/NA" pieces and avoid those in the SVD computation. Some sparse SVD implementations may have this, but I don't know any offhand in Python. You can still do 0/1 (2 score) rating with recommender systems, though if you have extra information (confidence) that can help. This 0/1 setup is really similar to "click through prediction", or CTR as well which is a huge field (and again, $$$ related) - check out some code that is awesome (I didn't write it, but learned a ton from it), also see the discussion in the old Kaggle competition I link to in that gist.

Having four arms would be an advantage for any musician, but they are just one of the many unique features of Shimon, the marimba playing robot. The machine has used its artificial intelligence and deep learning algorithms to analyse more than two million motifs, riffs and licks of music to create its own masterpiece. Aside from giving the machine the first four bars to use as a starting point, no humans are involved in either the composition or the performance of the music. Shimon (pictured) has used its artificial intelligence and deep learning algorithms to analyse over two million motifs, riffs and licks of music to create and perform its own masterpiece. Shimon is the creation of Mason Bretan, a PhD student at Georgia Tech, that uses eight sticks to play the wooden percussion instrument. He has worked with Shimon for seven years, enabling it to'listen' to music played by humans and improvise over composed chord progressions.

I'm happy to say that I've been able to create the first fully working demo here: I'm totally a noob but in the last 3 days I have learned a lot in creating this like SciKit, nltk, WSGI, Google Big Query, Google Datalabs, etc. Thanks everyone for the guidance in my last thread!

When Jeff Bezos first launched Amazon.com in 1994, he gave himself a 30% chance of success -- slightly better than the 1 in 10 odds for Internet start-ups. That's actually a very liberating expectation, expecting to fail," he said to Time magazine when it named him Person of the Year in 2000. By then, sales had ticked past $1 billion, but the company had yet to turn a profit. Some analysts remained skeptical that Bezos could deliver on his plan to sell everything and anything. But two decades after its launch, Amazon has conquered online retail, racking up $136 billion in sales in 2016.

Artificial intelligence has already proven its worth at performing routine tasks. But how good can it be at artistic creativity? A group of researchers at Kingston University and Queen Mary University of London are exploring the concept with AI that composes music. According to Dr. Oded Ben-Tal, lead researcher and senior lecturer in music technology at Kingston University, the results are impressive and can mark a new chapter in music, in which humans and machines collaborate in creating art. There are practical reasons to start with Irish music, Ben-Tal said, including the availability of data.

For an upcoming project we will be experimenting with Deep Learning approaches for NLP in an Azure environment (Amazon and Local are not an option right now). Azure offers NC6 (K80) and NV6 (M60) instances, but due to region restrictions it might be that only the M60 will be available. "In addition to the NC-Series, focused on compute, the NV-Series is focused more on visualization" Can anyone confirm that the M60 is appropriate for 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. Ph.D. student Mason Bretan is the man behind the machine. He's worked with Shimon for seven years, enabling it to "listen" to music played by humans and improvise over pre-composed chord progressions.