I built deepjazz in 36 hours for HackPrinceton, Spring 2016. It uses Keras & Theano, two deep learning libraries, to generate jazz music. Specifically, it builds a two-layer LSTM, learning from the given MIDI file. It uses deep learning, the AI tech that powers Google's AlphaGo and IBM's Watson, to make music -- something that's considered as deeply human.

Recurrent Neural Networks (RNNs), and specifically a variant with Long Short-Term Memory (LSTM), are enjoying renewed interest as a result of successful applications in a wide range of machine learning problems that involve sequential data. I will summarize my own experience with training these models for automated image captioning and for generating text character by character, with a particular focus on understanding the source of their impressive performance and their limitations.

John Markoff recently wrote in the New York Times, "Misconception: Computers will outstrip human capabilities within many of our lifetimes. Actually: You won't be obsolete for a long time, if ever, most researchers say. In March when Alphago, the Go-playing software program designed by Google's DeepMind subsidiary defeated Lee Se-dol, the human Go champion, some in Silicon Valley proclaimed the event as a precursor of the imminent arrival of genuine thinking machines. The achievement was rooted in recent advances in pattern recognition technologies that have also yielded impressive results in speech recognition, computer vision and machine learning. The progress in artificial intelligence has become a flash point for converging fears that we feel about the smart machines that are increasingly surrounding us.

What shortcomings do you see with deep learning? Deep Learning has been incredibly successful in recent years, but it is still merely a tool for classifying items into categories (or for nonlinear regression). We have seen outstanding results in mapping images, audio segments, even board positions, into categories with ever-increasing accuracy, but AI needs to go way beyond classification and regression. Let's talk about AlphaGo, which is a phenomenal technical achievement by the team at DeepMind. Yet, the overblown claims about the impressive success of AlphaGo are a case of a person climbing to the top of the tree and shouting "I'm on my way to the moon!"

More than 20 private companies working to advance artificial intelligence technologies have been acquired in the last 3 years by corporate giants competing in the space, including Google, Amazon, Apple, IBM, Yahoo, Facebook, Intel, and, more recently, Salesforce. There have been 4 major acquisitions already in 2016. Google has been the most prominent global player, with 5 key acquisitions under its belt (follow all of Google's M&A activity here, through our real-time Google acquisitions tracker). In 2013, the corporate giant picked up deep learning and neural network startup DNNresearch from the computer science department at the University of Toronto. This acquisition reportedly helped Google make major upgrades to its image search feature.

Microsoft is showing off its artificial intelligence abilities, with a new program that can produce an'original' Rembrandt painting based on the master's old works. The project is a collaboration between ING, Microsoft, Delft University of Technology, The Mauritshuis and Museum Het Rembrandthuis. 'The Next Rembrandt' project uses AI, capable of deep learning, was imprinted with 346 of Rembrandt's known works in the hopes that it can create a unique 3D printed image in his style. The computer analysed Dutch master Rembrandt's work, thoroughly tagged by humans. "We examined the entire collection of Rembrandt's work, studying the contents of his paintings pixel by pixel," the project team explained.

Stepping back from what may seem like hype and examples steeped in robotics, VR and infrastructure, the truth is, the announcements today show that deep learning in action is at most a year away, and as soon as now. In addition, the innovation coming out of robotics, VR and infrastructure will allow introduction of new form factors and channels to engage with customers and shape our workforce. In the end, it is a data challenge for the very reason that for every channel we use and add, it always ends up being a data challenge.

Software has lately become much, much better at understanding images. Last year Microsoft and Google showed off systems more accurate than humans at recognizing objects in photos, as judged by the standard benchmark researchers use. That became possible thanks to a technique called deep learning, which involves passing data through networks of roughly simulated neurons to train them to filter future data (see "Teaching Machines to Understand Us"). Deep learning is why you can search images stored in Google Photos using keywords, and why Facebook recognizes your friends in photos before you've tagged them. Using deep learning on images is also making robots and self-driving cars more practical, and it could revolutionize medicine.

H2O is feature-rich open source machine learning platform known for its R and Spark integration and it's ease of use. This is an overview of using H2O deep learning for data science with the Internet of Things. H2O is an Open Source machine learning platform for smarter applications. At the Data Science for IoT course, we have been following H2O for features such as Open Source, R integration, Spark integration, Deep Learning and it's ease of use. This blog is authored by Sibanjan Das and Ajit Jaokar as part of our work at the Data Science for IoT course exploring H2O Deep Learning for Internet of Things.

I like it because its almost as if this game was designed to be perfect for thumbstick controllers. You have highly precise control over the "character". You have 1 life and die if you touch the blue diamonds, or the orange parts of the "gates" (the white and orange barbell shaped things). You get points by killing the blue things. You can't touch the blue things, but if you hit the long white "bar" of the barbell shape, it explodes and kills blue things, and each blue thing gives off 3 green diamond "multipliers" which accumulate and multiply your points.