The details of the project are revealed in a research paper titled "A Neural Algorithm of Artistic Style." "Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality," reads the paper, penned by a group of researchers from the University of Tubingen in Germany. "The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images." Using an image of a street in Germany (above), the team demonstrated the ability to recreate the photo using the visual styles of Vincent van Gogh, Edvard Munch and Pablo Picasso. Each transformed street photo, at least upon casual inspection, looks like it was indeed painted by one of the masters. Over the last couple of days, Andrej Karpathy, a PhD student at Stanford studying Machine Learning, posted a few experiments on Twitter using the technique, and the results were incredibly accurate. Karpathy had since removed all examples of his experiments with the image transfer, but we managed to capture some screenshots of his tests, including one that turned a photo of Gandalf into a Picasso-style portrait. Does this mean that, with a little more code tweaking, we'll begin to see fake works (created using this technique) suddenly "discovered" in coming years?

I've recently switched (from my own custom NN code) to theano and its been a marvel to have the benefits of a flexible optimized graph but I often find its a bit of a black box. It can do a lot of very nice things but I always worry that they might cost me in performance. E.g. is it more efficient to vectors in order to do fewer larger matrix multiplications (e.g. in the units of an LSTM or a GRU) does is the loss in memory locality or shuffling stuff around in memory outweigh the gains of larger matrix multiplications? I still have figured out how to efficiently implement attention with minibatches. I realize I can try to profile and test this stuff (and I do) but I am wondering where people normally go to get this kind of info on theano?

In this tutorial I will discuss how reinforcement learning (RL) can be combined with deep learning (DL). There are several ways to combine DL and RL together, including value-based, policy-based, and model-based approaches with planning. Several of these approaches have well-known divergence issues, and I will present simple methods for addressing these instabilities. The talk will include a case study of recent successes in the Atari 2600 domain, where a single agent can learn to play many different games directly from raw pixel input.

Google's artificial intelligence program AlphaGo's overwhelming win over South Korean go grandmaster Lee Sedol in a five-game tournament this month has shown that machine intelligence is rapidly evolving and underlined the possibility that it will catch up with and eventually surpass human intelligence. The time has come for us to think how best to use AI in ways that will contribute to -- and not detract from -- our well-being. In the tournament held in Seoul, the program built by a Google subsidiary DeepMind defeated Lee, a 33-year-old 9-dan professional go player with 18 world titles, in a 4-1 victory. Google had chosen Lee as an opponent in view of his impressive records, considering him as the world's strongest player of the board game. The outcome has stunned go players, professional programmers and the public alike -- given that experts had previously expected it would take more than 10 years for an AI program to beat a world-class professional go player.

The folks over at x.ai – creators of Amy, the artificial intelligence answer to scheduling meetings – have had a shot at showing exactly what it looks like inside their bot's brain, using AI, of course. The team used a powerful deep-learning model, a Recurrent Neural Network (RNN), to trawl 500,000 words in its database, looking at their sequence in a sentence to understand what they mean, then predicting how to categorize them. Get your company on stage at TNW Europe. Without a human ever telling the RNN the definitions of different word groups, it has managed to understand that Stanford is different from Instagram, and that Jesse, Luke and Jason are names. This data was cut to down to the 3,500 most frequently used words and has then been projected into a 2D shape in order to show the relationships the AI has made between different words.

I thought that this interview deserved a repost here at Data Science Central. It is with the man responsible for Artificial Intelligence at Facebook: the AI director Yann Lecun; and might be of interested and appeal to the knowlegeable of AI here. IEEE Spectrum: We read about Deep Learning in the news a lot these days. What's your least favorite definition of the term that you see in these stories? Yann LeCun: My least favorite description is, "It works just like the brain."

If venture capital and research funding are any indication, artificial intelligence will play a leading role in shaping our future. And few tech innovators in the private or public sector have been as prominent in defining that role as Andrew Ng, chief scientist at China's search giant Baidu. Ng has taught AI at Stanford, led the Google Brain project, founded online education pioneer Coursera, and just last year took his post at "China's Google" in hopes of figuring out how to teach computers to see and hear, and to do that for the world's most populous country. Small wonder why China represents such a huge opportunity for machine intelligence applications. Baidu is the world's fifth most trafficked website.

Anyone who's ever shaken their head over the utterances coming out of Donald Trump's mouth will surely be glad to know that they're now being improved with deep learning. DeepDrumpf is a Twitterbot created by a postdoctoral researcher in MIT's Computer Science and Artificial Intelligence Lab that uses a deep-learning algorithm to make even Trumpier statements than Trump himself. Based on an artificial-intelligence algorithm trained on transcripts of Trump's victory speeches and debate performances, the bot shows a remarkable proficiency for taking the hyperbole even further. "I'm what ISIS doesn't need" is one creation of the bot, for example. "Great manufacturing, bring back our jobs, bring back our manufacturing, because my file, you know, I don't need anybody's money. The bot creates its tweets one letter at a time, apparently following Trump's campaign slogan, "Make America Great Again."

I just need a framework in which I configure the architecture of the network and not construct/code the entire network, I found a few but they seem to lag good community support, any good one's to recommend?

A neural-analogue calculator based upon a probability model of reinforcement.