We introduce the multiresolution recurrent neural network, which extends the sequence-to-sequence framework to model natural language generation as two parallel discrete stochastic processes: a sequence of high-level coarse tokens, and a sequence of natural language tokens. There are many ways to estimate or learn the high-level coarse tokens, but we argue that a simple extraction procedure is sufficient to capture a wealth of high-level discourse semantics. Such procedure allows training the multiresolution recurrent neural network by maximizing the exact joint log-likelihood over both sequences. In contrast to the standard log- likelihood objective w.r.t. natural language tokens (word perplexity), optimizing the joint log-likelihood biases the model towards modeling high-level abstractions. We apply the proposed model to the task of dialogue response generation in two challenging domains: the Ubuntu technical support domain, and Twitter conversations. On Ubuntu, the model outperforms competing approaches by a substantial margin, achieving state-of-the-art results according to both automatic evaluation metrics and a human evaluation study. On Twitter, the model appears to generate more relevant and on-topic responses according to automatic evaluation metrics. Finally, our experiments demonstrate that the proposed model is more adept at overcoming the sparsity of natural language and is better able to capture long-term structure.

The NHS could be applying machine learning-style processing to its patient, doctor and hospital data in an effort to improve efficiency within five years if plans by Google/DeepMind to push into the healthcare sector are approved. According to New Scientist, which has obtained a Memorandum of Understanding drawn up between DeepMind and the Royal Free NHS Trust in London, the two organisations are attempting to form a "broad ranging, mutually beneficial partnership, engaging in high levels of collaborative activity and maximising the potential to work on genuinely innovative and transformative projects." Among the areas the project aims to touch on are making improvements in clinical outcomes and patient safety, and reducing costs throughout the organisation. The memo also sets out a long list of "areas of mutual interest" where the two organisations could work together over the next five years, including bed and demand management software, financial control products, private messaging and task management for junior doctors, and even real-time health prediction. In fact, health prediction has formed the basis of the first project between the two partners, with Google/DeepMind creating an app called Streams that aims to study healthcare data to try to identify patients at risk of deterioration, readmission or even death.

People have a weird obsession with games likes Chess and Go. Achievement in them has long been seen as a marker of human intellect, and yet they're among the least human test you could devise; putting players in simplified situations where everything is known, every possible course of action is laid out for them, and the test is one of concentration and logic. We pass far greater tests daily, when we recognise a face in a crowd, when we dynamically balance in motion, when we predict the response our words and expressions will have on another sentient being, or when we do all of the above, effortlessly, at the same time. We don't think of these as challenging because they're so innately human, while playing Chess or Go seems far more impressive precisely because they're more rigid and computational in nature. There's an irony in making a board game one of the'grand challenges' of AI, and it surprises me that more people don't see it.

My collection of (mainly deep learning) Docker images is moving from using kaixhin/cuda to nvidia/cuda. If you are using one of these, please be aware that you will now need to use the nvidia-docker binary instead of docker. This means that you do not need a precise driver version, and moving forward NVIDIA can provide full-time support for CUDA-based images. After a few issues, I've decided that the project is stable enough to start the migration. It'll take me a while to test all the images, so as always please raise an (informative) issue if you encounter any problems.

"Well, I have to go to the skull," is a line that can only find place in a movie that does not make any sense. In the nine minute movie "Sunspring," however, it is far from the most nonsensical piece of dialogue. The credit (or the blame) for the creation of the disjointed script of the movie goes to Benjamin -- a neural network created by filmmaker Oscar Sharp and Ross Goodwin, who is an artificial intelligence researcher at New York University. Sharp and Goodwin fed the AI, which is a Long Short-Term Memory (LSTM) recurring neural network, a raft of sci-fi scripts written for movies like Aliens, Watchmen, Star Trek and the X-Files. Initially, Benjamin kept "spitting out conversations between Mulder and Scully [the X-Files protagonists], and you'd notice that Scully spends more time asking what's going on and Mulder spends more time explaining," Sharp told Ars Technica.

As 2015 draws to a close, all eyes are on the year's accomplishments, as well as forecasting technology trends of 2016 and beyond. One particular field that has frequently been in the spotlight during the last year is deep learning, an increasingly popular branch of machine learning, which looks to continue to advance further and infiltrate into an increasing number of industries and sectors. Over the last year we've had the privilege of hearing from many of the great minds working in artificial intelligence and computer science, at RE•WORK events, and we look forward to meeting and learning from many more in 2016! As part of our ongoing speaker Q&A series, we asked some of the top names in deep learning for their predictions for the field over the next 5 years. What developments can we expect to see in deep learning in the next 5 years?

This is a list of articles that are themselves lists of articles that are also lists on the English Wikipedia. In other words, each of the articles linked here is an index to multiple lists on a topic. Some of the linked articles are themselves lists of lists of lists. Please contact /u/GregMartinez with any questions or feedback. Some of the linked articles are themselves lists of lists of lists.

The video classification paper by Karpathy et al.: http://cs.stanford.edu/people/karpath... Recommended for you: Artistic Style Transfer For Videos - https://www.youtube.com/watch?v Uxax5... Deep Learning related Two Minute Papers videos - https://www.youtube.com/playlist?list... WE WOULD LIKE TO THANK OUR GENEROUS SUPPORTERS WHO MAKE TWO MINUTE PAPERS POSSIBLE: Sunil Kim, Julian Josephs. Subscribe if you would like to see more of these! - http://www.youtube.com/subscription_c... Splash screen/thumbnail design: Felícia Fehér - http://felicia.hu

By now everyone has heard of Google's RankBrain. It's the new artificial intelligence machine learning algorithm that is supposed to be the latest and greatest from Mountain View, California. What you might not realize, however, is just how fast the SEO industry is changing because of it. This article will take you through some clear examples of how some of the old rules of SEO no longer apply, and what steps you can take to stay ahead of the curve in order to continue to provide successful SEO campaigns for your businesses. When we talk about the context of Google's RankBrain, and the machine learning algorithms that are currently running on Google, we are talking about Artificial Narrow Intelligence (ANI).

Three actors in glittery metallic costumes stand in an office, spouting gibberish at one another: the outcome of an experiment that couldn't be any more predictable. Earlier this week, UK filmmaker Oscar Sharp released Sunspring, an experimental film based on a screenplay developed by an artificial intelligence. The project saw Mr Sharp and technologist Ross Goodwin feeding an artificial long short-term memory neural network (LSTM) an array of science fiction film scripts. Mr Sharp and Mr Goodwin then programmed the LSTM to process the scripts, using cinematic writing conventions to produce a screenplay of its own. Mr Sharp then spent 48 hours developing the script into a film, before releasing it on the Ars Technica Videos YouTube page on June 9.