This paper proposes a general method for improving the structure and quality of sequences generated by a recurrent neural network (RNN), while maintaining information originally learned from data, as well as sample diversity. An RNN is first pre-trained on data using maximum likelihood estimation (MLE), and the probability distribution over the next token in the sequence learned by this model is treated as a prior policy. Another RNN is then trained using reinforcement learning (RL) to generate higher-quality outputs that account for domain-specific incentives while retaining proximity to the prior policy of the MLE RNN. To formalize this objective, we derive novel off-policy RL methods for RNNs from KL-control. The effectiveness of the approach is demonstrated on two applications; 1) generating novel musical melodies, and 2) computational molecular generation. For both problems, we show that the proposed method improves the desired properties and structure of the generated sequences, while maintaining information learned from data.

In October 2016, "Westworld" topped the charts as the most-watched premiere season of an HBO original series ever. In the series, a science fiction thriller written and directed by novelist Michael Crichton based on a 1973 film of the same name, Anthony Hopkins takes on the role of Dr Ford, who creates a futuristic western-themed amusement park populated by android hosts to cater human guests, with Evan Rachel Wood playing the role of Dolores, the oldest android host working in the park. Further to the great script and the impressive casting, the success of the series is also undoubtedly linked to its timing. Just one year ago, Lee Sedol, 18-time world Go-board game champion, was beaten by DeepMind's AlphaGo, which was a monumental breakthrough of Artificial Intelligence (AI). Even before the AlphaGo's victory over Lee Sedol, there was growing interest in the potential and risks of humanoid robots and of AI, led by the likes of Stephen Hawking and Elon Musk.

Text data preparation is different for each problem. Preparation starts with simple steps, like loading data, but quickly gets difficult with cleaning tasks that are very specific to the data you are working with. You need help as to where to begin and what order to work through the steps from raw data to data ready for modeling. In this tutorial, you will discover how to prepare movie review text data for sentiment analysis, step-by-step. How to Prepare Movie Review Data for Sentiment Analysis Photo by Kenneth Lu, some rights reserved.

It's been a great season for horror, with Blumhouse's "Happy Death Day" becoming the latest horror film to top the domestic box office in its opening weekend. The $5-million film, a bloody riff on the classic "Groundhog Day" concept, brought in an estimated $26.5 million in the U.S. and Canada, according to figures from measurement firm ComScore, above the $15 million to $20 million analysts projected. "We are absolutely thrilled with the opening," said Universal's Executive Vice President of Domestic Distribution Jim Orr. "Happy Death Day" marks Blumhouse's ninth film to open at No. 1 and its third to debut at No. 1 this year alone, following "Split" and "Get Out." The latest from producer Jason Blum and Universal Pictures, the film, about a woman who relives the day of her murder until she learns her killer's identity, earned a B rating on CinemaScore and a 64% "fresh" rating on Rotten Tomatoes.

This class is offered as CS7641 at Georgia Tech where it is a part of the Online Masters Degree (OMS). Taking this course here will not earn credit towards the OMS degree. The first part of the course covers Supervised Learning, a machine learning task that makes it possible for your phone to recognize your voice, your email to filter spam, and for computers to learn a bunch of other cool stuff. This class is offered as CS7641 at Georgia Tech where it is a part of the Online Masters Degree (OMS).

It's just a very stupid idea to create an AI with emotions. There is no need for a human like AI. I understand just making one just for research but still is the risk worth it? Thinking AI is ultimately going to take over the world is naive, but it can certainly happen if you design an AI irresponsibly like I don't know... making it capable of producing irrational thoughts like emotions and giving it a lot of power. The people who work at Hanson Robotics appear pretty immature and irresponsible on tv and online but then again it is only publicity.

Maria, Marvin, Sonny, David, and Ava are all ordinary-sounding names -- but in film, television, and literature, these seemingly ordinary names belong to extraordinary individuals who, despite their exemplary skills and complex personalities, are not human. Since Brigitte Helm's 1927 portrayal of Maria in Metropolis, audiences have developed an increased love/hate fascination with artificial intelligence. While filmmakers continue to address the controversy regarding the acceptance and cohabitation between humans and their modern creations parallel to real-world technological advancements, just how accurate is this representation in modern film, and have cinematic depictions evolved at all? Early films reflected the heightened fear of technology that developed among the working class during the Industrial Age by depicting metal machines as unstoppable forces of mayhem. This successfully fed into the pre-existing "anti-immigrant" nervous anticipation that technological advancements would go from taking over people's jobs to taking over the world.

Lear what artificial intelligence is and how it can benefit content.

We are not used to the idea of machines making ethical decisions, but the day when they will routinely do this - by themselves - is fast approaching. So how, asks the BBC's David Edmonds, will we teach them to do the right thing? The car arrives at your home bang on schedule at 8am to take you to work. You climb into the back seat and remove your electronic reading device from your briefcase to scan the news. There has never been trouble on the journey before: there's usually little congestion.

Earlier this year, Amir Avni used neural networks to troll the subreddit /r/Colorization - a community where people colorize historical black and white images manually using Photoshop. They were astonished with Amir's deep learning bot - what could take up to a month of manual labour could now be done in just a few seconds. I was fascinated by Amir's neural network, so I reproduced it and documented the process. First off, let's look at some of the results/failures from my experiments (scroll to the bottom for the final result). Today, colorization is done by hand in Photoshop. To appreciate all the hard work behind this process, take a peek at this gorgeous colorization memory lane video. In short, a picture can take up to one month to colorize. A face alone needs up to 20 layers of pink, green and blue shades to get it just right. This article is for beginners. Yet, if you're new to deep learning terminology, you can read my previous two posts [1][2] and watch Andrej Karpathy's lecture for more background.