Scientists at IBM Research have collaborated with 20th Century Fox to create the first-ever cognitive movie trailer for the movie Morgan. Utilizing experimental Watson APIs and machine learning techniques, the IBM Research system analyzed hundreds of horror/thriller movie trailers. After learning what keeps audiences on the edge of their seats, the AI system suggested the top 10 best candidate moments for a trailer from the movie Morgan, which an IBM filmmaker then edited and arranged together. A corporate troubleshooter (Kate Mara) is sent to a remote, top-secret location, where she is to investigate and evaluate a terrifying accident. She learns the event was triggered by a seemingly innocent "human," who presents a mystery of both infinite promise and incalculable danger.

Workplace surveillance has been a controversial subject for as long as there have been workplaces. Advocates of surveillance insist that it is essential for productivity, quality control, and (in some cases) safety. Further, they'll insist, employees have no inherent right to privacy at work. Opponents of workplace surveillance argue that the practice actually is counter-productive; employees feel as though they aren't trusted, which lowers their morale and, inevitably, their effectiveness. Technologies such as video cameras, electronic ID badges, and keylogging software have made it easier for employers to track workers' locations, activities, and physical output.

Machine learning a subset of AI began to grow in the 1990s and has now become an integral part of many business strategies. Fuelled by an influx of data and unlimited storage it's been used to detect fraud, play (and win) games and enhance search engines. Through machine learning companies can track and respond to the personal preferences of individuals, theoretically giving the consumer more of what they want. It's easy to see how important this is for marketers and business strategists, but there's a less obvious benefactor. Within the media, data analysis is helping publications and websites to work out what information their users want to see, but as Facebook found recently, it can easily backfire.

The International Joint Conference of Artificial Intelligence (or in short IJCAI) is the most established, important and leading scientific event in Artificial Intelligence. Established in 1969 as the first ever international conference on Artificial Intelligence (AI), it is an extension of the seminal (AI first) Dartmouth workshop in 1956 (for the interested, read the inspirational first papers on AI). Practically, much of the leading AI science and technology was presented during previous IJCAI conferences. Before we talk more about the upcoming IJCAI-ECAI-18 event in Stockholm, Sweden (July 13-19, 2018), let us share with you our first-hand experience of IJCAI 2017 in Melbourne, Australia. IJCAI 2017 brings together the brightest researcher and technologists from around the world.

Apple announced three new iPhones this month: the iPhone 8, the iPhone 8 Plus, and the high-end iPhone X. Those three phones start at $699, $799, and $999, respectively. Based on the relatively diminutive launch-day lines for the iPhone 8, it seems likely that most people are waiting for the release of Apple's high-end iPhone X, which debuts November 3. That said, there are several reasons it's worth considering an iPhone 8 instead of holding out for the iPhone X: The iPhone 8 and 8 Plus are powered by the same brains as the iPhone X. This is probably the most important reason to consider the iPhone 8 and 8 Plus over the iPhone X: Functionally, they're all identical.

SELF-DRIVING, personal assistants, cleaning robots, smart homes - these are just some examples of autonomous systems. With many such systems already in use or under development, a key question concerns trust. My central argument is that having trustworthy, well-working systems is not enough. To enable trust, the design of autonomous systems also needs to consider other requirements, including a capacity to explain decisions and to have recourse options when things go wrong. The past few years have seen dramatic advances in the deployment of autonomous systems. These are essentially software systems that make decisions and act on them, with real-world consequences.

I will use {ordinal} clm() (and other cool R packages such as {text2vec} as well) here to develop a hybrid content-based, collaborative filtering, and (obivously) model-based approach to solve the recommendation problem on the MovieLens 100K dataset in R. All R code used in this project can be obtained from the respective GitHub repository; the chunks of code present in the body of the post illustrate the essential steps only. The MovieLens 100K dataset can be obtained from the GroupLens research laboratory of the Department of Computer Science and Engineering at the University of Minnesota. The first part of the study introduces the new approach and refers to the feature engineering steps that are performed by the OrdinalRecommenders_1.R script (found on GitHub). The second part, to be published soon, relies on the R code in OrdinalRecommenders_3.R and presents the model training, cross-validation, and analyses steps. The OrdinalRecommenders_2.R script encompasses some tireless for-looping in R (a bad habbit indeed) across the dataset only in order to place the information from the dataset in the format needed for the modeling phase.

I am working on developing a neural network from scratch. Each of my nodes are linear and each layer is fully connected. The neurons fire with Relu activation so the true value of N would be max{0, N}. As of right now all of the framework is complete and I have a simple game that I am testing the neural network on. However I am not getting the results I expect.

Submission statement: Finding the correct learning rate is a pain. But this paper shows how to find reasonable learning rate bounds. Then you can cyclically vary your learning rate to getting better accuracy and often a decreasing training time. P.S There is a PR for this in keras-contrib.

Hi, I'm books editor Carolyn Kellogg, and this week, our books newsletter has gotten a little bit interactive. Maybe you read Han Kang's "The Vegetarian" -- I know I did after it won the Man Booker International Prize. The novel is a spare, strange tale of a South Korean woman who stops eating meat, in an unspoken resistance to her husband, and wastes away. The book was an English-language bestseller and young translator Deborah Smith was lauded with praise. But a closer look at her translation caused a huge controversy in South Korea: Did it go too far?