If you close your eyes for a moment, stop and think about a customer experience which stands out to you. Think about what makes you remember that particular engagement and what this means to you. Customer Experiences which stay with you – which leave you with an impression – positive or negative – almost always have one thing in common. These interactions occur across multiple channels – in person, online, in social forums, by phone. And these experiences have a direct correlation to our engagement, and spend with that company. American Express have quoted that customers who have a positive customer experience spend 74% more! Whilst McKinsey have reported that 70% of buying experiences are based on how an individual "perceives" they are being treated. This perception comes back to the simplicity and personalisation of the engagement – and again the emotional reaction of the experience. Personal, simple and emotionally connected experiences – piece of cake right…??

One of the most useful features across both Google Home and the Amazon Echo series is getting a daily news briefing. Asking both devices to tell you what's going on for your day can include a customized look at news that matters to you from a variety of sources. As of today, there's a new option for getting a news update from your smart speaker, and it comes from a somewhat surprising source: former vice president Joe Biden. "Biden's Briefing" is essentially a short daily podcast featuring news and info curated by Biden -- the content itself is sourced from a wide variety of news publications, including Axios, Bloomberg, BuzzFeed, The Huffington Post, MSNBC, New York Review of Books, Politico, Slate, Vice and Wired. How exactly the format will work isn't quite clear -- it sounds like Biden will be "curating" the content, but what's unclear is if the program will just replay stories from its sources or if Biden will be offering his own take.

Fall of the House of Usher is a 12-minute animation based on the short story by Edgar Allan Poe, where each still is generated by artificial intelligence. This is done by using a neural net (pix2pix) trained on the artist's ink drawings made of stills from the 1929 version of the film. Each still shown in the animation is not merely a filter that is applied to an existing image, but an entirely new image by a neural net. As all the stills that it was given to learn from came from the first four minutes of the film, it can output this reasonably well. But as the animation progresses, it has less and less of a frame of reference to draw on, leading to uncanny moments where the information starts to break down, particularly at the end of the piece.

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.