A UK technology company is inserting customised product placement into films and TV shows – even those that were originally released decades ago. The firm uses artificial intelligence (AI) to analyse films and TV episodes for space where the ads or objects can be subtly inserted. It means old Hollywood classics like Casablanca or The Great Escape could soon appear on streaming services with the newest ads in the background, like a new Apple smartphone or the latest McDonald's whopper. Streaming services including Netflix and Amazon Prime Video could be temped by large offers from companies to insert their ads to content, to accompany the subscription fees from its userbase. Mirriad's technology could even allow different ads to be seen by different people, based on their internet search history, just like targeted ads on Facebook.

Video is on an exponential growth trajectory, and it's not just Netflix originals and HBO docs and new films on Amazon Prime. In today's world, when people aren't eating or sleeping (or perhaps even when they are), they are likely viewing a video. Each day, people watch over 1 billion hours of YouTube. Creating and delivering movies, news and other compelling visual content is no longer just for the Hollywood elite. In fact, some of today's most prolific storytellers are doing so with little resources and amateur tools.

Automatic unreliable news detection is a research problem with great potential impact. Recently, several papers have shown promising results on large-scale news datasets with models that only use the article itself without resorting to any fact-checking mechanism or retrieving any supporting evidence. In this work, we take a closer look at these datasets. While they all provide valuable resources for future research, we observe a number of problems that may lead to results that do not generalize in more realistic settings. Specifically, we show that selection bias during data collection leads to undesired artifacts in the datasets. In addition, while most systems train and predict at the level of individual articles, overlapping article sources in the training and evaluation data can provide a strong confounding factor that models can exploit. In the presence of this confounding factor, the models can achieve good performance by directly memorizing the site-label mapping instead of modeling the real task of unreliable news detection. We observed a significant drop (>10%) in accuracy for all models tested in a clean split with no train/test source overlap. Using the observations and experimental results, we provide practical suggestions on how to create more reliable datasets for the unreliable news detection task. We suggest future dataset creation include a simple model as a difficulty/bias probe and future model development use a clean non-overlapping site and date split.

There is a growing interest in the area of machine learning and creativity. This survey presents an overview of the history and the state of the art of computational creativity theories, machine learning techniques, including generative deep learning, and corresponding automatic evaluation methods. After presenting a critical discussion of the key contributions in this area, we outline the current research challenges and emerging opportunities in this field.

Artificial Intelligence is a topic which evokes mixed reactions among people. Some consider AI to be a technological revolution which will solve all our problems and transform our planet into a veritable paradise. Others equate AI with robots courtesy of Hollywood movies; not good robots but rather exceedingly intelligent but evil and villainous robots with nefarious plans to wipe out the entire human civilization. As a computer engineer, I have always had an insider view of the technologies and been witness to many technical changes over the decades. From BASIC, to C to C to Java – each iteration produced better and more sophisticated coding mechanisms.

Enterprise AI solutions platform LogicPlum has released its latest update to improve functionality and practicality on its automated machine learning platform. Because it is extremely difficult to find data scientists who can do all these tasks well, it is likely an organization would have to hire an expert for each category. That is where LogicPlum comes in. LogicPlum can assist in every aspect of these sub-specialties. LogicPlum Chief Technology Officer Anoop Muraleedharan said, "We are committed to helping organizations experience maximum benefit from their AI solutions. We are uniquely positioned to help companies understand the challenges they face, in addition to the opportunities they have, with AI innovation. Our LogicPlum 3.0 platform release has been precisely constructed to accelerate organizations that are becoming AI-driven."

Have you ever wondered how apps like Netflix or Spotify decide which movie or songs you're likely to prefer watching or listening to? Seems like magic, doesn't it? For instance, a lot of data is being mined and multiple complicated algorithms are developed by data science professionals in an attempt to make predictions more accurate. It is not magic but "machine learning." Machine learning is what allows the system to determine the movies and songs most relevant to your liking.

The increasing availability of textual corpora and data fetched from social networks is fuelling a huge production of works based on the model proposed by psychologist Robert Plutchik, often referred simply as the ``Plutchik Wheel''. Related researches range from annotation tasks description to emotions detection tools. Visualisation of such emotions is traditionally carried out using the most popular layouts, as bar plots or tables, which are however sub-optimal. The classic representation of the Plutchik's wheel follows the principles of proximity and opposition between pairs of emotions: spatial proximity in this model is also a semantic proximity, as adjacent emotions elicit a complex emotion (a primary dyad) when triggered together; spatial opposition is a semantic opposition as well, as positive emotions are opposite to negative emotions. The most common layouts fail to preserve both features, not to mention the need of visually allowing comparisons between different corpora in a blink of an eye, that is hard with basic design solutions. We introduce PyPlutchik, a Python library specifically designed for the visualisation of Plutchik's emotions in texts or in corpora. PyPlutchik draws the Plutchik's flower with each emotion petal sized after how much that emotion is detected or annotated in the corpus, also representing three degrees of intensity for each of them. Notably, PyPlutchik allows users to display also primary, secondary, tertiary and opposite dyads in a compact, intuitive way. We substantiate our claim that PyPlutchik outperforms other classic visualisations when displaying Plutchik emotions and we showcase a few examples that display our library's most compelling features.

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