Collaborating Authors


Why UX should guide AI


If we need to learn one thing about the numerous AI applications around us today, it is that they are examples of "artificial specific intelligence." In other words, they rely on algorithms that are great at very particular tasks, such as selecting a movie based on our watching history or keeping our car in the proper lane on the highway. Because it is so highly specialized, AI greatly outperforms human intelligence in those narrowly defined tasks. Take it from a person who recently spent 50 minutes picking a movie that itself lasted 77 minutes. However, AI's effectiveness at specialized jobs comes at the price of severe context blindness and a general inability to develop meaningful feedback loops: The typical algorithm does not and cannot consider the wider implications of the decisions it makes and hardly affords us users any control over its inner workings.

12 of the best suspense movies on Netflix to put you on edge


Given how stress-inducing the real world can be at the best of times, movies that rely on tension as a driving force might seem like an odd entertainment choice to some. But who are we to judge? Maybe spending 90 minutes sweating and wincing in front of the TV screen is actually a cathartic way to let off some steam -- and at the very least, a suspense movie is always a great way to get the ol' heart-rate up. But what actually is a suspense movie? How is it different to a thriller?

Data Science: Machine Learning


Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors. In this course, part of our Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system. You will learn about training data, and how to use a set of data to discover potentially predictive relationships.

Gwyneth Paltrow reveals which of her movies her kids have seen: 'It's weird if I'm on screen'

FOX News

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Gwyneth Paltrow's children are picky about the movies they watch. The Oscar-winning actress, 48, shares two children with ex-husband Chris Martin – Apple, 17, and Moses, 15 – and while Paltrow is one of Hollywood's biggest names, the kiddos aren't terribly interested in seeing her on-screen. During Thursday's episode of "Shop TODAY with Jill Martin," Paltrow revealed that Apple has never seen one of her movies while Moses has only seen her in the massively popular Marvel franchise.

6 Python Projects You Can Finish in a Weekend


Learning Python can be difficult. You might spend a lot of time watching videos and reading books; however, if you can't put all the concepts learned into practice, that time will be wasted. This is why you should get your hands dirty with Python projects. A project will help you bring together everything you've learned, stay motivated, build a portfolio and come up with ways of approaching problems and solving them with code. In this article, I listed some projects that helped me level up my Python code and hopefully will help you too.

Smart Recommendation System For OTT platforms


The recommendation engine has become quite popular across diverse industries in recent years. The recommendation engine is gaining rapid traction from OTT (Over the Top) platforms to e-commerce stores. Whether you have just started your OTT platform or plan to scale it up, recommendation engines can significantly improve your profitability. A Recommendation engine or recommendation system is an information filtering tool that provides the most relevant suggestions regarding products or services to various customers. A recommendation engine uses machine learning algorithms to collect and analyze user activities such as their preferences, search history, and others.

"Demon Slayer": The Viral Blockbuster from Japan

The New Yorker

One of the seismic cultural shifts of the pandemic era has been a migration into fantasies. Some of them are troubling, such as the conspiratorial prejudice that has fuelled QAnon and the recent surge in violence against those of Asian descent. Others are restorative: the immersive worlds of books, the virtual realities of video games, the hypnotic lull of binge-streamed television series. Many of the escapes that we use to nourish ourselves originated in Japan. The stunning success of Nintendo's Animal Crossing: New Horizons, which sold thirty-one million copies worldwide last year, is a striking example.

Artificial Intelligence will make marketing more creative and effective - Express Computer


AI can free up a lot of the marketers' time, currently spent on mundane tasks, so that they focus on what they do best--be creative, think, ideate and innovate. All of us have heard about driverless cars, automated machines, bots and virtual assistants, even if we don't fully understand what these terms mean. All of these are manifestations of self-learning algorithms, smart technologies such as Artificial Intelligence (AI) and Machine Learning (ML). The application of these technologies is no longer just limited to sci-fi movies and erudite research papers. Directed by data-driven insights from these powerful technologies, traditional decision-making by experienced professionals is slowly being transformed.

Council Post: The Reality Behind The AI Illusion


Though artificial intelligence has evolved recently and appears to be a new phenomenon in modern society, it is much older than you would imagine. Being actively involved in the global AI community, I've noticed that many people still associate AI with sci-fi Hollywood movies displaying the distant future powered by intelligent robots and machines. However, this perception is waning as AI becomes more commonplace in our daily lives. The early instances of intelligent machines were found in ancient Greek mythology with conceptions of mechanical robots made to help the Greek god Hephaestus. Following were some milestones in the history of AI, which started as a field of research in the late 1950s with the development of the first algorithms to solve complex mathematical problems.

Identifying Harmful Video Content With Movie Trailers And Machine Learning


A research paper from the Swedish Media Council outlines a possible new approach to the automatic identification of'harmful content', by considering audio and video content separately, and using human-annotated data as a guiding index for material that may disturb viewers. Learning to Predict Harmfulness Ratings from Video, the paper illustrates the need for machine learning systems to take account of the entire context of a scene, and illustrates the many ways that innocuous content (such as humorous or satirical content) could be misinterpreted as harmful in a less sophisticated and multimodal approach to video analysis – not least because a film's musical soundtrack is often used in unexpected ways, either to unsettle or reassure the viewer, and as a counterpoint rather than a complement to the visual component. They also observe that to date, similar experiments have suffered from a sparsity of labels for full-length movies, which has led to prior work oversimplifying the contributing data, or keying in on only one aspect of the data, such as dominant colors or dialogue analysis. To address this, the researchers have compiled a video dataset of 4000 video clips, trailers cut down into chunks of around ten seconds in length, which were then labeled by professional film classifiers that oversee the application of ratings for new movies in Sweden, many with professional qualifications in child psychology. Under the Swedish system of film classification, 'harmful' content is defined based on its possible propensity to produce feelings of anxiety, fear, and other negative effects in children.