Neural network based models for collaborative filtering have started to gain attention recently. One branch of research is based on using deep generative models to model user preferences where variational autoencoders were shown to produce state-of-the-art results. However, there are some potentially problematic characteristics of the current variational autoencoder for CF. The first is the too simplistic prior that VAEs incorporate for learning the latent representations of user preference. The other is the model's inability to learn deeper representations with more than one hidden layer for each network. Our goal is to incorporate appropriate techniques to mitigate the aforementioned problems of variational autoencoder CF and further improve the recommendation performance. Our work is the first to apply flexible priors to collaborative filtering and show that simple priors (in original VAEs) may be too restrictive to fully model user preferences and setting a more flexible prior gives significant gains. We experiment with the VampPrior, originally proposed for image generation, to examine the effect of flexible priors in CF. We also show that VampPriors coupled with gating mechanisms outperform SOTA results including the Variational Autoencoder for Collaborative Filtering by meaningful margins on 2 popular benchmark datasets (MovieLens & Netflix).

We discuss the objectives of automation equipped with non-trivial decision making, or creating artificial intelligence, in the financial markets and provide a possible alternative. Intelligence might be an unintended consequence of curiosity left to roam free, best exemplified by a frolicking infant. For this unintentional yet welcome aftereffect to set in a foundational list of guiding principles needs to be present. A consideration of these requirements allows us to propose a test of intelligence for trading programs, on the lines of the Turing Test, long the benchmark for intelligent machines. We discuss the application of this methodology to the dilemma in finance, which is whether, when and how much to Buy, Sell or Hold.

Mark Kingwell is a professor of philosophy at the University of Toronto. More than five decades ago, Marshall McLuhan argued that media are ecosystems, extensions of human consciousness. The famous adage that the medium is the message also means, as the often-misquoted title of McLuhan's famous book notes, that the medium is the mass age. We are all immersed in media and technology. Media have changed a lot since McLuhan wrote: less broadcast, more diffusion and unruliness.

Artificial intelligence is penetrating every aspect of modern business, and for a number of compelling reasons. As technology has become an inextricable part of our everyday lives, business leaders and forward-looking marketers are quick to pioneer the integration of innovative AI-driven technologies as the likes of chatbots, for example, in order to develop a competitive advantage and speed up the growth of their brands as a whole. Needless to say, AI helps them achieve this in a myriad of ways. Download our free e-book to learn everything you need to know about chatbots for your business. From enhanced customer service to easier SEO optimization, all the way to smart brand building and laser-precise advertising, artificial intelligence can make all the difference in how modern brands operate and plan their future.

"Terminator: Dark Fate" also marks the return of writer/producer James Cameron -- who directed the first two movies in the franchise, but wasn't involved in the three sequels that followed. Although monstrous machines have figured in movie plots since Fritz Lang's "Metropolis" in 1927, Schwarzenegger's performance in "The Terminator" set the stage for worries about out-of-control intelligent machines. Billionaire techie Elon Musk is among the best-known doomsayers. "I keep sounding the alarm bell, but until people see robots going down the street, killing people, they don't know how to react because it seems so ethereal," Musk said in 2017. On the other side of the debate, Oren Etzioni, the CEO of Seattle's Allen Institute for Artificial Intelligence, or AI2, keeps telling people to calm down.

Machine learning (ML) is an amazing field that enables a huge number of powerful and interesting techniques. ML is a broad field that has applications in many areas. From image processing to conservation, ML provides unique solutions to problems old and new. Here are some interesting and cool applications of machine learning. Neural networks (NNs) and deep neural networks (DNNs) are very popular machine learning techniques. This type of modeling is used in many of the best-known applications of ML. Image classification, face identification, and speech recognition are just a few examples.

It's commonly known that AI feeds on data, yet the importance of data is often underestimated. "The scope and scale of the data problem in AI is far larger than most people realize," explained Jen Snell, vice president of Verint, a chatbot development company. "So many organizations run into problems with their projects due to data -- from data quality to managing and wrangling data for meaningful insights to labeling and model building," she said. "In the beginning, it seems easy, but as you look to grow at scale, change models, manage and ensure control over the system, it gets tricky." Snell shared a painful statistic: While 59% of executives believe AI could improve their use of big data, 85% of big data or AI projects fail.

Six machine learning models were used to study and validate the genomic signatures.