And for people worried their jobs might soon be gone, that might be scary -- but the threat could actually be much worse. It might affect our entire race. The modern-day dolls that not only look like women but have a similar feel, and they react like real people in conversations too. For those who find it's too hard to form a real relationship, will they just turn to a robot as a long-term solution? Armed with questions and recording equipment, my fear took me to Realbotixs in San Diego to meet Harmony, a robot with artificial intelligence (and a working vagina).

Technologically, the 20-year jump from 2015 to 2035 will be huge. During that time some elements of our world will change beyond recognition while others will stay reassuringly (or disappointingly) familiar. Consider the 20 years to 2015. Back in 1995 we were in the early days of the internet, we worked in cubicles and our computers were chunky and powered by Windows 95. There were no touch screen phones or flat screen TVs; people laughed at the idea of reading electronic books, and watching a home movie meant loading a clunky cassette into your VCR.

Recent work has shown that collaborative filter-based recommender systems can be improved by incorporating side information, such as natural language reviews, as a way of regularizing the derived product representations. Motivated by the success of this approach, we introduce two different models of reviews and study their effect on collaborative filtering performance. While the previous state-of-the-art approach is based on a latent Dirichlet allocation (LDA) model of reviews, the models we explore are neural network based: a bag-of-words product-of-experts model and a recurrent neural network. We demonstrate that the increased flexibility offered by the product-of-experts model allowed it to achieve state-of-the-art performance on the Amazon review dataset, outperforming the LDA-based approach. However, interestingly, the greater modeling power offered by the recurrent neural network appears to undermine the model's ability to act as a regularizer of the product representations.

Whether consumers know it or not, three next-generation technologies are playing a major role in shaping their experience with brands -- and the future of consumer goods marketing: artificial intelligence (AI), machine learning (ML) and Internet of Things (IoT). To keep pace and effectively compete in an increasingly connected marketplace, brands are investing in these three technologies to continually fine-tune their customer strategies, using hyper-personalized information across touchpoints. Have you ever wondered how Netflix makes movie and TV show recommendations, how Facebook prompts friends to be tagged in photos, and how Alexa, Siri and Google Now assist in our day-to-day activities? These are real-life examples of machine learning -- a subset of AI. ML uses a customer's historic data and behavioral patterns to create high-quality predictions of their future behavior.

It's official, the rocket stored in the villain base will be launched next week in Fortnite. The moment has been broadcast in loading screens (The Visitor blockbuster skin is shown firing it up), teased in the game (a countdown timer shown on TVs ends Tuesday), and extracted from data files in the game (showing launch sequence text). So, it's coming, but what does that mean for the game? Is the rocket about to become season 4's comet, teasing what's to come in season 5? Or is something else at play here? One new wrinkle is that dataminers @twoepicbuddies are claiming that they have uncovered something that suggests that players themselves will be able to launch the rocket with the press of a button come Tuesday.

In this conversation with Elias Crespin, a Venezuelan-born artist who builds kinetic sculptures using complex algorithms, they discuss the evolution of Crespin's work and the future of Artificial Intelligence as it pertains to art. You started your career as a computer engineer. When and how did you start creating art? As a teenager I wanted to be an architect. I loved to draw blueprints.

Machine learning may give a tough time to songwriters and musicians because the algorithms are definitely getting better at generating music. Composing music using artificial intelligence isn't a new development as startups and researchers have been attempting it for a long time now. Remember the Chicago-based audio startup, Brain.fm? It promised to provide a new state of medication for the brain using AI-driven music. We also saw a collaborative album between artist Tayrn Southern and Amper software called I AM AI, which became the first album to be entirely composed by AI. These are just two of the many developments that are flooding the space.

Apparently, teaching artificial intelligence to read our innermost thoughts or turning them in terrifying psychopaths isn't enough--now researchers are …