From making tea to cleaning the floors, everyday life often feels like one huge chore. But the opportunity to offload such menial tasks to your own personal robot helper may arrive sooner than you think. In a promo clip, the advanced humanoid boils the kettle, vacuums floors, carries groceries, cleans windows and puts up a picture frame. At the end of the video, it takes a well-earned sit in the longue – while its blissfully-happy owners drink wine in the next room. Although it is currently a prototype, the creation could be autonomously completing chores in customers' homes by the end of the decade.

For die-hards, no horror movie can be too scary. But for you, a wimp, the wrong one can leave you miserable. Never fear, scaredies, because Slate's Scaredy Scale is here to help. We've put together a highly scientific and mostly spoiler-free system for rating new horror movies, comparing them with classics along a 10-point scale. And because not everyone is scared by the same things--some viewers can't stand jump scares, while others are haunted by more psychological terrors or can't stomach arterial spurts--it breaks down each movie's scares across three criteria: suspense, spookiness, and gore.

For die-hards, no horror movie can be too scary. But for you, a wimp, the wrong one can leave you miserable. Never fear, scaredies, because Slate's Scaredy Scale is here to help. We've put together a highly scientific and mostly spoiler-free system for rating new horror movies, comparing them with classics along a 10-point scale. And because not everyone is scared by the same things--some viewers can't stand jump scares, while others are haunted by more psychological terrors or can't stomach arterial spurts--it breaks down each movie's scares across three criteria: suspense, spookiness, and gore.

AI-written books are now an incoming reality. But can they write the next great bestseller? AI is already writing music, creating pictures for graphic novels, and winning art competitions, beating humans. One of the first experimental AI-written novels turned up as early as 2017. Called 1 the Road, it was an experiment by Ross Goodwin.

A horror film written by AI is about as confused and incongruous as you'd imagine it would be. However, it is highly entertaining in its own right, with ill-fitting and even nonsensical dialogue and misfiring concepts making for an oddly enjoyable – if odd – few minutes. The project, uploaded to the Netflix is a Joke YouTube channel, had a bot subjected to 400,000 hours of horror movie material before being tasked with producing its own horror movie script. The result is either a horrifically bad attempt at a horror film – or it is a superb attempt at satire. If it's the former, then we've nothing to worry about as far as AI being able to exercise genuine creativity or even display coherence.

That was the question Alan Turing posed at the top of his landmark 1950 paper, "Computing Machinery and Intelligence." The paper was published seven years after the British mathematician had cemented his place in history by decrypting the German Enigma machine during World War II. It was a time when rudimentary electronic computers were just starting to emerge and the concept of artificial intelligence was almost entirely theoretical. So, Turing could only explore his inquiry with a thought experiment: the imitation game. The game, commonly called the Turing test, is simple. One person, player C, plays the role of an interrogator who poses written questions to players A and B who are in a different room.

Horror films were wildly popular on streaming platforms over the past year, and 2020 saw the horror genre take home its largest share of the box office in modern history.1 In a year where the world was stricken by real horrors, why were many people escaping to worlds full of fictional horrors? As odd as it may sound, the fact that people were more anxious in 2020 may be one reason why horror films were so popular. A look at typical horror fans may provide some clues about the nature of this peculiar phenomenon. For example, horror fans often mention their own anxiety and how horror helps them deal with it.

A few months ago, a friend introduced me to this peculiar horror game he was playing on Nintendo Switch. The game, Dead by Daylight, originally came out in 2016, but quickly enveloped my life. Working from home with minimal social interaction and looming financial precarity put a heavy strain on my mental health, and a horror video game where I'm constantly fighting for survival felt like a kind of virtual exposure therapy. If you haven't played before, here are the gameplay basics for Dead by Daylight. Five players are in each round: one killer and a team of four survivors.

It's the year 2019--and the rise of the machines is transforming digital marketing. Nowadays, AI makes it much easier to generate information for online shoppers directly from a website or in the search results. Yes, that horror movie recommended in your streaming library is generated from AI. Artificial intelligence can both generate and curate content to personalize the user experience. The system is able to generate content based on matching data and information that has been indexed through the internet. For instance, you may notice this as a pop-up that appears as soon as a user visits your website.

Embedding models, which learn latent representations of users and items based on user-item interaction patterns, are a key component of recommendation systems. In many applications, contextual constraints need to be applied to refine recommendations, e.g. when a user specifies a price range or product category filter. The conventional approach, for both context-aware and standard models, is to retrieve items and apply the constraints as independent operations. The order in which these two steps are executed can induce significant problems. For example, applying constraints a posteriori can result in incomplete recommendations or low-quality results for the tail of the distribution (i.e., less popular items). As a result, the additional information that the constraint brings about user intent may not be accurately captured. In this paper we propose integrating the information provided by the contextual constraint into the similarity computation, by merging constraint application and retrieval into one operation in the embedding space. This technique allows us to generate high-quality recommendations for the specified constraint. Our approach learns constraints representations jointly with the user and item embeddings. We incorporate our methods into a matrix factorization model, and perform an experimental evaluation on one internal and two real-world datasets. Our results show significant improvements in predictive performance compared to context-aware and standard models.