Fans of will be happy to hear that there's been another entry into the world of scintillating gay romance. The film stars noted on-screen sex haver Alexander Skarsgård--he's equally provocative in the NC-17-rated --and some guy named Harry Melling, who seems to have been in . Melling plays Colin, a certified beta whose deepest desire is to serve. He gets his wish when he meets Ray (Skarsgård), a toppy, Tom of Finland -esque biker with an attitude so icy it could preserve food. The two enter into a full-time power-exchange relationship that fuels both of their desires, until their connection evolves to a heart-wrenching breaking point. Unlike other recent films about kink that were bound and gagged by their own corniness--think and -- has been lauded as realistic, sophisticated, and smart, and the movie is currently sitting at 100 percent on Rotten Tomatoes . Still, was it enough to satisfy senior editor Isabelle Kohn and How to Do It columnist Rich Juzwiak? Be a good boy and find out.

This paper offers a comprehensive analysis of collaborative bandit algorithms and provides a thorough comparison of their performance. Collaborative bandits aim to improve the performance of contextual bandits by introducing relationships between arms (or items), allowing effective propagation of information. Collaboration among arms allows the feedback obtained through a single user (item) to be shared across related users (items). Introducing collaboration also alleviates the cold user (item) problem, i.e., lack of historical information when a new user (item) arriving to the platform with no prior record of interactions. In the context of modeling the relationships between arms (items), there are two main approaches: Hard and soft clustering. We call approaches that model the relationship between arms in an \textit{absolute} manner as hard clustering, i.e., the relationship is binary. Soft clustering relaxes membership constraints, allowing \textit{fuzzy} assignment. Focusing on the latter, we provide extensive experiments on the state-of-the-art collaborative contextual bandit algorithms and investigate the effect of sparsity and how the exploration intensity acts as a correction mechanism. Our numerical experiments demonstrate that controlling for sparsity in collaboration improves data efficiency and performance as it better informs learning. Meanwhile, increasing the exploration intensity acts as a correction because it effectively reduces variance due to potentially misspecified relationships among users. We observe that this misspecification is further remedied by introducing latent factors, and thus, increasing the dimensionality of the bandit parameters.

We introduce a comprehensive Linguistic Benchmark designed to evaluate the limitations of Large Language Models (LLMs) in domains such as logical reasoning, spatial intelligence, and linguistic understanding, among others. Through a series of straightforward questions, it uncovers the significant limitations of well-regarded models to perform tasks that humans manage with ease. It also highlights the potential of prompt engineering to mitigate some errors and underscores the necessity for better training methodologies. Our findings stress the importance of grounding LLMs with human reasoning and common sense, emphasising the need for human-in-the-loop for enterprise applications. We hope this work paves the way for future research to enhance the usefulness and reliability of new models.

I'm writing this post from the guest room in my mom's house, which is peppered with old knick-knacks of mine--to summon the spirit of my childhood room, I suppose. While flipping through my photo albums, I was tickled to find a blurry picture of the poster for Phone Booth, clearly taken by me on a disposable camera outside of a movie theater. I was probably too young to be watching a gunman thriller--thanks, Mom--but I'm pretty sure my affection for it had a lot to do with Colin Farrell, who was a relative unknown when that movie came out in 2002. To this day, I'm a bit gaga over him, though I think part of the reason my puppy love has turned into something more enduring is that, as I've gotten older and my tastes have evolved, so has the actor's persona. Not to downplay his macho heartthrob phase in the aughts--I still go catatonic whenever I think about him salsa dancing in Miami Vice, and I sense noted MV-heads Bilge and David feel the same way--but it has been a delight to see him take on increasingly stranger, more cerebral roles for directors like Yorgos Lanthimos and Sofia Coppola while also pushing himself, unafraid to get ugly and unhinged, in blockbusters like The Batman.

Deepak Patel, 43, conducts a room inspection at the Country Inn and Suites, Baltimore North, a hotel he owns and manages with his family in Rosedale, Maryland. Deepak Patel, 43, conducts a room inspection at the Country Inn and Suites, Baltimore North, a hotel he owns and manages with his family in Rosedale, Maryland. This holiday season at the Garden City Hotel on Long Island, Merle Ayers is feeling especially grateful for the Whiz. At two feet tall and 66 pounds, the powerful robot vacuum doesn't mind working late into the night after the parties are over. It doesn't even need a day off.

Following best practices when building machine learning models is a time-consuming yet important process. There are so many things to do ranging from: preparing the data, selecting and training algorithms, understanding how the algorithm is making decisions, all the way down to deploying models to production. I like to think of the machine learning design and maintenance process as being comprised of ten steps (see the diagram above). But, if I want to save time, increase accuracy, and reduce risk, I don't manually go through the entire machine learning process in order to build my machine learning models. Instead, I turn to automated machine learning, using clever software that knows how to automate the repetitive and mundane steps, and freeing me up to do what humans are best at: communication, applying common sense, and being creative.

Chris Smith and Phil Sansom delve into the world of artificial Intelligence (AI) to find out how this emerging technology is changing the way we practise medicine... Mike - I think this is an area where AI stands a really good chance of making quite dramatic improvements to very large numbers of people's lives. Carolyn - Save lives and reduce medical complications. Beth - That's a concern - when machine-learning algorithms learn the wrong things. Andrew - Frankly revolutionary productivity that we are now starting to see from these AI approaches in drug design. Lee - It will replace all manual labor in all research laboratories. And then suddenly everyone can collaborate. Phil - But what was previously sci-fi is now closer to reality. AI technology exists, and there's a brand new frontier where it's being applied to the world of healthcare. Chris - But this isn't the AI you see in the movies. In the words of Kent University computer scientist Colin Johnson, "this is more software than Schwarzeneggar"... Colin - When scientists say AI, they often mean some piece of code that's running on a computer and it's taking some inputs.

Colin Priest is the Sr. Director of Product Marketing for DataRobot, where he advises businesses on how to build business cases and successfully manage data science projects. Colin has held a number of CEO and general management roles, where he has championed data science initiatives in financial services, healthcare, security, oil and gas, government and marketing. Colin is a firm believer in data-based decision making and applying automation to improve customer experience. He is passionate about the science of healthcare and does pro-bono work to support cancer research.

Black Mirror's "Bandersnatch" is the story of a 19-year-old software developer trying to turn a novel by a psychologically unstable science fiction visionary into a 1980s video game. The dystopian anthology's fifth "season" turns out to be a single project, an interactive movie (if that's not a contradiction in terms) that offers viewers dozens of distinct choices, ranging from the innocuous selection of a sugary breakfast cereal to life-or-death forks in the road. You control Stefan (Fionn Whitehead), a jittery teenager trying to create a joystick-controlled analogue for Bandersnatch, a multistranded novel by the science-fiction author Jerome F. Davies, whose views on fate and alternate realities are clearly modeled on those of Philip K. Dick. Stefan seeks out the already legendary game designer Colin Ritman (Will Poulter) for advice, but instead Colin leads him toward a psychotic break, insisting that freewill is an illusion, that people are controlled by unseen hands, and that what we take to be reality is just one possibility that exists simultaneously alongside many others. In other words, life is just one big game of choose your own adventure.

Colin Priest is the Director of Product Marketing for DataRobot, where he advises businesses on how to build business cases and successfully manage data science projects. Colin has held a number of CEO and general management roles, where he has championed data science initiatives in financial services, healthcare, security, oil and gas, government and marketing. Colin is a firm believer in data-based decision making and applying automation to improve customer experience. He is passionate about the science of healthcare and does pro-bono work to support cancer research.