My friend walks into the village hall, scene of my son's third birthday party, a mixture of panic and incredulity creeping across his face. "I didn't realise we were dressing up," he says, taking in my outfit. I'm wearing a mint-green tulle midi dress with sheer sleeves that balloon precociously and a tiered skirt that puffs out in such a way as to give me the appearance of either a Quality Street or a three-year-old at her own birthday party. It's not, if I'm entirely honest, the most practical of outfits for serving chocolate cake to 18 sticky-handed toddlers but, as I blurt out to my friend, keen to dispel any confusion, the avant-garde look wasn't actually my choice: it was AI's. My wardrobe is my identity, my refuge, my hobby, my happy place. Or, at least, it was.

Interior design involves the careful selection and arrangement of objects to create an aesthetically pleasing, functional, and harmonized space that aligns with the client's design brief. This task is particularly challenging, as a successful design must not only incorporate all the necessary objects in a cohesive style, but also ensure they are arranged in a way that maximizes accessibility, while adhering to a variety of affordability and usage considerations. Data-driven solutions have been proposed, but these are typically room- or domain-specific and lack explainability in their design design considerations used in producing the final layout. In this paper, we investigate if large language models (LLMs) can be directly utilized for interior design. While we find that LLMs are not yet capable of generating complete layouts, they can be effectively leveraged in a structured manner, inspired by the workflow of interior designers. By systematically probing LLMs, we can reliably generate a list of objects along with relevant constraints that guide their placement. We translate this information into a design layout graph, which is then solved using an off-the-shelf constrained optimization setup to generate the final layouts. We benchmark our algorithm in various design configurations against existing LLM-based methods and human designs, and evaluate the results using a variety of quantitative and qualitative metrics along with user studies. In summary, we demonstrate that LLMs, when used in a structured manner, can effectively generate diverse high-quality layouts, making them a viable solution for creating large-scale virtual scenes. Project webpage at https://flairgpt.github.io/

Text-based games (TBGs) have emerged as an important collection of NLP tasks, requiring reinforcement learning (RL) agents to combine natural language understanding with reasoning. A key challenge for agents attempting to solve such tasks is to generalize across multiple games and demonstrate good performance on both seen and unseen objects. Purely deep-RL-based approaches may perform well on seen objects; however, they fail to showcase the same performance on unseen objects. Commonsense-infused deep-RL agents may work better on unseen data; unfortunately, their policies are often not interpretable or easily transferable. To tackle these issues, in this paper, we present EXPLORER which is an exploration-guided reasoning agent for textual reinforcement learning. EXPLORER is neurosymbolic in nature, as it relies on a neural module for exploration and a symbolic module for exploitation. It can also learn generalized symbolic policies and perform well over unseen data. Our experiments show that EXPLORER outperforms the baseline agents on Text-World cooking (TW-Cooking) and Text-World Commonsense (TWC) games.

More than 12,000 people are turning 65 each day in the US. And with that, individuals and families are starting to make considerations on what might be entailed to better manage the aging process. There is a strong desire from seniors to age in place, meaning staying in their home instead of moving to a dedicated facility. Marc Glickman, CEO of long-term care planning experts BuddyIns, estimated that today, around 75% of seniors are using home care services to age in place instead of moving to an assisted living or nursing homes. An AARP survey showed 90% of individuals 65 and over would prefer to age in place.

'Make meetings more fun and personal with Decorate your background,' Microsoft said The fancy option adds sparkling fairy lights and glasses of champagne to your background. Meanwhile, the celebration option adds a festive Christmas tree and presents behind you. The feature will launch in early 2024 for Teams Premium users. The news comes shortly after a study revealed how your background on video calls can influence the first impression you make. Researchers from Durham University say that people who sit in front of houseplants or bookcases are deemed the most trustworthy.

Andreas Kronthaler and iconic British fashion designer Dame Vivienne, who died last year, were a fashion powerhouse designing together under the Westwood brand. The pair married in 1992.

The average person sees up to 10,000 ads every day. No wonder people just throw money at whatever -- they'll buy something they don't need, something that doesn't fit them or won't help them. It can be a perpetual need to spend. Then the guilt hits, and more than half of all Americans say that they regret spending on things they didn't need or can't use. A single Black Friday sale generates $74 billion worth of regretted spending in the U.S. alone.

Over the next decade, homes will become greener and smarter, with wardrobes folding clothes, toilets checking waste, and a space for video calls, a futurologist has claimed. Tom Cheesewright claims that trends were already pointing towards a more remote, flexible and sustainable life, but the pandemic and lockdown are making it happen faster. Research funded by Hive found that 88 per cent of people wanted to live in a more sustainable future but 41 per cent didn't know how to go about making it happen. There is also a push towards smart homes, with smart assistants, video doorbells and smart lights becoming more popular as people spent time indoors over lockdown. Speaking exclusively to MailOnline, Mr Cheesewright said: 'The pressure of the pandemic brought that forward,' adding that homes are going to change to reflect these trends over the next decade. These changes will include a rise in'smart technology', including things like smart wardrobes that can iron and fold your clothes, or a medical toilet that can analyse your waste for signs of cancer or other health problems and report back to doctors, according to the futurologist.

Given an outfit pieced together from a limitless wardrobe, what nips and tucks might improve its overall stylishness? That's the question researchers at Cornell, Georgia Tech, and Facebook AI Research recently investigated in a research paper published on the preprint server Arxiv.org. In it, they describe an approach that aims to identify small adjustments to outfits that might have an outsized impact on fashionability. It brings to mind Amazon's Echo Look, a connected camera that combines human and machine intelligence to recommend styles, color-filter clothes, compare two outfits, and keep track of what's in personal wardrobes. But the researchers assert their techniques are more sophisticated than most.

At the turn of the twentieth century, the swelling populations of newly arrived immigrants in New York City's Lower East Side reached a boiling point, forcing the City to pass the 1901 Tenement House Act. Recalling this legislation, New York City's Mayor's Office recently responded to its own modern housing crisis by enabling developers for the first time to build affordable micro-studio apartments of 400 square feet. One of the primary drivers of allocating tens of thousands of new micro-units is the adoption of innovative design and construction technologies that enable modular and flexible housing options. As Mayor de Blasio affirmed, "Housing New York 2.0 commits us to creating 25,000 affordable homes a year and 300,000 homes by 2026. Making New York a fairer city for today and for future generations depends on it."