Trending Now Safety Net Say More Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Creator Playbook Mashable Selects Getting Technical with Mashable Gift Ideas For Everyone On Your List Versus In My Bag Mashable @Home All Series Airbnb is cracking down on checkout chores. We are not running hotels. We are sharing our properties. Olivia Tauber is the deputy editor of digital culture, covering creators, media, movies, beauty, and more. Based in New York, her work has appeared in The New York Times, Vanity Fair, The Cut, Teen Vogue, Complex, and Interview Magazine.

Safety Net Creator Playbook Trending Now Mashable Selects Look Up Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Getting Technical with Mashable Gift Ideas For Everyone On Your List Say More Versus In My Bag Mashable @Home All Series Airbnb hosts can't make guests do chores under new rules Don't strip the bed: Airbnb's new policy limits checkout chores, prompting travelers to share horror stories. Olivia Tauber is the deputy editor of digital culture, covering creators, media, movies, beauty, and more. Based in New York, her work has appeared in The New York Times, Vanity Fair, The Cut, Teen Vogue, Complex, and Interview Magazine. She holds a Master's degree in Journalism from NYU and a Bachelor's from the University of Michigan. She also runs Fan Mail, a weekly pop-culture newsletter.

Airbnb has changed its rules to limit what property hosts may ask guests to do - including when it comes to cleaning. The updated guidelines state that hosts can now set seven standard house rules and can also add other reasonable requests. Airbnb states that guests - who are required to pay a cleaning fee - should not be asked to complete tasks normally done by a cleaner, including washing dishes and stripping beds. The new rules will apply to all reservations booked on or after 20 November on the accommodation marketplace. House rules help guests understand what hosts expect, but we've heard from our community that they can be confusing, onerous and hard to manage, Airbnb told the BBC.

Airbnb says the "anti-party system" it deploys ahead of major holiday weekends flags bookings with characteristics indicating guests are likely to use properties for unauthorized parties.

Samsung Galaxy Unpacked 2026 is Feb. 25 Valve's Steam Machine: Everything we know Airbnb is testing out AI search with a'small percentage' of users Beyond improving search, Airbnb wants to lean heavily into artificial intelligence to help users with with booking, managing listings and customer service. Airbnb plans to double down on artificial intelligence to improve its user experience for both guests and hosts. During a fourth-quarter earnings call, Airbnb's CEO, Brian Chesky, said the company is building an AI-native experience aimed at helping guests book trips, assisting hosts with their listings, and running the company more efficiently. According to Chesky, there's an AI search tool to help guests book trips that's live for a small percentage of users right now. In a shareholder letter posted on Airbnb's website, the company said it's conducting early testing with an AI-powered search that is focused on giving guests a more natural way to describe what they're looking for, and ask questions about the listing and location.

Generating an abstraction of a dynamic domain that aligns with a given purpose remains a significant challenge given that the choice of such an abstraction can impact an agent's ability to plan, reason, and provide explanations effectively. We model the agent's concrete behaviors in PDDL and investigate the use of in-context learning with large language models (LLMs) for the generation of abstract PDDL domains and problem instances, given an abstraction objective specified in natural language. The benchmark examples we use are new and have not been part of the data any LLMs have been trained on. We consider three categories of abstractions: abstraction of choice of alternative concrete actions, abstraction of sequences of concrete actions, and abstraction of action/predicate parameters, as well as combinations of these. The generated abstract PDDL domains and problem instances are then checked by symbolic validation tools as well as human experts. Our experiments show that GPT -4o can generally synthesize useful planning domain abstractions in simple settings, although it is better at abstracting over actions than over the associated fluents.

It is increasingly important to generate synthetic populations with explicit coordinates rather than coarse geographic areas, yet no established methods exist to achieve this. One reason is that latitude and longitude differ from other continuous variables, exhibiting large empty spaces and highly uneven densities. To address this, we propose a population synthesis algorithm that first maps spatial coordinates into a more regular latent space using Normalizing Flows (NF), and then combines them with other features in a Variational Autoencoder (VAE) to generate synthetic populations. This approach also learns the joint distribution between spatial and non-spatial features, exploiting spatial autocorrelations. We demonstrate the method by generating synthetic homes with the same statistical properties of real homes in 121 datasets, corresponding to diverse geographies. We further propose an evaluation framework that measures both spatial accuracy and practical utility, while ensuring privacy preservation. Our results show that the NF+VAE architecture outperforms popular benchmarks, including copula-based methods and uniform allocation within geographic areas. The ability to generate geolocated synthetic populations at fine spatial resolution opens the door to applications requiring detailed geography, from household responses to floods, to epidemic spread, evacuation planning, and transport modeling.

As Brian Chesky tells it, the reinvention of Airbnb started with the coup at OpenAI. On November 17, 2023, the board of OpenAI fired company CEO Sam Altman. His friend Chesky leapt into action--publicly defending his pal on X, getting on the phone with Microsoft's CEO, and throwing himself into the thick of Altman's battle to retake OpenAI. Five days later Altman prevailed, and Chesky--"I was so jacked up," he says--turned his buzzing mind to his own company, Airbnb. The Chesky extended family had already held their turkey get-together a week earlier, and the Airbnb CEO had no holiday plan.

This research examines whether Airbnb guests' positive and negative comments influence acceptance rates and rental prices across six U.S. regions: Rhode Island, Broward County, Chicago, Dallas, San Diego, and Boston. Thousands of reviews were collected and analyzed using Natural Language Processing (NLP) to classify sentiments as positive or negative, followed by statistical testing (t-tests and basic correlations) on the average scores. The findings reveal that over 90 percent of reviews in each region are positive, indicating that having additional reviews does not significantly enhance prices. However, listings with predominantly positive feedback exhibit slightly higher acceptance rates, suggesting that sentiment polarity, rather than the sheer volume of reviews, is a more critical factor for host success. Additionally, budget listings often gather extensive reviews while maintaining competitive pricing, whereas premium listings sustain higher prices with fewer but highly positive reviews. These results underscore the importance of sentiment quality over quantity in shaping guest behavior and pricing strategies in an overwhelmingly positive review environment.