In China, where labor shortages and soaring labor costs are increasingly serious problems, robots are replacing redundant, low value-added and sometimes dangerous human work. Hotels are no exception to this trend. As robot technology continues to improve, the service robots in hotels are attracting attention from venture capitalists and entrepreneurs. Established in 2019, Shanghai Jingwu Intelligent Technology makes robots, primarily for hotels. The startup's management and research and development team was once engaged in the robotics business of a listed company and has 17 years of experience in the field.
Newport, R.I., is renowned for its elegance, charm, beauty and history. Now there's a new hotel for visitors eager to take it all in. Newport, as much as it is a place full of the fancy and fabulous, can also be a perfect spot for a kicked back, sea soaked and super fun escape. From big rooms that let in lots of sun to the bright pops of artwork throughout, the nods to the sea, a fabulous restaurant and bar and, of course, a great pool area, the Wayfinder makes for a great escape. My husband and I used it as our home base for a recent weekend.
White people who host rental properties on Airbnb earn significantly more per year than Black hosts, but a "race blind" pricing algorithm could help close that income gap, new research shows. Black hosts who rely on Airbnb's algorithm to set enticing prices instead of manually choosing rates increase their occupancy rates significantly, bringing their earnings more in line with the higher rental incomes of white hosts, according to a study coauthored by Shunyuan Zhang, an assistant professor in the Marketing Unit at Harvard Business School. Zhang's findings come at a critical time for the travel industry. People eager to shake off their COVID-19 cabin fever are gearing up to take vacations, raising Airbnb bookings by 52 percent last quarter from a year earlier. Harnessing artificial intelligence to reduce racial economic disparities might help more property owners benefit from pent-up lodging demand.
Opened in November 2020, Hotel Sky in Sandton, Johannesburg, made its debut with three robots: Lexi, Micah, and Ariel. Lending a helpful hand to the human staff at the property, these robots are the hotel's answer to travelers' increased desire for socially distant interactions. Lexi, Micah, and Ariel can deliver room service, provide travel information, and carry up to 165 pounds of luggage each from the marble-floored lobby to the rooms.
In times of negative interest rates, investors are looking to get out of cash and move to assets that generate returns. Staking could be the answer. Many exchanges provide opportunities to stake tokens and in exchange earn rewards. One can safely earn 5% per annum on a popular digital asset such as ETH. At Modihost we have developed a similar mechanism to staking that rewards buying and holding of AIM tokens.
Modihost brings the power of AI to take the hospitality industry to the next stage in its evolution. ModiHost is a next generation cloud-based hotel management platform that integrates AI, Machine Learning, Cognitive Computing and Native Voice Recognition. It enables hotels to deliver a range of advanced, tailored services to their guests. ModiHost will also operate with a fairer and more transparent fee system than any HMS currently on the market. While most HMS products charge fixed rates, the ModiHost platform operates on a flexible Pay-Per-Use model wherein hotels only pay fees on delivered services. In effect, ModiHost will pay for itself as it delivers value to management.
Sender-receiver interactions, and specifically persuasion games, are widely researched in economic modeling and artificial intelligence, and serve as a solid foundation for powerful applications. However, in the classic persuasion games setting, the messages sent from the expert to the decision-maker are abstract or well-structured application-specific signals rather than natural (human) language messages, although natural language is a very common communication signal in real-world persuasion setups. This paper addresses the use of natural language in persuasion games, exploring its impact on the decisions made by the players and aiming to construct effective models for the prediction of these decisions. For this purpose, we conduct an online repeated interaction experiment. At each trial of the interaction, an informed expert aims to sell an uninformed decision-maker a vacation in a hotel, by sending her a review that describes the hotel. While the expert is exposed to several scored reviews, the decision-maker observes only the single review sent by the expert, and her payoff in case she chooses to take the hotel is a random draw from the review score distribution available to the expert only. The expert's payoff, in turn, depends on the number of times the decision-maker chooses the hotel. We consider a number of modeling approaches for this setup, differing from each other in the model type (deep neural network (DNN) vs. linear classifier), the type of features used by the model (textual, behavioral or both) and the source of the textual features (DNN-based vs. hand-crafted). Our results demonstrate that given a prefix of the interaction sequence, our models can predict the future decisions of the decision-maker, particularly when a sequential modeling approach and hand-crafted textual features are applied.
Multi-document summaritazion is the process of taking multiple texts as input and producing a short summary text based on the content of input texts. Up until recently, multi-document summarizers are mostly supervised extractive. However, supervised methods require datasets of large, paired document-summary examples which are rare and expensive to produce. In 2018, an unsupervised multi-document abstractive summarization method(Meansum) was proposed by Chu and Liu, and demonstrated competitive performances comparing to extractive methods. Despite good evaluation results on automatic metrics, Meansum has multiple limitations, notably the inability of dealing with multiple aspects. The aim of this work was to use Multi-Aspect Masker(MAM) as content selector to address the issue with multi-aspect. Moreover, we propose a regularizer to control the length of the generated summaries. Through a series of experiments on the hotel dataset from Trip Advisor, we validate our assumption and show that our improved model achieves higher ROUGE, Sentiment Accuracy than the original Meansum method and also beats/ comprarable/close to the supervised baseline.
Before we dive into the Airbnb dataset and our findings, let's do an in-depth review of the K-Means clustering algorithm. An unsupervised learner receives unlabeled training data and makes predictions for unseen points. Clustering analysis falls under unsupervised learning. A cluster is a collection of data objects that are similar (or related) to one another within the same group, or dissimilar (or unrelated) to the objects in other groups. "Good" clusters will have the following: high intra-class similarity (cohesiveness within clusters) and low inter-class similarity (distinctive between clusters). The K-Means algorithm is common a type of clustering analysis.