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Start-up Eddy Travels' group planning digital assistant launches with kiwi.com support

#artificialintelligence

Start-up Eddy Travels has launched a new artificial intelligence group planning digital assistant as part of a partnership with flight search specialist Kiwi.com. Tequila API powers the Eddy Travels AI assistant flight search which was launched in 2018 and has four million users. Eddy Travels, the co-located Lithuanian and Canadian firm, said the new group chat planning service will help families and friends planning to reconnect after months in lockdown. Edmundas Balčikonis, co-founder and chief executive of Eddy Travels, said: "The new chat app removes the burden for one person to be responsible for planning everything and makes it a fun shared activity, even if it's done remotely for now." Oliver Dlouhý, co-founder and chief executive of Kiwi.com, added: "Edmundas and his team did an amazing job building the first group travel booking app that actually works and is really fun to use. "We are proud to be providers of flight and ground transportation inventory, including Virtual Interlining.


American Airlines just revealed the future (you may feel very uncomfortable)

ZDNet

It's going to be different. Airlines offered so much good news last week that it was hard to know how happy to be. Delta, United, and Alaska all made noises about soon breaking even. Gary Kelly, Southwest's CEO chirped that this "feels like the beginning of the end." So along came American Airlines to helpfully prepare customers for what the end might really look like.


Here's how artificial intelligence and IoT can improve travel experience

#artificialintelligence

In interaction with Media, Aamir Junaid Ahmad, CEO - BusAndTicket, a technocrat himself, shared how the company is planning to use technology for improving bus ticket booking and travel experience for its customers. With the AI advancement in the world, we will soon implement new ways the technology can improve customer experience. AI and IoT together will give more personalised ticket booking experience and will help users find the best deals and recommendations to fulfill their travel plans with ease. The more they use the service, the more information will be available to further customize the search results. BusAndTicket.com is coming up for the first time with the concept of dynamic pricing in bus ticket booking using the Analytical Benefits of AI.


Artificial Intelligence and IoT can improve travel experience

#artificialintelligence

In interaction with Media, Aamir Junaid Ahmad, CEO - BusAndTicket, a technocrat himself, shared how the company is planning to use technology for improving bus ticket booking and travel experience for its customers. With the AI advancement in the world, we will soon implement new ways the technology can improve customer experience. AI and IoT together will give more personalized ticket booking experience and will help users find the best deals and recommendations to fulfill their travel plans with ease. The more they use the service, the more information will be available to further customize the search results. BusAndTicket.com is coming up for the first time with the concept of dynamic pricing in bus ticket booking using the Analytical Benefits of AI.


How Artificial Intelligence Could Reshape How Travelers Book Hotels

#artificialintelligence

It's easier to talk about people, companies, or events than to talk about ideas. But one idea worth discussing, despite its complexity, is how artificial intelligence could reorder hotel distribution. Some researchers are wondering if artificial intelligence could handle some of the more complex tasks of shopping and haggling. Could new algorithms and processes shrink the role of travel search engines and comparison apps? Could the cost of bringing buyers and sellers together shrink thanks to technical innovations?


Andrei Papancea, CEO at NLX – Interview Series

#artificialintelligence

Andrei Papancea, is the CEO at NLX a comprehensive SaaS platform for building and managing AI-powered conversational applications at scale. Previously, he built the Natural Language Understanding platform for American Express, processing millions of conversations across AmEx's main servicing channels. You grew up in Romania and started programming when you were 10 years old. What attracted you to programming at such a young age? It started off as curiosity: I've always been intrigued about how things worked and since my family has just gotten a computer, I wanted to figure out how it worked.


On the Subbagging Estimation for Massive Data

arXiv.org Machine Learning

This article introduces subbagging (subsample aggregating) estimation approaches for big data analysis with memory constraints of computers. Specifically, for the whole dataset with size $N$, $m_N$ subsamples are randomly drawn, and each subsample with a subsample size $k_N\ll N$ to meet the memory constraint is sampled uniformly without replacement. Aggregating the estimators of $m_N$ subsamples can lead to subbagging estimation. To analyze the theoretical properties of the subbagging estimator, we adapt the incomplete $U$-statistics theory with an infinite order kernel to allow overlapping drawn subsamples in the sampling procedure. Utilizing this novel theoretical framework, we demonstrate that via a proper hyperparameter selection of $k_N$ and $m_N$, the subbagging estimator can achieve $\sqrt{N}$-consistency and asymptotic normality under the condition $(k_Nm_N)/N\to \alpha \in (0,\infty]$. Compared to the full sample estimator, we theoretically show that the $\sqrt{N}$-consistent subbagging estimator has an inflation rate of $1/\alpha$ in its asymptotic variance. Simulation experiments are presented to demonstrate the finite sample performances. An American airline dataset is analyzed to illustrate that the subbagging estimate is numerically close to the full sample estimate, and can be computationally fast under the memory constraint.


Distributed Bootstrap for Simultaneous Inference Under High Dimensionality

arXiv.org Machine Learning

We propose a distributed bootstrap method for simultaneous inference on high-dimensional massive data that are stored and processed with many machines. The method produces a $\ell_\infty$-norm confidence region based on a communication-efficient de-biased lasso, and we propose an efficient cross-validation approach to tune the method at every iteration. We theoretically prove a lower bound on the number of communication rounds $\tau_{\min}$ that warrants the statistical accuracy and efficiency. Furthermore, $\tau_{\min}$ only increases logarithmically with the number of workers and intrinsic dimensionality, while nearly invariant to the nominal dimensionality. We test our theory by extensive simulation studies, and a variable screening task on a semi-synthetic dataset based on the US Airline On-time Performance dataset. The code to reproduce the numerical results is available at GitHub: https://github.com/skchao74/Distributed-bootstrap.


Graph Neural Network for Traffic Forecasting: A Survey

arXiv.org Artificial Intelligence

Traffic forecasting is important for the success of intelligent transportation systems. Deep learning models, including convolution neural networks and recurrent neural networks, have been extensively applied in traffic forecasting problems to model spatial and temporal dependencies. In recent years, to model the graph structures in transportation systems as well as contextual information, graph neural networks have been introduced and have achieved state-of-the-art performance in a series of traffic forecasting problems. In this survey, we review the rapidly growing body of research using different graph neural networks, e.g. graph convolutional and graph attention networks, in various traffic forecasting problems, e.g. road traffic flow and speed forecasting, passenger flow forecasting in urban rail transit systems, and demand forecasting in ride-hailing platforms. We also present a comprehensive list of open data and source resources for each problem and identify future research directions. To the best of our knowledge, this paper is the first comprehensive survey that explores the application of graph neural networks for traffic forecasting problems. We have also created a public GitHub repository where the latest papers, open data, and source resources will be updated.


MSC Cruise line reveals 'humanoid' robot bartender for new ship

FOX News

Cruises have been cancelled since last march, resulting in over 100,000 Americans losing their jobs per a November report. Florida has been especially hard hit with the state being home to the three busiest cruise ports in the world. Robots may be able to pour drinks, but can they listen to your troubles? The future of bartending is coming to MSC Cruises' new flagship ship when the MSC Virtuosa launches later this year. The cruise line recently revealed details of its "immersive, futuristic" bar and entertainment experience – including a "humanoid" bartender.