Eddy Travels closes pre-seed round led by Techstars to scale its AI travel assistant – TechCrunch


Eddy Travels, an AI-powered travel assistant bot which can understand text and voice messages, has closed a pre-seed round of around $500,000 led by Techstars Toronto, Practica Capital and Open Circle Capital VC funds from Lithuania, with angel investors from the U.S., Canada, U.K. Launched in November 2018, Eddy Travels claims to have more than 100,000 users worldwide. Travelers can send voice and text messages to the Eddy Travels bot and get personalized suggestions for the best flights. Because of this ease of use, it now gets 40,000 flight searches per month -- tiny compared to the major travels portals, but not bad for a bot that is available on Facebook Messenger, WhatsApp, Telegram, Rakuten Viber, Line and Slack chat apps. The team is now looking to expand into accommodation, car rentals and other travel services. Eddy Travels search is powered by partnerships with Skyscanner and Emirates Airline.

AI Can Rescue Hospitality By Kevin McCarthy – Hospitality Net


It's 2020, my name is Jack and I work at the front desk of a busy 3-star hotel at Amsterdam airport called the Grand Hotel (fictitious). Our clientele is the usual airport hotel clientele you would expect, crews, holidaymakers, business travelers, and in low season some tour groups. There are 300 rooms, and we have a large restaurant and a lobby bar as well as gym, meeting rooms etc. This morning was a particularly hard morning at checkout though. The car parking validator broke down and that just transcended us into a bad place.

How To Rise Above The ITOps Chaos Using AI


CIOs (Chief Information Officers) are both excited and scared about digital transformation and the pace of innovation. While the ability to drive forward their businesses using IT is exciting, along with the agility and flexibility of newer IT infrastructure models, the fact that business will come to a standstill if information technology (IT) services go down is a scary proposition. Yet, most enterprises are trying to fix problems after they occur instead of preventing them from happening in the first place. Gartner estimates that the data volumes generated by IT infrastructure are increasing two-to-three-fold every year. This combined with shrinking IT operations budgets is a clear recipe for disaster.

Coronavirus outbreak: Passengers stranded on Japan cruise plead for help from Trump, say situation is 'desperate'

FOX News

Passengers Milena Basso and her husband speak out about being quarantined aboard the Princess Cruise ship where dozens have tested positive for the coronavirus. Passengers Milena Basso and her husband Gaetano Cerullo are calling for help from President Trump after being trapped on a Diamond Princess cruise ship off the coast of Japan with at least 61 positive cases of coronavirus. The newlyweds -- on their honeymoon -- are two of more than 2,000 passengers who have been held on the ship since Tuesday. Appearing on "America's Newsroom" with host Ed Henry, the couple said that while their physical health is "pretty good," mentally they are "not so great." FOX NEWS' TODD PIRO REPORTS FROM NEW JERSEY AS CRUISE PASSENGERS ARRIVE TO BE TESTED FOR CORONAVIRUS Additionally, the pair told Henry they were disheartened to learn that updates were coming faster from their parents and news outlets than from those on the ship itself.

Artificial Intelligence: Future of Travel Industry


TECHNOLOGY TODAY is advancing at a rapid pace, and has permeated into every sector. Combining AI with advanced analytics principles can ensure personalized service, resulting in better value and memorable experience to customers. New age travellers in the country are exposed to the power of choice. Having options makes customers more satisfied. They do not wish to be bounded by the traditional rigid itineraries of old, and are looking for personalization and customisation.

SANST: A Self-Attentive Network for Next Point-of-Interest Recommendation Machine Learning

Next point-of-interest (POI) recommendation aims to offer suggestions on which POI to visit next, given a user's POI visit history. This problem has a wide application in the tourism industry, and it is gaining an increasing interest as more POI check-in data become available. The problem is often modeled as a sequential recommendation problem to take advantage of the sequential patterns of user check-ins, e.g., people tend to visit Central Park after The Metropolitan Museum of Art in New York City. Recently, self-attentive networks have been shown to be both effective and efficient in general sequential recommendation problems, e.g., to recommend products, video games, or movies. Directly adopting self-attentive networks for next POI recommendation, however, may produce sub-optimal recommendations. This is because vanilla self-attentive networks do not consider the spatial and temporal patterns of user check-ins, which are two critical features in next POI recommendation. To address this limitation, in this paper, we propose a model named SANST that incorporates spatio-temporal patterns of user check-ins into self-attentive networks. To incorporate the spatial patterns, we encode the relative positions of POIs into their embeddings before feeding the embeddings into the self-attentive network. To incorporate the temporal patterns, we discretize the time of POI check-ins and model the temporal relationship between POI check-ins by a relation-aware self-attention module. We evaluate the performance of our SANST model with three real-world datasets. The results show that SANST consistently outperforms the state-of-theart models, and the advantage in nDCG@10 is up to 13.65%.

Feature-based time series analysis


I used this example in my talk at useR!2019 in Toulouse, and it is also the basis of a vignette in the package, and a recent blog post by Mitchell O'Hara-Wild. The data set contains domestic tourist visitor nights in Australia, disaggregated by State, Region and Purpose. An example of a feature would be the autocorrelation function at lag 1 -- it is a numerical summary capturing some aspect of the time series. Autocorrelations at other lags are also features, as are the autocorrelations of the first differenced series, or the seasonally differenced series, etc. Values close to 1 indicate a highly seasonal time series, while values close to 0 indicate a time series with little seasonality.

Egencia Delivers Predictions for the Future of Tech and Travel Hotel Business


NATIONAL REPORT--What does the future hold for the travel industry? For starters, technology will continue to shape the guest experience as hotel brands make this a vital point of focus. Whether it's through in-room technology, service-oriented robots or the seamless integration of a new process or service, change is coming. "Personalization has begun to take off thanks to artificial intelligence and machine-learning technology. But, to date, the impact of personalization has been largely contained to rates and hotel options in the travel industry," said Kaluzny.

Reimagining the future of travel and hospitality with artificial intelligence


Over the years, the influence of artificial intelligence (AI) has spread to almost every aspect of the travel and the hospitality industry. Thirty percent of hospitality businesses use AI to augment at least one of their primary sales processes, and most customer personalisation is done using AI. The proliferation of AI in the travel and hospitality industry can be credited to the humongous amount of data being generated today. AI helps analyse data from obvious sources, brings value in assimilating patterns in image, voice, video, and text, and turns it into meaningful and actionable insights for decision making. Trends, outliers, and patterns are figured out using machine learning-based algorithms that help in guiding a travel or hospitality company to make informed decisions.

STREETS: A Novel Camera Network Dataset for Traffic Flow

Neural Information Processing Systems

In this paper, we introduce STREETS, a novel traffic flow dataset from publicly available web cameras in the suburbs of Chicago, IL. We seek to address the limitations of existing datasets in this area. Many such datasets lack a coherent traffic network graph to describe the relationship between sensors. The datasets that do provide a graph depict traffic flow in urban population centers or highway systems and use costly sensors like induction loops. These contexts differ from that of a suburban traffic body.