Gartner estimated that 40% of an organisation's data is fundamentally lacking accuracy and fails to communicate the full picture. They also highlighted in key findings that poor data quality affects operational efficiency. As you might aware, Hawaiian airlines caused an upsetting customer experience with their poor data management last year, for example. They mistakenly charged its customer $539k while other frequent travellers were charged zero. To make matters worse, when trying to fix the faux pas, the airlines canceled the tickets of customers instead; further upsetting them. Ok, don't start to hate them, they have an wonderful flying crew, btw.
A common problem for companies with strong business growth is that it is hard to find enough experienced staff to support expansion needs. This problem is particular pronounced for operations planners and controllers who must be very highly knowledgeable and experienced with the business domain. This article is a case study of how one of the largest travel agencies in Hong Kong alleviated this problem by using AI to support decision-making and problem-solving so that their planners and controllers can work more effectively and efficiently to sustain business growth while maintaining consistent quality of service. AI is used in a mission critical fleet management system (FMS) that supports the scheduling and management of a fleet of luxury limousines for business travelers. The AI problem was modeled as a constraint satisfaction problem (CSP).
Budget airline easyJet was aware of the data breach, which revealed personal information of nine million customers and the credit card information of over 2,200 customers, in January. News of the cyber attack broke yesterday, revealing that the attacker or attackers had access to the data of customers who booked flights from 17 October 2019 to 4 March 2020. In a statement, the airline said: "We're sorry that this has happened, and we would like to reassure customers that we take the safety and security of their information very seriously. "There is no evidence that any personal information of any nature has been misused." However, while there is no evidence the data was misused, that does not mean that it cannot be misused. Experts suggest that personal information "drives a higher price on the dark web" – the area of the internet inaccessible by mainstream search engines – and could be used for organised crime or ransomed. What does the easyJet data hack mean for you? What does the easyJet data hack mean for you? Two people with knowledge of the investigation have said that Chinese hackers are supposedly responsible for the hack based on similarities in hacking tools and techniques used in previous campaigns, but that has yet to be officially confirmed. In a statement, the Information Commissioners' Office (ICO) said: "We have a live investigation into the cyber attack involving easyJet.
Robots and drones equipped with infrared cameras could patrol holiday destinations and enforce social distancing rules under new EU plans to save the summer break. European Commission tourism proposals imaging'artificial intelligence and robotics [to] underpin public health measures', alongside infection tracing mobile apps. Automatons could appear in places like airports, beaches, resorts and restaurants to make sure that people keep at least 5 feet (1.5 metres) away from each other. On-board infrared cameras could allow the robots to measure people's temperatures from a distance and identify people with a fever that need to self-isolate. The plans come after Singapore employed a Boston Dynamics Spot robot to roam parks, broadcasting a message reminding pedestrians to keep their distance.
Whatever it is they're selling, businesses today are under more pressure than ever to provide a five-star experience. Customer service is not a'nice-to-have', it's taken for granted by customers who have plenty of eager competitors at their disposal and a multitude of public forums to share their negative experiences. In times of need, if customers can't open a chatbot or pick up a phone to quickly get resolution to their issues, they'll start shopping around – customer experience is now part of the package. Meeting these real-time demands, then, is a deal-breaker, but there are also rewards. According to McKinsey, 70 percent of buying experiences are based on how the customer feels they are being treated, and if you do it right, they will stick around.
The travel industry is overwhelmed. As the coronavirus continues spreading around the globe, thousands of customers are calling hotels and airlines to cancel or change plans based on highly variable and unpredictable changes – and as a result, millions of travel-related jobs will be lost. With an industry so dependent on reviews and word of mouth recommendations, customer service and experiences are the foundation of the travel industries. Yet, we're facing unprecedented times, and every customer is different. Each individual is having to make dramatic, sometimes emotional decisions about their plans for the year.
"We've set it to alert us if someone has a fever over 100.5 degrees Fahrenheit," Brett Smith, chief information officer of the airport's operator, Propeller Airports, said about the repurposed device. The camera screens passengers as they line up for standard security checks by the Transportation Security Administration. Passengers with high fevers are screened a second time, and ultimately the airline determines if they pose a danger to others on board, Mr. Smith said. The airport began operations in March 2019 and serves as a northwestern hub for Alaska Airlines and United Airlines. Developed in 2018, in the wake of a mass shooting in Las Vegas, Athena's gun-detecting camera operates by combining object detection, computer vision and machine-learning to identify weapons and automatically alert on-site workers and police.
Some cafes put dressed mannequins in poses as if they were buying products or sipping coffee at counters in their empty businesses. In towns from north to south, many small business owners on Friday stood at a safe distance from each other on the sidewalk or in town squares, their shuttered stores or restaurants behind them, wearing black masks and holding placards highlighting their economic troubles. On Monday, restaurants and cafes can start offering takeout. Non-essential shops can reopen on May 18 if Italy's rate of contagion with COVID-19 doesn't sharply rise again.
With the news that Data Airline is filing a lawsuit against its chatbot provider, among endless IT breaches and disasters, the reality is now starkly clear that chatbots need to be secure and well-managed to protect the business and customers. The cloud is so easy and seductive, sign up for a service, create something amazing and off you go. That flexibility and access has been a huge boon, driving startups and helping departments get ahead of their plodding IT departments. However, in the charge to cool AI and chatbot products, or using the cloud for storage and third-party solutions, the need for cast-iron security becomes all the greater, and most businesses lack the expertise to manage that facet. This issue was brought to light by US airline Delta filing suit against 7ai,claiming it lacked the proper security procedures for the product, allowing hackers to alter the chatbot's source code.
Unlike the traditional dock-based systems, dockless bike-sharing systems are more convenient for users in terms of flexibility. However, the flexibility of these dockless systems comes at the cost of management and operation complexity. Indeed, the imbalanced and dynamic use of bikes leads to mandatory rebalancing operations, which impose a critical need for effective bike traffic flow prediction. While efforts have been made in developing traffic flow prediction models, existing approaches lack interpretability, and thus have limited value in practical deployment. To this end, we propose an Interpretable Bike Flow Prediction (IBFP) framework, which can provide effective bike flow prediction with interpretable traffic patterns. Specifically, by dividing the urban area into regions according to flow density, we first model the spatio-temporal bike flows between regions with graph regularized sparse representation, where graph Laplacian is used as a smooth operator to preserve the commonalities of the periodic data structure. Then, we extract traffic patterns from bike flows using subspace clustering with sparse representation to construct interpretable base matrices. Moreover, the bike flows can be predicted with the interpretable base matrices and learned parameters. Finally, experimental results on real-world data show the advantages of the IBFP method for flow prediction in dockless bike sharing systems. In addition, the interpretability of our flow pattern exploitation is further illustrated through a case study where IBFP provides valuable insights into bike flow analysis.