'The search is soul-destroying': Young jobseekers on the struggle to find work Young people are bearing the brunt of the UK's weak labour market, according to new figures from the Office for National Statistics (ONS). Some 16.1% of people aged 16 to 24 are not able to find work, compared to a national unemployment figure of 5.1%. That does not include young people who are out of work but not looking for a job, due to ill health or who are still studying. Businesses, particularly in sectors that traditionally gave young people their first jobs, like retail and hospitality, say higher costs are leading them to cut staff or not take on new hires, which often hits young workers the hardest. But graduate-level roles are also proving harder to land.

FlashBot Arm is designed to interact more like a human. As you navigate through your daily routine, whether it's grabbing a meal at a restaurant, visiting a healthcare center or checking into a hotel, you might soon encounter a new kind of robot at these places. It's called the FlashBot Arm, and Pudu Robotics is behind it. This semi-humanoid robot combines advanced manipulation capabilities with intelligent delivery features, making it a significant innovation in the service robotics sector. Unlike traditional robots, the FlashBot Arm is designed to interact with its environment in a more human-like way.

Large language models (LLMs) are widely adopted in knowledge-intensive tasks and have achieved impressive performance thanks to their knowledge abilities. While LLMs have demonstrated outstanding performance on atomic or linear (multi-hop) QA tasks, whether they can reason in knowledge-rich scenarios with interweaving constraints remains an underexplored problem. In this work, we propose geometric reasoning over structured knowledge, where pieces of knowledge are connected in a graph structure and models need to fill in the missing information of this graph. Such geometric knowledge reasoning would require the ability to handle structured knowledge, reason with uncertainty, verify facts, and backtrack when an error occurs. Further analysis reveals that LLMs' ability of geometric reasoning over structured knowledge is still far from robust or perfect, susceptible to confounders such as the order of options, certain structural patterns, assumption of existence of correct answer, and more. Large language models (LLMs) have demonstrated an impressive ability on knowledge-intensive tasks such as open-domain QA (Petroni et al., 2019), misinformation detection (Karimi & Tang, 2019), and fact-checking (Gao et al., 2023). To assess the knowledge abilities of LLMs, existing tasks and datasets mostly focus on atomic (e.g., open-domain QA) (Rajpurkar et al., 2016; Das et al., 2022) or linear (e.g., multi-hop QA) (Press et al., 2022) settings, probing LLMs' responses to simple or multiple concatenated facts where each reasoning step has a unique definite answer. However, knowledge is not always arranged in a simple linear manner: it often involves more complex structural information, forming an interweaving network that connects various entities and relations through multiple chains as illustrated in Figure 1. Each reasoning step of atomic or linear QAs leads to a unique and definite (intermediate) answer, while multiple candidates exist before all constraints are jointly considered in geometric QA. Consequently, an underexplored yet crucial question arises: Can LLMs extend beyond linear compositionality and aggregate information from multiple chains along with various knowledge constraints? Specifically, when certain pieces of knowledge are missing, can LLMs successfully fill in the blanks based on existing constraints represented by other available information in the network? In this work, we evaluate how well models can aggregate information from the given constraints across a graph representing pieces of knowledge and figure out the blanks in this graph.

A monster AI machine with blades is revolutionizing the world of lawn maintenance. Don't yawn and click off the page just because this story begins with grass. Yes, the stuff that grows in your backyard. You see, the world of lawn care just got its equivalent of a supersonic jet -- the autonomous mega-mower by FireFly Automatix. This is the mega-mower, a monster that'll blitz an area equivalent to 19 NFL football fields on a single charge.

Recommender systems have become indispensable tools in the hotel hospitality industry, enabling personalized and tailored experiences for guests. Recent advancements in large language models (LLMs), such as ChatGPT, and persuasive technologies, have opened new avenues for enhancing the effectiveness of those systems. This paper explores the potential of integrating ChatGPT and persuasive technologies for automating and improving hotel hospitality recommender systems. First, we delve into the capabilities of ChatGPT, which can understand and generate human-like text, enabling more accurate and context-aware recommendations. We discuss the integration of ChatGPT into recommender systems, highlighting the ability to analyze user preferences, extract valuable insights from online reviews, and generate personalized recommendations based on guest profiles. Second, we investigate the role of persuasive technology in influencing user behavior and enhancing the persuasive impact of hotel recommendations. By incorporating persuasive techniques, such as social proof, scarcity and personalization, recommender systems can effectively influence user decision-making and encourage desired actions, such as booking a specific hotel or upgrading their room. To investigate the efficacy of ChatGPT and persuasive technologies, we present a pilot experi-ment with a case study involving a hotel recommender system. We aim to study the impact of integrating ChatGPT and persua-sive techniques on user engagement, satisfaction, and conversion rates. The preliminary results demonstrate the potential of these technologies in enhancing the overall guest experience and business performance. Overall, this paper contributes to the field of hotel hospitality by exploring the synergistic relationship between LLMs and persuasive technology in recommender systems, ultimately influencing guest satisfaction and hotel revenue.

This is a basic awareness level course with case studies multiple industries such as Retail, Railways, Pharma, Insurance, Banking, Hospitality, Beverages, Real Estate, Warehouse, etc. In the contemporary period of gigantic volumes and multi-regional global business internet reach, the new innovative technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have significant importance in the global business. This course can help to enhance the digital skills of any individual as it is a "street-smart view" of the AI usage in the industry. This course explains in the most simple manner giving examples such as Growth of the child, parentage, Chess Player, AlphaGO of Google, Robot usage, farming etc.

Trundling around the hallways of Hong Kong's Cyberport innovation hub, the little Rice Robot is on a mission. The stocky white cuboid resembles Star Wars' R2D2 robot in its build, but has the wide-eyed expression of Pixar's WALL-E. It's delivering drinks to patrons of the HFT Life cafe in a compartment in its "head" which is unlocked by the customer using a PIN code sent to their phone. While Rice's operations at the cafe are limited to distributing drinks, the compact robot is already providing a range of services at venues in Hong Kong and Japan. Rice is deployed as a bellhop at Hong Kong's Dorsett Wanchai hotel, providing room service to guests.

The age of artificial intelligence (AI) is upon us, and it's revolutionizing industries across the globe. AI is being used to create more efficient processes, enable better decision-making, and provide previously unattainable insights. From retail to healthcare, AI is changing the way we do business. The Artificial Intelligence (AI) revolution is rapidly transforming numerous industries in its wake. From retail and healthcare to marketing and manufacturing, businesses across the board are harnessing the power of AI to streamline processes, drive efficiencies and gain a competitive edge.

Following this review of the use of AI in the hospitality industry, it is clear now that it will become part of every daily business activity in the not too distant future. As professionals, questions remain about how to adapt the workforce to those new practices. Emotional intelligence (EQ), which groups a core of competencies (e.g., managing one's own emotions and the ones of others), was seen as the most essential skill-set for hospitality workers. With all the new technologies, is emotional intelligence still required today?

The Living Planet Index produced by WWF estimates that wildlife population sizes have dropped by 68 per cent since 1970. The charity advocates the use of artificial intelligence (AI) as a tool of conservation technology to monitor and curb this alarming rate of decline. One of the most useful applications is in acoustic monitoring, recording the sounds of wildlife ecosystems on weatherproof sensors. Many animals, from birds and bats to mammals and even invertebrates, use sound for communication, navigation and territorial defence, providing reams of rich data on how a species population is doing. AI provides a fast and cost-effective way to analyse hours of recordings for patterns of behaviour.