Large Language Models (LLMs) are increasingly proposed as near-autonomous artificial intelligence (AI) agents capable of making everyday decisions on behalf of humans. Although LLMs perform well on many technical tasks, their behaviour in personal decision-making remains less understood. Previous studies have assessed their rationality and moral alignment with human decisions. However, the behaviour of AI assistants in scenarios where financial rewards are at odds with user comfort has not yet been thoroughly explored. In this paper, we tackle this problem by quantifying the prices assigned by multiple LLMs to a series of user discomforts: additional walking, waiting, hunger and pain. We uncover several key concerns that strongly question the prospect of using current LLMs as decision-making assistants: (1) a large variance in responses between LLMs, (2) within a single LLM, responses show fragility to minor variations in prompt phrasing (e.g., reformulating the question in the first person can considerably alter the decision), (3) LLMs can accept unreasonably low rewards for major inconveniences (e.g., 1 Euro to wait 10 hours), and (4) LLMs can reject monetary gains where no discomfort is imposed (e.g., 1,000 Euro to wait 0 minutes). These findings emphasize the need for scrutiny of how LLMs value human inconvenience, particularly as we move toward applications where such cash-versus-comfort trade-offs are made on users' behalf.

For drivers who endure Britain's crumbling roads daily, there's no doubt we're stuck in an escalating'pothole crisis'. These dangerous holes can injure and even kill cyclists and motorists, and are popping up quicker than they can be filled. Now, interactive graphics reveal the shocking extent of the problem - and scientists think climate change is to blame. Climate organisation Round our Way reveals 952,064 potholes were reported in Britain between January and November last year, marking a five-year high. MailOnline's interactive map, based on the new data, reveals the local authorities with the most pothole reports during the period.

Video shows the moment drones started falling from the sky during a drone show at Eola Lake in Orlando, Florida on Dec. 21, 2024. A 7-year-old Florida boy who was injured when drones collided and fell into a crowd at a holiday airshow over the weekend underwent open heart surgery, his parents said. Adriana Edgerton and Jessica Lumsden, parents of Alexander, said one of the red and green-lit drones struck him and knocked him out upon impact, causing a chest injury, Fox Orlando reported. Hundreds of drones being used as part of a Saturday night aerial light show in Lake Eola Park in downtown Orlando appeared to be flying into position before several started falling from the sky before slamming to the ground, according to videos posted online. Alexander, a 7-year-old boy, has undergone heart surgery after he was struck by a falling drone during a holiday airshow in Orlando, his parents said.

Home repair and installation services require technicians to visit customers and resolve tasks of different complexity. Technicians often have heterogeneous skills and working experiences. The geographical spread of customers makes achieving only perfect matches between technician skills and task requirements impractical. Additionally, technicians are regularly absent due to sickness. With non-perfect assignments regarding task requirement and technician skill, some tasks may remain unresolved and require a revisit and rework. Companies seek to minimize customer inconvenience due to delay. We model the problem as a sequential decision process where, over a number of service days, customers request service while heterogeneously skilled technicians are routed to serve customers in the system. Each day, our policy iteratively builds tours by adding "important" customers. The importance bases on analytical considerations and is measured by respecting routing efficiency, urgency of service, and risk of rework in an integrated fashion. We propose a state-dependent balance of these factors via reinforcement learning. A comprehensive study shows that taking a few non-perfect assignments can be quite beneficial for the overall service quality. We further demonstrate the value provided by a state-dependent parametrization.

Most task-oriented dialogue (TOD) benchmarks assume users that know exactly how to use the system by constraining the user behaviors within the system's capabilities via strict user goals, namely "user familiarity" bias. This data bias deepens when it combines with data-driven TOD systems, as it is impossible to fathom the effect of it with existing static evaluations. Hence, we conduct an interactive user study to unveil how vulnerable TOD systems are against realistic scenarios. In particular, we compare users with 1) detailed goal instructions that conform to the system boundaries (closed-goal) and 2) vague goal instructions that are often unsupported but realistic (open-goal). Our study reveals that conversations in open-goal settings lead to catastrophic failures of the system, in which 92% of the dialogues had significant issues. Moreover, we conduct a thorough analysis to identify distinctive features between the two settings through error annotation. From this, we discover a novel "pretending" behavior, in which the system pretends to handle the user requests even though they are beyond the system's capabilities. We discuss its characteristics and toxicity while emphasizing transparency and a fallback strategy for robust TOD systems.

A couple of weeks ago I created a ChatGPT chatbot on my tech help website. The bot was meant to help answer people's tech queries: it's ended up fielding questions way outside of its remit. In the fortnight it's been running, the chatbot has been asked how to build a magical potato, how to bring down totalitarian regimes and how to safely remove a remove USB stick from, shall we say, a delicate area. At least that query was tech related... Here's a round-up of the weirdest requests the AI bot has been forced to answer. Even though I explicitly instructed the AI bot not to answer questions that aren't related to tech, it sometimes can't help itself. Such as on this occasion, when it's dragged into a world of utter fantasy: AI: Sure, let's build a magical potato!

Scientists are building autonomous repair robots that will use AI to identify and fix potholes in UK roads. The electric, self-driving bots – which are being built by a spin-out company from the University of Liverpool called Robotiz3d – can find small cracks in the road and cover them with asphalt. Researchers say the machines, which look like a cross between a tank and a road roller, will transform road maintenance when they hit the roads in 2021, and finally offer a cost effective fix for the UK's pothole problem. Currently, no autonomous technology solutions exist to tackle potholes, which are estimated to have cost UK taxpayers more than £1 billion to fix over the last decade. Artist's impression of the autonomous road repair system, which looks part-tank, part road roller.

Artificial intelligence (AI) might soon be your newest coworker. Many organizations have already implemented AI-powered solutions, such as virtual assistants or predictive software, to the benefit of workers nationwide. According to Gartner, the number of companies implementing AI grew 270% in the past four years and tripled in the last year. While some organizations have been quick to embrace AI, others are hesitant to make this change. IT leaders, product engineers and other innovators are often the ones who understand AI's big-picture potential in the workforce. For others, the advantages of AI augmentation are sometimes shrouded by misconceptions.

To have an effective conversation, it is important to understand the sentiment and respond appropriately. In a customer service call, a simple acknowledgment when talking to an unhappy customer might be helpful, such as, "Sorry to hear you are having trouble." Understanding sentiment is also useful in determining when you need to hand over the call to a human agent for additional support. To achieve such a conversational flow with a bot, you have to detect the sentiment expressed by the user and react appropriately. Previously, you had to build a custom integration by using Comprehend APIs.

Artificial intelligence (AI) is coming to a workplace near you -- in fact, maybe it's already arrived. From AI-powered virtual assistants to predictive features in everyday software, it's projected that one in five workers will experience the benefits of working with AI by 2022. Information technology is one segment of the workforce that understands AI's big-picture potential. IT leaders, product engineers and other forward-thinkers can easily envision how AI improves and augments the entire employee experience. Though there are still misconceptions and misleading hype surrounding AI, organizational leaders are already realizing the many benefits of the technology in business operations.