Traditional natural disaster response involves significant coordinated teamwork where speed and efficiency are key. Nonetheless, human limitations can delay critical actions and inadvertently increase human and economic losses. Agentic Large Vision Language Models (LVLMs) offer a new avenue to address this challenge, with the potential for substantial socio-economic impact, particularly by improving resilience and resource access in underdeveloped regions. We introduce DisasTeller, the first multi-LVLM-powered framework designed to automate tasks in post-disaster management, including on-site assessment, emergency alerts, resource allocation, and recovery planning. By coordinating four specialised LVLM agents with GPT-4 as the core model, DisasTeller autonomously implements disaster response activities, reducing human execution time and optimising resource distribution. Our evaluations through both LVLMs and humans demonstrate DisasTeller's effectiveness in streamlining disaster response. This framework not only supports expert teams but also simplifies access to disaster management processes for non-experts, bridging the gap between traditional response methods and LVLM-driven efficiency.

Learning to defer (L2D) aims to improve human-AI collaboration systems by learning how to defer decisions to humans when they are more likely to be correct than an ML classifier. Existing research in L2D overlooks key aspects of real-world systems that impede its practical adoption, namely: i) neglecting cost-sensitive scenarios, where type 1 and type 2 errors have different costs; ii) requiring concurrent human predictions for every instance of the training dataset and iii) not dealing with human work capacity constraints. To address these issues, we propose the deferral under cost and capacity constraints framework (DeCCaF). DeCCaF is a novel L2D approach, employing supervised learning to model the probability of human error under less restrictive data requirements (only one expert prediction per instance) and using constraint programming to globally minimize the error cost subject to workload limitations. We test DeCCaF in a series of cost-sensitive fraud detection scenarios with different teams of 9 synthetic fraud analysts, with individual work capacity constraints. The results demonstrate that our approach performs significantly better than the baselines in a wide array of scenarios, achieving an average 8.4% reduction in the misclassification cost.

Large Language Models (LLMs) [27], including Generative Pre-trained Transformer (GPT) [6] and Bidirectional Encoder Representations from Transformers (BERT) [13], have achieved state-of-theart performance on a wide range of Natural Language Processing (NLP) tasks. LLMs are gaining popularity and receiving increasing attention for their significant applications in knowledge reasoning [12, 52, 57]. ChatGPT is one of the LLMs applications, and probably the application, in the limelight. ChatGPT was used for collating literature and writing professional papers in fields like law [9], and medical education [30, 16]. OpenAI announced GPT-4 in March 2023 that can pass some of the bar exams to AP Biology [39]. These successful stories demonstrate that people have already gained experience in using LLMs, for their performance in handling complex content due to their massive training datasets and model capacity to process and learn from data, enabling their potential for complex tasks that require domain expert knowledge [38]. Given this, as researchers in the field of safety-critical systems, we pose a question: Can safety analysis make use of LLMs?

Artificial intelligence (AI) has become an increasingly popular technology in recent years, with many businesses turning to AI to enhance their operations, improve customer experiences, and drive innovation. However, the prospect of using AI can be daunting for many business owners, particularly those who are unfamiliar with the technology. In this blog post, we'll outline some key steps that businesses can take to effectively leverage AI for their operations. Before you start implementing AI, it's important to identify your business goals. Ask yourself: what are the specific problems or challenges that AI can help you solve? Are you looking to automate certain tasks, improve customer service, or gain insights into your business data?

Turbine maintenance is an expensive, high-risk task. According to a recent analysis from the news website, wind farm owners are expected to spend more than $40 billion on operations and maintenance over a decade. Another recent study finds by using drone-based inspection instead of traditional rope-based inspection, you can reduce the operational costs by 70% and further decrease revenue lost due to downtime by up to 90%. This blog post will present how drones, machine learning (ML), and Internet of Things (IoT) can be utilized on the edge and the cloud to make turbine maintenance safer and more cost effective. First, we trained the machine learning model on the cloud to detect hazards on the turbine blades, including corrosion, wear, and icing.

An AI-driven space debris-dodging system could soon replace expert teams dealing with growing numbers of orbital collision threats in the increasingly cluttered near-Earth environment. Every two weeks, spacecraft controllers at the European Space Operations Centre (ESOC) in Darmstadt, Germany, have to conduct avoidance manoeuvres with one of their 20 low Earth orbit satellites, Holger Krag, the Head of Space Safety at the European Space Agency (ESA) said in a news conference organized by ESA during the 8th European Space Debris Conference held virtually from Darmstadt Germany, April 20 to 23. There are at least five times as many close encounters that the agency's teams monitor and carefully evaluate, each requesting a multi-disciplinary team to be on call 24/7 for several days. "Every collision avoidance manoeuvre is a nuisance," Krag said. "Not only because of fuel consumption but also because of the preparation that goes into it. We have to book ground-station passes, which costs money, sometimes we even have to switch off the acquisition of scientific data. We have to have an expert team available round the clock."