Assessing the impact of a disaster in terms of asset losses and human casualties is essential for preparing effective response plans. Traditional methods include offline assessments conducted on the ground, where volunteers and first responders work together to collect the estimate of losses through windshield surveys or on-ground inspection. However, these methods have a time delay and are prone to different biases. Recently, various online data sources, including social media, news reports, aerial imagery, and satellite data, have been utilized to evaluate the impact of disasters. Online data sources provide real-time data streams for estimating the offline impact. Limited research exists on how different online sources help estimate disaster impact at a given administrative unit. In our work, we curate a comprehensive dataset by collecting data from multiple online sources for a few billion-dollar disasters at the county level. We also analyze how online estimates compare with traditional offline-based impact estimates for the disaster. Our findings provide insight into how different sources can provide complementary information to assess the disaster.

Wikipedia, a vast and continuously consulted knowledge base, faces significant challenges in maintaining up-to-date content due to its reliance on manual human editors. Inspired by the vision of continuous knowledge acquisition in NELL and fueled by advances in LLM-based agents, this paper introduces WiNELL, an agentic framework for continuously updating Wikipedia articles. Our approach employs a multi-agent framework to aggregate online information, select new and important knowledge for a target entity in Wikipedia, and then generate precise edit suggestions for human review. Our fine-grained editing models, trained on Wikipedia's extensive history of human edits, enable incorporating updates in a manner consistent with human editing behavior. Our editor models outperform both open-source instruction-following baselines and closed-source LLMs (e.g., GPT-4o) in key information coverage and editing efficiency. End-to-end evaluation on high-activity Wikipedia pages demonstrates WiNELL's ability to identify and suggest timely factual updates. This opens up a promising research direction in LLM agents for automatically updating knowledge bases in a never-ending fashion.

Two days after releasing o3-mini to the world, the company made a surprise announcement on Sunday evening, revealing Deep Research. The new feature allows ChatGPT to find, analyze and synthesize hundreds of websites and online sources to create reports "at the level of a research analyst." The chatbot will then take "anywhere from 5 to 30 minutes" to compile an answer, a side panel documenting the agent's progress and citations as it works. "It accomplishes in tens of minutes what would take a human many hours," OpenAI says of the new feature. "Our ultimate aspiration is a model that can uncover and discover new knowledge for itself," said Mark Chen, chief research officer at OpenAI, during the company's reveal livestream.

We analyze two Natural Language Inference data sets with respect to their linguistic features. The goal is to identify those syntactic and semantic properties that are particularly hard to comprehend for a machine learning model. To this end, we also investigate the differences between a crowd-sourced, machine-translated data set (SNLI) and a collection of text pairs from internet sources. Our main findings are, that the model has difficulty recognizing the semantic importance of prepositions and verbs, emphasizing the importance of linguistically aware pre-training tasks. Furthermore, it often does not comprehend antonyms and homonyms, especially if those are depending on the context. Incomplete sentences are another problem, as well as longer paragraphs and rare words or phrases. The study shows that automated language understanding requires a more informed approach, utilizing as much external knowledge as possible throughout the training process.

Like every other industry, digital transformation is also revolutionizing the pharmaceutical industry. There has been a substantial increase in the digital health market since 2010, and according to a recent study, around 74% of respondents agreed that Covid-19 has significantly accelerated digital transformation in the pharmaceutical and healthcare industry. In another study, 35% of respondents state that the pandemic has accelerated the digital transformation in the pharmaceutical sector by more than five years, as shown in Figure 1. Digital transformation in the pharmaceutical sector means implementing various digital technologies to improve the production and provision of healthcare products and services. As digital technologies take over the world, pharmaceutical companies must stay up to speed to survive.

We study the problem of finding fake online news. This is an important problem as news of questionable credibility have recently been proliferating in social media at an alarming scale. As this is an understudied problem, especially for languages other than English, we first collect and release to the research community three new balanced credible vs. fake news datasets derived from four online sources. We then propose a language-independent approach for automatically distinguishing credible from fake news, based on a rich feature set. In particular, we use linguistic ( n-gram), credibility-related (capitalization, punctuation, pronoun use, sentiment polarity), and semantic (embeddings and DB-Pedia data) features. Our experiments on three different testsets show that our model can distinguish credible from fake news with very high accuracy.