AI for Extreme Event Modeling and Understanding: Methodologies and Challenges

• URL: http://arxiv.org/abs/2406.20080v1
• Date: Fri, 28 Jun 2024 17:45:25 GMT
• Title: AI for Extreme Event Modeling and Understanding: Methodologies and Challenges
• Authors: Gustau Camps-Valls, Miguel-Ángel Fernández-Torres, Kai-Hendrik Cohrs, Adrian Höhl, Andrea Castelletti, Aytac Pacal, Claire Robin, Francesco Martinuzzi, Ioannis Papoutsis, Ioannis Prapas, Jorge Pérez-Aracil, Katja Weigel, Maria Gonzalez-Calabuig, Markus Reichstein, Martin Rabel, Matteo Giuliani, Miguel Mahecha, Oana-Iuliana Popescu, Oscar J. Pellicer-Valero, Said Ouala, Sancho Salcedo-Sanz, Sebastian Sippel, Spyros Kondylatos, Tamara Happé, Tristan Williams,
• Abstract summary: This paper reviews how AI is being used to analyze extreme events (like floods, droughts, wildfires and heatwaves) We discuss the hurdles of dealing with limited data, integrating information in real-time, deploying models, and making them understandable. We emphasize the need for collaboration across different fields to create AI solutions that are practical, understandable, and trustworthy.
• Score: 7.636789744934743
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences. Here, AI improved weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. However, the latter comes with specific challenges, such as developing accurate predictors from noisy, heterogeneous and limited annotated data. This paper reviews how AI is being used to analyze extreme events (like floods, droughts, wildfires and heatwaves), highlighting the importance of creating accurate, transparent, and reliable AI models. We discuss the hurdles of dealing with limited data, integrating information in real-time, deploying models, and making them understandable, all crucial for gaining the trust of stakeholders and meeting regulatory needs. We provide an overview of how AI can help identify and explain extreme events more effectively, improving disaster response and communication. We emphasize the need for collaboration across different fields to create AI solutions that are practical, understandable, and trustworthy for analyzing and predicting extreme events. Such collaborative efforts aim to enhance disaster readiness and disaster risk reduction.

Related papers

• Lessons for Editors of AI Incidents from the AI Incident Database [2.5165775267615205]
  The AI Incident Database (AIID) is a project that catalogs AI incidents and supports further research by providing a platform to classify incidents. This study reviews the AIID's dataset of 750+ AI incidents and two independent ambiguities applied to these incidents to identify common challenges to indexing and analyzing AI incidents. We report mitigations to make incident processes more robust to uncertainty related to cause, extent of harm, severity, or technical details of implicated systems.
  arXiv  Detail & Related papers  (2024-09-24T19:46:58Z)
• Towards Understanding Human Emotional Fluctuations with Sparse Check-In Data [2.8623940003518156]
  Data sparsity is a key challenge limiting the power of AI tools across various domains. This paper proposes a novel probabilistic framework that integrates user-centric feedback-based learning. It achieves 60% accuracy in predicting user states among 64 options.
  arXiv  Detail & Related papers  (2024-09-10T21:00:33Z)
• OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI [73.75520820608232]
  We introduce OlympicArena, which includes 11,163 bilingual problems across both text-only and interleaved text-image modalities. These challenges encompass a wide range of disciplines spanning seven fields and 62 international Olympic competitions, rigorously examined for data leakage. Our evaluations reveal that even advanced models like GPT-4o only achieve a 39.97% overall accuracy, illustrating current AI limitations in complex reasoning and multimodal integration.
  arXiv  Detail & Related papers  (2024-06-18T16:20:53Z)
• Predictable Artificial Intelligence [77.1127726638209]
  This paper introduces the ideas and challenges of Predictable AI. It explores the ways in which we can anticipate key validity indicators of present and future AI ecosystems. We argue that achieving predictability is crucial for fostering trust, liability, control, alignment and safety of AI ecosystems.
  arXiv  Detail & Related papers  (2023-10-09T21:36:21Z)
• VerifAI: Verified Generative AI [22.14231506649365]
  Generative AI has made significant strides, yet concerns about its accuracy and reliability continue to grow. We propose that verifying the outputs of generative AI from a data management perspective is an emerging issue for generative AI. Our vision is to promote the development of verifiable generative AI and contribute to a more trustworthy and responsible use of AI.
  arXiv  Detail & Related papers  (2023-07-06T06:11:51Z)
• Fairness in AI and Its Long-Term Implications on Society [68.8204255655161]
  We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time. We discuss how biased models can lead to more negative real-world outcomes for certain groups. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest.
  arXiv  Detail & Related papers  (2023-04-16T11:22:59Z)
• Forecasting Future World Events with Neural Networks [68.43460909545063]
  Autocast is a dataset containing thousands of forecasting questions and an accompanying news corpus. The news corpus is organized by date, allowing us to precisely simulate the conditions under which humans made past forecasts. We test language models on our forecasting task and find that performance is far below a human expert baseline.
  arXiv  Detail & Related papers  (2022-06-30T17:59:14Z)
• Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
  Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome. Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations. We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
  arXiv  Detail & Related papers  (2021-06-14T20:48:48Z)
• The Rise of AI-Driven Simulators: Building a New Crystal Ball [6.289422225292999]
  Continued U.S. and international prosperity, security, and health depend in part on continued improvements in simulation capabilities. The past decade has brought remarkable advances in complementary areas. In this paper, we lay out some themes that we envision forming part of a cohesive, multi-disciplinary, and application-inspired research agenda.
  arXiv  Detail & Related papers  (2020-12-11T00:13:40Z)
• Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
  We show how AI can empower the IoT to make it faster, smarter, greener, and safer. First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
  arXiv  Detail & Related papers  (2020-11-17T13:14:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.