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2025-01-11
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Google Launches Android XR, Its New AI-Powered Extended Reality Platform
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2025-01-07
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How AI Really Learns: The Journey from Random Noise to Intelligence
A Story of How Machines Learn to Think Through Language
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2025-01-06
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Fairness and Abstraction in Sociotechnical Systems
A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outcomes such as fairness, justice, and due process. Bedrock concepts in computer science - such as abstraction and modular design - are used to define notions of fairness and discrimination, to produce fairness-aware learning algorithms, and to intervene at different stages of a decision-making pipeline to produce "fair" outcomes. In this paper, however, we contend that these concepts render technical interventions ineffective, inaccurate, and sometimes dangerously misguided when they enter the societal context that surrounds decision-making systems. We outline this mismatch with five "traps" that fair-ML work can fall into even as it attempts to be more context-aware in comparison to traditional data science. We draw on studies of sociotechnical systems in Science and Technology Studies to explain why such traps occur and how to avoid them. Finally, we suggest ways in which technical designers can mitigate the traps through a refocusing of design in terms of process rather than solutions, and by drawing abstraction boundaries to include social actors rather than purely technical ones.
Andrew D. Selbst, Danah Boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, Janet Vertesi
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2025-01-06
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Projekt AUFGEHTS
Automatisiertes Verfahren zur Erstellung und Kalibrierung von Verkehrssimulationen auf Basis von Floating-Car-Daten
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2024-12-29
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Maps Mania: The AI Map Benchmark Test
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2024-12-21
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Map of walkable neighborhoods
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2024-12-17
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Maps Mania: Do You Live in 15 Minute City?
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2024-12-07
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The 15-Minute City
Let AI analyze any location worldwide based on the 15-minute city concept, where essentials like shopping, education, healthcare, transport, and leisure are within a 15-minute walk.
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2024-12-06
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The Spatial Edge (Newsletter)
Helping you become a better geospatial data scientist in less than 5 minutes a week. Click to read The Spatial Edge, by Yohan, a Substack publication with thousands of subscribers.
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2024-12-06
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Enhancing Ultra High Resolution Remote Sensing Imagery Analysis with ImageRAG
Ultra High Resolution (UHR) remote sensing imagery (RSI) (e.g. 100,000 × 100,000 pixels or more) poses a significant challenge for current Remote Sensing Multimodal Large Language Models (RSMLLMs). If choose to resize the UHR image to standard input image size, the extensive spatial and contextual information that UHR images contain will be neglected. Otherwise, the original size of these images often exceeds the token limits of standard RSMLLMs, making it difficult to process the entire image and capture long-range dependencies to answer the query based on the abundant visual context.
In this paper, we introduce ImageRAG for RS, a training-free framework to address the complexities of analyzing UHR remote sensing imagery. By transforming UHR remote sensing image analysis task to image’s long context selection task, we design an innovative image contextual retrieval mechanism based on the Retrieval-Augmented Generation (RAG) technique, denoted as ImageRAG. ImageRAG’s core innovation lies in its ability to selectively retrieve and focus on the most relevant portions of the UHR image as visual contexts that pertain to a given query. Fast path and slow path are proposed in this framework to handle this task efficiently and effectively. ImageRAG allows RSMLLMs to manage extensive context and spatial information from UHR RSI, ensuring the analysis is both accurate and efficient.
Zilun Zhang, Haozhan Shen, Tiancheng Zhao, Yuhao Wang, Bin Chen, Yuxiang Cai, Yongheng Shang, Jianwei Yin
