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Rachel Sussman: All the time in the world

KAUST ·

American artist Rachel Sussman spoke at KAUST's 2019 Winter Enrichment Program about her project documenting the world's oldest living organisms. Sussman photographed 30 species alive for over 2,000 years, including trees, coral, and bacteria. She collaborated with 30 scientists to identify and document these organisms. Why it matters: The lecture highlights KAUST's interdisciplinary approach to knowledge, connecting art, science, and philosophy to explore concepts of time and longevity.

The world's living oceans

KAUST ·

Princess Hala bint Khalid bin Sultan discussed the Khaled bin Sultan Living Oceans Foundation's marine preservation work at KAUST's Enrichment in the Fall program. The foundation focuses on research, education, and communication to preserve marine environments locally, regionally, and globally. Key projects include a five-year research expedition across 15 countries and the Mangroves Program in Jamaican and Bahamian schools. Why it matters: This highlights the ongoing efforts and commitment within Saudi Arabia to address critical environmental challenges in marine ecosystems through research and education.

A Benchmark and Agentic Framework for Omni-Modal Reasoning and Tool Use in Long Videos

arXiv ·

A new benchmark, LongShOTBench, is introduced for evaluating multimodal reasoning and tool use in long videos, featuring open-ended questions and diagnostic rubrics. The benchmark addresses the limitations of existing datasets by combining temporal length and multimodal richness, using human-validated samples. LongShOTAgent, an agentic system, is also presented for analyzing long videos, with both the benchmark and agent demonstrating the challenges faced by state-of-the-art MLLMs.

On Transferability of Machine Learning Models

MBZUAI ·

This article discusses domain shift in machine learning, where testing data differs from training data, and methods to mitigate it via domain adaptation and generalization. Domain adaptation uses labeled source data and unlabeled target data. Domain generalization uses labeled data from single or multiple source domains to generalize to unseen target domains. Why it matters: Research in mitigating domain shift enhances the robustness and applicability of AI models in diverse real-world scenarios.

Saving ghost cities

KAUST ·

In a 2018 KAUST lecture, MIT professor Kamal Youcef-Toumi discussed the case of Ordos Kangbashi, a Chinese city designed for a million residents that became a near-ghost town. Despite government incentives, the city struggled due to an economic downturn and lack of social and economic balance. Youcef-Toumi emphasized the importance of the public realm and a balance between social and economic development for successful cities. Why it matters: The analysis provides insights relevant to urban planning in Saudi Arabia and the broader GCC region, where new cities and megaprojects are being developed.