TL;DR
This research investigates how publicly available AI models and local infrastructure can reduce AI’s environmental footprint while giving researchers greater control over the tools they use.
An overview of the Rubin Observatory’s strategy to provide a sustainable community science model at scale, for thousands of users worldwide, including policies, guidelines, and workflows for documentation, tutorials, activities, user support, and issue resolution.
A new breed of software that promises impressive accuracy at spotting AI-written text is being tested, with results expected in the coming months.
This workshop explores how AI can be used responsibly across the research lifecycle, from literature discovery and question development to computational analysis, writing, and publication and gives a practical framework for deciding what AI can assist with and what must remain human-led.
This seminar explores statistical and data-driven methods for making AI systems robust when they move from simulation into physical environments. It covers domain shift, sensor noise, Bayesian estimation, and uncertainty-aware deployment for researchers working at the interface of AI and engineering.
This paper examines emerging trends in data science and management. We discuss AI integration, automated machine learning, explainable AI, and the impact of quantum computing on analytics. The paper provides strategic insights for researchers and practitioners to prepare for future developments.
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