🎄Day 15: Foundation models for science
Foundation models such as, large pre-trained AI models have become a cornerstone of modern scientific workflows. Foundation models are now…
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Day 14: Data quality beats model complexity
Data quality beats model complexity In 2025, the biggest performance wins in AI often come from better data, not bigger…
🎄Day 13: Ethics is not a checkbox
Ethics is not a checkbox Ethical AI is a practice, not a form to tick and forget. Treating ethics as…
🎄Day 12: Human-in-the-loop systems
Human-in-the-loop systems AI works best when humans stay actively involved, especially as adoption accelerates and risks become more complex. Human-in-the-loop…
🎄Day 11: Energy cost of AI models
Energy cost of AI models Training large AI models can consume significant energy, which has environmental and financial impacts. Today’s…
🎄Day 10: Open science and AI
Open science and AI Open science principles: transparency, accessibility, and collaboration are essential when using AI in research. Sharing data,…
🎄Day 9: Reproducibility in AI pipelines
Reproducibility in AI pipelines In scientific research, reproducibility is a cornerstone of trust. AI pipelines, from data pre-processing to model…
🎄Day 8: AI in astronomy: lessons learned
AI in astronomy: lessons learned Astronomy has become one of the most data-intensive sciences, and AI plays a growing role…
🎄Day 7: AI hallucinations in research
AI hallucinations in research AI hallucinations occur when a model generates plausible-looking but incorrect or misleading outputs. In scientific research,…
🎄AI Advent 2025 – Day 6
Explainability vs performance AI models often face a trade-off between performance (how accurate they are) and explainability (how well humans…