AI in Physics and Math: Philip Resnik and Maria Molina

Two 20 minute talks.  The first is from P Resnik

Title: Large Language Models are Biased Because They are Large Language Models

I argue that harmful biases are an inevitable consequence arising from the design of any large language model as LLMs are currently formulated, all the way down to their mathematical formulation. To the extent that this is true, it suggests that the problem of harmful bias cannot be properly addressed without a serious reconsideration of AI driven by LLMs, going back to the foundational assumptions underlying their design.
here are links to the relevant paper and to a short video.

The second is from Maria Molina

Learning Without Labels: New Insights into Climate and Extremes

Abstract:
Climate variability and weather extremes pose profound challenges for prediction, preparedness, and resilience. Traditional approaches often rely on predefined indices or supervised learning methods, which can overlook unexpected patterns or reinforce biases inherent in labeled datasets. This seminar explores how unsupervised learning techniques can uncover hidden patterns in high-dimensional climate data. I will highlight recent innovations that adapt established methods to reveal properties not captured by conventional architectures, offering new perspectives on modes of variability and extreme events. For instance, a knowledge-guided autoencoder can disentangle distinct Pacific climate modes with differing spectral signatures, while a custom hyperparameter search can optimize self-organizing maps to produce smooth, interpretable pathways among weather regimes. Together, these advances help uncover processes and mechanisms that may underlie established climate and weather phenomena. Ultimately, unsupervised learning provides a powerful lens for scientific discovery, with implications for understanding, prediction, and decision-making in a changing climate.