Speaker
Description
This talk explores the shift from classical critical rationalism to probabilistic rationalism in modern experimental physics. Using Plato's Cave as a metaphor, it argues that modern detectors produce probabilistically smeared shadows of underlying physical processes, making the inference from data to theory inherently non-unique. The presentation identifies three epistemological shifts in modern experiments: high-dimensional data volumes, the granularity of raw measurements, and the role of prior knowledge encoded as Bayesian priors. Because the inverse problem is ill-posed, regularization and controlled assumptions are required, and the resulting bias must be transparently quantified. Monte Carlo simulation provides a virtual reality for training machine-learning models, while Bayesian inference updates the plausibility of hypotheses rather than proving them.
The talk concludes that modern knowledge acquisition is a cyclical process in which AI extends rather than replaces scientific rationality. The resulting framework holds that knowledge remains rational and critical, yet fundamentally probabilistic: what is obtained is not a direct observation-to-theory inference, but a probabilistic statement grounded in data, model, simulation, and inference.