Topic: entropy

Workshop bringing together researchers working on AI for learning dynamics from data, with a special focus on information-theoretic methods. Topics include predictive information, the information bottleneck, latent-variable models, entropy and mutual-information estimation, world models of biological systems, and connections to dynamical systems and control theory. The workshop is part of the six-week NITMB program “Image-Based Scientific Machine Learning for Theories of Biological Dynamics Across Scales” (February 1–March 12, 2027). Program: AI in Biological Dynamics Scientific Focus

Nassim Nicholas Taleb examines three technical problems in applied probability. First, he considers how errors in probabilities derived from fat-tailed survival functions can translate into disproportionately large, potentially unbounded errors in thresholds. He relates this to uncertainty in disease growth rates and forecasting. Second, he discusses what he regards as fundamental probability errors in psychology papers. Third, he examines how correlation and relative distances can mislead in the geometry of information, proposing heuristics for applying entropy-based methods to genetic dista

Our goal is to extend the open thermodynamic model of cortical neurodynamic Freeman hypothesis using the theoretical Carnot and then Rankine Cycles to define biomarkers for mean field energy versus entropy change and relevancy to temperature. We are using the Rankine cycle to incorporate criticality which is at null spikes of theta rate (3-7 Hz) as onset of amplitude modulations with envelopes of gamma oscillations from one zero to another zero with gaps. We are analyzing the dynamics of zeroes establishing local equilibrium. We found gaps staying close to the global average gap size of whole