This work demonstrates how the concept of the entropic potential of events -- a parameter quantifying the influence of discrete events on the expected future entropy of a system -- can enhance uncertainty quantification, decision-making, and interpretability in artificial intelligence (AI). Building on its original formulation in physics, the framework is adapted for AI by introducing an event-centric measure that captures how actions, observations, or other discrete occurrences impact uncertainty at future time horizons. Both the original and AI-adjusted definitions of entropic potential are formalized, with the latter emphasizing conditional expectations to account for counterfactual scenarios. Applications are explored in policy evaluation, intrinsic reward design, explainable AI, and anomaly detection, highlighting the metric's potential to unify and strengthen uncertainty modeling in intelligent systems. Conceptual examples illustrate its use in reinforcement learning, Bayesian inference, and anomaly detection, while practical considerations for computation in complex AI models are discussed. The entropic potential framework offers a theoretically grounded, interpretable, and versatile approach to managing uncertainty in AI, bridging principles from thermodynamics, information theory, and machine learning.

The automatic curation of discussion forums in online courses requires constant updates, making frequent retraining of Large Language Models (LLMs) a resource-intensive process. To circumvent the need for costly fine-tuning, this paper proposes and evaluates the use of Bayesian fusion. The approach combines the multidimensional classification scores of a pre-trained generic LLM with those of a classifier trained on local data. The performance comparison demonstrated that the proposed fusion improves the results compared to each classifier individually, and is competitive with the LLM fine-tuning approach

Machine learning (ML) models often exhibit unexpected failures once deployed in the "wild".

We do not foresee direct negative societal impact from the current work. Also, one may argue that our work is catalytic to enhancing the'disclosure risk' of individuals, i.e. an adversary might be able to make accurate Granted, no existing privacy frameworks can guard against this. We prove its ergodicity in Theorem S-3.1, which implies Theorem 3.3 . The model is such that the set { x: f ( x |) > 0 } does not depend on . The Metropolis-within-Gibbs sampler is aperiodic by construction, since some proposals can be rejected.

Currently, the data augmentation parameters are chosen by human effort and costly cross-validation, which makes it cumbersome to apply to new datasets. We develop a convenient gradient-based method for selecting the data augmentation without validation data during training of a deep neural network.

Because the true underlying structure of the data is rarely accessible, this "representation learning" must be

For the GP, the bias-variance trade-off is made by optimization of the two hyperparameters: the length scale and noise-term.