Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by
enhancing the trust of end-users in machines. As the number of connected devices keeps on …

The state of the art for optimal water reservoir operations is rapidly evolving, driven by
emerging societal challenges. Changing values for balancing environmental resources …

Foundation models are first pre-trained on vast unsupervised datasets and then fine-tuned
on labeled data. Reinforcement learning, notably from human feedback (RLHF), can further …

We study the problem of generating diverse candidates in the context of Multi-Objective
Optimization. In many applications of machine learning such as drug discovery and material …

While Reinforcement Learning from Human Feedback (RLHF) aligns Large Language
Models (LLMs) with general, aggregate human preferences, it is suboptimal for learning …

The recent paper “Reward is Enough” by Silver, Singh, Precup and Sutton posits that the
concept of reward maximisation is sufficient to underpin all intelligence, both natural and …

Abstract Colombian drug lord Pablo Escobar, while on the run, purportedly burned two
million dollars in banknotes to keep his daughter warm. A stark reminder that, in life …

In this review, the industry's current issues regarding intelligent manufacture are presented.
This work presents the status and the potential for the I4. 0 and I5. 0's revolutionary …

In many real-world applications, reinforcement learning (RL) agents might have to solve
multiple tasks, each one typically modeled via a reward function. If reward functions are …

Customizing robotic behaviors to be aligned with diverse human preferences is an
underexplored challenge in the field of embodied AI. In this paper we present Promptable …