Reinforcement Learning (RL) agents often learn policies that do not generalise across tasks in which the environmental features and optimal skills are different [des Combes et al., 2018, Garcin et al., 2024].

Reinforcement Learning (RL) agents often learn policies that do not generalise across tasks in which the environmental features and optimal skills are different [des Combes et al., 2018, Garcin et al., 2024].

For example, in online learning, an AI social assistant may connect learners and thereby enhance social interaction. These social AI assistants too need to explain themselves in order to enhance transparency and trust with the learners. We present a method of self-explanation that uses introspection over a self-model of an AI social assistant. The self-model is captured as a functional model that specifies how the methods of the agent use knowledge to achieve its tasks. The process of generating self-explanations uses Chain of Thought to reflect on the self-model and ChatGPT to provide explanations about its functioning. We evaluate the self-explanation of the AI social assistant for completeness and correctness. We also report on its deployment in a live class.

Meta-Reinforcement Learning (Meta-RL) agents can struggle to operate across tasks with varying environmental features that require different optimal skills (i.e., different modes of behaviours). Using context encoders based on contrastive learning to enhance the generalisability of Meta-RL agents is now widely studied but faces challenges such as the requirement for a large sample size, also referred to as the $\log$-$K$ curse. To improve RL generalisation to different tasks, we first introduce Skill-aware Mutual Information (SaMI), an optimisation objective that aids in distinguishing context embeddings according to skills, thereby equipping RL agents with the ability to identify and execute different skills across tasks. We then propose Skill-aware Noise Contrastive Estimation (SaNCE), a $K$-sample estimator used to optimise the SaMI objective. We provide a framework for equipping an RL agent with SaNCE in practice and conduct experimental validation on modified MuJoCo and Panda-gym benchmarks. We empirically find that RL agents that learn by maximising SaMI achieve substantially improved zero-shot generalisation to unseen tasks. Additionally, the context encoder equipped with SaNCE demonstrates greater robustness to reductions in the number of available samples, thus possessing the potential to overcome the $\log$-$K$ curse.

Many hyper-personalized AI systems profile people's characteristics (e.g., personality traits) to provide personalized recommendations. These systems are increasingly used to facilitate interactions among people, such as providing teammate recommendations. Despite improved accuracy, such systems are not immune to errors when making inferences about people's most personal traits. These errors manifested as AI misrepresentations. However, the repercussions of such AI misrepresentations are unclear, especially on people's reactions and perceptions of the AI. We present two studies to examine how people react and perceive the AI after encountering personality misrepresentations in AI-facilitated team matching in a higher education context. Through semi-structured interviews (n=20) and a survey experiment (n=198), we pinpoint how people's existing and newly acquired AI knowledge could shape their perceptions and reactions of the AI after encountering AI misrepresentations. Specifically, we identified three rationales that people adopted through knowledge acquired from AI (mis)representations: AI works like a machine, human, and/or magic. These rationales are highly connected to people's reactions of over-trusting, rationalizing, and forgiving of AI misrepresentations. Finally, we found that people's existing AI knowledge, i.e., AI literacy, could moderate people's changes in their trust in AI after encountering AI misrepresentations, but not changes in people's social perceptions of AI. We discuss the role of people's AI knowledge when facing AI fallibility and implications for designing responsible mitigation and repair strategies.

SAMI researchers are utilizing AI/ML techniques to dramatically accelerate the development of technologies critical to minimizing environmental impacts of fossil fuels while working toward net-zero emissions. Science-based AI/ML modeling injects scientific knowledge and puts humans-in-the-loop by leveraging the data resources with advanced, applied energy models and AI/ML science to derive breakthrough technology innovations. Over recent decades, NETL has produced a suite of science-based computational tools to accelerate technology maturation and confront some of the most difficult energy challenges. These tools span multiple projects across the Lab, including multiphase flow science, materials discovery and qualification, geospatial and subsurface geologic understanding and visualization, and energy system optimization.

Earlier this year Microsoft announced it would integrate artificial intelligence-based productivity features into Excel. It was an exciting development for many users of the spreadsheet as it promised to make several tasks easier and more intuitive. Other users have noted how these promised features -- which are still in beta --could deliver far more productivity if Microsoft continues down the same path of improvement. One suggestion touched on a conundrum that productivity tools will face as they increasingly integrate with AI. Mark Sami, VP of Microsoft and Cloud Solutions at business consultancy SPR said for AI and machine learning to be useful, data from inside the organization is going to have to be made available in Excel.