On Rashomon sets, the mathematics of simplicity, and why we don't need black boxes: an interview with Cynthia Rudin Welcome back to AI Pioneers - in-depth conversations with those shaping the field . This time, we speak with Cynthia Rudin, a trailblazer in the field of interpretable machine learning. Winner of the 2022 Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity, Cynthia's algorithms are already predicting seizures, aiding crime detection, and powering biological research . We discuss black boxes, Rashomon sets, and what's next for her lab - from cancer detection to interpretable AI-generated music. Can you tell me a bit about your background - what drew you into the field of interpretable machine learning?

Power grids are critical infrastructures of paramount importance to modern society and, therefore, engineered to operate under diverse conditions and failures. The ongoing energy transition poses new challenges for the decision-makers and system operators. Therefore, we must develop grid analysis algorithms to ensure reliable operations. These key tools include power flow analysis and system security analysis, both needed for effective operational and strategic planning. The literature review shows a growing trend of machine learning (ML) models that perform these analyses effectively. In particular, Graph Neural Networks (GNNs) stand out in such applications because of the graph-based structure of power grids.

Therefore, developing grid analysis algorithms is important for supporting reliable operations.

Find out more about our session on Wednesday 21 January.

Power consumption has become a critical aspect of modern life due to the consistent reliance on technological advancements. Reducing power consumption or following power usage predictions can lead to lower monthly costs and improved electrical reliability. The proposal of a holistic framework to establish a foundation for IoT systems with a focus on contextual decision making, proactive adaptation, and scalable structure. A structured process for IoT systems with accuracy and interconnected development would support reducing power consumption and support grid stability. This study presents the feasibility of this proposal through the application of each aspect of the framework. This system would have long term forecasting, short term forecasting, anomaly detection, and consideration of qualitative data with any energy management decisions taken. Performance was evaluated on Power Consumption Time Series data to display the direct application of the framework.

Abstract--Critical infrastructures are becoming increasingly complex as our society becomes increasingly dependent on them. This complexity opens the door to new possibilities for attacks and a need for new defense strategies. Our work focuses on instability attacks on the power grid, wherein an attacker causes cascading outages by introducing unstable dynamics into the system. When stress is place on the power grid, a standard mitigation approach is load-shedding: the system operator chooses a set of loads to shut off until the situation is resolved. While this technique is standard, there is no systematic approach to choosing which loads will stop an instability attack. We show a proof of concept on the IEEE 14 Bus System using the Achilles Heel T echnologies Power Grid Analyzer, and show through an implementation of modified Prony analysis (MPA) that MPA is a viable method for detecting instability attacks and triggering defense mechanisms. Throughout the past two hundred years, the power grid has become a core part of the infrastructure of the world. Every modern facility relies on electricity to sustain the way of life that has become prevalent in first world countries, powering everything from life sustaining equipment to financial transaction infrastructure.

We first would like to thank the reviewers for their insightful comments and suggestions. F oreword: This paper is framed as a methodological and theoretical contribution, with simple experimental validation. There is in particular no specific ethical concern with this paper - something we will add in a "Broader Impact" section. The uniqueness is only proven for the linear system. We will add one sentence along this line.

The recent emergence of large language models (LLMs) such as GPT-3 has marked a significant paradigm shift in machine learning. Trained on massive corpora of data, these models demonstrate remarkable capabilities in language understanding, generation, summarization, and reasoning, transforming how intelligent systems process and interact with human language. Although LLMs may still seem like a recent breakthrough, the field is already witnessing the rise of a new and more general category: multi-modal, multi-task foundation models (M3T FMs). These models go beyond language and can process heterogeneous data types/modalities, such as time-series measurements, audio, imagery, tabular records, and unstructured logs, while supporting a broad range of downstream tasks spanning forecasting, classification, control, and retrieval. When combined with federated learning (FL), they give rise to M3T Federated Foundation Models (FedFMs): a highly recent and largely unexplored class of models that enable scalable, privacy-preserving model training/fine-tuning across distributed data sources. In this paper, we take one of the first steps toward introducing these models to the power systems research community by offering a bidirectional perspective: (i) M3T FedFMs for smart grids and (ii) smart grids for FedFMs. In the former, we explore how M3T FedFMs can enhance key grid functions, such as load/demand forecasting and fault detection, by learning from distributed, heterogeneous data available at the grid edge in a privacy-preserving manner. In the latter, we investigate how the constraints and structure of smart grids, spanning energy, communication, and regulatory dimensions, shape the design, training, and deployment of M3T FedFMs.