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Abstract With their advertising and audience revenues in decline, local news organizations have been experiencing comparatively high degrees of disruption in recent years. Artificial Intelligence (AI) offers opportunities for local news organizations to better cope with the economic challenges they face. However, local news organizations need to carefully prioritize where AI will create the most value. After all, they serve customers in the audience and advertising markets, with external effects on society. At the same time, they are limited by scarce resources, which constrains the implementa ..read more

Abstract The advance of machine learning (ML) systems in real-world scenarios usually expects safe deployment in high-stake applications (e.g., medical diagnosis) for critical decision-making process. To this end, provable robustness of ML is usually required to measure and understand how reliable the deployed ML system is and how trustworthy their predictions can be. Many studies have been done to enhance the robustness in recent years from different angles, such as variance-regularized robust objective functions and conformal prediction (CP) for uncertainty quantification on testing data. Al ..read more

Abstract Personalized news experiences powered by recommender systems permeate our lives and have the potential to influence not only our opinions, but also our decisions. At the same time, the content and viewpoints contained within news recommendations are driven by multiple factors, including both personalization and editorial selection. Explanations could help users gain a better understanding of the factors contributing to the news items selected for them to read. Indeed, recent works show that explanations are essential for users of news recommenders to understand their consumption prefe ..read more

Abstract This article considers the impact of AI on the economy and financing of journalism organizations. AI has structural implications on the news media beyond the practice of journalism and the management of news as a process. AI also shifts the premises of competition, competitive advantage, mergers and acquisitions, and IT capabilities in the news industries. Not least, it fundamentally challenges journalism's traditional business model. Considered hereunder is the two-sided market model, journalism's traditional platform function, its network effects, and its public good characteristics ..read more

Abstract The appearance of large language models (LLMs) and other forms of generative AI portend a new era of disruption and innovation for the news industry, this time focused on the production and consumption of news rather than on its distribution. Large news organizations, however, may be surprisingly well-prepared for at least some of this disruption because of earlier innovation work on automating workflows for personalized content and formats using structured techniques. This article reviews this work and uses examples from the British Broadcasting Corporation (BBC) and other large news ..read more

Abstract In this article, we present an overview of the 2023 International Planning Competition. It featured five distinct tracks designed to assess cutting-edge methods and explore the frontiers of planning within these settings: the classical (deterministic) track, the numeric track, the Hierarchical Task Networks (HTN) track, the learning track, and the probabilistic and reinforcement learning track. Each of these tracks evaluated planning methodologies through one or more subtracks, with the goal of pushing the boundaries of current planner performance. To achieve this objective, the compe ..read more

Abstract Earlier this year, OpenAI released their GPTs framework, allowing users to set up Large Language Model (LLM)-based personas, orchestrate them into a workflow and even offering their AI apps within an app store. This is the latest, and maybe the easiest to set up, in a string of agent-based LLM orchestration platforms in the past year, harkening a new age of agent-based engineering. But, like most breakthroughs, this one is also rooted in many years of research, and the reason the world is paying attention to it now is that, thanks to Generative AI and Large Language Models, we finally ..read more

Abstract I argue that ensuring artificial intelligence (AI) retains alignment with human values over time is critical yet understudied. Most research focuses on static alignment, neglecting crucial retention dynamics enabling stability during learning and autonomy. This paper elucidates limitations constraining provable retention, arguing key gaps include formalizing dynamics, transparency of advanced systems, participatory scaling, and risks of uncontrolled recursive self-improvement. I synthesize technical and ethical perspectives into a conceptual framework grounded in control theory and ph ..read more

Abstract The Institute for Foundations of Machine Learning (IFML) focuses on core foundational tools to power the next generation of machine learning models. Its research underpins the algorithms and data sets that make generative artificial intelligence (AI) more accurate and reliable. Headquartered at The University of Texas at Austin, IFML researchers collaborate across an ecosystem that spans University of Washington, Stanford, UCLA, Microsoft Research, the Santa Fe Institute, and Wichita State University. Over the past year, we have witnessed incredible breakthroughs in AI on topics that ..read more