Ai Magazine最新文献

Correcting misinformation is a complex task, influenced by various psychological, social, and technical factors. Most research evaluation methods for identifying effective correction approaches tend to rely on either crowdsourcing, questionnaires, lab-based simulations, or hypothetical scenarios. However, the translation of these methods and findings into real-world settings, where individuals willingly and freely disseminate misinformation, remains largely unexplored. Consequently, we lack a comprehensive understanding of how individuals who share misinformation in natural online environments would respond to corrective interventions. In this study, we explore the effectiveness of corrective messaging on 3898 users who shared misinformation on Twitter/X over 2 years. We designed and deployed a bot to automatically identify individuals who share misinformation and subsequently alert them to related fact-checks in various message formats. Our analysis shows that only a small minority of users react positively to the corrective messages, with most users either ignoring them or reacting negatively. Nevertheless, we also found that more active users were proportionally more likely to react positively to corrections and we observed that different message tones made particular user groups more likely to react to the bot.

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 implementation of AI. Therefore, based on Porter's value chain, this article pursues two goals. First, drawing on previous research, we provide a systematic overview of activities for which local news organizations see the biggest potential of AI to create value. Moreover, we highlight promising AI use cases based on benchmarking with national news organizations. Second, we discuss local news organizations’ challenges in implementing AI and how they might overcome such obstacles.

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. Although these tools provably improve the robustness of ML model, there is still an inevitable gap to integrate them into an end-to-end deployment. For example, robust objectives usually require carefully designed optimization algorithms, while CP treats ML models as black boxes. This paper is a brief introduction to our recent research focusing on filling this gap. Specifically, for learning robust objectives, we designed sample-efficient stochastic optimization algorithms that achieves the optimal (or faster compared to existing algorithms) convergence rates. Moreover, for CP-based uncertainty quantification, we established a framework to analyze the expected prediction set size (smaller size means more efficiency) of CP methods in both standard and adversarial settings. This paper elaborates the key challenges and our exploration towards efficient algorithms with details of background methods, notions for robustness measure, concepts of algorithmic efficiency, our proposed algorithms and results. All of them further motivate our future research on risk-aware ML that can be critical for AI–human collaborative systems. The future work mainly targets designing conformal robust objectives and their efficient optimization algorithms.

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 preferences and set intentions in line with their goals, such as goals for knowledge development and increased diversity of content or viewpoints. We give examples of such works on explanation and interactive interface interventions which have been effective in influencing readers' consumption intentions and behaviors in news recommendations. However, the state-of-the-art in news recommender systems currently fall short in terms of evaluating such interventions in live systems, limiting our ability to measure their true impact on user behavior and opinions. To help understand the true benefit of these interfaces, we therefore call for improving the realism of studies for news.

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. The aim of the article is, thus, to reconceptualize core economic features of the news industries in the context of AI to provide a vocabulary with which to assess the economic future of journalism in a data-driven platform economy.

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 competition introduced a combination of well-established challenges and entirely novel ones. Within this article, each track offers an exploration of its historical context, justifies its relevance within the planning landscape, discusses emerging domains and trends, elucidates the evaluation methodology, and ultimately presents the results.

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 providers to show how LLMs have recently been successfully applied to addressing significant barriers to the deployment of structured approaches in production, and how innovation using structured techniques has more generally framed significant editorial and product challenges that might now be more readily addressed using generative AI. Using the BBC's next-generation authoring and publishing stack as an example, the article also discusses how earlier innovation work has influenced the design of flexible infrastructure that can accommodate uncertainty in audience behavior and editorial workflows – capabilities that are likely to be well suited to the fast-approaching AI-mediated news ecosystem.

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 have artificial agents that are useful enough to scale to more serious problems.

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 philosophy to analyze dynamics. I argue priorities should shift towards capability modulation, participatory design, and advanced modeling to verify enduring alignment. Overall, I argue that realizing AI safely aligned throughout its lifetime necessitates translating principles into formal methods, demonstrations, and systems integrating technical and humanistic rigor.