The 27th European Conference on Artificial Intelligence (ECAI-2024) took place from 19-24 October in Santiago de Compostela, Spain. The venue also played host to the 13th Conference on Prestigious Applications of Intelligent Systems (PAIS-2024). During the week, both conferences announced their outstanding paper award winners. The winning articles were chosen based on the reviews written during the paper selection process, nominations submitted by individual members of the programme committee, additional input solicited from outside experts, and the judgement of the programme committee chairs. Abstract: Proper losses such as cross-entropy incentivize classifiers to produce class probabilities that are well-calibrated on the training data.

Research has shown the effectiveness of reasoning (e.g., Chain-of-Thought), planning (e.g., SelfAsk), and retrieval augmented generation strategies to improve the performance of Large Language Models (LLMs) on various tasks, such as question answering. However, using a single fixed strategy to answer different kinds of questions is suboptimal in performance and inefficient in terms of generated output tokens and performed retrievals. In our work, we propose a novel technique DyPlan, to induce a dynamic strategy selection process in LLMs, to improve performance and reduce costs in question-answering. DyPlan incorporates an initial decision step to select the most suitable strategy conditioned on the input question and guides the LLM's response generation accordingly. We extend DyPlan to DyPlan-verify, adding an internal verification and correction process to further enrich the generated answer. Experiments on three prominent multi-hop question answering (MHQA) datasets reveal how DyPlan can improve model performance by 7-13% while reducing the cost by 11-32% relative to the best baseline model.

Retrieval-Augmented Generation (RAG) has become a powerful paradigm for enhancing large language models (LLMs) through external knowledge retrieval. Despite its widespread attention, existing academic research predominantly focuses on single-turn RAG, leaving a significant gap in addressing the complexities of multi-turn conversations found in real-world applications. To bridge this gap, we introduce CORAL, a large-scale benchmark designed to assess RAG systems in realistic multi-turn conversational settings. CORAL includes diverse information-seeking conversations automatically derived from Wikipedia and tackles key challenges such as open-domain coverage, knowledge intensity, free-form responses, and topic shifts. It supports three core tasks of conversational RAG: passage retrieval, response generation, and citation labeling. We propose a unified framework to standardize various conversational RAG methods and conduct a comprehensive evaluation of these methods on CORAL, demonstrating substantial opportunities for improving existing approaches.

Teri Garr, known for her work in "Young Frankenstein" and "Tootsie," died Tuesday in Los Angeles. Garr's publicist confirmed to The Associated Press that the comedian died of multiple sclerosis. She began her career in the entertainment industry as a background dancer in a number of Elvis Presley movies, and went on to earn an Academy Award nomination for her role as Sandy Lester in the 1982 Dustin Hoffman comedy, "Tootsie." Actress Teri Garr died Tuesday of multiple sclerosis. She was 79. (Getty Images) The daughter of Eddie Garr, a well-known vaudeville comedian and Phyllis Lind, one of the original Rockettes at New York's Radio City Music Hall, Garr seemed destined for show business.

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we learn about research towards safe and reliable AI agent behaviour, discuss generative AI hype, congratulate the Nobel Prize winners in physics and chemistry, and take a tour of recent conferences. In the latest in our series of interviews featuring the AAAI/ACM SIGAI doctoral consortium participants, we heard from Pulkit Verma about his research on safe and reliable behavior of AI agents. He is currently investigating the minimal set of requirements in an AI system that would enable a user to assess and understand the limits of its safe operability. There has been a string of articles recently about the end of generative AI hype.

A new tool could improve the one-on-one tutoring sometimes used to supplement class instruction in these schools, by letting tutors tap into more experienced teachers' expertise during virtual sessions. It's been in chaos for the best part of five years, and the problems just keep piling up.

The Seventh AAAI/ACM Conference on AI, Ethics, and Society (AIES-24) was held in San Jose, California from October 21-23, 2024. During the opening session of the conference, the best paper award winners were announced. Abstract: In response to rising concerns surrounding the safety, security, and trustworthiness of Generative AI (GenAI) models, practitioners and regulators alike have pointed to AI red-teaming as a key component of their strategies for identifying and mitigating these risks. However, despite AI red-teaming's central role in policy discussions and corporate messaging, significant questions remain about what precisely it means, what role it can play in regulation, and how it relates to conventional red-teaming practices as originally conceived in the field of cybersecurity. In this work, we identify recent cases of red-teaming activities in the AI industry and conduct an extensive survey of relevant research literature to characterize the scope, structure, and criteria for AI red-teaming practices.

Autoregressive (AR) Large Language Models (LLMs) have demonstrated significant success across numerous tasks. However, the AR modeling paradigm presents certain limitations; for instance, contemporary autoregressive LLMs are trained to generate one token at a time, which can result in noticeable latency. Recent advances have indicated that search and repeated sampling can enhance performance in various applications, such as theorem proving, code generation, and alignment, by utilizing greater computational resources during inference. In this study, we demonstrate that diffusion language models are capable of generating at least 32 tokens simultaneously, while exceeding the performance of AR models in text quality and on the LAMBADA natural language understanding benchmark. This outcome is achieved through a novel distillation method for discrete diffusion models, which reduces the number of inference steps by a factor of 32-64. Practically, our models, even without caching, can generate tokens at a rate that is up to 8 times faster than AR models employing KV caching, and we anticipate further improvements with the inclusion of caching. Moreover, we demonstrate the efficacy of our approach for diffusion language models with up to 860M parameters.

Artificial Intelligence (AI) techniques, particularly machine learning techniques, are rapidly transforming tactical operations by augmenting human decision-making capabilities. This paper explores AI-driven Human-Autonomy Teaming (HAT) as a transformative approach, focusing on how it empowers human decision-making in complex environments. While trust and explainability continue to pose significant challenges, our exploration focuses on the potential of AI-driven HAT to transform tactical operations. By improving situational awareness and supporting more informed decision-making, AI-driven HAT can enhance the effectiveness and safety of such operations. To this end, we propose a comprehensive framework that addresses the key components of AI-driven HAT, including trust and transparency, optimal function allocation between humans and AI, situational awareness, and ethical considerations. The proposed framework can serve as a foundation for future research and development in the field. By identifying and discussing critical research challenges and knowledge gaps in this framework, our work aims to guide the advancement of AI-driven HAT for optimizing tactical operations. We emphasize the importance of developing scalable and ethical AI-driven HAT systems that ensure seamless human-machine collaboration, prioritize ethical considerations, enhance model transparency through Explainable AI (XAI) techniques, and effectively manage the cognitive load of human operators.

Nicolas Cage continues to share his fears about artificial intelligence in Hollywood. At the 25th Newport Beach Film Festival on Sunday, the actor gave a speech ahead of his Icon Award reception during the Honors Brunch where he emphasized the need to control your own image and performance as AI rises in popularity with studios. "There is a new technology in town. It's a technology that I didn't have to contend with for 42 years until recently. But these 10 young actors, this generation, most certainly will be, and they are calling it'EBDR.' This technology wants to take your instrument. We are the instruments as film actors. We are not hiding behind guitars and drums," Cage said, per Deadline.