Artificially sweetened beverages like Diet Coke are often considered healthier alternatives, but the debate over their impact on obesity persists. Previous research has predominantly relied on observational data or randomized controlled trials (RCTs), which may not accurately capture the causal relationship between Diet Coke consumption and obesity. This study uses causal inference methods, employing data from the National Health and Nutrition Examination Survey (NHANES) to examine this relationship across diverse demographics. Instead of relying on RCT data, we constructed a causal graph and applied the back-door criterion with its adjustment formula to estimate the RCT distributions. We then calculated the counterfactual quantity, the Probability of Necessity and Sufficiency (PNS), using both NHANES data and estimated RCT data. We propose that PNS is the essential metric for assessing the impact of Diet Coke on obesity. Our results indicate that between 20% to 50% of individuals, especially those with poor dietary habits, are more likely to gain weight from Diet Coke. Conversely, in groups like young females with healthier diets, only a small proportion experience weight gain due to Diet Coke. These findings highlight the influence of individual lifestyle and potential hormonal factors on the varied effects of Diet Coke, providing a new framework for understanding its nutritional impacts on health.

This paper surveys and organizes research works on medical dialog systems, which is an important yet challenging task. Although these systems have been surveyed in the medical community from an application perspective, a systematic review from a rigorous technical perspective has to date remained noticeably absent. As a result, an overview of the categories, methods, and evaluation of medical dialogue systems remain limited and underspecified, hindering the further improvement of this area. To fill this gap, we investigate an initial pool of 325 papers from well-known computer science, and natural language processing conferences and journals, and make an overview. Recently, large language models have shown strong model capacity on downstream tasks, which also reshaped medical dialog systems' foundation. Despite the alluring practical application value, current medical dialogue systems still suffer from problems. To this end, this paper lists the grand challenges of medical dialog systems, especially of large language models.

Federated Learning (FL) has gained attention for addressing data scarcity and privacy concerns. While parallel FL algorithms like FedAvg exhibit remarkable performance, they face challenges in scenarios with diverse network speeds and concerns about centralized control, especially in multi-institutional collaborations like the medical domain. Serial FL presents an alternative solution, circumventing these challenges by transferring model updates serially between devices in a cyclical manner. Nevertheless, it is deemed inferior to parallel FL in that (1) its performance shows undesirable fluctuations, and (2) it converges to a lower plateau, particularly when dealing with non-IID data. The observed phenomenon is attributed to catastrophic forgetting due to knowledge loss from previous sites. In this paper, to overcome fluctuation and low efficiency in the iterative learning and forgetting process, we introduce cyclical weight consolidation (CWC), a straightforward yet potent approach specifically tailored for serial FL. CWC employs a consolidation matrix to regulate local optimization. This matrix tracks the significance of each parameter on the overall federation throughout the entire training trajectory, preventing abrupt changes in significant weights. During revisitation, to maintain adaptability, old memory undergoes decay to incorporate new information. Our comprehensive evaluations demonstrate that in various non-IID settings, CWC mitigates the fluctuation behavior of the original serial FL approach and enhances the converged performance consistently and significantly. The improved performance is either comparable to or better than the parallel vanilla.

Conversational AI agents often require extensive datasets for training that are not publicly released, are limited to social chit-chat or handling a specific domain, and may not be easily extended to accommodate the latest advances in AI technologies. This paper introduces Jill Watson, a conversational Virtual Teaching Assistant (VTA) leveraging the capabilities of ChatGPT. Jill Watson based on ChatGPT requires no prior training and uses a modular design to allow the integration of new APIs using a skill-based architecture inspired by XiaoIce. Jill Watson is also well-suited for intelligent textbooks as it can process and converse using multiple large documents. We exclusively utilize publicly available resources for reproducibility and extensibility. Comparative analysis shows that our system outperforms the legacy knowledge-based Jill Watson as well as the OpenAI Assistants service. We employ many safety measures that reduce instances of hallucinations and toxicity. The paper also includes real-world examples from a classroom setting that demonstrate different features of Jill Watson and its effectiveness.

In recent years, the study of artificial intelligence (AI) has undergone a paradigm shift. This has been propelled by the groundbreaking capabilities of generative models both in supervised and unsupervised learning scenarios. Generative AI has shown state-of-the-art performance in solving perplexing real-world conundrums in fields such as image translation, medical diagnostics, textual imagery fusion, natural language processing, and beyond. This paper documents the systematic review and analysis of recent advancements and techniques in Generative AI with a detailed discussion of their applications including application-specific models. Indeed, the major impact that generative AI has made to date, has been in language generation with the development of large language models, in the field of image translation and several other interdisciplinary applications of generative AI. Moreover, the primary contribution of this paper lies in its coherent synthesis of the latest advancements in these areas, seamlessly weaving together contemporary breakthroughs in the field. Particularly, how it shares an exploration of the future trajectory for generative AI. In conclusion, the paper ends with a discussion of Responsible AI principles, and the necessary ethical considerations for the sustainability and growth of these generative models.

Abstract--Graph Machine Learning (Graph ML) has witnessed substantial advancements in recent years. With their remarkable ability to process graph-structured data, Graph ML techniques have been extensively utilized across diverse applications, including critical domains like finance, healthcare, and transportation. Despite their societal benefits, recent research highlights significant safety concerns associated with the widespread use of Graph ML models. Lacking safety-focused designs, these models can produce unreliable predictions, demonstrate poor generalizability, and compromise data confidentiality. In high-stakes scenarios such as financial fraud detection, these vulnerabilities could jeopardize both individuals and society at large. Therefore, it is imperative to prioritize the development of safety-oriented Graph ML models to mitigate these risks and enhance public confidence in their applications. In this survey paper, we explore three critical aspects vital for enhancing safety in Graph ML: reliability, generalizability, and confidentiality. We categorize and analyze threats to each aspect under three headings: model threats, data threats, and attack threats. This novel taxonomy guides our review of effective strategies to protect against these threats. Our systematic review lays a groundwork for future research aimed at developing practical, safety-centered Graph ML models. Furthermore, we highlight the significance of safe Graph ML practices and suggest promising avenues for further investigation in this crucial area. To prevalent across a wide range of real-world applications, narrow this gap, our survey seeks to resolve two critical including drug discovery [15], traffic forecasting questions: (1) What are the key aspects involved in the safety [76], and disease diagnosis [96]. Within these domains, issues of Graph ML? (2) What specific types of threats might Graph Machine Learning (Graph ML) plays a pivotal role in arise within each aspect, and how can they be effectively modeling this data and executing graph-based predictive handled? To address the first question, we introduce a novel tasks [83], [187]. However, as the scope of Graph ML taxonomy that facilitates a thorough categorization of safety applications expands, concerns about their underlying safety issues in Graph ML. To answer the second question, we issues intensify [37].

Training on large amounts of rationales (i.e., CoT Fine-tuning) is effective at improving the reasoning capabilities of large language models (LLMs). However, acquiring human-authored rationales or augmenting rationales from proprietary models is costly and not scalable. In this paper, we study the problem of whether LLMs could self-improve their reasoning capabilities. To this end, we propose Self-Explore, where the LLM is tasked to explore the first wrong step (i.e., the first pit) within the rationale and use such signals as fine-grained rewards for further improvement. On the GSM8K and MATH test set, Self-Explore achieves 11.57% and 2.89% improvement on average across three LLMs compared to supervised fine-tuning (SFT). Our code is available at https://github.com/hbin0701/Self-Explore.

The evolution of artificial intelligence (AI) has profoundly impacted human society, driving significant advancements in multiple sectors. Yet, the escalating demands on AI have highlighted the limitations of AI's current offerings, catalyzing a movement towards Artificial General Intelligence (AGI). AGI, distinguished by its ability to execute diverse real-world tasks with efficiency and effectiveness comparable to human intelligence, reflects a paramount milestone in AI evolution. While existing works have summarized specific recent advancements of AI, they lack a comprehensive discussion of AGI's definitions, goals, and developmental trajectories. Different from existing survey papers, this paper delves into the pivotal questions of our proximity to AGI and the strategies necessary for its realization through extensive surveys, discussions, and original perspectives. We start by articulating the requisite capability frameworks for AGI, integrating the internal, interface, and system dimensions. As the realization of AGI requires more advanced capabilities and adherence to stringent constraints, we further discuss necessary AGI alignment technologies to harmonize these factors. Notably, we emphasize the importance of approaching AGI responsibly by first defining the key levels of AGI progression, followed by the evaluation framework that situates the status-quo, and finally giving our roadmap of how to reach the pinnacle of AGI. Moreover, to give tangible insights into the ubiquitous impact of the integration of AI, we outline existing challenges and potential pathways toward AGI in multiple domains. In sum, serving as a pioneering exploration into the current state and future trajectory of AGI, this paper aims to foster a collective comprehension and catalyze broader public discussions among researchers and practitioners on AGI.

As a pivotal extension of the renowned ChatGPT, the GPT The development of Large Language Models (LLMs) has been Store serves as a dynamic marketplace for various Generative a transformative force in human life, reshaping interactions, Pre-trained Transformer (GPT) models, shaping the frontier enhancing communication, and influencing decision-making of conversational AI. This paper presents an in-depth measurement processes. A notable manifestation of this impact is ChatGPT, study of the GPT Store, with a focus on the categorization which, since its inception, has garnered widespread popularity, of GPTs by topic, factors influencing GPT popularity, evidenced by its millions of active users and its profound and the potential security risks. Our investigation starts with integration into various sectors such as education, business, assessing the categorization of GPTs in the GPT Store, analyzing and entertainment [17]. This surge in popularity not only how they are organized by topics, and evaluating the highlights the effectiveness of ChatGPT in understanding effectiveness of the classification system. We then examine and generating human-like text but also underscores the the factors that affect the popularity of specific GPTs, looking growing public interest in AI-driven solutions.

The emergence of large language models (LLMs) capable of generating realistic texts and images has sparked ethical concerns across various sectors. In response, researchers in academia and industry are actively exploring methods to distinguish AI-generated content from human-authored material. However, a crucial question remains: What are the unique characteristics of AI-generated text? Addressing this gap, this study proposes StyloAI, a data-driven model that uses 31 stylometric features to identify AI-generated texts by applying a Random Forest classifier on two multi-domain datasets. StyloAI achieves accuracy rates of 81% and 98% on the test set of the AuTextification dataset and the Education dataset, respectively. This approach surpasses the performance of existing state-of-the-art models and provides valuable insights into the differences between AI-generated and human-authored texts.