Thomson Reuters (Canada)

The advent of artificial intelligence (AI) has driven an emergence in applications demanding online responses to immense amounts of data. Typically, the cloud-based deployment of these applications requires resource management to be optimized for high performance while meeting cost efficiency. However, conventional static resource allocation methods may only partially apply to the context of real-time AI applications as they are dynamic, unpredictable, and require more efficient & adaptive ways of allocating resources. To address this challenge, and as a solution in this research, we introduce an RL-based method for online resource management of cloud-based AI systems. Reinforcement learning ($\mathbf{R L}$) is a machine-learning algorithm that allows systems to learn how best to perform tasks in changing environments based on feedback from those interactions with the environment. The third way our proposed approach utilizes the weapon of RL entails allowing for dynamic resource allocation so that resources are allocated when needed rather than from a static method as set at design time.

Cloud computing in the healthcare industry allows medical organizations to store patient data securely and in compliance with privacy regulations. Furthermore, it enables remote monitoring of device performance, enabling clinicians to proactively identify and eliminate any potential problems. By identifying and predicting risks, clinicians can make well-informed decisions and conduct preventative measures such as targeted medication, avoiding risks and minimizing medical errors. Monte Carlo simulation enables healthcare organizations to accurately model and simulate complex systems, enabling them to analyze and assess the impact of each decision taken on the risk profile of the organization. Monte Carlo simulation can help healthcare organizations proactively identify, quantify and manage risks before they become real, enhancing the ability to ensure patient safety and maximize outcomes. This helps them to make more informed decisions and to improve overall efficiency and reduce costs. The combination of cloud computing and Monte Carlo simulation is revolutionizing the telemetry functions in the healthcare industry. By securely storing, monitoring and analyzing medical data, healthcare organizations can gain valuable insights for making better decisions faster. Furthermore, by simulating and modeling outcomes in advance, it is now possible to identify healthcare risks and operational issues in real-time, reducing the potential for unexpected costs and failures. In the future, this will enable healthcare organizations to apply a preventative approach to risk management and continue to provide safe and effective healthcare services.

Traditionally, writing assistance systems have focused on short or even single-word suggestions. Recently, large language models like GPT-3 have made it possible to generate significantly longer natural-sounding suggestions, offering more advanced assistance opportunities. This study explores the trade-offs between sentence- vs. message-level suggestions for AI-mediated communication. We recruited 120 participants to act as staffers from legislators’ offices who often need to respond to large volumes of constituent concerns. Participants were asked to reply to emails with different types of assistance. The results show that participants receiving message-level suggestions responded faster and were more satisfied with the experience, as they mainly edited the suggested drafts. In addition, the texts they wrote were evaluated as more helpful by others. In comparison, participants receiving sentence-level assistance retained a higher sense of agency, but took longer for the task as they needed to plan the flow of their responses and decide when to use suggestions. Our findings have implications for designing task-appropriate communication assistance systems.

PurposeThis paper examines user generated social media content bearing on consumers’ attitude and belief systems taking the domain of natural food product as illustrative case. This research sheds light on how consumers think and talk about natural food within the context of food well-being and health.Design/methodology/approachThe authors used a keyword-based approach to extract user generated content from Twitter and used both food as well-being and food as health frameworks for analysis of more than two million tweets.FindingsThe authors found that consumers mostly discuss food marketing and less frequently discuss food policy. Their results show that tweets regarding naturalness were significantly less frequent in food categories that feature naturalness to an extent, e.g. fruits and vegetables, compared to food categories dominated by technologies, processing and man-made innovation, such as proteins, seasonings and snacks.Research limitations/implicationsThis paper provides numerous implications and contributions to the literature on consumer behavior, marketing and public policy in the domain of natural food.Practical implicationsThe authors’ exploratory findings can be used to guide food system stakeholders, farmers and food processors to obtain insights into consumers' mindset on food products, novel concepts, systems and diets through social media analytics.Originality/valueThe authors’ results contribute to the literature on the use of social media in food marketing on understanding consumers' attitudes and beliefs toward natural food, food as the well-being literature and food as the health literature, by examining the way consumers think about natural (versus man-made) food using user generated content of Twitter, which has not been previously used.

In this study, we explore the performance oflarge language models (LLMs) using differ-ent prompt engineering approaches in the con-text of legal text classification. Prior researchhas demonstrated that various prompting tech-niques can improve the performance of a di-verse array of tasks done by LLMs. However,in this research, we observe that professionaldocuments, and in particular legal documents,pose unique challenges for LLMs. We experi-ment with several LLMs and various promptingtechniques, including zero/few-shot prompting,prompt ensembling, chain-of-thought, and ac-tivation fine-tuning and compare the perfor-mance on legal datasets. Although the newgeneration of LLMs and prompt optimizationtechniques have been shown to improve gener-ation and understanding of generic tasks, ourfindings suggest that such improvements maynot readily transfer to other domains. Specifi-cally, experiments indicate that not all prompt-ing approaches and models are well-suited forthe legal domain which involves complexitiessuch as long documents and domain-specificlanguage.

Studies have shown that some Natural Language Processing (NLP) systems encode and replicate harmful biases with potential adverse ethical effects in our society. In this article, we propose an approach for identifying gender and racial stereotypes in word embeddings trained on judicial opinions from U.S. case law. Embeddings containing stereotype information may cause harm when used by downstream systems for classification, information extraction, question answering, or other machine learning systems used to build legal research tools. We first explain how previously proposed methods for identifying these biases are not well suited for use with word embeddings trained on legal opinion text. We then propose a domain adapted method for identifying gender and racial biases in the legal domain. Our analyses using these methods suggest that racial and gender biases are encoded into word embeddings trained on legal opinions. These biases are not mitigated by exclusion of historical data, and appear across multiple large topical areas of the law. Implications for downstream systems that use legal opinion word embeddings and suggestions for potential mitigation strategies based on our observations are also discussed.

Creative problem solving and innovation powered by Artificial Intelligence (AI) requires detection of user needs that can be reframed into data science problems. We propose a framework of 10 creativity triggers for creative human centered AI opportunity detection, based on research and categorization of information retrieval tasks and cognitive task analysis. The method aims to facilitate a dialog between data scientists and underrepresented groups such as non-technical domain experts.

A oralidade e seus elementos têm um caráter fundamental nas conquistas políticas ao redor do mundo, em toda a história da humanidade. À luz de uma análise na interface entre Semiótica Social (KRESS, 2010), Análise da Conversa (MARCUSCHI, 2007) e Nova Retórica (PERELMAN; OLBRECHTS-TYTECA, 2005), objetivamos discutir como os recursos semióticos atrelados à oralidade, especificamente os elementos cinésicos, se constituem essenciais para a argumentação e seus propósitos no debate político, com o intuito de persuadir um público-alvo. Para atender ao propósito, desenvolvemos esse exercício analítico em um corpus de um debate do segundo turno das eleições brasileiras para a Presidência da República no ano de 2014, televisionado pela Rede Globo. Quanto aos procedimentos metodológicos, analisamos o material e selecionamos excertos em que verificamos como os presidenciáveis se utilizaram desses recursos semióticos da oralidade como estratégia persuasiva. Os resultados apontam que elementos cinésicos, como movimentos corporais, expressões faciais, gestos, olhares e risos desempenham importantes funções argumentativas, como o descrédito do oponente e a convicção dos pontos de vista defendidos, em busca do voto do eleitor.