GPT-in-the-Loop: Adaptive Decision-Making for Multiagent Systems

This paper introduces the "GPT-in-the-loop" approach, a novel method combining the advanced reasoning capabilities of Large Language Models (LLMs) like Generative Pre-trained Transformers (GPT) with multiagent (MAS) systems. Venturing beyond traditional adaptive approaches that generally require long training processes, our framework employs GPT-4 for enhanced problem-solving and explanation skills. Our experimental backdrop is the smart streetlight Internet of Things (IoT) application. Here, agents use sensors, actuators, and neural networks to create an energy-efficient lighting system. By integrating GPT-4, these agents achieve superior decision-making and adaptability without the need for extensive training. We compare this approach with both traditional neuroevolutionary methods and solutions provided by software engineers, underlining the potential of GPT-driven multiagent systems in IoT. Structurally, the paper outlines the incorporation of GPT into the agent-driven Framework for the Internet of Things (FIoT), introduces our proposed GPT-in-the-loop approach, presents comparative results in the IoT context, and concludes with insights and future directions.Comment: 8 page

A Sonoridade de Eros nas canções do Blues Clássico de Bessie Smith

The aims of this article is show how Bessie Smith's Classic Blues exemplifies the possibility of changes occurring through the aesthetic dimension. As references we will use the works Eros and Civilization: A Philosophical Inquiry into Freud, An Essay on Liberation and The Aesthetic Dimension: Toward a Critique of Marxist Aesthetics, by Herbert Marcuse, in order to demonstrate how music presents a persuasive power over minds, souls and bodies of listeners. We will also use Blues Legacies and Black Feminism: Gertrude "Ma" Rainey, Bessie Smith and Billie Holiday, by Angela Davis, in which the Blues is present as the means for the expression and transmission of the ideas and thoughts of black women capable of provoking changes in African-American consciousness. Bessie Smith was one of the best-known singers of the Classic Blues and is featured in this article. The singer preserved in her songs an African heritage to be presented to African-American, and that served as an incentive to alternative models of behavior and attitudes toward the established.O objetivo deste artigo é apresentar como o Blues Clássico de Bessie Smith exemplifica a possibilidade de mudanças ocorrerem através da dimensão estética. Como referências utilizaremos as obras Eros e Civilização: Uma interpretação filosófica do pensamento de Freud, An Essay on Liberation e The Aesthetic Dimension: Toward a Critique of Marxist Aesthetics, de Herbert Marcuse, a fim de demonstrar como a música apresenta um poder persuasivo sobre as mentes e corpos dos ouvintes. Utilizaremos também a obra Blues Legacies and Black Feminism: Gertrude “Ma” Rainey, Bessie Smith and Billie Holiday, de Angela Davis, na qual o Blues é apresentado enquanto o meio para a expressão e transmissão das ideias e pensamentos das mulheres negras capaz de provocar mudanças na consciência negra norte-americana. Bessie Smith foi uma das mais conhecidas cantoras do Blues Clássico e recebe destaque neste artigo. A cantora preservou em suas canções uma herança africana a ser apresentada aos negros norte-americanos que servia de incentivo à modelos alternativos de comportamento e atitudes em relação aos estabelecidos.&nbsp

GPT in Data Science: A Practical Exploration of Model Selection

There is an increasing interest in leveraging Large Language Models (LLMs) for managing structured data and enhancing data science processes. Despite the potential benefits, this integration poses significant questions regarding their reliability and decision-making methodologies. It highlights the importance of various factors in the model selection process, including the nature of the data, problem type, performance metrics, computational resources, interpretability vs accuracy, assumptions about data, and ethical considerations. Our objective is to elucidate and express the factors and assumptions guiding GPT-4's model selection recommendations. We employ a variability model to depict these factors and use toy datasets to evaluate both the model and the implementation of the identified heuristics. By contrasting these outcomes with heuristics from other platforms, our aim is to determine the effectiveness and distinctiveness of GPT-4's methodology. This research is committed to advancing our comprehension of AI decision-making processes, especially in the realm of model selection within data science. Our efforts are directed towards creating AI systems that are more transparent and comprehensible, contributing to a more responsible and efficient practice in data science.Comment: 11 pages. To appear in IEEE BigData 202