Reinforcement Learning and Physics

Machine learning techniques provide a remarkable tool for advancing scientific research, and this area has significantly grown in the past few years. In particular, reinforcement learning, an approach that maximizes a (long-term) reward by means of the actions taken by an agent in a given environment, can allow one for optimizing scientific discovery in a variety of fields such as physics, chemistry, and biology. Morover, physical systems, in particular quantum systems, may allow one for more efficient reinforcement learning protocols. In this review, we describe recent results in the field of reinforcement learning and physics. We include standard reinforcement learning techniques in the computer science community for enhancing physics research, as well as the more recent and emerging area of quantum reinforcement learning, inside quantum machine learning, for improving reinforcement learning computations.Ministerio de Ciencia e Innovación PGC2018- 095113-B-I00, PID2019-104002GB-C21 and PID2019-104002GB-C2

What If ChatGPT Wrote It? A Research On Consumers Perception

openThis research thesis can be divided in two parts, the first part (chapter 1, chapter 2 and chapter 3) focuses on the literature, while the second part (chapter 4) sees the unravelling of the research, starting from the research question and the methodology, and ending with the research itself and its results. Chapter 1, simply called “Artificial Intelligence”, starts with a series of definitions in order to set the ground for the subsequent chapters. Following, the history of AI is recalled from the 1950s till today, and finally I list the risks and bring real-life examples of the benefits of this technology. Chapter 2, “Artificial Intelligence in Marketing”, starts with a recollection of academic studies concerning AI marketing divided into four subjects (1) technical AI algorithms for solving specific marketing problems, (2) customer’s psychological reactions to AI, (3) effects of AI on jobs and society and (4) managerial and strategic issues related to AI, focusing on the last one in particular (Huang & Rust 2020). Then it shows through reports and case studies how companies are implementing AI in their marketing strategies. The chapter closes considering the benefits and the risks of using AI for marketing reasons and finally how consumers perceive it. Chapter 3 builds the foundations for the research, focusing on one of the most discussed AI tools, ChatGPT. It explains what kind of artificial intelligence it is, referencing also how OpenAI has improved its algorithm over time, but also its pervasive adoption and media coverage. Then I rely on Sohail’s (2023) taxonomy to explain the possible applications of ChatGPT in different fields – medicine, marketing, finance, software engineering, academic writing, education, environmental science, natural language processing and tourism. I focus on how marketers are in fact using it now, explaining the best practices to benefit the most from its use. Lastly, I consider its risks and limitations, like biases, hallucinations, inaccuracies, and ethical dilemmas. The last chapter is dedicated to the research, where I delve into the specific use of ChatGPT for writing copy and the perception of people towards it. To answer the research question « Does the perception of the copy changes when potential customers know that it has been written by ChatGPT? » I chose a well-known brand – IKEA –, rewrote the written content of a post using ChatGPT and surveyed 115 people on their perception of the copy. Even though the sample is not representative of the Italian population aged between 20 and 35, my analysis shows that there are in fact some statistical differences on the perception between those who knew and those who didn’t know that I used an artificial intelligence to write the promotional content

Digital Food Marketing to Children and Adolescents: Problematic Practices and Policy Interventions

Examines trends in digital marketing to youth that uses "immersive" techniques, social media, behavioral profiling, location targeting and mobile marketing, and neuroscience methods. Recommends principles for regulating inappropriate advertising to youth

Analytical customer relationship management in retailing supported by data mining techniques

Tese de doutoramento. Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 201

Artificial intelligence (AI) competencies for organizational performance : A B2B marketing capabilities perspective

The deployment of Artificial Intelligence (AI) has been accelerating in several fields over the past few years, with much focus placed on its potential in Business-to-Business (B2B) marketing. Early reports highlight promising benefits of AI in B2B marketing such as offering important insights into customer behaviors, identifying critical market insight, and streamlining operational inefficiencies. Nevertheless, there is a lack of understanding concerning how organizations should structure their AI competencies for B2B marketing, and how these ultimately influence organizational performance. Drawing on AI competencies and B2B marketing literature, this study develops a conceptual research model that explores the effect that AI competencies have on B2B marketing capabilities, and in turn on organizational performance. The proposed research model is tested using 155 survey responses from European companies and analyzed using partial least squares structural equation modeling. The results highlight the mechanisms through which AI competencies influence B2B marketing capabilities, as well as how the later impact organizational performance.© 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).fi=vertaisarvioitu|en=peerReviewed

The emerging landscape of Social Media Data Collection: anticipating trends and addressing future challenges

[spa] Las redes sociales se han convertido en una herramienta poderosa para crear y compartir contenido generado por usuarios en todo internet. El amplio uso de las redes sociales ha llevado a generar una enorme cantidad de información, presentando una gran oportunidad para el marketing digital. A través de las redes sociales, las empresas pueden llegar a millones de consumidores potenciales y capturar valiosos datos de los consumidores, que se pueden utilizar para optimizar estrategias y acciones de marketing. Los beneficios y desafíos potenciales de utilizar las redes sociales para el marketing digital también están creciendo en interés entre la comunidad académica. Si bien las redes sociales ofrecen a las empresas la oportunidad de llegar a una gran audiencia y recopilar valiosos datos de los consumidores, el volumen de información generada puede llevar a un marketing sin enfoque y consecuencias negativas como la sobrecarga social. Para aprovechar al máximo el marketing en redes sociales, las empresas necesitan recopilar datos confiables para propósitos específicos como vender productos, aumentar la conciencia de marca o fomentar el compromiso y para predecir los comportamientos futuros de los consumidores. La disponibilidad de datos de calidad puede ayudar a construir la lealtad a la marca, pero la disposición de los consumidores a compartir información depende de su nivel de confianza en la empresa o marca que lo solicita. Por lo tanto, esta tesis tiene como objetivo contribuir a la brecha de investigación a través del análisis bibliométrico del campo, el análisis mixto de perfiles y motivaciones de los usuarios que proporcionan sus datos en redes sociales y una comparación de algoritmos supervisados y no supervisados para agrupar a los consumidores. Esta investigación ha utilizado una base de datos de más de 5,5 millones de colecciones de datos durante un período de 10 años. Los avances tecnológicos ahora permiten el análisis sofisticado y las predicciones confiables basadas en los datos capturados, lo que es especialmente útil para el marketing digital. Varios estudios han explorado el marketing digital a través de las redes sociales, algunos centrándose en un campo específico, mientras que otros adoptan un enfoque multidisciplinario. Sin embargo, debido a la naturaleza rápidamente evolutiva de la disciplina, se requiere un enfoque bibliométrico para capturar y sintetizar la información más actualizada y agregar más valor a los estudios en el campo. Por lo tanto, las contribuciones de esta tesis son las siguientes. En primer lugar, proporciona una revisión exhaustiva de la literatura sobre los métodos para recopilar datos personales de los consumidores de las redes sociales para el marketing digital y establece las tendencias más relevantes a través del análisis de artículos significativos, palabras clave, autores, instituciones y países. En segundo lugar, esta tesis identifica los perfiles de usuario que más mienten y por qué. Específicamente, esta investigación demuestra que algunos perfiles de usuario están más inclinados a cometer errores, mientras que otros proporcionan información falsa intencionalmente. El estudio también muestra que las principales motivaciones detrás de proporcionar información falsa incluyen la diversión y la falta de confianza en las medidas de privacidad y seguridad de los datos. Finalmente, esta tesis tiene como objetivo llenar el vacío en la literatura sobre qué algoritmo, supervisado o no supervisado, puede agrupar mejor a los consumidores que proporcionan sus datos en las redes sociales para predecir su comportamiento futuro

Information Diffusion and Summarization in Social Networks

Social networks are web-based services that allow users to connect and share information. Due to the huge size of social network graph and the plethora of generated content, it is difficult to diffuse and summarize the social media content. This thesis thus addresses the problems of information diffusion and information summarization in social networks. Information diffusion is a process by which information about new opinions, behaviors, conventions, practices, and technologies flow from person-to-person through a social network. Studies on information diffusion primarily focus on how information diffuses in networks and how to enhance information diffusion. Our aim is to enhance the information diffusion in social networks. Many factors affect information diffusion, such as network connectivity, location, posting timestamp, post content, etc. In this thesis, we analyze the effect of three of the most important factors of information diffusion, namely network connectivity, posting time and post content. We first study the network factor to enhance the information diffusion, and later analyze how time and content factors can diffuse the information to a large number of users. Network connectivity of a user determines his ability to disseminate information. A well-connected authoritative user can disseminate information to a more wider audience compared to an ordinary user. We present a novel algorithm to find topicsensitive authorities in social networks. We use the topic-specific authoritative position of the users to promote a given topic through word-of-mouth (WoM) marketing. Next, the lifetime of social media content is very short, which is typically a few hours. If post content is posted at the time when the targeted audience are not online or are not interested in interacting with the content, the content will not receive high audience reaction. We look at the problem of finding the best posting time(s) to get high information diffusion. Further, the type of social media content determines the amount of audience interaction, it gets in social media. Users react differently to different types of content. If a post is related to a topic that is more arousing or debatable, then it tends to get more comments. We propose a novel method to identify whether a post has high arousal content or not. Furthermore, the sentiment of post content is also an important factor to garner users’ attention in social media. Same information conveyed with different sentiments receives a different amount of audience reactions. We understand to what extent the sentiment policies employed in social media have been successful to catch users’ attention. Finally, we study the problem of information summarization in social networks. Social media services generate a huge volume of data every day, which is difficult to search or comprehend. Information summarization is a process of creating a concise readable summary of this huge volume of unstructured information. We present a novel method to summarize unstructured social media text by generating topics similar to manually created topics. We also show a comprehensive topical summary by grouping semantically related topics