Chatbots are computer programs that interact with users utilizing natural language. Businesses benefit from using chatbots as they can provide a better and more satisfactory customer experience. This thesis investigates differences in user satisfaction with two types of e-commerce chatbots: a keyword-based chatbot and a GPT-based chatbot. The study focuses on user interactions with IKEA's chatbot "Billie" compared to a prototype GPT-based chatbot designed for similar functionalities. Using a within-subjects experimental design, participants were tasked with typical e-commerce queries, followed by interviews to gather qualitative data about each participants experience. The research aims to determine whether a chatbot based on GPT technology can offer a more intuitive, engaging and empathetic user experience, compared to traditional keyword-based chatbots in the realm of e-commerce. Findings reveal that the GPT-based chatbot generally provided more accurate and relevant responses, enhancing user satisfaction. Participants appreciated the GPT chatbot's better comprehension and ability to handle natural language, though both systems still exhibited some unnatural interactions. The keyword-based chatbot often failed to understand user intent accurately, leading to user frustration and lower satisfaction. These results suggest that integrating advanced AI technologies like GPT-based chatbots could improve user satisfaction in e-commerce settings, highlighting the potential for more human-like and effective customer service.

Denna kandidatuppsats undersöker implementeringen av artificiell intelligens (AI) och dess påverkan på den svenska spelutvecklingsbranschen. Genom kvalitativa intervjuer med branschprofessionella har vi kartlagt hur AI integreras i spelutvecklingsprocesser, påverkar yrkesroller och förändrar kompetensbehov. Studien belyser både möjligheter och utmaningar som AI medför, inklusive organisatoriska förändringar, ekonomiska fördelar och sociala aspekter. Resultaten visar att AI inte bara bidrar till teknisk innovation utan också kräver en strategisk anpassning av företagets arbetsprocesser och strukturer. Vår analys indikerar att en framgångsrik AI-implementering kan stärka företagens konkurrenskraft på en kompetitiv marknad. / This bachelor's thesis examines the implementation of artificial intelligence (AI) and its impact on the Swedish game development industry. Through qualitative interviews with industry professionals, we have mapped how AI is integrated into game development processes, affects job roles, and changes competency requirements. The study highlights both opportunities and challenges brought by AI, including organizational changes, economic benefits, and social aspects. The results show that AI not only contributes to technical innovation but also requires a strategic adjustment of companies work processes and structures. Our analysis indicates that successful AI implementation can enhance companies competitiveness in a competitive market.

Denna studie undersöker utmaningarna och möjligheterna med implementeringen av artificiell intelligens (AI) på arbetsplatsen. Genom en kombination av intervjuer, enkäter och litteraturöversikt analyseras olika aspekter av AI-implementering, inklusive effektivitet, produktivitet, kompetensutveckling, etiska dilemman och samarbete. Resultaten visar att AI-implementering kan leda till ökad effektivitet och produktivitet genom automatisering av rutinmässiga uppgifter. Dock finns det utmaningar relaterade till anställdas acceptans, utbildning och säkerhetsfrågor. Vidare betonas vikten av att etablera etiska riktlinjer för att hantera potentiella risker och dilemman som uppstår med AI-användning. Kommunikation och samarbete identifieras som nyckelfaktorer för en framgångsrik integration av AI på arbetsplatsen. Denna studie belyser behovet av att förstå och navigera de komplexa dynamikerna som omger AI-implementering för att maximera dess fördelar och minimera dess risker. / This study examines the challenges and opportunities associated with the implementation of artificial intelligence (AI) in the workplace. Through a combination of interviews, surveys, and literature review, various aspects of AI implementation are analyzed, including efficiency, productivity, skills development, ethical dilemmas, and collaboration. The results indicate that AI implementation can lead to increased efficiency and productivity by automating routine tasks. However, there are challenges related to employee acceptance, training, and security issues. Furthermore, the importance of establishing ethical guidelines to address potential risks and dilemmas arising from AI usage is emphasized. Communication and collaboration are identified as key factors for successful integration of AI in the workplace. This study highlights the need to understand and navigate the complex dynamics surrounding AI implementation to maximize its benefits and minimize its risks.

[ES] La presente tesis aborda el problema del encaminamiento en las redes definidas por software (SDN). Específicamente, aborda el problema del diseño de un protocolo de encaminamiento basado en inteligencia artificial (AI) para garantizar la calidad de servicio (QoS) en transmisiones multimedia. En la primera parte del trabajo, el concepto de SDN es introducido. Su arquitectura, protocolos y ventajas son comentados. A continuación, el estado del arte es presentado, donde diversos trabajos acerca de QoS, encaminamiento, SDN y AI son detallados. En el siguiente capítulo, el controlador SDN, el cual juega un papel central en la arquitectura propuesta, es presentado. Se detalla el diseño del controlador y se compara su rendimiento con otro controlador comúnmente utilizado. Más tarde, se describe las propuestas de encaminamiento. Primero, se aborda la modificación de un protocolo de encaminamiento tradicional. Esta modificación tiene como objetivo adaptar el protocolo de encaminamiento tradicional a las redes SDN, centrado en las transmisiones multimedia. A continuación, la propuesta final es descrita. Sus mensajes, arquitectura y algoritmos son mostrados. Referente a la AI, el capítulo 5 detalla el módulo de la arquitectura que la implementa, junto con los métodos inteligentes usados en la propuesta de encaminamiento. Además, el algoritmo inteligente de decisión de rutas es descrito y la propuesta es comparada con el protocolo de encaminamiento tradicional y con su adaptación a las redes SDN, mostrando un incremento de la calidad final de la transmisión. Finalmente, se muestra y se describe algunas aplicaciones basadas en la propuesta. Las aplicaciones son presentadas para demostrar que la solución presentada en la tesis está diseñada para trabajar en redes heterogéneas. / [CA] La present tesi tracta el problema de l'encaminament en les xarxes definides per programari (SDN). Específicament, tracta el problema del disseny d'un protocol d'encaminament basat en intel·ligència artificial (AI) per a garantir la qualitat de servici (QoS) en les transmissions multimèdia. En la primera part del treball, s'introdueix les xarxes SDN. Es comenten la seva arquitectura, els protocols i els avantatges. A continuació, l'estat de l'art és presentat, on es detellen els diversos treballs al voltant de QoS, encaminament, SDN i AI. Al següent capítol, el controlador SDN, el qual juga un paper central a l'arquitectura proposta, és presentat. Es detalla el disseny del controlador i es compara el seu rendiment amb altre controlador utilitzat comunament. Més endavant, es descriuen les propostes d'encaminament. Primer, s'aborda la modificació d'un protocol d'encaminament tradicional. Aquesta modificació té com a objectiu adaptar el protocol d'encaminament tradicional a les xarxes SDN, centrat a les transmissions multimèdia. A continuació, la proposta final és descrita. Els seus missatges, arquitectura i algoritmes són mostrats. Pel que fa a l'AI, el capítol 5 detalla el mòdul de l'arquitectura que la implementa, junt amb els mètodes intel·ligents usats en la proposta d'encaminament. A més a més, l'algoritme intel·ligent de decisió de rutes és descrit i la proposta és comparada amb el protocol d'encaminament tradicional i amb la seva adaptació a les xarxes SDN, mostrant un increment de la qualitat final de la transmissió. Finalment, es mostra i es descriuen algunes aplicacions basades en la proposta. Les aplicacions són presentades per a demostrar que la solució presentada en la tesi és dissenyada per a treballar en xarxes heterogènies. / [EN] This thesis addresses the problem of routing in Software Defined Networks (SDN). Specifically, the problem of designing a routing protocol based on Artificial Intelligence (AI) for ensuring Quality of Service (QoS) in multimedia transmissions. In the first part of the work, SDN is introduced. Its architecture, protocols and advantages are discussed. Then, the state of the art is presented, where several works regarding QoS, routing, SDN and AI are detailed. In the next chapter, the SDN controller, which plays the central role in the proposed architecture, is presented. The design of the controller is detailed and its performance compared to another common controller. Later, the routing proposals are described. First, a modification of a traditional routing protocol is discussed. This modification intends to adapt a traditional routing protocol to SDN, focused on multimedia transmissions. Then, the final proposal is described. Its messages, architecture and algorithms are depicted. As regards AI, chapter 5 details the module of the architecture that implements it, along with all the intelligent methods used in the routing proposal. Furthermore, the intelligent route decision algorithm is described and the final proposal is compared to the traditional routing protocol and its adaptation to SDN, showing an increment of the end quality of the transmission. Finally, some applications based on the routing proposal are described. The applications are presented to demonstrate that the proposed solution can work with heterogeneous networks. / Rego Máñez, A. (2020). Intelligent multimedia flow transmission through heterogeneous networks using cognitive software defined networks [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/160483

Syfte: Syftet med denna studie har varit att utforska och skapa ökad förståelse hur konsumenternas köpbeslut och efterköpsbeteende kan förändras av att heminredningsföretag inom e-handeln använder Artificiell Intelligens (AI) samt vilka eventuella konsekvenser AI användandet har på konsumenternas förtroende för företagen.  Problembakgrund: Med tanke på e-handelns framfart och att AI ständigt utvecklas är det relevant att ämnet diskuteras. Förtroende för AI skiljer sig mellan de svenska medborgarna samtidigt som implementeringen av AI ökar hos företagen.   Metod: Studien behandlar hur AI påverkar köpbeslut, efterköpsbeteende och förtroende med utgångspunkt i en kvalitativ studie och med en induktiv forskningsansats. Semistrukturerade intervjuer med femton personer genomfördes för att kunna besvara forskningsfrågorna.  Slutsats: Utifrån studiens empiri och analys kunde slutsatser dras att AI påverkar konsumenternas köpbeslut, efterköpsbeteende och förtroende på olika sätt i vardera steg. Detta ger en indikation från studien att företag som använder AI bör se till att det är genomtänkt och välutvecklat. Slutsatsen är att AI i dagsläget inte används av företag på ett tillräckligt utvecklat sätt utifrån dess potentiella kompetens, därav fyller det inte sin fulla funktion och konsumentens förtroende kan antas påverkas mer negativt än positivt. Om AI istället används på ett mer genomtänkt och välutvecklat sätt påverkar det konsumenternas förtroende positivt. / Purpose: The purpose of this study has been to explore and create greater understanding of how consumers' purchase decisions and post-purchase behavior change as a result of interior design companies in e-commerce using Artificial Intelligence (AI) and what possible consequences AI use has on consumers' trust in the companies. Problem background: Considering the progress of e-commerce and that AI is constantly developing, makes the topic relevant to discuss. Trust in AI differs between Swedish citizens, while the implementation of AI is increasing among companies. Method: The study investigates how AI affects purchase decisions, post-purchase behavior and trust based on a qualitative study with an inductive research approach. Semi-structured interviews with fifteen people were conducted in order to answer the research questions. Conclusion: Based on the study's empirical data and analysis, conclusions could be drawn that AI affects consumers' purchase decisions, post-purchase behavior and trust, in different ways at each stage. This gives an indication from the study that companies using AI should ensure that it is well thought out and well developed. The conclusion is that AI is currently not used by companies in a sufficiently developed way based on its potential competence, therefore it does not fulfill its full function and consumer trust can be assumed to be affected more negatively than positively. Instead, customer trust is enhanced when AI is applied in a well thought out and well developed manner.

Under de senaste åren har revisionsbranschen genomgått betydande förändringar med införandet av digitalisering och olika digitala verktyg. Under det senaste året har Artificiell Intelligens (AI) blivit alltmer framträdande och förväntas fortsätta att utvecklas. Revisionsbyråer integrerar digitala verktyg för att optimera revisionsprocessen, förbättra kvaliteten på revisionerna och reducera risken för konkurrens. Användning av AI-teknik kan signalera att revisionsbyrån ligger i framkant med tekniska verktyg och därmed öka revisionens trovärdighet. Tidigare forskning betonar fördelarna med att implementera digitala verktyg, som AI-teknik, i revisionsarbetet. Det förväntas att detta ska medföra effektivisering, flexibilitet, resursbesparing samt möjlighet att fokusera på andra mer komplexa arbetsuppgifter för revisorn. Syftet med den föreliggande studien är att undersöka hur användningen av AI-teknik och digitalisering påverkar revisionsprocess, revisionskvalitet och revisorns kompetenskrav. Studien utforskar dessutom revisorers uppfattning och acceptans av teknologiska innovationer hos revisionsbyråer som ingår i The Big Four. För att åstadkomma detta genomfördes intervjuer med elva revisorer av olika yrkesbefattning. Resultaten från den föreliggande studien indikerar att majoriteten av respondenterna har en positiv inställning till AI-tekniken och dess framtida utveckling. Dessutom framhåller respondenterna att tekniken är ett värdefullt hjälpmedel och stöd under revisionsprocessen genom att erbjuda vägledning och insikter.  Studien framhäver vikten av att använda AI-teknik inom revision med försiktighet och betonar att revisorns mänskliga kompetens och expertis fortfarande är avgörande för både revisionskvalitet och klientförtroende. Trots att majoriteten av respondenterna ser potential i hur AI-teknik kan förbättra deras arbete, påpekar studien att tekniken fortfarande är i ett tidigt utvecklingsstadium. Studien har resulterat i att AI-teknik inte är så etablerad som tidigare forskning har indikerat. Det har också observerats att revisorer behöver ökad kunskap om AI för att effektivt kunna använda tekniken i sin revision, vilket kan åstadkommas genom utbildning. Dessa slutsatser indikerar att AI-tekniken fortsätter att genomgå betydande förbättringar och att dess fulla potential ännu inte har realiserats. / Artificial Intelligence and Digitalization in the Auditing Industry: Challenges and Opportunities for the Auditing Process and Audit Quality In recent years, the auditing industry has undergone significant changes with the introduction of digitalization and various digital tools. Over the past year, Artificial Intelligence (AI) has become increasingly prominent and is expected to continue developing. Audit firms are integrating digital tools to optimize the auditing process, improve audit quality, and reduce the risk of competition. The use of AI-technology can signal that the audit firm is at the forefront of technological tools, thereby increasing the credibility of the audit. Previous research emphasizes the benefits of implementing digital tools, such as AI-technology, in auditing work. It is expected that this will lead to increased efficiency, flexibility, resource savings, and the ability for auditors to focus on other, more complex tasks.The purpose of the present study is to examine how the use of AI-technology and digitalization affects the auditing process, audit quality, and the competence requirements for auditors. The study also explores auditors' perceptions and acceptance of technological innovations at audit firms within The Big Four. To achieve this, interviews were conducted with eleven auditors of various professional positions. The results from the present study indicate that the majority of respondents have a positive attitude towards AI-technology and its future development. Additionally, the respondents highlight that the technology is a valuable tool and support during the auditing process by providing guidance and insights.The study highlights the importance of using AI-technology in auditing with caution and emphasizes that the auditor's human competence and expertise remain crucial for both audit quality and client trust. Although the majority of respondents see potential in how AI-technology can enhance their work, the study points out that the technology is still in an early stage of development. The study has shown that AI-technology is not as established as previous research has indicated. It has also been observed that auditors need increased knowledge about AI to effectively use the technology in their audits, which can be achieved through education. These conclusions indicate that AI-technology continues to undergo significant improvements and that its full potential has yet to be realized.

Background: The increasing awareness of digitalization and specifically the emergence of Artificial Intelligence (AI) has made it possible for companies to apply chatbots. With the enhanced use of chatbots in digital settings, companies have applied chatbots to their digital customer service.  Purpose: This bachelor thesis aimed to explore chatbots' influence on customer satisfaction with digital customer service.  Method: The used research method for the study was qualitative research. To collect data for the research, the methods used were two focus groups. There were 12 participants, Swedish-speaking students from Linnaeus University, in Generation Z, both males and females.  Results: The results from the focus groups indicated that the chatbots' different characteristics and performance influenced the participants in variance. Half of the participants indicated the personal chatbot with friendly interaction influenced their customer satisfaction, and the other half influenced the impersonal chatbot. The participants agreed that it also depends on what situation the digital matter considers. All the participants agreed that the chatbots performance in general of response time and availability influence customer satisfaction.  Findings: Chatbots that are personal with friendly interactions influence customer satisfaction, for customers with complex digital matters. The second finding indicates that chatbots that are impersonal with intelligent interaction influence customer satisfaction, for customers with easy digital matters. The last findings indicates that, general performance of a chatbot, in terms of time-efficiency and availability, influences customer satisfaction for all digital matters.

Background: Managing credit risks is an integral part of the banking sector and is crucial for banks’ success. Effective risk management ensures stable and profitable operations, addressing challenges like information asymmetry between lenders and borrowers. To combat these challenges, banks are shifting from manual methods to automated processes in credit assessment and credit risk management.Purpose: The purpose of the study was to investigate how the use of AI has contributed to credit risk management and the handling of risk assessments within Swedish banks. Additionally, the study explored the factors driving the use of AI in this area.  Methodology: An abductive research approach was employed within the framework of a qualitative research method. Four banks were included in the study: two major banks and two niche banks. Semi structured interviews provided the primary data for the study, while secondary data, such as articles and literature, were used to support and explain the findings during the analysis and discussion.  Theory: The study was based on two models and the theory of information asymmetry. The first model focuses on the credit assessment process, while the second addresses critical success factors for the implementation of AI. The theory of information asymmetry consists of moral hazard and adverse selection. Conclusions: The study’s conclusion indicated that AI has contributed to increased efficiency and precision in credit risk management. Furthermore, AI supports addressing information asymmetry by automating data collection, analysis, and fraud detection. The study concludes that effective AI usage necessitates a balanced combination of management support, strategic vision, organizational culture, and structure.

This study explores the potential of artificial intelligence (AI) in enhancing the diagnosis of brain tumours, specifically through a comparative analysis of two advanced deep learning (DL) models, ResNet50 and YOLOv8, applied to detect and classify brain tumours in MRI images. The study addresses the critical need for rapid and accurate diagnostic tools in the medical field, given the complexity and diversity of brain tumours. The research was motivated by the potential benefits AI could offer to medical diagnostics, particularly in terms of speed and accuracy, which are crucial for effective patient treatment and outcomes. The performance of the ResNet50 and YOLOv8 models was evaluated on a dataset of 7023 MRI images across four tumour types. Key metrics used were accuracy, precision, recall, specificity, F1-score, and processing time, to identify which model performs better in detecting and classifying brain tumours. The findings demonstrates that although both models exhibit high performance, YOLOv8 surpasses ResNet50 in most metrics, particularly showing advantages in speed. The findings highlight the effectiveness advanced DL models in medical image analysis, providing a significant advancement in brain tumour diagnosis. By offering a thorough comparative analysis of two commonly used DL models, aligning with ongoing approaches to integrate AI into practical medical application, and highlighting their potential uses, this study advances the area of medical AI providing insight into the knowledge required for the deployment of future AI diagnostic tools.

Trots de framsteg som gjorts inom artificiell intelligens (AI) och maskininlärning (ML), uppkommer utmaningar gällande deras miljöpåverkan. Fokuset på att skapa avancerade och träffsäkra modeller innebär ofta att omfattande beräkningsresurser krävs, vilket leder till en hög energiförbrukning. Syftet med detta arbete är att undersöka ämnet grön AI och sambandet mellan prestanda och energiförbrukning hos två ML-algoritmer. De algoritmer som undersöks är beslutsträd och stödvektormaskin (SVM), med hjälp av två dataset: Bank Marketing och MNIST. Prestandan mäts med utvärderingsmåtten noggrannhet, precision, recall och F1-poäng, medan energiförbrukningen mäts med verktyget Intel VTune Profiler. Arbetets resultat visar att en högre prestanda resulterade i en högre energiförbrukning, där SVM presterade bäst men också förbrukade mest energi i samtliga tester. Vidare visar resultatet att optimering av modellerna resulterade både i en förbättrad prestanda men också i en ökad energiförbrukning. Samma resultat kunde ses när ett större dataset användes. Arbetet anses inte bidra med resultat eller riktlinjer som går att generalisera till andra arbeten. Däremot bidrar arbetet med en förståelse och medvetenhet kring miljöaspekterna gällande AI, vilket kan användas som en grund för att undersöka ämnet vidare. Genom en ökad medvetenhet kan ett gemensamt ansvar tas för att utveckla AI-lösningar som inte bara är kraftfulla och effektiva, utan också hållbara. / Despite the advancements made in artificial intelligence (AI) and machine learning (ML), challenges regarding their environmental impact arise. The focus on creating advanced and accurate models often requires extensive computational resources, leading to a high energy consumption. The purpose of this work is to explore the topic of green AI and the relationship between performance and energy consumption of two ML algorithms. The algorithms being evaluated are decision trees and support vector machines (SVM), using two datasets: Bank Marketing and MNIST. Performance is measured using the evaluation metrics accuracy, precision, recall, and F1-score, while energy consumption is measured using the Intel VTune Profiler tool. The results show that higher performance resulted in higher energy consumption, with SVM performing the best but also consuming the most energy in all tests. Furthermore, the results show that optimizing the models resulted in both improved performance and increased energy consumption. The same results were observed when a larger dataset was used. This work is not considered to provide results or guidelines that can be generalized to other studies. However, it contributes to an understanding and awareness of the environmental aspects of AI, which can serve as a foundation for further exploration of the topic. Through increased awareness, shared responsibility can be taken to develop AI solutions that are not only powerful and efficient but also sustainable.