The Internet of Things (IoT) is a network of interconnected devices and objects that have various functions,such as sensing, identifying, computing, providing services and communicating. It is estimated that by the year 2030, there will be approximately 29.42 billion IoT devices globally, facilitating extensive data exchange among them. In response to this rapid growth of IoT, Artificial Intelligence (AI) has become a pivotal technology in automating key aspects of IoT systems, including decision-making, predictive data analysis among others. The widespread use of AI across various industries has brought about significant transformations in business ecosystems. Despite its immense potential, IoT systems still face several challenges. These challenges encompass concerns related to privacy and security, data management, standardization issues, trust among others. Looking at these challenges, AI emerges as an essential enabler, enhancing the intelligence and sophistication of IoT systems. Its diverse applications offer effective solutions to address the inherent challenges within IoT systems. This, in turn, leads to the optimization of processes and the development of more intelligent and smart IoT systems.This thesis presents a semi-systematic literature review (SSLR) that aims to explore the role of AI in IoT systems. A systematic search was performed on three (3) databases (Scopus, IEEE-Xplore and the ACM digital library), 29 scientific and peer reviewed studies published between 2018-2022 were selected and examined to provide answers to the research questions. This study also encompasses an additional study within the context of AI and trustworthiness in IoT systems, user acceptance within IoT systems and AIoT's impact on sustainable economic growth across industries. This thesis also presents the DIMACERI Framework which encompasses eight dimensions of IoT challenges and concludes with recommendations for stakeholders in AIoT systems. AI is strategically integrated across the DIMACERI dimensions to create reliable, secure and efficient IoT systems.

AI in art saw a huge spike in popularity with text-to-image models like Midjourney and Stable Diffusion. The AI aids in creation of 2D art and is at times able to save massive amounts of time. Creation of 3D assets is an incredibly time consuming task but the field is currently lacking in research pertaining to artificial intelligence. The goal of this study was to produce an AI-aided workflow that would be compared to a standard workflow of 3D art students. Participants were given one hour per workflow to produce game-ready sci-fi chair assets, one with their standard workflow and one with the study’s AI workflow where the AI tools supplemented or replaced parts of their regular workflow. They began with concepting and researching, moved onto modeling, sorted out the model’s UVs and finally textured the asset. Data taken from semi-structured interviews post-experiment was analyzed with thematic analysis to produce a vivid picture of their thoughts on the experience. The tools proved to be lackluster in both quality and user experience. It seems the tool that was most probable to see use in the future was a TTI tool for concepting. However, almost all tools – and the ideas behind the tools used showed great potential if developed further. The concept of AI in art was met with mixed emotions, excitement over the potential of improvements it might provide, and a small fear over the threat of being replaced. Considering how fast AI has developed in recent years, there is no doubt that further research on the topic is important. Even as the study was being conducted, new tools were being developed and released which could have found a way into the study or could prove useful for the next one.

In today's world, Artificial Intelligence (AI) has seamlessly integrated into ourdaily lives without us even realising it. We witness AI-driven innovations allaround us, subtly enhancing our routines and interactions. Ranging from Siri, Alexa, to Google Assistant, voice assistants have become prime examples of AI technology, assisting us with simple tasks and responding to our inquiries. As these once futuristic ideas have now become an indispensable part of our everyday reality, they also become relevant for the field of GUI. This thesis explores the views of stakeholders, such as designers, alumni, students and teachers, on the inevitable implementation of artificial intelligence(AI) into the graphical user interface (GUI) development. It aims to provide understanding on stakeholders thoughts and needs with the focus on two research questions: RQ1: What are the viewpoints of design stakeholders regarding using Artificial Intelligence tools into GUI development? And RQ2: What characteristics should be considered in including AI in GUI development? To collect data, the thesis will use A/B testing and question sessions. In the A/B testing, participants will watch two videos, one showing how to digitise asketch using an AI tool (Uizard) and the other showing how to do the samething using a traditional GUI design tool (Figma). Afterwards, the participants will answer questions about their experience regarding the two different ways to digitise a sketch. The study highlighted a generally positive outlook among the participating stakeholders. Students and alumni expressed more enthusiasm whereas experienced professionals and teachers were cautious yet open to AI integration. Concerns werevoiced regarding potential drawbacks, including limited control and issues of over-reliance. The findings underscored AI's potential to streamline tasks but also emphasised the need for manual intervention and raised questions about maintaining control and creative freedom. We hope this work serves as a valuable starting point for other researchers interested in exploring this topic.

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Artificial Intelligence has recently attracted attention due to its rapid advancement in various industries such as the healthcare and finance industry. The intersection between AI and entrepreneurship is still being studied, and this study explores the impact of AI-tools on startups, with a focus on Swedish startups. The study explores the utilization of AI- tools to optimize their operations or capture new opportunities. It also examines their alignment to The Lean Startup Development, designed to help entrepreneurs to navigate through challenges they face when launching a product or a service. The primary contribution is of qualitative nature, using semi-structured interviews with individuals from startups implementing AI-technologies. Interpretation of the data is done through thematic analysis, which involves identifying themes and core categories.  The startups use the AI-tools for strategic internal planning and operations. The findings suggest that the AI- tools are commonly used to minimize costs, automate certain tasks, saving time to focus more on complex tasks and thereby enhancing efficiency which gradually leads to strengthened competitiveness. Interestingly, the participating startups show a consideration for ethical risks making more careful decisions on the information provided by the AI-tools.

Studien utforskade användningen av den Artificiell Intelligens chatbot, ChatGPT, i undervisningen av engelska (ELT) och hur elever och lärare på svenska gymnasieskolor uppfattade användningen av ChatGPT i engelskundervisningen. Studien har samlat båda kvantitativa data från 63 gymnasieelever genom en online-enkät och kvalitativa data från intervjuer med två engelsklärare på gymnasienivå. Forskningen undersökte i vilken utsträckning och för vilka syften elever använde ChatGPT, förändringarna i undervisningsmetoder inom ELT, samt fördelar och utmaningar med ChatGPT ur lärarnas perspektiv. Studien använde teoretiska ramverk som The Unified Technology Acceptance and Use of Technology (UTAUT), Language teacher cognition och Learner Autonomy. Resultaten indikerar att elever huvudsakligen använder ChatGPT för idegenerering och inspiration. Dock har anvädningen av ChatGPT för engelskinlärning inte blivit populär bland eleverna. Förändringar i undervisningsmetoder märks främst i klassrum bedömningar, aktiviteter, och hjälp med lektionsplanering och materialförberedelse. Fördelar med ChatGPT inkluderar idegenerering, främjande av Learner Autonomy, medan utmaningar inkluderar oro för tillförlitlighet, begränsad inlärning, och frågor om akademisk ohederlighet. Detta understryker behovet av noggrant övervägande vid inkluderingen av ChatGPT i pedagogiska sammanhang. / The study explored the application of artificial intelligence chatbot, ChatGPT, in English language teaching (ELT) and learning, exploring how Swedish upper secondary school students’ and teachers’ perceived ChatGPT in English class. The study collected quantitative data consisting of 63 upper secondary school students’ through an online questionnaire, and qualitative data from interviews with two upper-secondary ELT Teachers. The research explores the extent and purposes of students’ use of ChatGPT, the changes in ELT instructional practices, and the affordances and challenges of ChatGPT from teacher’s perspectives. This study adopts the unified technology acceptance and use of technology theory (UTAUT), Language teacher cognition and Learning autonomy as theoretical frameworks. The results indicate that students primarily use ChatGPT for brainstorming and inspiration, however, using ChatGPT for English learning has not become popular among students. Changes in instructional practices are noticeable in in-class assessments, activities, and assistance with lesson planning and material preparation. The affordances of ChatGPT are brainstorming, promoting learner autonomy, and the challenges include reliability concerns, limited learning, and issues of academic dishonesty. This emphasises the need for careful consideration when including ChatGPT in pedagogical implications.

Using the data collected during the hyperparameter tuning for Google's Dependency Parser Parsey McParseface, Feedforward neural networks and the correlation between its hyperparameter during the networks training are explained and analysed in depth.:1 Introduction to Neural Networks 4 1.1 History of AI 4 1.2 The role of Neural Networks in AI Research 6 1.2.1 Artificial Intelligence 6 1.2.2 Machine Learning 6 1.2.3 Neural Network 8 1.3 Structure of Neural Networks 8 1.3.1 Biology Analogy of Artificial Neural Networks 9 1.3.2 Architecture of Artificial Neural Networks 9 1.3.3 Biological Model of Nodes – Neurons 11 1.3.4 Structure of Artificial Neurons 12 1.4 Training a Neural Network 21 1.4.1 Data 21 1.4.2 Hyperparameters 22 1.4.3 Training process 26 1.4.4 Overfitting 27 2 Natural Language Processing (NLP) 29 2.1 Data Preparation 29 2.1.1 Text Preprocessing 29 2.1.2 Part-of-Speech Tagging 30 2.2 Dependency Parsing 31 2.2.1 Dependency Grammar 31 2.2.2 Dependency Parsing Rule-Based & Data-Driven Approach 33 2.2.3 Syntactic Parser 33 2.3 Parsey McParseface 34 2.3.1 SyntaxNet 34 2.3.2 Corpus 34 2.3.3 Architecture 34 2.3.4 Improvements to the Feed Forward Neural Network 38 3 Training of Parsey’s Cousins 41 3.1 Training a Model 41 3.1.1 Building the Framework 41 3.1.2 Corpus 41 3.1.3 Training Process 43 3.1.4 Settings for the Training 44 3.2 Results and Analysis 46 3.2.1 Results from Google’s Models 46 3.2.2 Effect of Hyperparameter 47 4 Conclusion 63 5 Bibliography 65 6 Appendix 74

The artificial intelligence (AI) technology has developed unexpectedly fast for the last five years. There is an ongoing discussion about whether the technology should be seen as a contributing tool that could make life easier for people or if it is a risky innovation threatening human systems, particularly among journalists. The different opinions of AI within the journalism profession contributes to the interest in studying how artificial intelligence is written about in newspapers. Despite this, there is a lack of studies that investigate the portrayal of artificial intelligence in Swedish news. This paper examines the depiction of artificial intelligence in the most read daily newspaper in Sweden to contribute to this research gap. It also offers a comparison of the portrayal for the year 2018 and 2022. The study is based on Entman´s framing theory and explores the framing of AI within different subjects, perspectives of time and the representation of AI as an aid or a risk. The study also applies theorized concepts of media panic and technological optimism to enable a deeper analysis and discussion of the results. The analysis was conducted through qualitative content analysis of twentyone news articles from Dagens Nyheter. By analyzing significant words and sentences the results of this study shows that AI is mostly framed along the following subject categories: ethics and culture. Further on the results show a predominantly framing of AI as a contributing aid and a discussion from a present or future perspective of time. The comparison between the portrayal of AI during 2018 and 2022 presented a rather similar result. The most noticeable differences are that more subjects, such as economics, politics, education and climate, were identified in articles from 2018 and that articles from 2022 tend to discuss risks of AI to a slightly higher extent. Due to the predominant framing of AI as an aid, there could be traces of technical optimism in the empirical data but there is nothing in the material to clearly indicate this.

Yes / The technological choices facing the manufacturing industry are vast and complex as the industry contemplates the increasing levels of digitization and automation in readiness for the modern competitive age. These changes broadly categorized as Industry 4.0, offer significant transformation challenges and opportunities, impacting a multitude of operational aspects of manufacturing organizations. As manufacturers seek to deliver increased levels of productivity and adaptation by innovating many aspects of their business and operational processes, significant challenges and barriers remain. The roadmap toward Industry 4.0 is complex and multifaceted, as manufacturers seek to transition toward new and emerging technologies, whilst retaining operational effectiveness and a sustainability focus. This study approaches many of these significant themes by presenting a critical evaluation of the core topics impacting the next generation of manufacturers, challenges and key barriers to implementation. These factors are further evaluated via the presentation of a new Industry 4.0 framework and alignment of I4.0 themes with the UN Sustainability Goals.

Rapid technological advances in artificial intelligence (AI), data analytics, big data, the semantic web, the Internet of Things (IoT), and cloud and mobile computing have given rise to a new generation of AI-driven recommendation agents (RAs). These agents continue to evolve and offer potential for use in a variety of application domains. However, extant information systems (IS) research has predominantly focused on user perceptions and evaluations of traditional non-intelligent product-brokering recommendation agents (PRAs), supported by empirical studies on custom-built experimental RAs that heavily rely on explicit user preference elicitations. To address the lack of research in the new generation of intelligent RAs (RAs 2.0), this dissertation aims to study consumer responses to AI-driven RAs using an affordance perspective. Notably, this research is the first in the IS discourse to link RA design artifacts, RA affordances, RA outcomes, and user continuance. It examines how actualized RA affordances influence user engagements with and evaluations of these highly personalized systems, which increasingly focus on user experiences and long-term relationships. This three-essay dissertation, consisting of one theory-building paper and two empirical studies, conceptually defines "RAs 2.0," proposes a comprehensive theoretical framework with testable propositions, and conducts two empirical studies guided by smaller carved-out models to test the validity of the comprehensive framework. The research is expected to enrich the IS literature on RAs and identify potential areas for future research. Moreover, it offers key implications for industry professionals regarding the effective system development of the new generation of intelligent RAs. / Doctor of Philosophy / Rapid technological advances in artificial intelligence (AI), data analytics, big data, the semantic web, the Internet of Things (IoT), and cloud and mobile computing have given rise to a new generation of AI-driven recommendation agents (RAs). These agents continue to evolve and offer potential for use in a variety of application domains. This three-essay dissertation, consisting of one theory-building paper and two empirical studies, conceptually defines "RAs 2.0," proposes a comprehensive theoretical framework with testable propositions, and conducts two empirical studies guided by smaller carved-out models to test the validity of the comprehensive framework. The research is expected to enrich the IS literature on RAs and identify potential areas for future research. Moreover, it offers key implications for industry professionals regarding the effective system development of the new generation of intelligent RAs.