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.

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.

The emergence of 6G and beyond networks is set to enable a range of novel services such as personalized highly immersive experiences, holographic teleportation, and human-like intelligent robotic applications. Such applications require a set of stringent sensing, communication, control, and intelligence requirements that mandate a leap in the design, analysis, and optimization of today's wireless networks. First, from a wireless communication standpoint, future 6G applications necessitate extreme requirements in terms of bidirectional data rates, near-zero latency, synchronization, and jitter. Concurrently, such services also need a sensing functionality to track, localize, and sense their environment. Owing to its abundant bandwidth, one may naturally resort to terahertz (THz) frequency bands (0.1 − 10 THz) so as to provide significant wireless capacity gains and enable high-resolution environment sensing. Nonetheless, operating a wireless system at the THz band is constrained by a very uncertain channel which brings forth novel challenges. In essence, these channel limitations lead to unreliable intermittent links ergo the short communication range and the high susceptibility to blockage and molecular absorption. Second, given that emerging wireless services are "intelligence-centric", today's communication links must be transformed from a mere bit-pipe into a brain-like reasoning system. Towards this end, one can exploit the concept of semantic communications, a revolutionary paradigm that promises to transform radio nodes into intelligent agents that can extract the underlying meaning (semantics) or significance in a data stream. However, to date, there has been a lack in holistic, fundamental, and scalable frameworks for building next-generation semantic communication networks based on rigorous and well-defined technical foundations. Henceforth, to panoramically develop the fully-fledged theoretical foundations of future 6G applications and guarantee affluent corresponding experiences, this dissertation thoroughly investigates two thrusts. The first thrust focuses on developing the analytical foundations of THz systems with a focus on network design, performance analysis, and system optimization. First, a novel and holistic vision that articulates the unique role of THz in 6G systems is proposed. This vision exposes the solutions and milestones necessary to unleash THz's true potential in next-generation wireless systems. Then, given that extended reality (XR) will be a staple application of 6G systems, a novel risk and tail-based performance analysis is proposed to evaluate the instantaneous performance of THz bands for specific ultimate virtual reality (VR) services. Here, the results showcase that abundant bandwidth and the molecular absorption effect have only a secondary effect on the reliability compared to the availability of line-of-sight. More importantly, the results highlight that average metrics overlook extreme events and tend to provide false positive performance guarantees. To address the identified challenges of THz systems, a risk-oriented learning-based design that exploits reconfigurable intelligent surfaces (RISs) is proposed so as to optimize the instantaneous reliability. Furthermore, the analytical results are extended to investigate the uplink freshness of augmented reality (AR) services. Here, a novel ruin-based performance is conducted that scrutinizes the peak age of information (PAoI) during extreme events. Next, a novel joint sensing, communication, and artificial intelligence (AI) framework is developed to turn every THz communication link failure into a sensing opportunity, with application to digital world experiences with XR. This framework enables the use of the same waveform, spectrum, and hardware for both sensing and communication functionalities. Furthermore, this sensing input is intelligently processed via a novel joint imputation and forecasting system that is designed via non-autoregressive and transformed-based generative AI tools. This joint system enables fine-graining the sensing input to smaller time slots, predicting missing values, and fore- casting sensing and environmental information about future XR user behavior. Then, a novel joint quality of personal experience (QoPE)-centric and sensing-driven optimization is formulated and solved via deep hysteretic multi-agent reinforcement learning tools. Essentially, this dissertation establishes a solid foundation for the future deployment of THz frequencies in next-generation wireless networks through the proposal of a comprehensive set of principles that draw on the theories of tail and risk, joint sensing and communication designs, and novel AI frameworks. By adopting a multi-faceted approach, this work contributes significantly to the understanding and practical implementation of THz technology, paving the way for its integration into a wide range of applications that demand high reliability, resilience, and an immersive user experience. In the second thrust of this dissertation, the very first theoretical foundations of semantic communication and AI-native wireless networks are developed. In particular, a rigorous and holistic vision of an end-to-end semantic communication network that is founded on novel concepts from AI, causal reasoning, transfer learning, and minimum description length theory is proposed. Within this framework, the dissertation demonstrates that moving from data-driven intelligence towards reasoning-driven intelligence requires identifying association (statistical) and causal logic. Additionally, to evaluate the performance of semantic communication networks, novel key performance indicators metrics that include new "reasoning capacity" measures that could go beyond Shannon's bound to capture the imminent convergence of computing and communication resources. Then, a novel contrastive learning framework is proposed so as to disentangle learnable and memoizable patterns in source data and make the data "semantic-ready". Through the development of a rigorous end-to-end semantic communication network founded on novel concepts from communication theory and AI, along with the proposal of novel performance metrics, this dissertation lays a solid foundation for the advancement of reasoning-driven intelligence in the field of wireless communication and paves the way for a wide range of future applications. Ultimately, the various analytical foundations presented in this dissertation will provide key guidelines that guarantee seamless experiences in future 6G applications, enable a successful deployment of THz wireless systems as a versatile band for integrated communication and sensing, and build future AI-native semantic communication networks. / Doctor of Philosophy / To date, the evolution of wireless networks has been driven by a chase for data rates, i.e., higher download or upload speeds. Nonetheless, future 6G applications (the generation succeeding today's fifth generation 5G), such as the metaverse, extended reality (encompassing augmented, mixed, and virtual reality), and fully autonomous robots and vehicles, necessitate a major leap in the design and functionality of a wireless network. Firstly, wireless networks must be able to perform functionalities that go beyond communications, encompassing control, sensing, and localization. Such functionalities enable a wide range of tasks such as remotely controlling a device, or tracking a mobile equipment with high precision. Secondly, wireless networks must be able to deliver experiences (e.g. provide the user a sense of immersion in a virtual world), in contrast to a mere service. To do so, extreme requirements in terms of data rate, latency, reliability, and sensing resolution must be met. Thirdly, intelligence must be native to wireless networks, which means that they must possess cognitive and reasoning abilities that enable them to think, act, and communicate like human beings. In this dissertation, the three aforementioned key enablers of future 6G experiences are examined. Essentially, one of the focuses of this dissertation is the design, analysis, and optimization of wireless networks operating at the so-called terahertz (THz) frequency band. The THz band is a quasi-optical (close to the visible light spectrum) frequency band that can enable wireless networks to potentially provide the extreme speeds needed (in terms of communications) and the high-resolution sensing. However, such frequency bands tend to be very susceptible to obstacles, humidity, and many other weather conditions. Therefore, this dissertation investigates the potential of such bands in meeting the demands of future 6G applications. Furthermore, novel solutions, enablers, and optimization frameworks are investigated to facilitate the successful deployment of this frequency band. To provide wireless networks with their reasoning ability, this dissertation comprehensively investigates the concept of semantic communications. In contrast to today's traditional communication frameworks that convert our data to binary bits (ones and zeros), semantic communication's goal is to enable networks to communicate meaning (semantics). To successfully engineer and deploy such networks, this dissertation proposes a novel suite of communication theoretic tools and key performance indicators. Subsequently, this dissertation proposes and analyzes a set of novel artificial intelligence (AI) tools that enable wireless networks to be equipped with the aforementioned cognitive and reasoning abilities. The outcomes of this dissertation have the potential to transform the way we interact with technology by catalyzing the deployment of holographic societies, revolutionizing the healthcare via remote augmented surgery, and facilitating the deployment of autonomous vehicles for a safer and more efficient transportation system. Additionally, the advancements in wireless networks and artificial intelligence proposed in this dissertation could also have a significant impact on various other industries, such as manufacturing, education, and defense, by enabling more efficient and intelligent systems. Ultimately, the societal impact of this research is far-reaching and could contribute to creating a more connected and advanced world.

The marketing process has undergone significant changes over the years, due to new technologies. Among these advancements, artificial intelligence (AI) has been increasingly used to generate advertising messages and images. This study examines consumers' perceptions of AI-generated advertisements, with a focus on the influence of age. Participants were divided into two groups, with one group believing the ads were human-created and the other assuming they were AI-generated. Results indicated that perceived ad falsity led to more negative perceptions. However, younger individuals exhibited more favorable attitudes toward AI-generated ads compared to older individuals. Despite an overall negative bias towards AI-generated ads, one AI-generated ad was received as most positively across all age groups, supporting prior research that AI-generated content can be well-received. A theoretical model was built and tested to explore the relationship between age and ad perception, suggesting that prior experience with AI, attitudes towards AI, and AI credibility sequentially mediate this relationship. While the effect of machine heuristics was found to be nonsignificant, it did influence AI credibility, indicating potential avenues for future research.