The AI-based chatbot ChatGPT has taken the world by storm, especially the education sector, as it has the ability to generate full reports, write scripts, assess reports and much more. The disruptive technology ChatGPT is changing the way students learn and the way lecturers teach by offering an exciting new approach to teaching and learning. As ChatGPT has had rapid development, a need for knowledge about the technology has been formed in higher education. The idea of ​​this study is to fill that need and give higher education better knowledge of what ChatGPT has to offer. This study investigates the potential impact of ChatGPT, an artificial intelligence-based algorithm, on the practices and routines in higher education. The study has a qualitative approach and was conducted through three interviews with teachers from different institutions at Umeå University. We also conducted a document study to gather input from teachers across the country to get a wider perspective. The results suggest that the emergence of ChatGPT has the potential to change the way teachers approach their teaching and interact with students. ChatGPT can provide students with quick access to information and feedback, and assist teachers in providing personalized learning experiences and support to students. Additionally, we found that it is important to be critical of the responses generated by ChatGPT since it generates answers based on a series of guesses, which can also be biased. Due to ChatGPTs capabilities of generating realistic texts, institutions may also have to modify examinations and assessment methods to ensure academic integrity. Overall, the findings of this study suggest that the emergence of ChatGPT has the potential to bring significant changes to the practices and routines in higher education, and that teachers should be prepared to adapt to these changes in order to effectively leverage the benefits of this technology.

Background: The technological development has contributed to an increased implementation and use of AI in organizations. Since AI contributes to both opportunities and risks, leaders need knowledge of how intelligent systems can be used. AI and leadership is an unexplored phenomenon that needs to be investigated more closely, in order for leaders to be able to keep up with our rapidly digitized world. Purpose: The purpose of the thesis is to increase understanding of how implementation and use of AI in organizations affects leadership. Furthermore, the thesis aims to find out how leaders can benefit from AI in their role as well as navigate its challenges. Method: The data has been collected through qualitative interviews of semi-structured form. Using an inductive approach, the cross-sectional study treats AI and leadership as a phenomenon.  Conclusion: The role of a leader is changing with the implementation and use of AI in organizations. When AI takes over tasks or facilitates the work of a leader, the central part of leadership becomes the human, relational and social interactions. The positive effects identified are efficiency, improvement and that AI facilitates rather than replaces human resources. Risks with implementing AI in organizations have been identified as ethical dilemmas, ignorance and resistance. / Bakgrund: Den teknologiska utvecklingen har bidragit till en ökad implementering och användning av AI i organisationer. Eftersom AI bidrar med såväl möjligheter som risker behöver ledare kunskap om hur intelligenta system kan användas. AI och ledarskap är ett outforskat fenomen som behöver undersökas närmare för att ledare ska kunna följa med i vår snabbt digitaliserade värld.  Syfte: Uppsatsens syfte är att öka förståelsen för hur implementering och användning av AI i organisationer påverkar ledarskap. Vidare syftar uppsatsen till att ta reda på hur ledare kan dra nytta av AI i sin roll samt navigera dess utmaningar.  Metod: Datan har samlats in genom kvalitativa intervjuer av semistrukturerad form. Med ett induktivt tillvägagångssätt behandlar tvärsnittsstudien AI och ledarskap som ett fenomen.   Slutsats: Rollen som ledare förändras i och med implementering och användning av AI i organisationer. När AI tar över arbetsuppgifter eller underlättar ledarens arbete, blir den centrala delen av ledarskapet det mänskliga, relationella och sociala interaktioner. De positiva effekter som identifierats är effektivisering, förbättring och att AI underlättar snarare än ersätter mänsklig arbetskraft. Risker med implementering av AI i organisationer har identifierats vara etiska dilemman, okunskap och motstånd.

Slutsatsen av denna studie är att en mängd varierande faktorer påverkar en konsuments attityd, både vad gäller artificiell intelligens (AI) men också attityder gentemot företagen som brukar den sagda teknologin. I enlighet med The Technology Acceptance Model (Davies, 1989) är konsumentens uppfattning om huruvida AI personligen kommer gynna dem av yttersta vikt. Såvida ingen vinning uppfattas kommer attityden gentemot AI påverkas negativt. Ytterligare har även faktorer som riskbenägenhet, annonsrelevans och subjektiva normer betydelse i vad som är en konsuments attityd gentemot AI och företag. / The conclusion of this study is that a number of varying elements are affecting the consumer attitude, against artificial intelligence (AI) –but also towards the specific companies who utilize AI. Corresponding with The Technology Acceptance Model (Davies, 1989), the perception of whether AI will benefit the consumer or not is essential. Should no gain be perceived, the attitude towards AI will be affected negatively. Furthermore, aspects such as risk beliefs, ad relevance and subjective norms are elements to consider regarding consumer attitude towards AI and subsequently retail companies.

Deep neural networks (DNNs) based on satellite and radar data have shown promising results for precipitation nowcasting, beating physical models and optical flow for time horizons up to 8 hours. “MetNet”, developed by Google AI, is a 225 million parameter DNN combining three different types of architectures that was trained on satellite and radar data over the United States. They claim to be the first machine learning model to outperform physical models at such a scale. In this work, we implemented a similar but simplified model trained on radar-only Swedish data, with the aim to perform precipitation nowcasting for up to 2 hours into the future. Furthermore, we compare the model to another, simpler model that omits the spatial aggregator of the DNN architecture which is a state-of-the-art vision transformer. Our results show that, although the adopted training dataset was too small to prevent overfitting, the model is still able to outperform the persistence benchmark for lead times longer than 30 minutes with a threshold of 0.2mm/h precipitation. Our simplified model, perhaps unsurprisingly, is outperformed by MetNet because of having too few training data samples or variances in the models’ implementation. We show, nonetheless, that the adopted spatial aggregator fulfills a vital role as expected, aggregating global information into spatial and temporal contexts.  Due to the limitations imposed by the reduced size of the model, we cannot, unfortunately, draw definitive conclusions on whether a radar-only model could yield similar forecast skills as MetNet. To improve on these results, more training data is certainly needed. This would require that more robust computation resources are available, but pre-training the model on a larger dataset — or even implementing a model that takes in different geographical locations for training — can naturally lead to significant improvements in the predictions. / Djupa neurala nätverk (DNN) baserade på satellit och radar data har gett bra resultat för korta nederbördsprognoser och kan slå fysikaliska modeller och optical flow f ̈or prognoser upp till 8 timmar i framtiden. “MetNet” ̈ar ett 225 million DNN publicerat av Google som kombinerar tre olika typer av djupa arkitekturer, det är tränat på satellit och radar data över USA och är enligt dom den första maskininlärningsmodellen som presterar bättre än fysikaliska modeller. I denna uppsats har vi konstruerat en modell som liknar deras på ett nedskalat problem. Vi har färre parametrar, lägre upplöst data, endast 2 timmar prognostisering och använder bara radar data över Sverige för att träna modellen. Vi använder F1-score för att evaluera modellens prestanda och jämför prognosen mot persistens som referens. Vidare undersöker vi en mindre komplicerad modell där den tredje arkitekturen inte används för att se vilken roll vision transformern har. Våra resultat visar att datasetet vi tränat på är för litet och modellen överanpassas men modellen lyckas ändå slå persistens referensen för prognoser 30–120 minuter när en 0.2mm/h regntröskel tillämpas. Resultaten är sämre än MetNet av Google och vi kan inte dra några slutsatser huruvida en modell med endast radar-data skulle kunna ge liknande resultat eller inte, eftersom modellen inte tränats till dess fulla potential. Vi visar att den tredje arkitekturen, vision transformern, är en viktig del av nätverket och aggregerar global information till lokala kontexter över tid och rum. För att förbättra våra resultat skulle vi pröva att låta modellen träna på det amerikanska datasetet använt av Google och implementera en modell vars input varierar geografisk position.

Idag är artificiell intelligens inom sjukvården ett omtalat ämne. Tidigare litteratur visar att det finns en tydlig potential för AI inom sjukvården där det kan göra stor nytta. Syftet med denna studien har varit att belysa var den svenska sjukvården befinner sig idag och identifiera de begränsningar som finns och försvårar sjukvårdens arbete med AI. Studien har fokuserat på att tala med experter inom området för att få deras perspektiv på situationen.  Empiriska materialet tyder på att den svenska sjukvården inte är tillräckligt förberedd och digitalt mogen för någon större utbreddhet av AI idag. Det påpekas att det krävs en digital transformation där digitala rutiner och processer blir en större del av den svenska sjukvårdens vardag först. Det visar sig att regelverken kring AI och datahantering inte är kompatibla med de ambitioner som finns vad gäller AI inom sjukvården. Denna uppsats visar att det finns ett tydligt glapp mellan förväntningarna kring AI och det faktiska arbetet i sjukvården där optimismen inte är lika tydlig. Det krävs en tydligare nationell samordning, större digital kompetens och framför allt en större ödmjukhet inför att det tar tid att bli digitalt mogen för en sådan stor förändring som AI. / Today, AI in healthcare is a popular topic. Previous literature shows that there is a clear potential for AI in healthcare where it can be of great benefit. The purpose of this study has been to shed light on where Swedish healthcare is today and identify the limitations that exist and complicate healthcare's work with AI. The study has focused on talking to experts in the field to get their perspective on the situation. Empirical evidence suggests that Swedish healthcare is not sufficiently prepared and digitally mature for any greater prevalence of AI today. It is pointed out that a digital transformation is required where digital routines and processes become a larger part of Swedish healthcare's everyday life first. It turns out that the regulations regarding AI and data management are not compatible with the ambitions that exist with regard to AI in healthcare. This essay shows that there is a clear gap between the expectations around AI and the actual work in healthcare where the optimism is not as clear. Clearer national coordination, greater digital competence and, above all, greater humility are required before it takes time to become digitally mature for such a major change as AI.

This study examines how new technologies, particularly generative artificial intelligence (AI), have become an innovative tool for communicators and how it affects their productivity and creativity. Through a combination of literature review, theoretical frameworks, and empirical research, we analyze how communicators integrate generative AI into their work process and how this affects their workflow and conditions. The study is based on the following questions: How are software-based text chat robots used by communicators in their professional role; To what extent do communicators perceive that there is an impact on creativity and productivity when co-writing between human and machine; What opportunities and challenges do communicators imagine that software-based text chat robots can contribute to? The findings indicate that generative AI has the potential to transform the communications industry by increasing efficiency and freeing up time for more strategic thinking and creativity. Communicators report increased productivity and that generative AI has the ability to help communicators manage large text bases in an agile way. At the same time, the study also identifies challenges and potential risks with the use of generative AI. Among these challenges are issues related to ethics, quality assurance and the need to maintain human control and creative input in the creation process. Communicators are aware of these challenges and emphasize the importance of balancing automation with human skills and insight. Finally, the study highlights the opportunities and challenges of the use of generative AI for communicators and identifies areas for future research and development. By understanding the potential benefits and limitations of this technology, communicators can develop strategies to maximize its positive effects and manage its challenges effectively.

This study aims to investigate how communication specialists perceive the integration of generative artificial intelligence (AI) within their profession and how these changes affect their professional role. The purpose of the study was achieved by conducting qualitative interviews with eight communication professionals regarding their view on the integration of generative AI within their profession. Particularly, we found it interesting to examine how the communication specialists use generative AI within their professional role, what risks and/or opportunities they identify concerning the use of generative AI tools within the communication profession and what factors they deem as crucial for successfully and effectively integrating AI tools within their professional roles. The theoretical framework of the study consists of diffusion theory, actor-network theory, risk society and reflexive modernity.  The results of the study indicate that communication specialists see several opportunities in integrating generative AI tools into their work. Key opportunities identified included using generative AI for inspiration and automating repetitive tasks, thereby allowing communication professionals to allocate more time to the strategic aspects of their work. There was no expressed concern that generative AI could currently replace the communication specialist’s professional role. Thus, the risks identified did not seem to revolve around the job security of the communication specialists but rather around the uncertainty about how generative AI functions, its capabilities, and how it handles the input data. Therefore, the establishment of clearer guidelines and policies on AI usage was something that the communication specialists in this study expressed a great need for. Additional factors identified by communication specialists as crucial for successful AI integration included individual characteristics such as curiosity, a willingness to learn, and openness to change. Thus, the results of the study indicate that achieving successful and effective integration of generative AI is perceived to be a shared responsibility at both organizational and individual levels.

In this thesis a new concept called domain knowledge management for informationproviding dialogue systems is introduced. Domain knowledge management includes issues related to representation and use of domain knowledge as well as access of background information sources, issues that previously have been incorporated in dialogue management. The work on domain knowledge management reported in this thesis can be divided in two parts. On a general theoretical level, knowledge sources and models used for dialogue management, including domain knowledge management, are studied and related to the capabilities they support. On a more practical level, domain knowledge management is examined in the contexts of a dialogue system framework and a specific instance of this framework, the ÖTRAF system. In this system domain knowledge management is implemented in a separate module, a Domain Knowledge Manager. The use of a specialised Domain Knowledge Manager has a number of advantages. The first is that dialogue management becomes more focused as it only has to consider dialogue phenomena, while domain-specific reasoning is handled by the Domain Knowledge Manager. Secondly, porting of a system to new domains is facilitated since domain-related issues are separated out in specialised domain knowledge sources. The third advantage with a separate module for domain knowledge management is that domain knowledge sources can be easily modified, exchanged, and reused. / <p>Report code: LiU-Tek-Lic-2001:27.</p>

This research paper is designed as a position paper with empirical elements to analyze the role of AI in the field of art. This work is separated in different parts of analysis, starting by the philosophy of art and its possible meaning in our society, human mind and its correlation with art and Artificial Intelligence. Then, the question of usage of AI in art will be risen. The discussion about AI in art can be separated into 2 parts: AI as a tool for art and AI as an artist. In order to see how those positions in the artistic field are possible, a theoretical research will be made. Moreover, an interview and the ”Turing” test will be launched with people who are related to the field of art, AI or none of them. This will be made in order to reflect on the matter from a practical point of view. The practical research and the theoretical one will be analyzed together. / Denna forskningsartikel är utformad som en positionsuppsats med empiriska inslag för att analysera AI:s roll inom konstområdet. Detta arbete är separerat i olika delar av analysen, med början i konstens filosofi och dess möjliga betydelse i vårt samhälle, mänskliga sinne och dess samband med konst och artificiell intelligens. Då kommer frågan om användningen av AI i konsten att väckas. Diskussionen om AI i konst kan delas upp i 2 delar: Ai som verktyg för konst och AI som konstnär. För att se hur dessa positioner inom det konstnärliga området är möjliga kommer den teoretiska forskningen att göras, liksom intervjun och ”turing”-testet kommer att lanseras med personer som är relaterade till konstområdet, AI eller ingen av dem kommer att göra det. göras för att reflektera över saken från den praktiska synpunkten. Den praktiska forskningen och den teoretiska kommer att analyseras tillsammans.

Följande studie har undersökt hur implementering av AI kan påverka IT-företag inom olika aspekter som exempelvis fördelar, nackdelar, utmaningar och verksamhetsprocesser. Studien är baserad på en kvalitativ metod där det insamlade materialet utgör grunden till detta arbete. Materialet kommer i form av informationen som har samlats in från tre semistrukturerade intervjuer som har genomförts med respondenter från 3 olika företag, dessa företag är Sogeti, Exsitec ett IT-företag som utvecklar digitala verksamhetsstöd, och Vinga som säljer olika produkter till företag. Denna studie borde genomföras eftersom dagens samhälle är väldigt digitaliserat och många företag i världen har redan implementerat någon form av artificiell intelligens för att förbättra deras arbetsprocesser. Rapporten inleds med att förklara bakgrunden till AI, dvs dess historia och uppkomst, därefter kommer forskningsfrågorna att försöka besvaras, och dessa är: ? Hur har verksamhetens processer ändrats efter implementeringen av AI? ? Vilka fördelar/nackdelar kan uppstå från implementeringen av AI? Eftersom AI omfattar ett stort område så avgränsar sig studien till de områden där företagen utnyttjar AI. Dessa områden innefattar kommunikation, skapandet avannons/reklam och dataanalys. Relevant litteratur väljs till de områden som täcks av IT-företagen. Rapporten förklarar hur verksamheten som har det implementerat har ändrats, men också konsekvenserna av denna implementering. Vilken roll som AI och har och kommer att ha är också något som kommer att tas upp i rapporten, vilket är en av anledningarna till att gruppen valde just detta ämne. Informationen som samlades in från respondenterna var att implementeringen av AI kan ha en betydande inverkan på affärsprocesser, men det har också sina fördelar och nackdelar. Vinga och deras beslut att implementera AI i sina affärsprocesser resulterade i beslutet att inte anlita externa byråer för att hantera annonser åt dem. Detta har visat sig vara mycket kostnadseffektivt för Vinga enligt respondenten, och samtidigt visat sig vara lika effektivt om inte mer, än att anlita en extern byrå. Nackdelen med detta är dock att det ur en anställds perspektiv så resulterade det i att någon gick miste om en jobbmöjlighet. Sammanfattningsvis har användningen av AI visat sig vara ett effektivt sätt att förbättra affärsprocesser, men det kräver också kompetens från de personer som bestämmer sig för att implementera det. Företag som bestämmer sig för att implementera AI i sina processer bör ha ett tydligt syfte med implementeringen, eftersom det kan leda till onödiga kostnader, om det inte underhålls och används på rätt sätt. Det finns många framtida forskningsmöjligheter inom områdena AI och dess inverkan på affärsprocesser. Exempelvis kan en jämförelse mellan IT-företag som har liknande arbetsprocesser genomföras, vilket inte kunde utföras i denna studie, på grund av tidsbrist och bristande respons från IT-företagen som skulle ha intervjuats. AI är också ett väldigt brett och ständigt utvecklande område samt används för många olika syften, vilket också möjliggör för framtida forskningar. / The following study has examined how the implementation of AI can affect IT companies in various aspects such as advantages, disadvantages, challenges and business processes. The study is based on a qualitative method where the collected material forms the basis for this work. The material comes in the form of information collected from three semi-structured interviews conducted with respondents from 3 different companies, these companies are Sogeti which is a subsidiary of the company Capgemini, Exsitec an IT company that develops digital business support, and Vinga, who sells various products to companies. This study should be conducted because today’s society is highly digitalized, many companies in the world have already implemented some form of artificial intelligence to improve their work processes. The report begins by explaining the background of AI, ie its history and origin, then the research questions will be answered, and these are: ? How have business processes changed after the implementation of AI? ? What are the advantages and disadvantages that can arise from implementing AI? Since AI covers a large area, the study is limited to areas where companies use AI. These areas include communication, ad creation and advertising and data analytics. Relevant literature is selected for the areas covered by the IT companies. This report also explains how the implementation of AI has affected the business, but also the consequences of said implementation. The information that was gathered from the respondents was that the implementation of AI can have a significant impact on business processes, but it also has its advantages and disadvantages. Vinga and their decision to implement AI in their business processes resulted in them not hiring external agencies to handle adverts for them. This has proven to be very cost efficient for Vinga according to the respondent, and at the same time proven to be as effective if not more effective than hiring an external agency. The disadvantage of that is that from an employee perspective, it means that someone has been denied a job opportunity. In conclusion, the use of AI has proven to be an effective way of improving business processes, but it also requires competency from the people who decide to implement it. Businesses who decide to implement AI in their processes should have a clear purpose with the implementation, since it can lead to unnecessary costs, if it is not maintained and used properly. There are many future research opportunities in the areas of AI and its impact on business processes. For example, a comparison between IT companies that have similar work processes can be performed, which could not be performed in this study, due to lack of time and lack of response from the IT companies that would have been interviewed. AI is also a very broad and constantly evolving area and is used for many different purposes, which also enables future research.