Objectives were to evaluate time of insemination relative to estrus and synchronization with melengestrol acetate (MGA) plus prostaglandin (PG) or progesterone insert (CIDR) plus PG on AI pregnancy rate in beef heifers (n = 662) during Fall or Spring. Fall heifers (n = 349) received MGA-PG (MGA for 14 d followed by PG on d 18) or CIDR-PG (CIDR for 7 d, PG administered 1 d before CIDR removal). Estrus was monitored by HeatWatch® (n = 200) or visually (n = 149). Spring heifers (n = 313) underwent CIDR-PG with detection of estrus by HeatWatch®. Heifers not in estrus by 96-100 h after PG were bred AI as non-responsive AI (NRAI). Across seasons, 548 heifers were bred following estrus (EAI). Heifers synchronized during the Fall with MGA received more (P < 0.05) mounts than Fall CIDR heifers (76.8 ± 6.7 and 47.6 ± 7.4, respectively), but duration of estrus was similar. Fall CIDR heifers had greater (P < 0.05) mounting activity and duration of estrus (47.9 ± 5.2 mounts and 15.5 ± 1.1 h) compared to Spring CIDR heifers (34.5 ± 3.1 mounts and 12.7 ± 0.6 h). Heifers grouped in 4 h blocks from 0 to 24 h had no difference (P > 0.05) in pregnancy rates (mean 62.5 %). Treatment had no effect (P > 0.05) on EAI pregnancy rates. Pregnancy rates across seasons for EAI, NRAI and overall was 61.0 %, 26.3 %, and 54.5%. In conclusion, a 24 h window may exist to successfully AI heifers. / Master of Science

Det finns en växande trend och ett ökat intresse för implementeringen av AI i olika branscher. Potentialen som AI har är stor med möjligheten att hantera stora mängder digital information och effektivisera processer. Det ökade intresset att implementera AI inkluderar byggbranschen som är i behov av ökad produktivitet. Branschen befinner sig i ett tidigt skede med implementeringen av AI, således finns det ett behov att studera forskningsområdet vidare. Denna kvalitativa fallstudie syftar till att öka kunskapen om AI-mognad i byggproduktion genom att utforska mognaden och dess påverkande faktorer. Studien görs i samarbete med ett stort entreprenadföretag där fyra fokusgruppsintervjuer och sex enskilda semistrukturerade intervjuer genomfördes. Alla intervjudeltagare är anställda på företaget och har en koppling till byggproduktionen. Analysen av empirin görs utifrån de teoretiska ramverken Diffusion of Innovation (DOI) och Technology- Organization- Environment (TOE). Studiens resultat visar att det finns en begränsning i kunskapen och användningen av AI. Generellt finns intresse att använda AI i större utsträckning i byggproduktionen och förväntningar att AI kommer bidra till positiva förändringar. Fokus i dagsläget ligger på att utforska nyttan av AI och uppmuntra anställda att använda teknologin. Det finns en övergripande positiv attityd gentemot AI, däremot är kunskapen och användningen begränsade vilket indikerar att AI-mognaden är låg i byggproduktionen. De faktorer som har identifierats påverka AI-mognaden är: outvecklad teknologi, säkerhet, storlek, kännedom, kultur, produktionsskedet, IT-leverantörer, marknadsläget, konkurrenter, kunder, underentreprenörer och byggbranschen. Denna studie är ett tidigt bidrag inom det relativt nya forskningsområdet och ökar förståelsen för utvecklingen av AI i byggbranschen. / There is a growing trend and increasing interest in the implementation of AI in various industries. The potential of AI is vast, with an ability to manage large amounts of digital information and streamline processes. The rising interest in implementing AI includes the construction industry, which needs increased productivity. The industry is in an early stage of AI implementation, hence, there is a need to further study this research area. This qualitative case study aims to increase the knowledge about AI maturity in production by exploring the maturity and its influencing factors. The study is conducted in collaboration with a large contractor company in which four focus group interviews and six individual semi-structured interviews were conducted. All interviewees are employed at the company and are connected to the production. The empirical analysis is based on the theoretical frameworks Diffusion of Innovation (DOI) and Technology- Organization- Environment (TOE).  The study’s results show that there is a limitation in the knowledge and the use of AI. Generally, there is an interest in using AI to a greater extent in the production and expectations that AI will contribute to positive changes. Currently, the focus is on exploring the benefits of AI and encouraging employees to use the technology. There is an overall positive attitude towards AI, however, knowledge and usage are limited, indicating that the AI maturity is low in the production. The factors identified as influencing the AI maturity are: Underdeveloped Technology, Security, Size, Awareness, Culture, The Production Phase, IT Suppliers, The Market Condition, Competitors, Customers, Subcontractors, and The Construction Industry. This study is an early contribution to the relatively new research area and increases the understanding of the development of AI in the construction industry.

<p>In real-time strategy (RTS) games, the human player faces tasks such as resource allocation, mission planning, and unit coordination. An Artificial Intelligence (AI) system that acts as an opponent against the human player need to be quite powerful, in order to create one cohesive strategy for victory. Even though the goal for an AI system in a computer game is not to defeat the human player, it might still need to act intelligently and look credible. It might however also need to provide just enough difficulty, so that both novice and expert players appreciates the game. The behavior of computer controlled opponents in RTS games of today has to a large extent been based on static algorithms and structures. Furthermore, the AI in RTS games performs the worst at the strategic level, and many of the problems can be tracked to its static nature. By introducing an adaptive AI at the strategic level, many of the problems could possibly be solved, the illusion of intelligence might be strengthened, and the entertainment value could perhaps be increased.</p><p>The aim of this dissertation has been to investigate how dynamic scripting, a technique for achieving adaptation in computer games, possibly could be applied at the strategic level in an RTS game. The dynamic scripting technique proposed by Spronck, et al. (2003), was originally intended for computer role-playing games (CRPGs), where it was used for online creation of scripts to control non-player characters (NPCs). The focus in this dissertation has been to investigate: (1) how the structure of dynamic scripting possibly could be modified to fit the strategic level in an RTS game, (2) how the adaptation time possibly could be lowered, and (3) how the performance of dynamic scripting possibly could be throttled.</p><p>A new structure for applying dynamic scripting has been proposed: a goal-rule hierarchy, where goals are used as domain knowledge for selecting rules. A rule is seen as a strategy for achieving a goal, and a goal can in turn be realized by several different rules. The adaptation process operates on the probability of selecting a specific rule as strategy for a specific goal. Rules can be realized by sub-goals, which create a hierarchical system. Further, a rule can be coupled with preconditions, which if false initiates goals with the purpose of fulfilling them. This introduces planning.</p><p>Results have shown that it can be more effective, with regard to adaptation time, re-adaptation time, and performance, to have equal punishment and reward factors, or to have higher punishments than rewards, compared to having higher rewards than punishments. It has also been shown that by increasing the learning rate, or including the derivative, both adaptation, and re-adaptation times, can effectively be lowered.</p><p>Finally, this dissertation has shown that by applying a fitness-mapping function, the performance of the AI can effectively be throttled. Results have shown that learning rate, and maximum weight setting, also can be used to vary the performance, but not to negative performance levels.</p>

In real-time strategy (RTS) games, the human player faces tasks such as resource allocation, mission planning, and unit coordination. An Artificial Intelligence (AI) system that acts as an opponent against the human player need to be quite powerful, in order to create one cohesive strategy for victory. Even though the goal for an AI system in a computer game is not to defeat the human player, it might still need to act intelligently and look credible. It might however also need to provide just enough difficulty, so that both novice and expert players appreciates the game. The behavior of computer controlled opponents in RTS games of today has to a large extent been based on static algorithms and structures. Furthermore, the AI in RTS games performs the worst at the strategic level, and many of the problems can be tracked to its static nature. By introducing an adaptive AI at the strategic level, many of the problems could possibly be solved, the illusion of intelligence might be strengthened, and the entertainment value could perhaps be increased. The aim of this dissertation has been to investigate how dynamic scripting, a technique for achieving adaptation in computer games, possibly could be applied at the strategic level in an RTS game. The dynamic scripting technique proposed by Spronck, et al. (2003), was originally intended for computer role-playing games (CRPGs), where it was used for online creation of scripts to control non-player characters (NPCs). The focus in this dissertation has been to investigate: (1) how the structure of dynamic scripting possibly could be modified to fit the strategic level in an RTS game, (2) how the adaptation time possibly could be lowered, and (3) how the performance of dynamic scripting possibly could be throttled. A new structure for applying dynamic scripting has been proposed: a goal-rule hierarchy, where goals are used as domain knowledge for selecting rules. A rule is seen as a strategy for achieving a goal, and a goal can in turn be realized by several different rules. The adaptation process operates on the probability of selecting a specific rule as strategy for a specific goal. Rules can be realized by sub-goals, which create a hierarchical system. Further, a rule can be coupled with preconditions, which if false initiates goals with the purpose of fulfilling them. This introduces planning. Results have shown that it can be more effective, with regard to adaptation time, re-adaptation time, and performance, to have equal punishment and reward factors, or to have higher punishments than rewards, compared to having higher rewards than punishments. It has also been shown that by increasing the learning rate, or including the derivative, both adaptation, and re-adaptation times, can effectively be lowered. Finally, this dissertation has shown that by applying a fitness-mapping function, the performance of the AI can effectively be throttled. Results have shown that learning rate, and maximum weight setting, also can be used to vary the performance, but not to negative performance levels.

Computational art is a creative field that refers to a futuristic idea of artificial intelligence. Contrary to the common belief that a machine cannot create art, technological advancements made the rise of a new form of art possible. Artificial intelligence programs can generate various art forms, such as poetry, music, visual art, design and architecture.  The aim of this thesis is to analyse and understand how the emerging community around type-generated art perceives AI in the practice, as well as to assess the main themes of discussion among the community. The study focused on Midjourney (a type-based generative art system) ’s communities on both Facebook and Twitter, two online social media platforms. The methods of netnography and content analysis were applied as a means to study these communities. Netnography helped identify members’ behaviours inside the community as well as the mutual engagement among them. Several discussions were considered in this thesis, where content analysis helped in dividing and analysing the main recurrent categories.  The theoretical framework of communities of practice and actor-network theory is applied in order to understand the findings in this research. Communities of practice refer to a group of people who engage in a practice of collective learning guided by the same interests. Whilst actor-network theory is used to attribute equally agency to humans and nonhumans. Several concepts (the myth of technology and technophobia) emerged throughout the analysis phase, which have been used to support the findings. This research applies the research paradigm of interpretivism, which lead to generalisations.  The conclusions drawn from this study show that the community sees AI as a tool for collaboration and a means for supporting and augmenting the creative process of type-based generative art. Lastly, limitations and further research were discussed in this thesis.

For a long-term period, the construction industry has been challenged by low growth rate performance, with strong relationships between reduced productivity, inefficient processes, and low level of digitalization. The industry is an important part of the development of society and generates considerable economic values that motivate change, by utilizing the forces of digitalization to a greater extent. Artificial Intelligence (AI) is a technology in today's digital revolution that has received a lot of media attention about its capabilities and how it can be implemented to create a higher degree of efficiency. In the construction industry, AI is a relatively new concept that is predicted to have great potential to challenge the conventional construction process to promote a more efficient and productive construction project. The aim of the study is to increase knowledge about AI, its potential and to develop a guideline for implementing AI. The study applied a qualitative research approach where qualitative data was analyzed. Data collection techniques for the study consisted of literature review consisting of book, reports and articles to create the theoretical framework of the topic. A comprehensive interview study was conducted with individuals in three different segments; Construction companies, consulting companies and academia to be able to cover the questions of the study. The results of the study indicate that AI is applied to a limited extent in the Swedish construction industry. However, the potential is considered high in many of the different phases and processes of the construction process, where the greatest extraction can be obtained in early stages and during the actual project management phase. Guidance of the implementation and its key factors is reinforced by the identification of the challenges that, according to the interviews and theory, can be found in both external and internal factors. Furthermore, AI technology requires investment and challenges the structure of the industry in terms of collaboration and data collection. Today's fragmented industry and the unwillingness to share data requires updated business models to achieve the expected impact that AI may generate. Implementation in construction projects often remains in test environments and, according to the study, struggles to scale up. Lack of knowledge about AI and low willingness to invest are identified as two reasons. An implementation of AI differs from "traditional" digitization since the technology is more closely connected to the business strategy and not only a technological issue. A knowledge boost within company management is crucial for an implementation to be strategic and incorporate the company's business strategy. The analysis further indicated that sector-wide initiatives are required to ensure that AI can be applied throughout the construction process. From the results and the subsequent analysis, there is a strong interest in AI and how itcan be applied. An increased utilization and investment in the technology can provide competitive advantages and consequently there is a risk that more activities will occur than what has been identified in the study. The result is in alignment with previous research but also indicates that Sweden as a country is generally behind in the development of AI. Further research and more use cases of AI are necessary to increase knowledge in the industry and to motivate companies participating in a construction project to increase their willingness to invest. / Byggbranschen har under lång tid utmanats av låg tillväxtutveckling där minskad produktivitet, ineffektiva processer och låg grad av digitalisering har starka samband. Branschen utgör en viktig del i samhällsutvecklingen och genererar stora ekonomiska värden som motiverar till förändring, genom att i större utsträckning nyttja digitaliseringens krafter. Artificiell Intelligens (AI) är en teknik i nutidens stora tekniska revolution som har fått ett stort medialt utrymme om dess förmåga och hur det kan implementeras för att bland annat skapa högre grad av effektivitet. Inom byggbranschen är AI ett relativt nytt begrepp som spås ha stor potential att utmana den konventionella byggprocessen för att främja ett effektivare och produktivare byggprojekt. Målet med studien är att öka kunskapen om AI, dess potential samt att forma en vägledning i implementeringen av AI. I studien tillämpades en kvalitativ undersökning där kvalitativa data analyserades. Datainsamlingstekniker för studien bestod av litteraturgenomgång i form av bok, rapporter och artiklar för att bygga upp det teoretiska ramverket kring ämnet. En omfattande intervjustudie utfördes med aktörer inom tre olika segment; Byggföretag, konsultföretag och akademin för att kunna besvara studiens frågeställningar. Resultatet i studien visar att AI används i begränsad utsträckning inom svensk byggbransch. Dock bedöms potentialen stor i många av byggprocessens olika skeden och processer, där den största utvinningen kan fås i tidiga skeden genom användning av historiska data samt under själva projektstyrningen. En vägledning i implementeringen och dess nyckelfaktorer förstärks genom identifieringen av utmaningarna som enligt intervjuerna och teorin bottnar i både externa och interna faktorer. AI-tekniken kräver dock investeringar och ställer krav på branschens struktur när det kommer till samarbete och datainsamlingar. Dagens fragmenterade bransch och motviljan att dela data kräver uppdaterade affärsmodeller för att få den förväntade effekt som AI kan generera. Införandet i byggprojekt stannar ofta i testmiljöer och har enligt studien svårt att skalas upp. Kunskapsbrist om AI och låg investeringsvilja pekas ut som två anledningar. En implementering av AI särskiljer sig från ”vanlig” digitalisering då tekniken i högre utsträckning är kopplad till affärsstrategin och inte enbart en teknisk fråga. Ett kunskapslyft inom företagsledningar anses nödvändigt för att en implementering ska ske strategiskt och genomsyra företagets affärsstrategi. Analysen och vägledningen visar också att det behövs branschgemensamma initiativ för att AI ska kunna tillämpas genom hela byggprocessen. I resultatet samt i den efterföljande analysen framhålls ett tydligt intresse för AI och hur det kan användas. En utökad tillämpning och satsning på tekniken kan ge konkurrensfördelar och det finns därför en risk att det görs mer än vad som framkommit i studien. Resultatet går i linje med tidigare forskning men visar också på att Sverige som land generellt ligger efter i utvecklingen av AI. Vidare forskning och fler användarfall av AI är nödvändigt för att öka kunskapen i branschen och också motivationen till att öka investeringsviljan hos bolag verksamma i ett byggprojekt.

Svärm-AI är en variant av artificiell intelligens (AI) som används för att simulera ett bettende liknande det man kan se i svärmar, flockar eller stim. Syftet med detta arbete är att undersöka ifall man kan använda svärm-AI för att förbättra en AI i ett top down shooter spel där två lag slåss mot varandra. Ett program skapades för att testa detta där de två lagen båda var datorstyrda enheter och fick slåss mot varandra för att samla mätvärden. Resultaten visar att svärm-AI presterade bättre i labyrintliknande banor utan stora tomma områden. De visade även att svärm-AI använde betydligt mycket mer prestanda. / <p>Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.</p><p>There are other digital material (eg film, image or audio files) or models/artifacts that belongs to the thesis and need to be archived.</p>

Graphic design has throughout history been affected by disruptive technologies, every technical advancement within the industry has led to a need for adapting in order to stay relevant. The biggest disruptor within the industry today is without doubt artificial intelligence. AI has already changed the workflow and design processes, and it is evident that AI is here to stay. This emerging technology has the potential to further revolutionise the creative process by providing designers with new tools and techniques to enhance efficiency and improve quality of their outputs. However, many uncertainties and concerns persist regarding how AI will impact the design profession and the space for human creative abilities. This essay aims to contribute to the ongoing discourse surrounding the role of AI in the creative industry through a thematic literature review. The findings of this study disproves the notion that AI poses a threat to creativity and emphasises the perpetual need for human expertise in a design process. This research underscores the value of human creativity and highlights how AI can complement and empower designers rather than replace them.

Artificial intelligence (AI) in the public sector is a debated topic that has the potential to enhance performance. The public sector is complex and characterized by bureaucratic challenges, posing significant procedural and financial difficulties. Using AI systems can substantially enhance the efficiency of administrative processes and the quality of services provided to citizens. However, the use of AI, in general and in the public sector, has mainly been focused on ethical issues such as trust, bias, or surveillance. One crucial factor directly affecting the efficient and ethical use of AI systems is employee competence. Employees' competence is, however, one of the least explored areas when it comes to working with AI systems, especially in the public sector. This study addresses this gap by examining the perceived necessary competences of employees within the Swedish public sector utilizing a qualitative research method and semi-structured interviews. The findings reveal three overarching areas of competences: 1) systemic competence, encompassing AI literacy and general knowledge; 2) Particular competence relating to expertise where the AI is being used; 3) Contextual competence involving data governance, legal implications, and personal data protection.

Artificial Intelligence (AI) holds transformative potential for many fields including the finance sector. However, identifying suitable tasks for artificial intelligence implementation remains a challenge. This study proposes the artificial intelligence readiness task assessment tool, empowering finance professionals to assess task suitability for AI implementation from a bottom-up perspective. Artificial intelligence adoption often encounters barriers such as costs, compatibility, and skill gaps. The proposed tool addresses these challenges by allowing finance professionals to gauge artificial intelligence suitability for specific tasks without requiring extensive AI knowledge. The tool follows a design science research approach, ensuring it is user-friendly and effectively addresses real world challenges. The proposed tool is comprised of three sections: task framing, task assessment, and results interpretation. Unlike existing methodologies that focus on organization wide artificial intelligence readiness, the proposed tool centers on task specific readiness. This innovative approach provides practical guidance for finance professionals seeking to leverage artificial intelligence and helps organizations realize the potential of AI more effectively.