Thesis (M.A.)--University of Wisconsin--Madison, 1971. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (M.S.)--University of Alaska Fairbanks, 2005. / Title from PDF t.p. (viewed Aug. 22, 2006). Includes bibliographical references (p. 59-67).

Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (leaves [235]-251).

The development of video games is a long and expensive process, and it is not uncommon for studios to require their workers to work overtime to meet deadlines, resulting in stressful work environments and reduced worker performance. Recent advancements in artificial intelligence (AI) research have people excited that perhaps this might change. Perhaps AI could supplement game developers, saving time and resources. A case study was performed to explore the use of AI models Chat-GPT and DALL-E in computer game development, assessing their viability in different computer game development areas such as programming, game development and 2D art. With predetermined criteria, based on feedback from professionals and students within the area of game development, the AI models were tasked with ten separate challenges for each category. The results showed that the AI models are not yet ready to become full-fledged developers. However, they are viable collaborative partners for a wide array of game development tasks.

The field of image synthesis has seen significant progress recently, including great strides with generative models like Generative Adversarial Networks (GANs), Diffusion Models, and Transformers. These models have shown they can create high-quality images from a variety of text prompts. However, a comprehensive analysis that examines both their performance and possible biases is often missing from existing research. In this thesis, I undertake a thorough examination of several leading text-to-image models, namely Stable Diffusion, DALL-E Mini, Lafite, and Ernie-ViLG. I assess their performance in generating accurate images of human faces, groups, and specified numbers of objects, using both Frechet Inception Distance (FID) scores and R-precision as my evaluation metrics. Moreover, I uncover inherent gender or social biases these models may possess. My research reveals a noticeable bias in these models, which show a tendency towards generating images of white males, thus under-representing minorities in their output of human faces. This finding contributes to the broader dialogue on ethics in AI and sets the stage for further research aimed at developing more equitable AI systems. Furthermore, based on the metrics I used for evaluation, the Stable Diffusion model outperforms the others in generating images from text prompts. This information could be particularly useful for researchers and practitioners trying to choose the most effective model for their future projects. To facilitate further research in this field, I have made my findings, the related data, and the source code publicly available. / Master of Science / In my research, I explored how cutting-edge computer models, namely Stable Diffusion, DALL-E Mini, Lafite, and Ernie-ViLG, can create images from text descriptions, a process that holds exciting possibilities for the future. However, these technologies aren't without their challenges. An important finding from my study is that these models exhibit bias, e.g., they often generate images of white males more than they do of other races and genders. This suggests they're not representing our diverse society fairly. Among these models, Stable Diffusion outperforms the others at creating images from text prompts, which is valuable information for anyone choosing a model for their projects. To help others learn from my work and build upon it, I've made all my data, findings, and the code I used in this study publicly available. By sharing this work, I hope to contribute to improving this technology, making it even better and fairer for everyone in the future.

Thesis (Ph. D.)--University of California, Santa Cruz, 2001. / Typescript. Includes bibliographical references (leaves 190-199).

Tools using artificial intelligence are being developed at a rapid pace. One of these tools is the text-to-image generator DALL·E 2 which has been developed by OPEN-AI and is meant to generate images solely through text input. One of the problems with tools like these is that they are not as good at generating material as AI generators for speech or text are. As DALL·E 2 has been made available to be used for commercial purposes there is a need to evaluate whether the tool is good enough to be used for that purpose. The study was conducted through 3 focus groups with a total of 15 participants. The participants were 20 to 25 years old. During the focus groups, already generated images were discussed and they were allowed to test use of the tool themselves. The theoretical framework Technology Acceptance Model 2(TAM-2) was used to analyze the qualitative data according to its already established determining factors. The result of the study presents area of usage as a new extending factor for the perceived usefulness when using TAM-2 in a qualitative study. The conclusion is drawn that visual material created by DALL·E is not seen as accepted for use in a commercial purpose but its application within certain areas of usage could raise the perceived usefulness, and thereby acceptance. / Verktyg som bygger på artificiell intelligens utvecklas i snabb takt. Ett av dessa verktyg är text-till-bild-generatorn DALL·E 2 som utvecklats av OPEN-AI och är menat att generera bilder utifrån text. Ett problem med verktyg som DALL-E 2 är att de inte är lika bra på att skapa visuellt material som generatorer är för ljud eller text. I och med att verktygen börjat göras tillgängliga kommersiellt så finns det ett behov att undersöka huruvida verktygen är tillräckligt bra för att kunna användas till det. Studien genomfördes med tre fokusgrupper med totalt 15 deltagare. Deltagarna hade en ålder mellan 20 och 25 och man diskuterade acceptans till tidigare genererat bildmaterial samt upplevelsen av att använda verktyget själva. Technology Acceptance Model 2 (TAM-2) användes som teoretiskt ramverk för analys av den kvalitativa datan och för påverkande faktorer. Resultatet presenterar sammanhang som en ny påverkande faktor för den uppfattade användbarheten i användningen av TAM-2 i kvalitativa studier. Slutsatsen dras att bildmaterial skapat av DALL·E 2 inte anses accepterat för användning i ett kommersiellt sammanhang men tillämpning i specifika sammanhang skulle kunna höja den uppfattade användbarheten och därmed acceptansen.

I slutet av år 2022 introducerades det nya AI-verktyget ChatGPT, en AI-modell som använder maskininlärning för att generera människoliknande svar i stor skala. ChatGPT:s snabba framväxt medför en ovisshet kring hur AI-verktyget kommer påverka praxis för digital marknadsföring. Denna studie utreder därför vilken roll ChatGPT kommer spela inom olika praxis för digital marknadsföring och ämnar därmed att utreda forskningsfrågan: Hur kommer ChatGPT att påverka praxis för digital marknadsföring? Den valda forskningsstrategin för denna studie är en kartläggning där ansikte-mot-ansikte kartläggning tillämpas. Detta stöds med hjälp av intervjuer som datainsamlingsmetod och vidare appliceras en tematisk analys för att analysera insamlad data. Fem marknadsföringsexperter intervjuades i denna studie och samtliga menade att ChatGPT på något vis påverkar praxis inom digital marknadsföring. Slutsatsen pekar därför mot att ChatGPT, trots dess nya upptäckt, redan börjat påverka processer inom praxis för digital marknadsföring och att det troligtvis i bredare utsträckning kommer fortsätta göra det på olika vis, genom att fortsätta inspirera, effektivisera och optimera. Vidare hade alla respondenter en positiv inställning till att se ChatGPT som ett komplement till dagens marknadsföringspraxis, dock en mer negativ inställning till att se det som ett substitut. / In late 2022, the new AI tool, ChatGPT, was introduced. It is an AI-model that uses machine learning to generate human-like responses on a large scale. The rapid rise of ChatGPT has resulted in a lack of sufficient knowledge about the effect that ChatGPT will have on digital marketing practices. Therefore, this study investigates the role of ChatGPT in different digital marketing practices and aims to address the research question: How will ChatGPT af ect digital marketing practices? The chosen research strategy for this study is a survey strategy, as well as the application of the face-to-face survey. This is supported by the data collection method interviews and then a thematic analysis is applied to analyse the collected data. Five marketing experts were interviewed in this thesis and all believed that ChatGPT will, and already has, in some way influenced digital marketing practices. The conclusion therefore points to the fact that ChatGPT, despite its recent discovery, has already begun to influence processes within the practice of digital marketing. Furthermore ChatGPT will most likely continue to enhance digital marketing in a variety of ways on a wider scale, through continuing to inspire as well as contribute with efficiency and optimisation. In addition, all respondents had a positive attitude towards seeing ChatGPT as a complement to current marketing practices, however a more negative attitude towards seeing it as a substitute.

This paper analyzes the current possible implementations and limitations of generative AI applications such as Chat-GPT, DALL-E, and Midjourney. The applications were used in a specific scenario to gauge whether it was able to effectively handle a potential request from a client, the scenario was to create a visual identity for a shoe company called WalkWise. The creations are then analyzed using Gestalt theories of perception and the machine-learning mechanisms that run these applications. To understand just how graphic designers may introduce these tools into their process, a process chart describing a typical graphic design process for a project has been created using data gathered from 8 professionals in the field who were interviewed. Using a thematic analysis, common occurring themes/activities were found and visualized in a process chart. The process was later analyzed using theories in process value analysis. Using all this information a conclusion was made that AI-generated art has several limitations that inhibit it from completely replacing human designers. These included: not understanding/generating vector graphics, not understanding objects in 3D space from less natural angles, often generating visual clichés, some potential copyright issues, and not being able to generate words. The implementation was therefore limited to a visual brainstorming tool which could aid graphic designers in quickly visualizing an idea or visualizing several different versions of one idea without having to sketch these differences, thereby making the idea-generating parts of the process more efficient.

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