Full bibliography

Artificial intelligence (AI) is changing the way we operate as a society. Generative AI models are especially known for being used to generate synthetic artifacts, such as texts, music, and images. This doctoral thesis explores generative AI's ability to create accurate images from prompt text. Our work aims to prove how generative AI tools are creating images that are remarkably similar in appearance as those created by humans. In addition to the theoretical contributions, this thesis explores broader secondary open questions about generative AI: what implications arise for the perception of what is virtual and non-virtual in our contemporary visual landscape? How does the new nature of interaction with generative AI change human-machine communication? Generative AI tools saw a series of breakthroughs these last years, which led to models that generate texts and images that are increasingly more difficult to distinguish from human- made creative content. As of 2022, Open AI developed and released ChatGPT, a chatbot enabling human users to converse, ask questions, explain concepts, and create new text-based content. However, the capabilities of generative AI went far beyond text generation. For example, gen AI models, Midjourney and DALL-E 3, are specifically designed to create images based on text prompts. These images are artificially created, meaning every screen pixel was produced using AI. Throughout this research, we explore new concepts of creative content generation, perception of virtual and non-virtual, memory, and trustworthiness in our contemporary imagery. Using an interdisciplinary methodological framework, this thesis engages with the creation of synthetic imagery as an opportunity for an infinite source of creativity or a detrimental disruption of contemporary visual culture.