Diffusions in Architecture: Artificial Intelligence and Image Generators
by Matias del Campo, Lev Manovich
De‐Coding Visual Cliches and Verbal Biases: Hybrid Intelligence and Data Justice
Sina Mostafavi and Asma Mehan
The truth is the whole. An empty signifier can, consequently, only emerge if there is a structural impossibility in signification as such, and only if this impossibility can signify itself as an interruption (Subversion, distortion, etcetera) of the structure of the sign (Laclau, 1996b)1.
The emergence of text prompt to image generation by using various Artificial Intelligence (AI) enabled tools has resulted in a profound impact in the fields of design and architecture. From a technical point of view, it is anticipated that further advancements in Generative Adversarial Networks (GANs), Diffusion Models (DM), and other generative methods will expand the application of AI to 3D and 4D domains. In this context, it is becoming increasingly clear that beyond the technical aspects of these technologies, common sense, critical thinking, and ethics are of utmost importance. Therefore, next to advancing machine intelligence, human consciousness is instrumental in the formation of what we refer to as Hybrid Intelligence2 that can aid us in surpassing visual clichés and verbal biases when interacting with AI.
In a recently published article titled The Dark Risk of Large Language Models, Gary Marcus argues that Humans aren’t ready for convincing conversations with AI and the consequences will be serious. He further elaborates that technically AI is moving faster than people predicted ...