ARTIFICIAL INTELLIGENCE IN ARCHITECTURE AND BUILT ENVIRONMENT DEVELOPMENT 2024: A CRITICAL REVIEW AND OUTLOOK, 6th part: Lawsuits

Obviously, training dataset quality in terms of size, comprehensivity, and relevance is of critical meaning for an AI application´s performance. Not always but often, the objects that assemble the set represent the intellectual property of respective authors. The authors feel mishandled and affected if someone - no matter whether a human or an AI - takes and compiles their creations (or their digital representations) to put them on display or to submit them individually.

ARTIFICIAL INTELLIGENCE IN ARCHITECTURE AND BUILT ENVIRONMENT DEVELOPMENT 2024: A CRITICAL REVIEW AND OUTLOOK, 4th part: Production ecosystems and sensational novelties

There are ecosystems of natural language processing, image and video processing, voice processing, and code or software processing and development, further robotics, and expert systems or business intelligence [99, 100], altogether represented by DALL-E (DALL-E3 newly), ImageGPT, InstructGPT and ChatGPT, Bard or Gemini, Ernie Bot, Tongyi Qianwen, Sense Time SenseChat, Bedrock, and many other tools by OpenAI, Microsoft, Google, Baidu, Alibaba Group, Amazon, also MidJourney (that released version 6 recently), Stable Diffusion (currently released version 3, which demonstrates unmatched performance on the ControlNet network, designed to control diffusion models in image generation, and LayerDiffusion that introduces latent transparency, which allows the generation of a single transparent image or multiple transparent layers, combined into a single blended image [101]), Gong.io, Tellius, OPENNN, Theano, and many other tools by multiple producers.

ARTIFICIAL INTELLIGENCE IN ARCHITECTURE AND BUILT ENVIRONMENT DEVELOPMENT 2024: A CRITICAL REVIEW AND OUTLOOK, 3rd part: Artificial neural networks

An artificial neural network is a collection of connected units or nodes called artificial neurons designed to model loosely how the neurons in a biological brain have been supposed to look and work. Like synapses in a biological brain, each connection can transmit a signal to other neurons. A deep neural network is an artificial neural network with multiple layers between the input and output layers; in a shortcut, a deep neural network makes machine learning deep learning [59, 60]. In essence, two computing principles apply in artificial neural networks today: feedforward computing and backpropagation. The goal is always to train the models generated to cope with the criteria typically inserted by vast sample datasets. Feedforward computing refers to a type of workflow without feedback connections that would form closed loops; the latter term marks a way of computing the partial derivatives during training. When training a model in the feedforward manner, the input “flows” forward through the network layers from the input to the output. By backpropagation, the model parameters update in the opposite direction: from the output layer to the input one. Backpropagation, a strategy to compute the gradient in a neural network, is a general technique; it is not restricted to feedforward networks, it works for recurrent neural networks (to be introduced soon) as well [61].

ARTIFICIAL INTELLIGENCE IN ARCHITECTURE AND BUILT ENVIRONMENT DEVELOPMENT 2024: A CRITICAL REVIEW AND OUTLOOK, 2nd part: 2) State-of-the-art

Since around 2010, young enthusiasts combining information technology and architecture background have been trying to turn the attention of well-doing, mostly global-star architectural studios towards AI’s potential contribution to architectural design or, better to say, disclose where such a contribution might stem from and what it might consist of. In 2020, DeepHimmelb(l)au - a video of a journey through an imaginary landscape of Coop Himmelb(l)au-like building forms - came into existence. The result of the elaboration of datasets comprising reference images of geomorphic formations on the one hand and actual Coop Himmelb(l)au projects on the other by CycleGAN and other forms of AI technologies provided "machine hallucinations" [10, 11, 12] - represented prevailingly in two dimensions, substantially lacking both spatial comprehensivity and the for architecture inherent interconnectedness of the experiential (poetic, in other words) and material attributes.

ARTIFICIAL INTELLIGENCE IN ARCHITECTURE AND BUILT ENVIRONMENT DEVELOPMENT 2024: A CRITICAL REVIEW AND OUTLOOK, 1st part

Architectural practices and the planning and management of built environment development lag in adopting artificial intelligence (machine learning techniques, more correctly), and the sparse tools and approaches implemented provide only marginal contributions. A contrast reveals not only comparing to other industries and creative disciplines but to the opportunities at hand. The paper evaluates the situation both in the context of the AI field and in the sector of architecture and the built environment, points to the causes of the sector´s current setup in terms of the starting points of creativity, the technologies used, and approaches to their development, as well as in terms of the economic, social, and political framework, subsequently introduces the opportunities to overcome the falling behind, and outlines the paths. Across the paper, the critical review applies three fundamental perspectives: authentic, poetic creativity that passes and precedes parameterization and algorithmization, second, novel, in architectural designing not yet applied learning strategies and training approaches, and third, concurrence of the fundamental three- and more-dimensional spatiality of both architecture and recently developed virtual reality technology, as well as the new theory of human thinking and intelligence that waits for implementation in machine learning (together with other novel computing approaches). Given the coincidence of the three aspects, a singularity is predicted for the next development of architectural craft and field.

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