AI-chitect – 人工智能建筑师 Future Architecture Lab – 未来建筑实验室 Artificial intelligence technology nowadays such as Generative Adversarial Neural Networks (GAN) is able to achieve human’s creativity to generate anything based on mechanism that set by human. The model mechanism consists of one “generator” that synthesizes “fake” samples from scratch, and a “discriminator” that evaluates the generated fake samples if they are “real (similar to a real sample)” or “fake (does not fit to any real sample)”. Generator and discriminator learn from each other, hence higher discrimination level ensures higher quality of generation. Such mechanism makes machine capable to synthesis images that are almost similar to what you can found in real world.
We need real sample dataset to train GAN model. Human needs to control the quality of samples as to ensure the generated output could be reflect on those real sample in some extent. This exhibition’s output is a new kind of “architectural representation”.Trained model needs new input as trigger to carry out generation. Input here normally is digital image, in this exhibition input will be a roughly sketch silhouette that drawn by human on a sketchpad. Based on input, machine could start its generation without human intervention. Hence, it is only machine itself to be participated in the most “creative” generation part, and human will not know what the outcome will be.
Uncertainty exists in generation phases; the unpredictable generated images might inspire one’s imagination or to provoke extensive thinking. Human do not limit themselves in interpretation from these generated images. Such process could analogous to sketching, where one could keep developing his or her design from the sketches on paper. Sketching involves communication between eyes, hand and brain, allowing design idea develops either from narrow to broad, or inversely. The installation is taken part in diverging one’s thinking. Hence, this indicates that, by integration of GAN, machine could assist human to broaden creative thinking. Besides, this interactive human-machine installation might also could “reconstruct” the design method or workflow that architects have been practiced for several centuries.
当今人工智能技术，如生成对抗性神经网络（Generative Adversarial Neural Networks, GAN）已能实现人的创造性，即根据人为预设的机制生成任何事物。该模型机制由一个“生成器“和一个”判别器”所组成，前者从零生成“伪”样本，后者则评估所生成的伪样本是否真实（与某真实样本相似）或虚假（不匹配任何真实样本）。生成器和判别器相互学习，判别水平越高，生成质量越高。这种机制使机器能够生成与现实世界几乎相似的图像。
Credits: Wanyu He 何宛余, Xiaodi Yang 杨小荻, Jackie Yong Leong Shong 杨良崧, Kan Liu 刘勘.
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