Description:
Key Words:
Solar Design,Bio form-finding,CA aggregation,Human-machine Intelligence
Required Skills:
basic rhino/grasshopper basic knowledge
Required Software:
Rhino, grasshopper, GhWasp, Rabbit, Ladybug, Visual Studio, Python, Houdini
Required Hardware:
A computer with discrete gpu (recommended) with 8GB ram (16 recommended)
Maximum number of participating students:
24
AI In+form is a workshop focusing on building the relationship between rationalizing energy circularity and human-machine interaction. More specifically, it stands at the convergence of artificial intelligence, information feedback, form-finding, and socio-economic inclusivity. In the search for tools that fall between the established fields of expertise, the collective collaborates with professionals across cultural and disciplinary boundaries, from natural to social sciences. This workshop aims to build interrelations between energy problem mapping, bio-inspired form-finding, and human-machine interactions to reflect and rethink the future of design.
The approach of this workshop emphasizes options over optimal solutions, channelling this to diversify solar designs that can be aggregated for diverse building topologies. Instead of generating one design to be used for every solar problem, this workshop focuses on AI informed workflows that generate families of designs that can be fabricated in similar methods using the same mathematical conception. How to enable variational designs that win over diverse solar vectors and environmental conditions by adaptivity without contributing to a competitive light-harvesting model to its surroundings? This frames design as an iterative decision-making strategy that feedback between input/output of human-machine, which demands a studying of tasks within a design process and how they can be distributed between designers/algorithms that are each better different tasks.
This workshop empowers participants with an understanding of readily available digital design tools:
1-Problem identification/mapping (from socio-economic to climatic).
2-Translating bio-inspirations into parametric-enabled visual algorithms.
3-The practice of iterative negotiation between designer intuition and algorithmic generation, specifically, will study AI as rule-based systems (i.e. CA, GA) and Machine Learning (ML), which helps automate the translation between voxel-pixel data input/output.
This workshop hopes to prompt discussions around the relationship between nature/human/machine and the future roles of architects as designers through theoretical and technical means.