Kyle Steinfeld

Almost Home ∈ The MLxART Collection

Kyle Steinfeld

Work related to the Almost Home project and the Fresh Eyes toolkit was featured in the MLxART Collection, an online showcase of "creative machine learning experiments" curated by Emil Wallner at the Google Arts & Culture Lab.

Fresh Eyes - CAAD Futures

A Framework for the Application of Machine Learning to Generative Architectural Design, and a Report of Activities at Smartgeometry 2018

Kyle Steinfeld, Kat Park, Adam Menges, Samantha Walker

This paper presents a framework for the application of Machine Learning (ML) to Generative Architectural Design (GAD), and illustrates this framework through a description of a series of projects completed at the Smart Geometry conference in May of 2018 (SG 2018) in Toronto.

Fresh Eyes - Toronto Exhibition

Kyle Steinfeld

In work exhibited at the University of Toronto in the context of the 2018 Smart Geometry Conference, three-dimensional architectural massings for single-family homes are generated by a generative adversarial network. This GAN is trained on a small dataset of three-dimensional models of homes falling into seventeen architectural styles, and that are represented as multi-view heightfield images.

Fresh Eyes - Smart Geometry Workshop

Hosted by the University of Toronto

Adam Menges, Kat Park, Kyle Steinfeld, Samantha Walker

This workshop cluster offered at the 2018 Smart Geometry Conference in Toronto was the initial catalyst for the Fresh Eyes project, and was the first incorporation of user-generated image recognition models into the evaluation step of a traditional generative design workflow.

This project uniquely links the familiar parametric environment of Grasshopper with cloud-hosted models trained using a user-friendly ML graphic programming environment that runs Tensorflow.

Fresh Eyes - Universität der Künste Berlin Workshop

Applying machine learning to generative architectural design

Adam Menges, Kat Park, Kyle Steinfeld, Matt Turlock, Nono Martinez Alonso

This workshop offered at the Design Modeling Symposium in Berlin presents tools and techniques for the application of Machine Learning (ML) to Generative Architectural Design (GAD).

Fresh Eyes for Grasshopper

Kyle Steinfeld

A toolkit for connecting parametric models in Grasshopper with hosted image classification machine learning models.