In this op-ed, Nathan Miller demonstrates the ease and efficiency for generating AI content to 'fabricate a design process.' Is this a race to the bottom for design?
My Project Doesn’t Exist
Why is AI adoption different from past digital transformations in design?
AI adoption in design differs from past digital transformations like CAD/BIM. AI is already familiar and doesn't require expertise, making guardrails and governance essential. Successful AI strategies need broad stakeholder engagement, including non-experts, to discover valuable uses. Though potential is high, the actual business impact of AI is not yet widely measured. Businesses must define measurable outcomes and strategies for managing change to realize value.
Weekly Workflow: Use LunchBox ML to Predict Solar Radiation
LunchBox includes Machine Learning components (LunchBoxML) that make Accord.NET and ML.NET workflows accessible within Grasshopper. These components allow users to train, save, and test machine learning models using a variety of algorithms. This example uses LunchBox’s Regression Trainer alongside Ladybug’s environmental modeling tools to predict incident solar radiation for an irregular surface.
Weekly Workflow: Train an Algorithm to Label Spaces for You
Labeling spaces can be a tedious process, but Machine Learning tools have the potential to give designers reprieve from manually typing in each space name. Classifier algorithms are able to reference training data and group lists of new elements into classes based on their characteristics and similarities to the data on which it was trained.