While we will present the work
completed by this workshop cluster momentarily,
we'd like to begin with a brief discussion
of the thinking that produced it.

This was a year ago,
    which feels like a decade
    considering how intense and quickly
    things have been progressing in the ML world.

A year ago,
we came up with a strategy
to test one model
of integrating ML
into creative computational architectural design.  

It was one model,
which quickly forked
into other directions during the workshop
as well as by others
in the field during the past year.

As we have seen
    in previous moments
    in the history of design technology,
the early stages
in the adoption of a new technology
    is often marked
    by intense periods
    of experimentation
    and disruptions of existing technical and social frameworks.

Clarity is not easy to come by in such moments;

Those who seek certainty in these times
are quickly humbled.



---------------------

While we will present the work completed by this workshop cluster momentarily, we'd like to begin with a brief discussion of the thinking that produced it.

In short,
this cluster sought to test one model
for the integration of Machine Learning
and Creative Computational Architectural Design:
    a model that has continued to bear fruit
    over the past year.

However,
it should be noted that
we tested just one model
for this integration.

Given the nature of our current moment
    of intense interest
    and impressive progress
    in Artificial Intelligence generally,
we can be sure that this one model
for integrating ML and design practice
will not stand alone.

As we have seen in previous moments
in the history of design technology,
the early stages in the adoption of a new technology
is often marked by
    intense periods of experimentation
    and disruptions of existing technical and social frameworks.
Clarity is not easy to come by in such moments;
Those who seek or who claim to have found certainty
in these times are quickly humbled.

The thought of being able to train a computer
to synthesize images,
    to "draw" pictures
    with such fidelity,
... this is more than a little jarring.

However,
    taken from a creative authorship point of view,
    it is also quite inspiring.

And so,
one important contribution of the cluster
    involves the developing of methods
for **describing architectural forms and spaces as images**
    in ways that allow the salient qualities
    of form and space
    to be captured by our critic

To secure
    this new locus of subjectivity,
and to offer participants
    the capacity to train their own models,    
we partnered with a company called Lobe.ai
    which sponsored our cluster

Lobe provided an essential platform
    for training ML models
    relevant to architectural evaluation.

As we see here,
Lobe is a web-based visual tool for
    constructing models
    for training them
    and for allowing them to serve up predictions

An anlaogy might be helpful:
    as Grasshopper is to Rhino
    so Lobe is to Tensorflow

So,
to illustrate how these pieces go together,
we see in this animation an actor and critic
coming together on a stage.

Here,
a critic is trained on 3d models that describe
typologies of detached single-family homes
    cape cod house
    shotgun
    dogtrot
    etc..

The job of the critic
    is to evaluate the performance of an actor,

In this case,
our actor generates house-like forms,
    such as the ones we see flashing by
    in this animation.

These two intelligences
are brought together in an optimization,

wherein the actor
generates new potential house forms,
these forms are scored by the critic
    in terms of how much they resemble
    a known type of house,
        such as the California Eichler style
        shown here
and then the process iterates
in a classic optimization.

and so,
by modestly adjusting
the nature of the evaluation step
    of the generative design process,

we find a way forward
from optimizing for **quantifiable objectives**,
    as is typical in generative design
to optimizing for more **qualitative objectives**,
    such as architectural typology
    or spatial experience

These qualitative objectives
    need not be strictly rational.

They might be more whimsical,
    as demonstrated by the Actor-Critic relationship
    shown in this animation.

Here,
an actor that generates balloon-like forms
separately attempts to please two critics:
    one that prefers zucchinis,
    and a second that prefers bananas.

So this is the framework
that was introduced to cluster participants,
who, over the course of the four-day workshop,
    probed
    extended
    exploded and re-assembled
this framework toward a variety of individual ends.

Embodying the Intangible: | Recognizing Experiential Qualities of Space | TODO: layout two-figure slides

One of the most interesting value ML offers to the design world is its ability to embody Quali - things we can't quite describe with words (and therefore script into explicit code) and as designers we are constantly thinking imagistically and qualitatively.

Some of the workshop participants wanted to craft an optimization that speaks to this experiential concept, such as what it feels like to occupy various types of forests. This particular project here extended our isovist representation to attempt to capture the spatial experience of scale.

There are experiments by others who used ML to capture the intangible - like styles of art and architecture.  These are a bit easier since they are artists with distinct styles and we have numerous data but I think the next step is to try to embody the critique that designers engage in, such as what makes a space work or a plan that has a nice flow.

Feature Detection in Floor Plans

the work we see here
took a deep dive into feature detection
in images of floor plans
with the goal of
eventually generating these graphics
artificially

This is a clear initial application of ML in design that many are attempting in architecture - but since the image of our plans are a representation of the final product, not actually the final product in the way art or music is, there is an extra step necessary to make ML actually relevant here. The symbolic representations we use in plans is coded as colors in this example from GSD which allows the ML algorithms to translate those colors to different objects or spaces.

Shaping Tall Buildings for Wind Effects

Some participants were interested in
focusing just on a configuration of an actor,
pitting a parametric model that they made
against an existing critic,
in this case one that was trained to predict
the performance of tower forms under wind loads

Replacing a time-consuming or costly simulation
with a trained ML model
is another popular direction that people are looking into

here you see CFD analysis results that were used as training sets but there are others doing the same with EUI prediction.

These are all simulations that have a large impact on the design in practice, especially early on but take longer to generate so by the time results are returned, the designers have moved on.

With trained Machine Learning model an API call away, people in practice are trying to reduce this time factor hurdle.

Insider View: | A 3D visualization of what a critic sees

to conclude,
another group was interested
in better understanding
how our critics "see" architectural form
and how this is different
from the way we see it.

to explore this question,
they "reverse engineered"
our raster representations of form
back to three-dimensional form
which validated that what the ML algorithm sees
is actually quite similar to what we see.

this work enabled an entirely new approach based on a Generative Adversarial Network or "GAN"

In short, a GAN replaces not just the evaluation step, but the generative step as well, such that actor and critic are both ML models.