This
is the last episode of our 3-part mini-series, the second of which, I
concluded with the following words:
“As
we have been discussing so much about digital design and
architecture, I should say that nowadays,
AI technology is becoming so popular and widely used in the design
industry. It is so phenomenal, and its rapid growth far exceeds all
the recorded advancements in the world of digital technology. It is
so imminent that even non-professionals can create something that appears professional to many, not only in architecture and interior design, but in almost every field: from song composition and music arrangement to creating artworks, graphic design, and
creative writing, among many others. Now, we couldn’t hide the fact
that it seems so threatening for designers like us, huh? What do you
think? Let’s talk more about it on the last episode of this series.
Thanks for following.”
As
I have always said, “I have nothing against the digital method...” and now we have finally arrived at the most awaited discussion on the
role of Artificial Intelligence (AI) in the field of Architecture. In
this article, we will explore the term “machine learning,” a subfield of AI, based on a conference presentation written by Mr. Giuseppe Gallo, a PhD candidate in Architecture at the University of Palermo, Italy, and submitted to Academia.edu. In my interpretation, it is through the subfield of machine learning that
AI is being applied to the architectural design process.
OBJECTIVE
Although
AI is being highlighted in this article, I would still maintain my
full support for the importance of manual sketching in the design
process. After all, that is actually my main goal, or should I say, an advocacy that I have supported ever since I
started discussing this subject matter. Moreover, some parts of this
article shall serve as a commentary on the writings of Gallo in order
to help us understand more deeply how AI affects the
architectural design process. So, some excerpts may be expected
occasionally to meet our objective.
According
to Gallo, “The
proliferation of data together with the increase of computing power
in the last decade has triggered a new interest in artificial
intelligence methods. Machine learning and in particular deep
learning techniques, inspired by the topological structure of neurons
network in brains, are omnipresent in the IT discourse, and generated
new enthusiasms and fears in our society.” And
so, this article is somehow aimed at alleviating that growing fear,
especially to the professionals who have spent a lifetime of devotion to
the field using just their acquired knowledge and manual
sketching method.
DEFINING
ARTIFICIAL INTELLIGENCE
According
to Wikipedia, “Artificial
intelligence (AI) refers to the capability of computational
systems to
perform tasks typically associated with human
intelligence,
such as learning, reasoning, problem-solving, perception, and
decision-making. It is a field
of research in computer
science that
develops and studies methods and software that
enable machines to perceive
their environment and
use learning and intelligence to
take actions that maximize their chances of achieving defined
goals. Such machines may be called AIs.”
For
me, I still find definitions on Wikipedia sound a little technical
in nature, and you still need further reading or more research efforts
to get the definition you
need, regardless of the specific purpose. I have read the definition
of AI in the Academia.edu article I am referring to, but somehow
found it a little profound, receiving a combination of opinions from
different architects being interviewed at the conference. Most of
them are pointing to different subfields of the subject AI, and so,
it prompted me to consult with ChatGPT about the different subfields
of AI, and here is exactly what I got (exactly what the selected
architects interviewed by Gallo are trying to say):
Artificial
Intelligence (AI) is a broad and diverse field with many subfields.
Each subfield focuses on different aspects of intelligence and
problem-solving. Here are some of the major subfields of AI:
1.
Machine Learning (ML)
•
Definition: A subset of AI that enables systems to learn and improve
from experience without being explicitly programmed.
•
Types:
○
Supervised Learning: Learning from labeled data to make predictions.
○
Unsupervised Learning: Learning from unlabeled data to identify
patterns or structures.
○
Reinforcement Learning: Learning through trial and error, receiving
rewards or penalties based on actions taken.
○
Semi-supervised Learning: Combines labeled and unlabeled data for
learning.
○
Deep Learning: A type of machine learning based on neural networks,
especially deep neural networks.
2.
Natural Language Processing (NLP)
•
Definition: Focuses on the interaction between computers and human
language.
•
Applications: Machine translation, sentiment analysis, chatbots, text
summarization, speech recognition, and more.
•
Tasks: Syntax and semantics analysis, text generation, language
translation, etc.
3.
Computer Vision
•
Definition: AI field that enables machines to interpret and
understand the visual world.
•
Tasks: Image recognition, object detection, facial recognition, video
analysis, and image generation.
•
Applications: Autonomous vehicles, medical image analysis, security
systems, etc.
4.
Robotics
•
Definition: Focuses on creating intelligent machines that can perform
tasks autonomously or semi-autonomously.
•
Applications: Manufacturing robots, drones, medical robots, service
robots, and more.
•
Sub-disciplines: Kinematics, control theory, and robot perception.
5.
Expert Systems
•
Definition: AI systems designed to emulate the decision-making
ability of a human expert in specific domains.
•
Components: Knowledge base, inference engine, and user interface.
•
Applications: Medical diagnosis, technical troubleshooting, financial
planning, etc.
6.
Cognitive Computing
•
Definition: AI systems designed to simulate human thought processes
and decision-making.
•
Related Technologies: Natural language processing, machine learning,
and reasoning.
•
Applications: Personal assistants (like Siri or Alexa), decision
support systems, and intelligent automation.
7.
Knowledge Representation and Reasoning (KR&R)
•
Definition: Focuses on representing information about the world in a
form that a computer system can use to solve complex tasks.
•
Tasks: Logic-based reasoning, ontologies, semantic networks, and
probabilistic reasoning.
•
Applications: Question answering, inference systems, and AI reasoning
tasks.
8.
Artificial General Intelligence (AGI)
•
Definition: A hypothetical AI system that can perform any
intellectual task that a human can do.
•
Research Goal: Achieving human-like cognitive abilities in machines.
•
Current Status: Still theoretical and not yet realized.
9.
Fuzzy Logic
•
Definition: Deals with reasoning that is approximate rather than
fixed and exact, mimicking human reasoning.
•
Applications: Control systems, decision-making, and systems where
precision is not required.
10.
Swarm Intelligence
•
Definition: Inspired by the collective behavior of decentralized,
self-organized systems (e.g., bird flocks, insect colonies).
•
Applications: Optimization problems, robotic control, and network
design.
11.
AI Ethics
•
Definition: The study of ethical issues related to AI development and
usage.
•
Issues: Bias in AI models, data privacy, accountability, AI fairness,
and ensuring AI systems are aligned with human values.
12.
Neural Networks
•
Definition: A computational model inspired by the way biological
neural networks in the brain process information.
•
Applications: Deep learning, image recognition, natural language
processing, and more.
13.
Automated Reasoning
•
Definition: Focuses on the automation of logical reasoning and
proofs.
•
Applications: Theorem proving, verification of software/hardware
systems, and automated decision-making.
14.
Human-Computer Interaction (HCI)
•
Definition: The design and use of computer technology that interacts
with human users.
•
Applications: User interfaces, voice assistants, and smart devices.
These
subfields often overlap, and researchers may work in multiple areas
simultaneously. Together, they contribute to making AI systems more
capable, adaptive, and intelligent.
I
think I am now finding myself getting too far from my real
objective.
But
somehow, this time I just have to use my
intuition to simplify
the definition.
Let’s just begin
with
a machine that can think like
a human.
Perhaps now, you are thinking about a robot. But this robot, although
it can talk, perhaps write, or communicate with us in
any way it can, still, the information it could provide will depend on
the data received
from
humans, initially from its creator or inventor, then from the users
themselves. The learning process could be both supervised and non-supervised, allowing the
AI to process available data by itself or while being trained by the
inventor or the users. In
the long run,
both the AI and the users tend
to benefit
from each other and
develop a reciprocal and infinite
learning process.
Now,
in the light of architecture, I would rather attribute the
traditional sketching to human ability and the parametric
architecture to AI ability. For
me, both are useful tools in the design process and cannot be
separated from each other. Well,
by
integrating some
of the
technical definitions initially
derived from research,
I hope I am able to satisfy the objective
that
we
are trying to reach here.
WILL
AI BE USEFUL IN ARCHITECTURE?
This
was an important question Mr. Gallo asked when he interviewed ten
architects of different specialties from
February to July 2019.
It somehow turned into a survey where the participants tend to vote
on certain categories. So, to cut the story short, here’s a table and an excerpt of the result:
“Machine
Learning is the technology that obtained the highest score with a
total of 53 out of 70 achievable points, followed by digital
manufacturing with 51, third “other computational
methods” with 47, then Internet of things with 38, BIM and
Augmented Reality with 37, last Virtual Reality with 30. It is
interesting
to note that Machine Learning and "other computational
methods" both obtained the first
place in the personal rankings of the designers four times,
as well as happened twice for BIM and once for Digital Manufacturing.
It is therefore clear that based on the experiences and expectations
of the interviewed designers, machine learning and its derivations
are expected to play a role within the architectural practice,
a role that, for many of the interviewees, will be decisive in ten
years.”
Such a piece of concrete information somehow supports my own definition of AI, as I have provided above, and has somehow satisfied the objective
of this article. However, if you still need further clarification, let's discuss further, and please feel free to leave a comment.
EXPECTATIONS
OF AI TECHNOLOGY IN ARCHITECTURE
It
has been five years since Gallo conducted his research work, and
the above survey was projected for ten years. Here’s an excerpt:
“Architecture
is a complex practice. On the contrary, sectors where Artificial Intelligences are showing an important impact, have a more linear
nature than that of our profession. The interviewee goes on saying
that, by breaking up the architect's work into separate tasks,
describing the process rigorously, it is easier to imagine an AI
capable of solving these operations individually. It is therefore
important to ask ourselves several questions: Are we able to manage
these enormous potentials to generate new concepts and ideas? Can we
describe this complexity so that a machine can process it? Maybe in
the future.”
Now
that we are actually more than halfway through the projected time of the
survey, do you think that at that time, they might have overlooked
the capability of AI technology in the field of Architecture? The way
I experienced it, it came so drastically at a very high speed, that
even non-professionals could produce professionally looking
products on their own. I guess that’s where it becomes intimidating
for the professional community.
AI
may sound intimidating due to the unpredictable speed in terms of the
development of technology. However, as we continue reading Gallo’s
research, he says:
“It
is therefore still too early to understand how much these
technologies will erode from an architect's professional practice,
and certainly nothing in terms of responsibility. In this sense,
Arthur Mamou-Mani declares that even by using AI, designers retain
the right to control the design process at any time, making choices
and questioning answers provided by artificial intelligence.”
This
was exactly what I was trying to point out as I concluded in my first episode:
“In
the next episodes, we can expect AI to enter the arena. Oh well,
let's just welcome it, but I believe we should not let it
dominate the show. Instead, let
us use our own creativity and use AI as
a modern tool only that we have full control of. Use
it to enhance our own ingenuity, nothing more, nothing
less.”
Now, it seems like the majority of the comments I heard from other famous architects add up to my confidence that I am on the right track when I say I support the advocacy of retaining the manual sketching method in the design process. For me, this is some sort of sustainability that matters in the field of architecture.
FINAL
THOUGHTS
In the first episode of this mini-series, I mentioned in my conclusion that "it
is the cultural identity and the sense of originality of the
architect or artist that I want to emphasize
and preserve
in this endeavor. The
bottom line is that we should stop arguing about which one is best.
Let's discuss this with a sense of balance." That
was when I discussed the integration of computer technology in the
manual sketching method used in the design process. Well, I would say
it would be the same thing in the use of AI technology. Instead of
being intimidated, let us be confident that AI is a helpful tool in
our professional practice. If we could train it, then we could definitely control it. Let’s be friends with them, a new colleague
whom we can trust and rely on based on accuracy and consistency. But
what about loyalty? Oh well, don’t you dare compare them to a
colleague next to your cubicle. Just kidding aside...
Thanks
for joining me throughout this mini-series. Hoping we could have more
of this...what do you think?
JOEY CASTANEDA, Architect
CITATIONS:
Thanks to Wikipedia for the initial definition of "Artificial Intelligence."
https://en.wikipedia.org/wiki/Artificial_intelligence
Some
excerpts and a table derived from a research work submitted to Academia.edu are as follows:
The
role of Artificial Intelligence in architectural design: conversation
with designers and researchers
By Giuseppe
Gallo and fulvio
wirz
https://www.academia.edu/44902106/The_role_of_Artificial_Intelligence_in_architectural_design_conversation_with_designers_and_researchers?nav_from=f119e482-b1bb-4831-ad11-df870f718f49
Photos
courtesy of Pixabay.
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