Course Syllabus

In 2022, OpenAI's ChatGPT and DALL-E popularized the latest milestones in computational techniques called "generative artificial intelligence," which have since captivated the world with remarkable capabilities. Simple text prompts can now appear to write essays, perform deep research, reason deductively, generate striking imagery and video, and even author and debug code.

Today, so-called "AI agents" are performing even more complex tasks – often with questionable quality – that are disrupting the nature of work, upending our notions of workplace roles, and forcing us to refine our understanding of the value of human labor and authorship.

At the same time, AI already pervades the built environment. Government agencies partner with companies like Flock.ai to track and identify cars on public roadways; autonomous vehicles and dog-like robots can continuously map environments in real time; environmental sensors record changes in air quality and climate; tech companies listen to and monitor our homes; and satellite data feed increasingly sophisticated predictive models of urban and natural growth and destruction.

At the frontier of AI's capabilities is spatial design. Spatial designers (architects, urban planners, etc.) face particularly complex problems when proposing changes to the built environment, a task which at best requires anticipating the consequences of such decisions, for the inhabitants of our buildings and cities in its immediacy but longer term economically and socially.

But current software tools don't intrinsically carry robust semantics of "space;" while useful, they rather focus on manipulating 3D geometries, modeling building componentry, or mining measured physical data.

"Spatial AI" refers to artificial intelligence as applied to spatial reasoning employed in the design, operation, and occupation of space. Spatial AI could enable designers to develop techniques that make the semantics of spatial design propositions the core of a critical creative medium, rather than relying on the features of commercial software packages.

Starting with what we know best, this seminar will explore the potential of 3D and physical AI to facilitate insights, decisions, and predictions for problems involving higher-level spatial reasoning. That is, _can we, spatial designers, imbue an AI agent with spatial reasoning skills?_

In this course, students will:

- explore the definitions, affordances, and inner workings of generative and discriminative artificial intelligence,
- scrutinize canonical writings from relevant technological, architectural, and computational theories regarding notions of "space,"
- experiment with the rapidly evolving landscape of AI methods, including spatial / vision language models, computer vision algorithms, AI agents, robotics simulations, and more,
- develop a critical and technical understanding of the technologies, and
- speculate on new spatial AI methods at human, architectural, and urban scales.

New AI methods are introduced weekly using modern platforms, services, and languages (Python, HuggingFace, OpenAI, Google AI Studio).

Class sessions each involve a brief lecture, an intensive technical workshop, and student presentations, with readings and technical prep work in-between sessions.

## Recommendations

+ Experience with Python or another programming language is preferred.
+ Students should plan to bring their own laptops to class.

## Setting Expectations

This course supports an environment to experiment, invent, and develop a series of ideas together. It's not about becoming an "expert" in a fixed set of skills.

==Expect more questions than answers.== The course uses several frameworks to structure our experiments. We will ground them with a conceptual and technical understanding of today's AI technologies alongside various notions of "space," using them as creative inspiration. We will learn by doing.

==Expect that AI will be inconsistent and nondeterministic.== If your projects aren't producing consistent results, this is expected. We are researching and inventing, meaning we're investing our time and effort, but we don't quite know what might result.

==This course is quite technical==, so keep in mind that you'll need to invest the proper effort to keep up. We do have generative coding tools at our disposal that will help, but they aren't perfect.

==The hope for the course is that you will understand how AI really works and what it _really_ is==, and that you will gain a fundamental perspective on spatial design relevant to architecture and urbanism, one that presents an alternative to the tool-based approaches that are perhaps more akin to data journalism or data science.

## Learning Objectives

By the end of the course, you will:

- be familiar with concepts of space, 
- know how to engage modern AI platforms (more than just chatbot products), 
- know how to construct basic computational models that represent spatial reasoning.