Ai: The Latest Architecture and News

Designer and Fulbright fellow Stanislas Chaillou has created a project at Harvard utilizing machine learning to explore the future of generative design, bias and architectural style. While studying AI and its potential integration into architectural practice, Chaillou built an entire generation methodology using Generative Adversarial Neural Networks (GANs). Chaillou's project investigates the future of AI through architectural style learning, and his work illustrates the profound impact of style on the composition of floor plans.

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Is Artificial Intelligence (AI) the doom of the architecture profession and design services (as some warn) or a way to improve the overall design quality of the built environment, expanding and extending design services in ways yet to be explored? I sat down with my University of Hartford colleague Imdat As. Dr. As is an architect with an expertise in digital design who is an assistant professor of architecture and the co-founder of Arcbazar.com, a crowd-sourced design site. His current research on AI and its impact on architectural design and practice is funded by the US Department of Defense. Recently we sat down and talked about how this emerging technology might change design and practice as we now know it—and if so, would that be such a bad thing?

This article was originally published as "Doom or Bloom: What Will Artificial Intelligence Mean for Architecture?" on CommonEdge. It has been slightly abridged for publication on this platform; the full interview can be read on CommonEdge here.

Programmer Joel Simon has created an experimental research project, Evolving Floor Plans, to explore speculative and optimized plan layouts using generative design. Interested in the intersection of computer science, biology and design, Joel organized rooms and expected flow of people through a genetic algorithm to minimize walking time, the use of hallways, etc.

The creative goal is to approach floor plan design solely from the perspective of optimization and without regard for convention or constructability. The research aims to see how a combination of explicit, implicit and emergent methods allow floor plans of high complexity to evolve.

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Amazon’s innovative, checkout-free convenience store concept, Amazon Go, has opened to the public in Seattle.

Located in the base of an existing Amazon office building, the 1,800-square-foot (167-square-meter) store offers grocery and convenience items. To begin shopping, customers simply scan an Amazon Go smartphone app and pass through a turnstile.

Using machine learning, computer vision and artificial intelligence technologies (incorporated into the software powering cameras and weight sensors), the store can then track the actions of customers as they remove items from the shelves, creating a virtual shopping list as they go. When a customer is finished shopping, they simply exit the store through the turnstiles and the user’s Amazon account is automatically charged.

Benjamin Bratton, Professor of Visual Arts and Director of the Center for Design at the University of California, San Diego, is the new Programme Director at Moscow's Strelka Institute. The New Normal is based on the premise that "something has shifted. [...] We are making new worlds faster than we can keep track of them, and the pace is unlikely to slow."

Have our technologies have advanced beyond our ability to conceptualize their implications? "One impulse," the course advocates, "is to pull the emergency brake and to try put all the genies back in the bottle." According to Bratton, this is hopeless. "Better instead to invest in emergence, in contingency: to map The New Normal for what it is, and to shape it toward what it should be."

The following essay by Carla Leitão and Ed Keller was first published by Volume Magazine in their 49th issue, Hello World! You can read the Editorial of this issue, Going Live, here.

What are the philosophical consequences of automation after the integration of pervasive AI into the architecture, landscapes and cognitive maps of our planet and its populations? We suggest that "natural models" of automation pre-exist our technology, with profound implications for human and planetary systems. We’re interested in specific examples and models outside of our cultural milieu that test the limits of bodies, that map habits and their disruption through noise, and reframe the relation between life and consciousness. The following examples index the performance of networks in tight cycles of feedback loops: machines teaching machines. To go to the root of the philosophical consequences of automation our path is through abstract and universalist models of ‘natural laws’, redeployed into specific local situations. We use the term ‘drive’ for its myriad implications connecting across the examples we have chosen.

The following essay by Nick Axel (Volume's Managing Editor) first published by the magazine in their 49th issue, Hello World!

With the rise of computational networks and power, cognitive models developed and debated over in the postwar decades have finally been able to be put to work. Back then, there was a philosophical debate raging alongside the burgeoning field of computer science theory on the nature of consciousness, in which machines of artificial intelligence served as a thought experiment to question humanity. Yet with the proliferation of data and the centralization of its archives, theoretical practice moved from conceptual experiments to empirical tests.