How will human and machine collaboration evolve, in the mold of AI as an emergent technology, to shape autonomy, agency, and indeterminacy in architecture?

RESEARCH PROBLEM

The contemplation of the human-machine collaboration and its potential in revolutionising the field of of architecture is highly anticipated, discussed, and sought out, yet the exact form and function of it is either in the form of scattered research or novels that fail to collaborate all the viable research; lacking a balance between research and vision to inform the audience. For example, in the works of Yuval Noah Harari the added description of future technologies, to speculate the change of humanity is based on Technology so far and study of human habitation and lifestyle as a major means of study; but lacks to describe the form and function of human-machine architecture, as compared to, desperation of technology, capitalism, medicine, anthropology, sociology and many other fields which can be used to help define future architecture.

Harari’s representation of growth in any industry

Diagram representing uncertainty in the field of architectural growth

HYPOTHESIS

Architecture or built forms are a reflection of our activities as they are shaped by them. Deciding where we lead emergent technologies can indicate systemic abilities for goal setting and operational strategy concepts closely associated not only with the performative aspects of architectural interventions but also with the nature of the techniques and tools that produce them.These terms might imply an enhanced relationship between architecture, its inhabitants, and its environment; a complex link between architecture, its production, and its tools; or a capacity of architecture to produce new conditions and grounds on which it could be engaged. This issue takes a closer look at artificial intelligence (AI) and human-machine collaboration in architecture.

Application of technology in the AEC industry is undergoing tremendous research, but is not out reached as the other fields. One of the main reasons for growth in technology in every other field in comparison to the AEC industry is due to the lack of motivation or collaboration, this is mainly because the plausibility of growing architectural research in making a change is a foreign topic to most common people. Breakthroughs in machine and deep learning have produced computational tools capable of interpreting only structured data, bits, pixels, and vectors. These systems are capable of becoming incrementally more robust and autonomous over time as they gather more information reinforcing success and failure.

In other territories, disciplines, and frontiers, AI advances are met with both excitement and trepidation. Their application threatens one of two polemics: augmentation or obsolescence.The possibilities and ramifications for more creative, artistic, or even “human” disciplines—where outputs are not linear or pre-conceived but negotiated and synthesised—are more contested and unknown. The power and potential of such approaches lies in their lack of specificity. The contributions to this explore a spectrum of novel design explorations and exploitations that illustrates the divergence and convergence of human, machine, and mixed agencies at the intersection with adaptivity, adjustable autonomy, and architecture.

Diagram representing similarities within different fields of research

HUMAN-MACHINE COLLABORATION

Architectural

• Potential – misbehavior analignment between intentions and design outcomes.

• Working link – unexpected and the indeterminate.

• Active approach – designing, evaluating, and materializing architecture, collaborating and partnering with our tools. • Execution – enactive process, adapting their collective actions to the requirements of their situation.

• Machine (Tehnical Agent) – Human (Information) – As this process matures, delineations between virtual and physical become blurred.

• Evolution of tools > Emancipication – “A Framework for Generating and Evaluating Façade Designs Using a Multi-Agent Systems Approach” by Evangelos Pantazis and David Gerber.

Non architectural

• Evolution of tools > Emancipication (within gaming to Siri/Alexa) – the communication and collaboration between humans, devices, machines,and systems enable a shared learning process and the establishment of collective knowledge.

CONTROL VERSUS AUTONOMY

Architectural

• Drivers – degree to which information gathering and processing is impacting design workflows.

• Considerations – Functional organization, behavior, utility, and aesthetics are considered in relation to each other.

• New vision – as an integrative tool of active production not only of material conditions but of new forms of information.

• Execution – interdependent associations between hardware, software, and ideas. • Simulating > real-life conditions – “Subtractive Digital Fabrication with Actual Robot and Virtual Material Using a MARI Platform” by Ebrahim Poustinchi.

Non architectural

• Simulating > Opportunity – “Digital Provenance and Material Metadata: Attribution and Co-Authorship in the Age of Artificial Intelligence” by Shelby Doyle and Nick Senske.

REFRAMING FORM AND FUNCTION

Architectural

• Drivers – qualitative versus quantitative interplay elicits new dimensions of agency.

• Utilty, functionality and poetic > Learnig (served|servant) – “Shattering the Black Box: Technicities of Architectural Manipulation” by Stavros Kousoulas.

• Considerations – technical solution > feedback < creative process

• New vision – promise enhanced performance through their emancipation from previous dependencies. • Simuating > Form to Function (interdependant) – “Artificial Intelligence in Architecture: Generating Conceptual Design via Deep Learning” by Imdat As, Siddharth Pal, and Prithwish Basu.

• Execution – programmable adjacencies and dispositions, as well as latent poetic and uplifting parameters.

Non architectural

• Simuating > Form to Function (independant) – “Digital Provenance and Material Metadata: Attribution and Co-Authorship in the Age of Artificial Intelligence” by Shelby Doyle and Nick Senske.

• Analytics > new representational language – “Deep Learning in Design Workflows—The Elusive Design Pixel” by Ranjeeth Mahankali, Brian Johnson, and Alex Anderson.

WORKPLAN

BIBLIOGRAPHY

NON ARCHITECTURAL

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STUDENT: Owaze Ansari.

Program