Significant others

Steinfeld, Kyle

doi:10.4324/9780367824259-2

Cited by 3 publications

(4 citation statements)

References 1 publication

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“…Looking at the responses of our participants, it seems that ML-powered tools for the early-stage design should function as provocateurs (or points of departure) for new designs, rather than as actors (partners and co-creators) or materials (the designers create and use the tools similarly to craftsmanship), in Kyle Steinfeld’s categorization (Steinfeld, 2021). 35…”

Section: Discussionmentioning

confidence: 99%

“…responses of our participants, it seems that ML-powered tools for the early-stage design should function as provocateurs (or points of departure) for new designs, rather than as actors (partners and co-creators) or materials (the designers create and use the tools similarly to craftsmanship), in Kyle Steinfeld's categorization (Steinfeld, 2021). 35 Some generated outputs presented features not included in the initial dataset (e.g., differentiated curves, see Figure 10). As a fact, we can argue that the framework produced unexpected results (incompleteness) that fit within the regulations we imposed on them (precision).…”

Section: On the Speculation: Navigating A Space Between Incompletenes...mentioning

confidence: 97%

“…As the project evolves, all the stakeholders involved in the design process make design decisions that help disentangle the complex problems faced in the beginning. responses of our participants, it seems that ML-powered tools for the early-stage design should function as provocateurs (or points of departure) for new designs, rather than as actors (partners and co-creators) or materials (the designers create and use the tools similarly to craftsmanship), in Kyle Steinfeld's categorization (Steinfeld, 2021). 35 Some generated outputs presented features not included in the initial dataset (e.g., differentiated curves, see Figure 10).…”

Section: On the Speculation: Navigating A Space Between Incompletenes...mentioning

confidence: 97%

“…34 Hansmeyer (2017) 32 suggested that we should see machines as our muse, as a partner in design, or as a tool to expand our imagination . Steinfield (2021) 35 categorized the use of machine learning tools in design, art, and architecture into three categories: machine learning as actor (models that co-design along with the designer), machine learning as material (generative models that provide new forms of design “material,” usually curated by a designer), and machine learning as provocateur (models that provoke new models of thinking that later serve as sources of inspiration).…”

Section: Architectural Design and Machine Learningmentioning

confidence: 99%

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Speculative hybrids: Investigating the generation of conceptual architectural forms through the use of 3D generative adversarial networks

Pouliou

Horvath

Palamas

2023

International Journal of Architectural Computing

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The process of architectural design aims at solving complex problems that have loosely defined formulations, no explicit basis for terminating the problem-solving activity, and where no ideal solution can be achieved. This means that design problems, as wicked problems, sit in a space between incompleteness and precision. Applying digital tools in general and artificial intelligence in particular to design problems will then mediate solution spaces between incompleteness and precision. In this paper, we present a study where we employed machine learning algorithms to generate conceptual architectural forms for site-specific regulations. We created an annotated dataset of single-family homes and used it to train a 3D Generative Adversarial Network that generated annotated point clouds complying with site constraints. Then, we presented the framework to 23 practitioners of architecture in an attempt to understand whether this framework could be a useful tool for early-stage design. We make a three-fold contribution: First, we share an annotated dataset of architecturally relevant 3D point clouds of single-family homes. Next, we present and share the code for a framework and the results from training the 3D generative neural network. Finally, we discuss machine learning and creative work, including how practitioners feel about the emergence of these tools as mediators between incompleteness and precision in architectural design.

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Section: Discussionmentioning

confidence: 99%

Section: On the Speculation: Navigating A Space Between Incompletenes...mentioning

confidence: 97%

Section: On the Speculation: Navigating A Space Between Incompletenes...mentioning

confidence: 97%

Section: Architectural Design and Machine Learningmentioning

confidence: 99%

See 2 more Smart Citations

Speculative hybrids: Investigating the generation of conceptual architectural forms through the use of 3D generative adversarial networks

Pouliou

Horvath

Palamas

2023

International Journal of Architectural Computing

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How we talk(ed) about it: Ways of speaking about computational architecture

Horvath

2022

International Journal of Architectural Computing

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If we understand architecture as a three-part system formed by the building, its image, or drawings and images describing buildings, and the critical discourse around architecture, then the texts or ways of speaking about architecture play a key role in understanding the field and its development. By analysing a corpus of around 4.6 million words from texts written between 2005 and 2020 that form a part of critical discourse in computational architecture (understood as the result of the intense digitalization of the field), this paper aims to map ways of speaking about computational architecture. This contributes to architectural theory and might help gain a better understanding of the evolution of the digitalization of construction in general. Findings show that computational architecture is surrounded by a specific way of speaking, hybridized with words from fields such as biology, neuroscience, arts and humanities, and engineering. While some topics such as ‘sustainability’ or ‘biology’ come up consistently in the discourse, others, such as ‘people’ or ‘human’, have periods when they are more and less popular. After highlighting open research questions, the paper concludes by presenting a map of periodic and recurring topics in ways of speaking about computational architecture over the last 15 years, thus tracking and documenting long-term trends, and illuminating patterns in the broader field of digital construction.

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Principles of Algorithmic Management

Stark,

Broeck

2024

Organization Theory

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Our title harks back to Frederick W. Taylor’s influential book. But our identification of the principles of algorithmic management is certainly not advocacy. Our task in this essay is to develop a theory of algorithmic management in relation to fundamental changes in the shape and structure of organization in the 21st century that are reconfiguring boundaries, roles, and relations among managers, workers, engineers, professionals, consumers, and other user categories. In particular, such a theory must be attentive to transformations in the topology of organization. Today, many of the most valuable actors, assets, and activities are not located within the firm but involve a complex entanglement of information flows, practices, and users. This topology gives rise to a distinctive challenge: how to manage when the most valuable assets and activities are not in the firm. Whereas actors in hierarchies command, in markets they contract, and in networks collaborate, on platforms they are co-opted. The co-optation of actors, assets, and activities is undertaken by algorithmic management. To grasp the distinctive principles of algorithmic management, we compare and contrast the features of its ideology and practices with those of scientific management and the more recent collaborative management. Algorithmic management, we argue, operates within a different organizational form, articulates a different ideology, and addresses different managerial problems with different governance principles along different lines of accountability.

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Significant others

Cited by 3 publications

References 1 publication

Speculative hybrids: Investigating the generation of conceptual architectural forms through the use of 3D generative adversarial networks

Speculative hybrids: Investigating the generation of conceptual architectural forms through the use of 3D generative adversarial networks

How we talk(ed) about it: Ways of speaking about computational architecture

Principles of Algorithmic Management

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