IaaC bits 1.3.1

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Implementing Advanced Knowledge


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Autopoietic Architecture – Can buildings think? Dennis Dollens

Autopoietic Architecture – Can buildings think? What follows is a theory-based proposition for research methods directed to support generative design in the context of IAAC’s Intelligent Constructions Design Studio. The text is formulated to underpin advanced architectural experiments and prototyping both theoretically and in practice. It’s a technologically enabled set of options for considering breakthroughs in science, informatics, robotics, mathematics and their importance to design. I argue that hybridized autopoiesis and extended cognition theories — what I call, autopoietic-extended design — accommodate methods of research and practice to reconceptualize technology and architecture as part of nature (Jonas 1966. Odling-Smee et al. 2013). The architectural quest becomes one of making systems, materials, and buildings biologically responsive while keeping in sight the arrival of living technology (Bedau et al. 2010. Rieffel et al. 2013. Spiller 2009). Living technology is defined as “based on the powerful core features of life . . . explained and illustrated with examples from artificial life software, reconfigurable and evolvable hardware, autonomously self-reproducing robots, chemical protocells, and hybrid electronic-chemical systems” (Bedau et al. 2010). In this frame, raw data from nature is scanned or streamed via technology (smartphones, apps, networks) for designers to interpret as information during in-field research phases. Input from nature is then critical to morphological, phenomenological, and metabolic functions generative architecture requires to explore the domains of biological intelligence (Clark 2008. Di Paolo 2005. Maturana & Varela 1980. Weber & Varela 2002). The fundamental points of engagement factor in visualization of nature, coding, drawing, modeling, and machine fabrication. Students observationally and technologically source design-relevant data to develop systems supporting their ideas for metabolically and synthetically intelligent buildings. To construct an OS process supporting such an endeavor, I track bio-computation and algorithmic simulation back to Allan Turing’s AI and morphogenetic research (Turing 1952). Thereafter, I date generative architecture to Turing’s research in order to provide a trailhead through which we approach bio-simulation and AI as they emerge in living technology (Langton 1988. Markoff 2013b. Modha 2013. Turing 1936). Deploying simulation and computation from nature then foregrounds Turing’s (1950) question: “Can machines think?” as parallel to: Can buildings think? With the second question, I evolve autopoietic-extended design as a research program from which we may investigate morphology, cognition, and computation for biologically performative architecture. 2

Before explaining theories, it is useful to recognize landmark biological and technological breakthroughs because they set markers impacting architecture from fields such as biomaterials, biorobotics, and biofabrication. For example, contemporary with this text, Stanford University and the J. Craig Venter Institute (Markoff 2012) announced a fully computational bacterium, and IBM (Markoff 2013) announced SyNapse chips (Systems of Neuromorphic Adaptive Plastic Scalable Electronic chips). Based on biological cognition, SyNapse are the first “cognitive chips” not programmed, but capable of learning (IBM 2011). And, the digital bacterium (Covert 2012) is a “simulation, which runs on a cluster of 128 computers, [and] models the complete life of the cell at the molecular level.” The Stanford/Venter model then provides scientists with “computerized laboratories,” from which they may experiment “without the need for traditional instruments” (Markoff 2012). In a landscape where biology and technology are evolving to reinterpret biological performance, industrial and robotic design are far in advance of architecture for recognizing sensory intelligence, networked communication, and synthetic life. To catch up, architecture needs experience with laboratory equipment supporting design initiatives and experimentation consistent with the observation that “traditional architectural and engineering ways of thinking about materials as something independent of form and structure are obsolete” (Weinstock 2006). Architecture further needs scientific imaginand

Figure 1 - Botanical Towers, Dennis Dollens

advanced scanning equipment of its own — new types of design labs, tools, and research facilities that are collaborative with existing fablabs to enable bio-experimentation linking visualization and theory. Interestingly, one potential model for such a lab is currently emerging between IAAC and Fablab Barcelona through their biologically and environmentally oriented Green Fablab at Valldaura. Synthetic biology, learning chips, and computational intelligence, or their equivalents, are then of theoretical and practical autopoietic and extended-cognition significance for architecture as ways of making buildings. Theories of autopoiesis and extended cognition are capable of focusing and influencing student’s conceptualizations, research, and resulting design. A multidisciplinary example supporting this view is Openworm.org. The research website functions through crowd-sourced participation, illustrating for generative architecture, the potential of virtual biological structure and organization sourced digitally (Fig 1). For the Openworm project, CAD designed structure (body), and bio-simulation (organs), come into being as online citizen science in ways some architectural research should emulate. If the synthetic bacterium and the SyNapse chips foretell lines of experimentation, they nevertheless exist as mainline research. I think we should also seek examples of emergent, yet equally rigorous new research. Living technology and biorobotics (Bedau et al. 2010. Webb & Consi 2001) exemplify potential non-conscious intelligence (AI, A-life, simulated bacteria) that even the most basic single-cell life possesses — but that no machine or building yet possesses. More controversial, but conceivably critical to metabolic architectural experimentation, entails finding alternative models to animal cognition and intelligence (and subsequent problems of consciousness) by focusing on plants to define intelligence and memory through the new discipline of “plant neurobiology” (Brenner et al. 2006. Gagliano et al. 2014. Pollan 2013).


Visualization of intelligent materials from nature and biology could, for example, influence the design of pliable building skins — able to fold like a leaf — as shape-shifting morphological facades demonstrating aerodynamic efficiency (Rieffel et al. 2013). In this respect, Rachel Armstrong has written about the role of synthetic biology where: “The building envelope could be constructed not with . . . traditional inert surfaces but with a ‘living’ cladding, which could house a range of synthetic-biology-based technologies” (Armstrong 2012 loc 483). With leaf performance as an example of nature-toarchitecture interfacing, buildings may themselves become environmentally relevant — embedded with biosensors and actuators that configure surface topologies and actions. Changing a building’s surface impacts architectural performance (Figs 2, 3) through functions such as light control, passive ventilation, air filtration, and aerodynamic stress (wind-load and sheer). Thereafter, changing the ontological state of a building from inert to intelligent (via bio- and syntheticlife materialization) changes its status in nature to favor autopoiesis. Performative facades are then indicative of next-generation architectural bioremediation and biorobotics — fully metabolic systems are a next step (Spiller 2009, Venter 2013). In this hypothesis, one living example is Physarum polycephalum, a single-cell amoeba that, under stress, self-assembles into a multicellular organism popularly known as slime mold. As an organism, it demonstrates emergent intelligence currently under investigation for living computational uses (Adamatzky 2013). For architecture, living robotics and living technology (Bedau et al. 2010. Rieffel et al. 2013) potentially align building functionality with both animate and machine intelligence for a degree of program autonomy through “wetware of a secondary class of living technologies” (Adamatzky 2013).

With biochips, simulated bacteria, and learning algorithms as near-future architectural components, I think as designers we need to conceptualize how to work with them. To this end, I read Humberto Maturana and Francesco Varela’s autopoiesis (1980) as a framework for living “machines.” Early on, Maturana and Varela’s thinking helped situate biology as systemic for AI and cybernetics. Openworm, robotics, SyNapse chips, and simulated bacterial life are current signposts that inorganic or synthetic biological intelligence may sooner-than-later approach ubiquity and lead us toward living technology and computation once acknowledged only in live organisms (Bedau et al. 2010. Modha 2013. Covert 2013). As examples of pervasive AI and then digital sensing we read first from The Atlantic and then from The New York Times: AI pervades heavy industry, transportation, and finance. It powers many of Google’s core functions, Netflix’s movie recommendations, Watson, Siri, autonomous drones, the self-driving car (Somers 2013). And of neuromorphic processors — chips that learn — we hear: “Instead of bringing data to computation as we do today, we can now bring computation to data,” said Dharmendra Modha, an I.B.M. computer scientist who leads the company’s cognitive computing research effort. “Sensors become the computer, and it opens up a new way to use computer chips that can be everywhere” (Markoff 2013b). Lifelike computing systems and reformulated AI are now emerging after decades of experiments theoretically supported, though not conceptualized, in Alan Turing’s papers: “Intelligent Machinery” (1948), “Computing Machinery and Intelligence” (1950), and “The Chemical Basis of Morphogenesis” (1952). Turing’s papers were hypothetical projections of future-possible computation and algorithmic simulation with far reaching implications for today’s technologies, scripted architecture, and fabricationby-code. From them I see the origins of generative architecture in the biological simulations Turing first programmed after joining the University of Manchester in September 1948 (Copeland 2013. Dollens 2014. Swinton 2004. 2011). Ideas from his research still grow, prompted by newer algorithms and

Figure 2 - Siggraph Tower, Dollens - Shlian Figure 3 - Performative design, Supervisor Dollens 6

computational power branching through iterations of his imitation-game (Turing Test) and reaction-diffusion morphogenesis (Reinitz 2012. Turing 1952); they support the reexamination of his question: “Can machines think?” (Dennett 1998. Turing 1950). Asked in “Computing Machinery and Intelligence,” Turing’s (1950) query was substantiated by later computational programming and computer simulations. John Reinitz wrote in Nature that algorithmic models from this period were: “the first computer simulations of [biological] pattern formation.” He continued: “What Turing should receive credit for is opening the door to a new view of developmental biology” (Reinitz 2012 464). “Computing Machinery and Intelligence” (Turing 1950) and his morphogenetic research (Turing 1952) quietly impacted 1960s and 1970s botanic coding (L-systems) and development of computational biology as instrumental for generative architecture (Floreano & Mattiussi 2008. Hayles 1999. Lindenmayer 1968. Prusinkiewicz & Lindenmayer 1990). Today, deep-rooted complexity and recursion (Luhmann 1990) mixes generative data feeding design, synthetic biology, algorithmic botany, and cognition in the environment; sometimes instantiating autopoiesis and living technologies in nature (Bedau et al. 2010. Clark 2008. Maturana & Varela 1980). Without direct reference, Maturana and Varela (1980) carried onward Turing’s search for machine intelligence and morphogenesis that included programming cellular automata (McMulling & Varela 1997. Varela et al. 1974) to study properties of autopoiesis (Langton 1988. Ray 1994. Turing 1952). Turing’s plant observations and simulations may thus be seen as origin points where translated data from nature to hand drawings, computer programming, computer simulations, and theory (Swinton 2004) informed attempts at computational autopoiesis. I discuss those morphological and embryological translations in an issue of Leonardo (Dollens 2014), and note them here as experiments channeling data from nature into biodigitalarchitectural information. Seen as foundational, Turing brings to generative design a computational heritage that includes machines, code, and biological simulation as they have been referenced and evolved into generative design and programming. Now, as much as anything, the acceptance of intelligent architecture is a matter of understanding that architecture could (wholly or partially) function biologically in nature, taking on animate roles in ecological remediation — buildings alive, but not conscious. Against the backdrop of Turing’s algorithmic morphology and pioneering digital simulations, Maturana and Varela’s autopoiesis (1980) should be factored in when biological properties and attributes are contemplated for generative and biorobotic design.

Maturana & Varela’s text charts a theory of life and cognition continually amended by later cognitive scientists, biologists, engineers, and philosophers (Di Paolo 2005. Weber & Varela 2002). Autopoiesis theorizes necessary minimal components and conditions to define, distinguish, and recognize life and cognition in nature and autonomous social systems (Maturana & Varela 1980). The amended theory followed, opening new pathways for AI and computational botany as well as to systems theory, cognitive science, and cybernetics (Clarke & Hansen 2009. Luhmann 1990. Hayles 1999). As I develop elsewhere, Maturana and Varela’s hybridized thesis, paired with the environmentally oriented hypothesis of extended cognition underpins autopoietic-extended design (Clark 2008. Di Paolo 2005. Maturana & Varela 1980. Weber & Varela 2002). From that foundation, my research results in a mobile OS methodology and practice supporting observational, computational, and biological architecture experimentation. In this context, two theoretical systems: 1) biological self-organization via autopoiesis (Maturana & Varela 1980) and 2), cognition-to-environment communication via extended cognition (Clark 2008), are hybridized to support design research methods where: . . . autopoiesis is a singularity among self-organizing concepts . . . close to strictly empirical grounds, yet provid[ing] the decisive entry point into the origin of individuality and identity, . . . connecting . . . into the phenomenological realm (Weber & Varela 2002). And where extended cognition stipulates that: It is possible that sometimes at least, some of the activity that enables us to be the thinking, knowing, agents that we are occurs outside the brain? . . . Minds like ours are the products not of neural processing alone but of the complex and iterated interplay between brains, bodies, and the many designer environments in which we increasingly live and work (Clark 2008). In the wake of these quotations, I forge autopoietic-extended design as a protocol (OS) for investigating biological input/output in the development of metabolic/intelligent generative architecture, materials, systems, and theory. Maintaining “origin of individuality and identity” (Weber & Varela 2002) is then important in order to recognize the thinking of the architect as autonomous and creative. Coded and scripted architecture needs to be registered and phased with living technology so that design processes and pressures play constructive roles sensed and driven by living functions. In this way, new buildings will range as cognitive extensions (Clark 2008), tuned (Coyne 2010) with the designer’s thinking, as well as with living technology matched to specific environments. Figure 4 - Botanical Tower, Dennis Dollens 8

Related to those processes, I listen to Katherine Hayles strategizing the transfer of theory into practice. We should, according to Hayles, be “using for different ends the very technologies applying this [generative] pressure” (Hayles 2012). In that statement, she supports subverting technology to experimental research goals. Subversion for generative architecture is ontologically feasible since machines and buildings exist in the same phylum (category) of human construction and may be conceptualized in related existential and “phenomenological realms.” In this view, evolutionary and phenomenological input gets acknowledged in physical buildings as extended phenotypes (Dawkins 1999. Langton 1988) generated from an autopoietic scaffold of extended cognition (Clark 2008) addressing performative structures through nature (Maturana & Varela 1980). I express the process as an iterative equation: nature = machine: machine = architecture: architecture = nature. Consider: we build machines and we build architecture — architecture is a big machine anchored in the ground. Then, if architecture is a big machine and the problems of machines are seen as heuristic (think, for example smartphone/apps as models of intelligent machines and systems architecture), the problems of technology, intelligence, and metabolism

Figure 5 - Tower, Archive 10

that exist for machines simultaneously exist for architecture and cities (Komninos 2013). Thereafter: “Can machines think?” (Turing 1950) = Can buildings think? The Clark (2008), Turing (1952), Dawkins (1999), Di Paolo (2005), and Maturana and Varela (1980) theory-hybrid enacts new OS-like network links to support learning underwritten with autopoietic-extended design. And that OS, again in Hayles’s words: “intervene[s] in the cycles of continuous reciprocal causality [feedback]” (Hayles 2012). We may then physically, technologically, and phenomenologically nurture network intervention — feedback/feedforth — because it is theoretically embodied in autopoieticextended design methods generating perceptions, ideas, and design. Furthermore, the deployment of theory supports engaging environmental operations as they impact analogue and digital evolutionary processes funneling bio-data into building research (Hayles 2012. Jonas 1966. OdlingSmee et al. 2013). The autopoietic-extended design program then intervenes in Maturana and Varela’s, Turing-like formulation guiding our theorizing of machinic intelligence, smart materials, and biological systems as contributing to intelligent buildings (Clark 2008. Maturana & Varela 1980. Turing 1950). To recap that scenario, cultivated design ideas feedback from nature via technology as bio-data manifesting research information (Luhmann 1990. Turing 1952). Therein, we may channel information’s flow and affordances as sources of insight from nature and biology, or as components of living technology, toward architectural functions (Clark 2008. Gibson 1986). The cultivation of data-from-nature for buildings then becomes a fundamental task for design research and architectural pedagogy. Having mentioned both data and information in the course of this text, I would like to close with a clarification to differentiate data from information. I distinguish data as the raw material of information — data flows from nature and subsets of nature such as objects and buildings — thereafter, information is constructed by human cognition. This distinction follows from Niklas Luhmann’s theoretical view that information is a functional creation of thinking: “. . . what is usually called “information,” are purely internal achievements. There is no information that moves from without to within the system. . . . [information] doesn’t exist in the external world, but is a construct” — i.e. internal to the system (Luhmann 1990 69).

References Adamatzky, Andrew. (2013) “Slimeware: Engineering Devices with Slime Mold.” Artificial Life. 19. 317-330. Armstrong, Rachel. (2012) Living Architecture: How Synthetic Biology Can Remake Our Cities and Reshape Our Lives. New York. TED Conferences. Kindle Edition. Bedau, Mark A. McCaskill, John S. Packard, Norman H. & Rasmussen, Steen. (2010) “Living Technology: Exploiting Life’s Principles in Technology.” Artificial Life. Cambridge, MA. The MIT Press. 16: #1. 89-97. Accessed online May 2012. http://www.mitpressjournals.org/toc/artl/16/1 Brenner, Eric D. Stahlberg, Rainer. Mancuso, Stefano. Vivanco, Jorge. Baluška, František. & Van Volkenburgh, Elizabeth. (2006) “Plant Neurobiology: An Integrated View of Plant Signaling.” Trends in Plant Science. 11:8. 413-419. Clark, Andy. (2008) Supersizing the Mind: Embodiment, Action, and Cognitive Extension. New York. Oxford University Press. Clarke, Bruce & Hansen, Mark B.N. Eds. (2009) Emergence and Embodiment: New Essays on Second- Order Systems Theory. Durham, NC. Duke University Press. Copeland, B. Jack, (2013) Turing: Pioneer of the Information Age. Oxford. Oxford University Press. Covert, Markus W. Karr, Jonathan R. Sanghvi, Jayodita C. Macklin, Derek N. Gutschow, Miriam V. Jacobs, Jared M. Bolival Jr., Benjamin. Assad-Garcia, Nacyra. & Glass, John I. (2012) “A Whole-Cell Computational Model Predicts Phenotype from Genotype.” Cell. 150:2. 389-401. 20 July 2012. Coyne, Richard. (2010) The Tuning of Place: Sociable Spaces and Pervasive Digital Media. Cambridge, MA. The MIT Press. Dawkins, Richard. (1999) The Extended Phenotype: The Long Reach of the Gene. New York. Oxford University Press. Dennett, Daniel C. (1998) Brainchildren: Essays on Designing Minds. London. Penguin Books. Di Paolo, Ezequiel. (2005) “Autopoiesis, Adaptivity, Teleology, Agency.” Phenomenology and the Cognitive Sciences. 4: 429–452. DOI: 10.1007/s11097-005-9002-y C _ Springer 2005 Dollens, Dennis. (2014 forthcoming) “Universal Machinic: Alan Turing & Autopoietic Architecture.” Leonardo: The International Society for the Arts, Sciences and Technology. Cambridge, MA. The MIT Press. Accepted: 9 May 2013. In press for: May/June 2014. 47:3. Floreano, Dario & Mattiussi, Claudio. (2008) Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies. Cambridge, MA. The MIT Press. 12

Gagliano, Monica. Renton, Michael. Depczynski, Martial. & Mancuso, Stefano. (2014) “Experience Teaches Plants to Learn Faster and Learn Slower in Environments Where it Matters.” Oecologia. 5 January 2014. Gibson, James J. (1986/1979) The Ecological Approach to Visual Perception. Hillsdale, New Jersey. Lawrence Erlbaum Associates. Publishers. Hayles, N. Katherine. (1999) How We Became Posthuman: Virtual Bodies in Cyberspace, Literature, and Informatics. Chicago. University of Chicago Press. _______________. (2012) How We Think: Digital Media and Contemporary Technogenesis. Chicago. The University of Chicago Press. IBM. (2011) IBM Unveils Cognitive Computing Chips. NY. Armonk. 18 Aug 2011. Press release. Consulted: 29 May 2012. http://www-03.ibm.com/press/ us/en/pressrelease/35251.wss Jonas, Hans. (1966) The Phenomenon of Life: Toward a Philosophical Biology. Chicago. The University of Chicago Press. Komninos, Nicos (2013) “What Makes Cities Intelligent? Architectures of Spatial Intelligence of Cities.” URENIO Research. Aristotle University of Thessaloniki. Email: komninos@urenio.org Langton, Christopher G. (1988) “Artificial Life.” In: Langton, Christopher G. (1988) Artificial Life. Santa Fe, NM. Addison Wesley & The Santa Fe Institute. 6:1-47. Lindenmayer, Aristid. (1968) “Mathematical Models for Cellular Interaction in Development.” Journal of Theoretical Biology. 18. 1968. Luhmann, Niklas. (1990) “The Cognitive Program of Constructivism and a Reality that Remains Unknown.” 64-85. In: Krohn, Wolfgang. Kuppers, Gunter. Nowotny, Helga. (1990) Selforganization: Portrait of a Scientific Revolution. Dordrecht. Kluwer Academic Publishers. Markoff, John. (2012) “In First, Software Emulates Lifespan of Entire Organism.” The New York Times. 20 July 2012. Accessed: 20 July 2012. http:// www.nytimes.com/2012/07/21/science/in-a-first-an- entire-organism-issimulated-by-software.html _______________. (2013) “Brainlike Computers, Learning from Experience.” The New York Times. 29 December 2013. Accessed 29 December 2013. http:// www.nytimes.com/2013/12/29/science/brainlike-computers-learning-fromexperience.html?ref=technology Maturana, Humberto & Varela, Francisco. (1980) Autopoiesis and Cognition: The Realization of the Living. Dordrecht, Holland. D. Reidel Publishing Company. McMulling, Barry & Varela, Francisco J. (1997) “Rediscovering Computational Autopoiesis.” SFI (Santa Fe Institute) Working Paper 97-02-012. Accessed 26 June 2013. www.santafe.edu/media/workingpapers/97-02-012.pdf

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