PUMA publications for /tag/Learning%20Machine%20codesignWed Jan 19 17:25:49 CET 2022133-142Spiralling Slope as a Real Life Co-Design Laboratory2018biodiversity codesign ecosystem gigamapping learning life machine myown performance The paper and its presentation is to discuss a family house Spiraling Slope (sophia) that is co-designed, inhabited, tested and developed prototype by the second and third author of this submission, the clients. The eco-systemically performing house, literally twisted as a helix into the sloping terrain, gaining its thermal energy, is also covered by extensive greenery to gain this property on its top. Algae, grown on the glass roof, is to moderate its atrium clime. Through its sloping disposition, the house employs natural ventilation for its airing. Though the first author is conducting research that this performance is operated by nature of material properties (Davidová 2016c), in the time of the house’s design stage, this research was not developed enough to meet the building practice. Therefore, the house’s eco-systemic performance that could not have been reached by biology is achieved through the technology of autonomous environment control (sysloop). Sysloop is a real-time knowledge processing software cowering physical computing, where the clients are the main developer, co-designing with all the other professions involved in the house design and construction, including its architects and systemic designer. Since the house’s design is based on natural performance, both its environmental, social, cultural and practical performance is operated through a computer based system AI that relates to BIG Data, the paper therefore presents one of the first attempts of fusion of abiotic and biotic agency with artificial intelligence in architectural practice. Testing such prototype by life co-living experience brings true insights into its design in time. This approach has been defined by Sevaldson as Time-Based Design at the start of this millennium (Sevaldson 2004; Sevaldson 2005). However, at that time the crucial leading design team member was not at the same time the subject of testing. This brings the Shön’s discussion on ‘reflective practitioner’ (Schön 1983) few steps further. It is not the case when her/his designing and lecturing is enriched by tacit knowledge of i.e. building practice experience, but furthermore, the co-designer’s experience is gained through living within the system s/he is co-designing and co-prototyping in real life and for real life. Therefore, through such case studies, as the approach fuses the life performance with its design and eco-systemic design and living processes, the first author defends to ratify a new design field, Systemic Approach to Architectural Performance that fights for the shift from Anthropocene.Wed Jan 19 17:21:39 CET 2022132-141sysloop2018architecture codesign intelligent learning machine myown responsive systems Unlike preceding “autonomous house systems” sysloop is cross-layered and highly scalable concept of “allopoietic system”, a system that is autonomous though dependent on the exchange across its environment (Dekkers 2015). This is performed through three types of co-design: • co-designing of trans-disciplinary co-authors; • co-designing of environment from which it is learning, users included; • co-designing of artificial intelligence and big data. At the scale of local environment, sysloop is focused mainly on interrelations of individual life space qualities, providing contextual autonomous behaviour across many aspects such as climate, light, sound, smell, safety, access control, etc. Due to such scope, the trans-disciplinary team of experts developing sysloop technology is evolving in time in reference to related fields. We specify key aspects of an alternative information system with ability to make decisions based on automated interpretation of meanings, instead of (conventional) symbol processing. We verify such information system in practice of environment automation, introducing technological support of overlapping values such as information hygiene, lifelong learning, aesthetics, overall comfort, etc. Such environments are integrated at “buildings” units scale in phenomenological terms and at “industrial” units scale focused on adaptive automation and reliability engineering, both processing micro-sensorial data and performing qualified decision making in real-time. These together with other big data available are integrated to support the “cities’” scale layer. This layer is to serve for informed city planning and emergency situations solutions, including automated, personalised assistance to individual citizens, etc. This multi-scaled system is feedback looping across its layers of scales and types of co-design and thus evolving by data and most importantly, its ever-changing relations. It gives to the term “smart buildings” its meaning across the scales towards sustainable development, performance and ecosystems. The authors, among all the team, built the first prototypical family and office building for real-world testing and further development. This “real life co-design laboratory” is elaborated at separate paper for this conference.Learning Machine codesignCommunity for tag(s) Learning Machine codesign