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Managing the benefits and risks of architectural artificial intelligence
By Barie Fez-Barringten   
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Last edited: Friday, November 23, 2012
Posted: Wednesday, July 25, 2012

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This is not the script for the movie called “Artificial Intelligence” about AI Brian Addis (Brian Wilson Aldiss, a British writer, anthologist, and critic,) says; I found we both agreed that AI, as they call it, is not going to be achieved by present-day machines. 'Artificial Intelligence' -- that makes it sound simple, but what you're really talking about is artificial consciousness, AC. And I don't think there's any way we can achieve artificial consciousness, at least until we've understood the sources of our own consciousness. I believe consciousness is a mind/body creation, literally interwoven with the body and the body's support systems. Well, you don't get that sort of thing with a robot."

Managing the benefits and risks of architectural artificial intelligence (c)

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                It is reported that on working with Stanley Kubrick and Steven Spielberg on Artificial Intelligence: AI (2001):] "Kubrick was obsessed by Pinocchio. He wanted David to become a real boy. In a future world of runaway global warming and awe-inspiring scientific advances, humans share every aspect of their lives with sophisticated companion robots called Mechas. But when an advanced prototype robot child named David (Haley Joel Osment) is programmed to show unconditional love, his human family isn't prepared for the consequences. Suddenly, David is a sovereign entity in a strange and dangerous world. Befriended by a streetwise Mecha (Jude Law), David embarks on a spectacular quest to discover the startling secret of his own identity. As a variation of idolatry, AI suffers from a tendency to ascribe life to the inanimate ascribing “good” or “bad” characterizations. Preface: As I argue the benefits’ and risks’ of architectural axioms I condition one with the other even though the risk to building design application is minimal and any consequences benign.                                                                                                          I present this intertwined argument because such dangers are currently on the minds of many in the AI community. To talk about one without consideration of the other might seem presumptuous and naïve. However, in my opinion as a licensed design professional, the benefits to an AI user-context would far outweigh the risks. Whatever malfunctions and dangers would only affect a specific well contained user and be easily controlled. Worst case would be a cost of time and expense to repair and redo as is the profession’s current practice. Relevance: The resolution to my claims is that architectural metaphoric axioms themselves sufficiently manage the marginal risk [ff] of AI being a potential adversary limiting the intelligence of machines and explaining the essential difference between human intelligence and artificial intelligence. In my view architectural AI is best viewed as a surrogate and not an adversary! While architectural metaphoric axioms contribute managing the risk [ff] of AI being a potential adversary, it is left to society to debate whether machines have a mind and consciousness. Within this context the challenge for AI managers is AI’s capacity to discern metaphors (humans have the capacity and capability to make use and discern metaphors). AI challenge is to abridge these architectural metaphoric axioms into their platform’s programs and systems, when they do this AI’s and architecture’s mutual interactions will both be improved by metaphoric axioms and mange risk [ff]. To achieve this goal I believe the AI community can regulate, legislate, monitor and license AI and its architectural devices and thus engraft AI with sympathetic human characteristics and concerns.  

                              Abstract: As AI and architecture mediate and control their mutual interactions metaphoric axioms will have cognitive impact on both the future of architecture and AI because there is common metaphor between natural (NI) and artificial intelligence (AI). The inference warrants that for both architectures’ (AI and building) , master builder is an interdisciplinary, multi-crafted and multi-venue team, They are also both arts since they wed intentional ideas to craft and they both make metaphors, the commonality to all the arts. While “architect” actually means master builder and “architecture” the product of the master builder, this is historically identified with habitable buildings. The warrant to the inference of the resolution is that the computer industries (and virtual designers) have made a metaphor referring to the word “architecture” with its conceptual design and fundamental operational structures of computer systems.  

                          Already, IT and AI industry metaphorically compare their sciences and art of selecting and interconnecting hardware components to create computers that meet functional, performance and cost goals with the ways and means traditional architects design buildings. There is an interconectivity between the metaphor of computer’s instruction set architecture, or ISA, machine language (or assembly language), Microarchitecture and system design.

                      Theoretically, I warrant that the as the body and mind of AI has identified itself with “architecture” there is an opportunity to use those links to apply and manage risks of AI to building architecture. However, benign, risks include operating system downtime, programming errors, inaccuracy in labeling and dimensions, misreading building codes, local ordinances, misinterpreting FEMA regulations and potential tampering with building security systems. . Further risks include erroneous selection of material and building systems that may expose architects to errors and omissions suits, so many of the general and specific axioms guidelines can be uploaded into the AI architectural system. So with AI potential risk [ff] what can be the impact of artificial intelligence on the future of building architecture?

 Biographical note: IBM FORTRAN 4 classes at Yale, Program planning for several Silicone Valley data companies and Gulf Oil Corp computerized Project Management System (PMS) later published by John Wiley and sons. Columbia University coursework in behavioral psychology under Ralph Hefferline and others in voice/linguistics, Bachelor’s of Fine Arts from Pratt Institute and Master of Architecture from Yale University where I was mentored in metaphors and metaphysics by Dr. Paul Weiss. For research I founded the New York City not-for–profit corporation called Laboratories for Metaphoric Environments. In addition to authoring over fifteen published monographs by learned journals I have spent 20 years in Saudi Arabia and have written a book containing pen and ink drawings on perceptions of 72 European cities. Institutional affiliations: Global University ;American Institute of Architects; Florida Licensed Architect; Programming Chairperson for the Gulf Coast Writers Association; National Council of Architectural Registration Boards; Al-Umran association of Saudi Arabia, American Society of Interior Designers; and founding president of Architects International Group of the Mid-East.

Introduction:

                Because artificial intelligence is inherently axiomatic, interdisciplinary [aa] and metaphoric it is uniquely suited to combine risk management and building architecture. Metaphoric axioms improve AI’s and architecture’s interactions by likening it to architecture. As AI architecture, the “strange” of AI becomes linked to the “familiar” architecture and the two can be compared: AI and architecture, they both can benefit from a metaphoric vocabulary. As most AI/IT activities, they work through digital and mechanical devices, mainframes, hard drives, processors, motherboards and chips, as well as application software, firmware, middleware, (which controls and co-ordinates distributed systems) , and system software (such as operating systems) , which interface with hardware to provide the necessary services for application software, these are all the body to the brain of AI. To warrant my claim as other disciplines these bodies are driven by some form of axioms (structured vocabulary) however, about AI architectural work, there is presently little in the way of axioms. Historically, in the early eighties, Silicone Valley data companies (I consulted such companies in Sunnyvale between 1979 and 1981) scoured the market for soft information to build proposed programs for computer aided design (CAD) intended to be driven by design professionals to actually lay down graphic images instead of hand drafted (pencil on paper) drawings. Having put traditional draftsman out of the loop, and, developed “master specs” for computerized specifications, the next step is now to reduce the expense of design personal and extend the design capability and capacity. Thirty years later the design industry claims that what can be done for the design of manufacturing plants, machine parts and assemblies may be applicable to creating communities, environments, developments and specific buildings. The resolution’s presumed context is that it is not just limited to information technology (IT) but a presumption of intelligence assuming man can make something which can think for itself as today’s computer games, medical procedures, aircraft and military devices

                 The below examples show that when programmed, systems can make judgments in a strange environment and metaphorically make the strange familiar (metaphorically) and systematically design buildings. (Where design is intentionally originating and developing a plan for a product, structure, system, or component). The impact of artificial intelligence on the future of architecture: practice, process and products are that today there are “smart buildings” with internal mechanical and electrical systems that respond to the specific behavioral patterns of occupants. Below you will find potentials for the use of metaphoric architectural axioms where artificial intelligence examples have been applied to designing buildings without necessarily acting as an “architect”, where design is only one architectural function. No more than would we have diagnostic equipment and robotics perform sovereign surgery on a doctor’s patient. Currently all other systems use protocols, parameters and axiomatic frameworks, axioms and guidelines needed to facilitate artificial diagnostics, analysis, and design of buildings at one or another level is the impact of artificial intelligence on the future of architecture. To complete the case for the resolution that AI’s and architecture’s mutual interactions will be improved and managed risks [ff] by metaphoric axioms I have provided a short summary of the claims and examples a of the 83 axioms I have authored in another much longer monograph [T]. Leaving those details of all the axioms for another essay suffice it to say that these axioms are essential drivers of AI architectural activities. As a predicate this

                      AI system can be used by the architectural profession to expand its use of metaphors and services to manage the design process by interfacing with clients, society, culture, contactors and building authorities and finally selecting the appropriate axioms and managing the overall design process [aa]. These architectural metaphoric axioms will have an impact on the future of AI and building architecture. Since a host for the architectural metaphoric axioms is needed I warrant my inference that even today’s architectural practice has changed, communicating between many disciplines via the internet. “The availability of reliable, high-speed electronic connectivity enabled collaborative design team’s function irrespective of physical distance. [V] This calls for new type of design and simulation environment—one that facilitates automated searching and locating of satisfying and optimizing parts, integration of selected parts in an assembly, and simulation of the overall design that is distributed over the Internet”. An increasing quantity of building applications of AI work is based on [W] “Building Information Modeling (BIM) generating and managing building data during its life cycle”. AI neither promises uncontrolled sovereign operations, inventions, creativity, and innovative design but instead it promises to operate within the parameters and limits designed by man and if it could innovate, invent and create it would only do so with either specific geometry or geometric axioms. However said, Science fiction writers extrapolate the potential of AI beings aimed at ultimately destroying their creators.

                         This metaphor to Frankenstein is to our culture as intimidating as is other unsavory results of cloning. Examples to the inferences where already industrial design for automobiles, aircraft and boats use design applications to meet aerodynamic, seismic, wind, structural loads, etc. These already account for the strength of materials, if given, or can optimally select materials based on its library of manufactured products. In addition [U] virtual building environments (VBE) are now producing graphic scenarios to estimate, plan, buy and build; already artificial intelligence is having an impact of on the future of architecture Examples and concerns applying AI to building design. Without concerns for risks the practical and the esoteric applications of AI to the built environment is often the result of metaphoric inventive processes, shocks and imaginative invention such as [M] ANTS which is an innovative example of an AI application to design buildings. “The Autonomic Nanotechnology Swarm (ANTS) is a generic mission architecture consisting of miniaturized, autonomous, self-similar, reconfigurable, addressable components forming structures. The components/structures have wide spatial distribution and multi-level organization. This ‘swarm’ (metaphor) behavior is inspired (metaphoric association) by the success of social insect colonies where within their specialties, individuals outperform generalists and with sufficiently efficient social interaction and coordination, groups of specialists outperform groups of generalists. [M] (Multi-disciplinary)

                  Axiomatically, the type of information that is preserved in the traditional built environment is organized-complexity: precisely the type of information that defines living systems themselves. Thus, the traditional built environment consists of evolved and discovered solutions (schemata) that make our life easier and more meaningful” [N]. That having been said as ACTS combines design and construction “Research in construction automation at the University of Reading led to the formulation of a computer-integrated, component-based construction system. [Q} The Reading Building System was rationalized for automation following a systematic study of the construction processes involved in the design and erection of a variety of building types, especially high-tech offices. Computer-aided design (CAD) packages were written that used Parts Set components as primitives and that offered flexibility in design that was so often lacking in earlier approaches to system building. At the same time, a family of automation aids was developed to manipulate the parts that were modeled in the CAD In the Netherlands [S] “Artificial Design focuses on the application in architecture and design of the algorithmic approach to art being developed at the Institute of Artificial Art Amsterdam.

              Once a style has been defined the tool can suggest any desired number of alternative designs for a given document. The Department of Artificial Architecture develops programs which generate random specifications of 3-dimensional objects. Each of these programs employs a "visual grammar" to define an infinite set of structures, and then draws random samples from this space”. “The science of design usually conceives of AI as a set of tools for structuring the process, or planning, or optimizing. [R] This further warrants that “ Rarely does the computer explore a space of designs, and in doing so, it is generally following a set of precise rules, so the machine is doing little else than repeating a series of mechanical steps, faster than a human could. Creativity is usually considered to lie outside the realm of what computers can do”. Evolutionary Design (ED), the creation of designs by computers using evolutionary methods is a new research area with an enormous potential”. To manage some of the risk [ff] using existing metaphoric architectural axioms manufactured buildings, pre-engineered steel buildings, mobile homes, decks, kitchens, lighting, structures which are just some of the examples of pre-designed programs allows user to input variables to receive a design result.

                  There are both similarities and differences between human natural intelligence and artificial intelligence which are metaphorically associated with the concerns of people and their aspirations to shape the post-industrial society. Metaphorical fears that people and not machines shape society adopted from the critics of the industrial and information revolution. In a way this is risk mitigation by reducing adopting metaphors that make the strange familiar and limit the unknowns. However, on closer examination, reality and fiction are different since artificial intelligence is authored by humans (the imagined fear is that what was created by man could turn against man when the AI capability to design, redesign and rebuild goes awry). Especially in building design, I argue that since there is a difference between the imagined, possible the reality of the probable is marginal, isolated, miniscule and therefore contained. The challenge to the AI community is to contain runaway metaphorical thinking, where the public looks to close down human capacity for social innovation and sustainability.[5] Military, design, engineering, accounting, medical, scientific, manufacturing and education are just some of the fields already augmenting artificial intelligence with human management.

                    As AI, Metaphor is one of the tools of a [1] 'knowledge society' and to 'human-centered' technologies and systems. One the attributes of anything artificial is that it is stagnant, engrafted and reflective of its creator, it does not have its own free will at least not that beyond what has been given by its designers. While humans change and adopt the artificial remains as it was unless it also has the ability to rebuild, adopt and change. This scope, range and amplitude of this capacity are likewise conditioned by its creator. Like a work of architecture, machine, weapons and medical equipment, self analysis, reprogramming and change are built-in. Dividing the discipline's metaphors between technical [hh] and conceptual can improve AI’s and architecture’s mutual interactions. The brain can be simulated. Hans Moravec, Ray Kurzweil and others have argued that it is technologically feasible to copy the brain directly into hardware and software, and that such a simulation will be essentially identical to the original. [K] "The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds.

                 Searle counters this assertion with his Chinese room argument, which asks us to look inside the computer and try to find where the "mind" might be. The resolution to my claims is that AI’s and architecture’s mutual interactions will be improved by metaphoric axioms is supported by claims, inferences and warrants as AI’s and architecture’s mutual interactions will not only manage marginal risks but be improved by metaphoric axioms which will have an impact on the future of architecture and AI field. Philosophers and scientists concerned with ethics, morals and sociopolitical agreements critically challenge [J] the limits of intelligent machines while proponents of architectural metaphoric axioms recreate the capabilities of the human mind. These philosophers and scientists question if there is an essential difference between human intelligence and artificial intelligence. They wonder can a machine have a mind and consciousness. There is already a difference in perception between scholars and practitioners. Since both humans and machines perceive their environment and take actions they maximize their chances of success and manage risks while they likewise wonder if machines have a similar human capacity and capability to discern metaphors. “The field (artificial intelligence) was founded on the claim that a central property of humans, intelligence—the sapience of Homo sapiens—can be so precisely described that it can be simulated by a machine.[2] Can the artificial find the range of unpredictable, whimsical, and historical stored in the human be replicated.

                    While for one it may be replicated but what about the trillions of other possibilities and potentials in humans not inherent in the artificial, as man, so does AI manage risks. [3] “Roughly speaking, AI is the attempt by computer scientists to model or simulate intelligent behavior on computers” This in and of itself is metaphoric, where one thing is stated in terms of the other. The intelligent behavior is the commonplace/commonality to both the human and the machine. We seem to want to make machines like us because we are the commonality. If we cannot clone mankind we can clone our body similar to the ancients who strove to be immortalized and as man so does AI manage risks. The mind-machine metaphor, central to AI, appears in jurisprudence as well. Sometimes it is explicit, as in Jerome Frank's image of the judicial slot machine: Judging is seen as a process wherein cases are fed into the hopper of the machine, a crank is turned, and justice is dispensed at the output. [3] The field of artificial intelligence is interesting to a student of metaphor, because it was explicitly founded upon a metaphor - several of them, in fact. In the 1950s, a group of scientists decided to try to provide the computer with intelligence. Their goal seemed attainable due to a common metaphorical identification of the computer with a brain. [4] From their efforts emerged the field of artificial intelligence, or AI. As I thought about the basic, or root metaphors of AI,

                   I realized that they took a form resembling a classical syllogism, a mode of argument that forms the core of the body of Western logical thought. Aristotle defined syllogistic logic, and his formulations were thought to be the final word in logic; they underwent only minor revisions in the subsequent 2,200 years: one of the axioms driving the relationship is that the computer is a brain, the premise in a syllogism containing the minor term, which will form the subject of the conclusion. “Thinking is computing, [Y] concluding that if we provide the computer with sophisticated programs, it will develop a mind similar to human minds. [4], in risk free circumstances. Artificial systems and the biological ones are similar for their dynamicity, because they cope with the new situations in a way that is controlled and creative at the same time. [H]. In the case of architectural design this can only leads to safer, healthier and “greener’ buildings. [5] There is a body of study comparing AI to metaphors as I did in 1967 comparing architecture to metaphors. [C]. There is ample discussions on the analogies, symbolisms and metaphors linking machines and minds, computers and humans , and artificial intelligence with natural intelligence it is therefore beneficial to apply the science, claims and axioms about metaphors.

                  [D]. But what about axioms derived by social, psychological, philosophical, cognitive scientist? In other works [T] I have derived 83 axioms which I could apply both here have only discussed the ones with major comparative value. As they did with AI we did with architecture and are using these axioms and findings to compare human and machines. For example [7] humans are able to generate metaphors by describing an operation in an unfamiliar way and thus able to make what was already somewhat known dominant. The generative metaphor is the name for a process of symptoms of a particular kind of seeing-as, the “meta-pherein” or “carrying –over” of frames or perspectives from one domain of experience to another. This process he calls generative which many years earlier WJ Gordon called the Metaphoric Way of Knowing [E] and 2.1 Paul Weiss called “associations” [F]. Both humans and computers can generate dead metaphors where one really does not contain any fresh metaphor insofar as it does not really “get thoughts across”; [8] “language seems rather to help one person to construct out of his own stock of mental stuff something like a replica, or copy, of someone’s else’s thoughts”. Man’s natural culture is a product of man-made, unnatural things, that instead of culture shaping the computer it is the computer (artificial intelligence) that shapes the culture. At first, culture creates the machines then the artificial intelligence modifies the culture. Then new modified culture creates new machines, etc. [9] The affect of the metaphor on other metaphors with all its links and consequences is manifest in the conduit [8] which leads to one after the other and a continuation of the first. On the one hand AI can result in prescriptive design vs. abnormal, different, irregular, occasional, rare, sometime, and unusual design solutions with such projects as CFS truss system[cc], Arup/cultural society[ee] and emergence [ee].

                   Emergence [ee] is an important new concept in artificial intelligence, information theory, digital technology, economics, climate studies, material science and biometric engineering. It is a development which is set to inform not only the construction of buildings, but also the composition of new materials. As a new science, coupled with material and technological innovations, it is set to enter architecture into a new phase of transition including new material processes and technologies that enable the production of complex architectural forms and effects. Mathematics of emergence underlies advanced manufacturing processes, how it is incorporated in the design process by scientists developing new materials, by mass market and niche product manufacturers, by engineers and by architects. The new science demands new strategies for design, strategies that have a remarkable similarity to the evolutionary design development and optimization processes of nature. It involves the intersection of a broad scope of disciplines including advanced structural and biomimetic engineering, the mathematics of morphogenesis and computer science with particular respect to artificial life and evolutionary computation, in order to set forth an operative notion of emergence for architectural design [aa] . Axiom Digest Within the parameters of risk management [ff] these axioms are self-evident principles that can be accepted on face-value as a true basis for argument since they have already been proven and described by the noted referent for each. Here they are postulates (or inferences) without their warrants. As such each is noted as to source and location for reference gleaned from “Metaphor and Thought” [6] (footnoted as 1._._ throughout).There are additional references noted below. The footnotes are sub-axioms meant to both support the axiom while also being useful as an independent principle.

                The below axioms are predominantly derived from “Metaphors and Thought” [6] by Andrew Ortony, earlier mentoring by Dr. Paul Weiss and are in addition to over forty years of work about my stasis to architecture as art being that “architecture as the making of metaphors” (please see background [C] below after the monograph for your information). Axioms are self-evident principles that I have deduced out of Ortony’s Metaphor and Thought [6] and accept as true without proof as the basis for future arguments; a postulates or inferences including their warrants (which I have footnoted as 1._._ throughout). These axioms are in themselves clarification, enlightenment, and illumination removing ambiguity where the derivative reference (Ortony) has many applications. Hopefully, these can be starting points from which other statements can be logically derived. Unlike theorems, axioms cannot be derived by principles of deduction as I wrote: "The metametaphor theorem" published by Architectural Scientific Journal, Vol. No. 8; 1994 Beirut Arab University. [gg] The below axioms define properties for the domain of a specific theory which eve loved out of the stasis defending architecture as an art and in that sense, a "postulate” and "assumption" .

                   Thusly, I presume to axiomatize a system of knowledge to show that these claims can be derived from a small, well-understood set of sentences (the axioms). “Universality, Global uniqueness, Sameness, Identity, and Identity abuse” are just some of the axioms of web architecture. Francis Hsu of Rutgers writes that “Software Architecture Axioms is a worthy goal. First, let's be clear that software axioms are not necessarily mathematical in nature”. Furthermore, in his book titled The Book of Architecture Axioms Gavin Terrill wrote: “Simplify essential complexity; diminish accidental complexity; You're negotiating more often than you think ;It's never too early to think about performance and resiliency testing; Fight repetition; Don't Control, but Observe and Architect as Janitor”. In “Axiomatic design in the customizing home building industry published by Engineering, Construction and Architectural Management; 2002;vol 9; issue 4;page 318-324 Kurt Psilander wrote that “the developer would find a tool very useful that systematically and reliably analyses customer taste in terms of functional requirements (FRs).

                     Such a tool increases the reliability of the procedure the entrepreneur applies to chisel out a concrete project description based on a vision of the tastes of a specific group of customers. It also ensures that future agents do not distort the developer's specified FRs when design parameters are selected for the realization of the project. Axiomatic design is one method to support such a procedure. This tool was developed for the manufacturing industry but is applied here in the housing sector. Some hypothetical examples are presented”. Aside from building-architect’s axioms claiming that “form follows function”; “follow manufacturer’s requirements and local codes and ordinances”, “AIA standards for professional practice” architectural axioms are few and far between. Each has been summarized, paraphrased and translated into architectural terms. Because of the speed and memory capacity it is not far fetched that an AI architectural system could receive, analyze and match requirements with codes, ordinances and industry standards which will impact the future of both building and AI architecture. Compendium of Axioms The first axiom permits all the others in that it claims that ideas and concepts are the reality of what we create. These images are also the commonality linking our impression with facts on the ground.

                    [10] Novel images and image metaphors are conceptual and not the works themselves, but their mental images. “All metaphors are invariant with respect to their cognitive topology, that is, each metaphorical mapping preserves image-schema structure:” Likewise when we look at the geometrical formal parts of an AI architectural metaphor we note those common elements where fit, coupling and joints occur. We remember that which exemplified the analogous match. This observation of the metaphor finds that the commonality, commonplace and similarity are the chief focus of the metaphor. Humans and mechanized readers both may note either the obscure or subtle as the way two horizontal axes of the land and then a building are wed by their commonality of horizontality affecting the future of both architectures. Natural Intelligence (NI) and AI note the 90 degree angles and shape that slide into one another. AI and NI and note the way like metals, clips and angles fit; the way ceiling ducts are made to fit between structures and hung ceiling, etc. While it is less possible for AI to spontaneously imagine the way AI could relate the human form to a building when humans circulate through its halls, rooms and closets its accommodation to our needs and necessities; to our self preservation and the maintenance of the building become apparent. Both can map the building structure to humans by finding the one commonality amongst all the others. Very often we will hear someone (user) say this place is” me”.

           The common image has been located and the fit made. The way to arrive at generic-level schemes for some knowledge structure is to extract its image; its image-schematic structure is the Invariance Principle. Obviously this is best done by architecture’s human inhabitant; this is called the Generic’s Specific Structure. It is an extremely common mechanism for comprehending the general from the specific. So what you can deduce for part you can assume is true of the whole. The human architect controls the mechanical and artificial; however, they both must share their intelligence where the artificial analyses and presents it findings to man for further action. Whether a human can preprogram an AI devise to perceive the infinite number of human “fit” characteristics seems formidable. As today, much depends on the return on investment and the capacity of creators to program the device, the programmer, scientist and operator will only do what is efficient and necessary. You may call it the biggest bang for the buck axiom, where humans may falter and awake to a new paradigm where AI devices are designed to always succeed. It is any of the three levels of [aa] Axioms contextual forms. Plausible accounts [10] rather than scientific results are why we have conventional metaphors and why conceptual systems contain a preference for one set of metaphorical mappings over another. An artificial intelligence establishes its own vocabulary which once comprehended become the way in which we experience its’ product’s finding. Its discrepancies and fits seek the first and all the other similar elements while humans judge consistency, integrity and aesthetics of AI. The two have their respective roles. The human monitors, manages and controls wile the AI system performs anticipated intelligent operations leaving the human to find variances and reprogram. In this way AI in general and the application in particular evolves and impacts both architectures Metaphor is the main mechanism through which humans comprehend abstract concepts and perform abstract reasoning.

                Whether it is one or thousands public cultures is influenced, bound and authenticated by its’ metaphors. Not withstanding “idolatry” the metaphors are the contexts of life’s dramas and as our physical bodies are read by our neighbors finding evidence for inferences about social, political and philosophical claims about our culture and its place in the universe. For humans much subject matter, from the most mundane to the most abstruse scientific theories, can only be comprehended via metaphor However, AI capacity is limited only by its microprocessor chips and RAM. Metaphor is fundamentally conceptual, not linguistic, in nature. Human’s free will, whim, natural functions and being the original indigenous native inhabitants characterize man over his artificial creations. Left to its own AI would create a world of possible machine parts, systems and structures well suited for artificial intelligent life. Metaphorical language is a surface manifestation of conceptual metaphor. As language is to speech so is output to AI where each has a content and inner meaning of the whole as well as each of its parts. As each word, each attachment, plain, material, structure had first been conceived to achieve some purpose and fill some need. Hidden from the reader is the inner psychology, social background, etc of the man when speaking and the programming deign and contacting process from the reader of a building metaphor. As in completing an argument the reader perceives the inferences with its warrants and connects the evidence of the seen to the claims to make the resolution of the whole, all of which are surmised from the surface.

                 Though much of our conceptual system is metaphorical a significant part of it is non-metaphorical. Metaphorical understanding is grounded in non-metaphorical understanding. AI is well suited to the architectural science of the strength of materials, mathematics, structures, indeterminate beams, truss design, mechanical systems, plumbing systems, electricity, cladding, finishes, lighting, etc. as are each understood metaphorically and their precepts applied metaphorically. But often random selections, trials and feasibility are random and rather in search of the metaphor without knowing whether it is or is not a metaphor and fit to be part of the metaphor at hand. AI will not know the relevant commonality. It may select some commonality but chances are it will be irrelevant and as incongruous as often are language translations selecting incongruous phrases and usages. It is for such a risk that human management and monitoring may be required. On the other hand we may select one or another based on non-metaphorical, empirical test and descriptions of properties. We then try to understand the metaphor in the selection, its commonality, how it contributes to the new application, how it has properties within itself which are alone strange and unrelated yet when coupled with the whole or part of the created metaphor contributes to metaphor. Metaphor allows humans to understand a relatively abstract or inherently unstructured subject matter in terms of a more concrete or at least more highly structured subject matter. Like the onomatopeics metaphors mappings of conceptions override the overt spoken and descriptive, and rely much more on mnemonics (something intended to assist the memory, as a verse or formula).

                      Peculiar to the human, assistance comes from something much more primordial (constituting a beginning; giving origin to something derived or developed; original; elementary: primordial forms of life) to the person’s or societies experiences. Again, it is for such a risk that human management and monitoring may be required, an architectural design may warrant human invasiveness into the process. However, once completed these become the matrix (encyclopedic) of schemas (in argument; the warrants {where a warrant is a license to make an inference and as such must have reader's agreement} supporting the inferences (mappings) where in the metaphor becomes real). In this way the reader maps, learns and personalizes the strange into the realm of the familiar. The reader does so by the myriad of synaptic connections he is able to apply to that source. Hence humans translate their conceptions from philosophy, psychology, sociology, etc into two dimensional scaled drawings and then to real life full scale multi dimensions convention consisting of conventional materials, building elements the task of upload axioms to AI would take a lifetime of dedicated specialist [aa]. Well suited to AI as maps are the result of cartographers rendering existing into a graphics for reading so is mapping to the reading of metaphors where the reader renders understanding from one source to another.

                   Doing so mentally and producing a rendition of understanding (as a pen and ink of a figure) not as a graphic but a conceptual understanding. The best risk management is when reader sees in a critical way the existing culling through and encyclopedia of referents to make the true relationship; the mapping which best renders the reality; the relationship which informs and clarifies as the map the location, configuration and characteristic of the reality. As the cartographer seeks lines, symbols and shadings to articulate the reality so the reader choices of heretofore unrelated and seemingly unrelated are found to have and essence common to both the reality and the rendition so that the metaphor can be repeated becoming the readers new vocabulary . In fact architects do the opposite as graphic renditions are made of synapses between amorphic and seemingly desperate information. This relationship between axiom and performance assure for program conformity and reliability. Yet the process of mapping is no less intense as architect review the matrix of conditions, operation , ideal and goals of the thesis to find similarities and differences , commonalities, and potential for one to resonate with another to make a “resolution” on the experience of a cognitive mapping which becomes the metaphor, parte and overwhelming new reality.

                  The new reality is the target of the source and finally can be read. In the case of the birth of an infant metaphor readers may find a wide variety of source information which is germane to their own experience. Before the public ever sees the constructed metaphor Building Officials, manufactures, city planners, owners, estimators, general contactors, specialty contractors, environmentalist, neighbors and community organization first read the drawings and map their observations to their issues to form a slanted version of the reality. Human manager can easily monitor this variance and modify the performance. Their mappings are based on the warrants which are their licensed to perform. Each warrant will support a different mapping (inference) and result in its own metaphor. In effect each will see a kind of reality of the proposed in the perspective of their peculiar warrant, where license is permission from authority to do something. It is assumed if one gets permission it has met the conditions, operations, ideal and goals of the proposed metaphor. As risk is managed by other professions, operations and systems mapping is critical at this read to assure that the architect’s rendering of the program is faithful to the cognitive, lawful, physical and legal realities. It s like a map which gets tested by scientist, navigators , pilots and engineers before they build a craft to use the map, or set out on a journey using the map. Before the contracts start committing men and material the metaphor must map and be the metaphor meeting all expectations.

                    As there is metaphor between natural (NI) and artificial intelligence (AI) in cognitive linguistics, conceptual metaphor, or cognitive metaphor, refers to the understanding of one idea, or conceptual domain, in terms of another, for example, understanding quantity in terms of directionality (e.g. "prices are rising"). A conceptual domain can be any coherent organization of human experience. The regularity with which different languages employ the same metaphors, which often appear to be perceptually based, has led to the hypothesis that the mapping between conceptual domains corresponds to neural mappings in the brain. Each mapping (where mapping is the systematic set of correspondences) that exist is between constituent elements of the source and the target domain. [I] Many elements of target concepts come from source domains and are not preexisting. To know a conceptual metaphor is to know the set of mappings that applies to a given source-target pairing. The same idea of mapping between source and target is used to describe analogical reasoning and inferences) is a fixed set of ontological (relating to essence or the nature of being) correspondences between entities in source domain and entities in target domain. There is a list of over 100 schemas in many categories about basic human behavior, reactions and actions.

                These schemas are the realms in which the mappings takes place much the same as the inferences in arguments have warrants and link evidence to claims so do these schemas, architects carry-over their experiences with materials, physics, art, culture, building codes, structures, plasticity, etc. to form metaphor. Identifying conditions, operations, ideals and goals are combined to form plans, sections and elevations which are then translated in to contract documents. Later the contractors map this metaphor based on their schemes of cost, schedule and quality control into schedules and control documents. Humans interact with their environments based on their physical dimensions, capabilities and limits. [F] The field of anthropometrics (human measurement) has unanswered questions, but it's still true that human physical characteristics are fairly predictable and objectively measurable. Buildings scaled to human physical capabilities have steps, doorways, railings, work surfaces, seating, shelves, fixtures, walking distances, and other features that fit well to the average person. [F] Humans also interact with their environments based on their sensory capabilities. The fields of human perception systems, like perceptual psychology and cognitive psychology, are not exact sciences, because human information processing is not a purely physical act, and because perception is affected by cultural factors, personal preferences, experiences, and expectations, so human scale in architecture can also describe buildings with sightlines, acoustic properties, task lighting, ambient lighting, and spatial grammar that fit well with human senses.

                      However, one important caveat is that human perceptions are always going to be less predictable and less measurable than physical dimensions. For humans mappings are not arbitrary, but grounded in the body and in every day experience and knowledge. Mapping and making metaphors are synonymous. The person and not the work make the metaphor. Without the body and the experience of either the author or the reader nothing is being made. The thing does not have but the persons have the experiences. As language, craft, and skills are learned by exercise, repetition and every day application so are mappings. Mappings are not subject to individual judgment or preference: but as a result of making seeking and finding the commonality by practice. Humans learn to associate, create and produce by years of education and practice while users have a longer history approaching and mapping for use and recognition. Yet new metaphors are difficult to assimilate without daily use and familiarity.

                        AI overcomes this and stores limited memory in RAM. A conceptual system contains thousands of conventional metaphorical mappings which form a highly structured subsystem of the conceptual system. Over the year’s society, cultures, families and individuals experience and store a plethora of mapping routines which are part of our mapping vocabulary. There are two types of mappings: conceptual mappings and image mappings; both obey the Invariance Principle. “A. Image metaphors are not exact “look-alikes” ;many sensory mechanisms are at work, which can be characterized by Langacker’s focal adjustment (selection, perspective, and abstraction); B. images and Image-schemas are continuous; an image can be abstracted/schematized to various degrees; and C. image metaphors and conceptual metaphors are continuous; conceptual metaphorical mapping preserves image-schematic structure (Lakoff 1990) and image metaphors often involve conceptual aspects of the source image. (“All metaphors are invariant with respect to their cognitive topology, that is, each metaphorical mapping preserves image-schema structure:” Likewise when we look at the geometrical formal parts of an architectural metaphor we note those common elements where fitting, coupling and joints occur), again this simultaneity of ideas and image operating in tandem where we see and know an idea simultaneously; where the convention of the architectural space and the metaphor of the conception converge. Such an axiom is the commonality between man and machine, AI and human architecture and AI mechanism and its manager.

                  For both AI and humans the invariance principle offers the hypothesis that metaphor only maps components of meaning from the source language that remain coherent in the target context. The components of meaning that remain coherent in the target context retain their "basic structure" in some sense, so this is a form of invariance. For humans there will be all sorts of incongruities, similarities and differences. Both humans and AI can experience onomatopeics metaphors that are onomatopoeic (grouping of words that imitates the sound it is describing, suggesting its source object, such as "click", "bunk", "clang", "buzz", "bang", or animal noises such as "oink", "moo", or "meow") ? In this case an assemblage instead of a sound. As a non-linguistic it has impact beyond words and is still a metaphor. Then a metaphor is much more than the sum of its parts and is beyond any of its constituent constructions, parts and systems, its very existence a metaphor. The cost to convey inconsistencies, variables and nuances of human life can be formidable. Elegant architectural metaphors are those in which the big idea and the smallest of details echo and reinforce one another manifest in paraphrasing where “people ascertain the deep metaphor that underlies one or more surface metaphors by filling in terms of an implicitly analogy”. [11] It is the “filling in” wherein the [L] synapse (a region where nerve impulses are transmitted and received, encompassing the axon terminal of a neuron that releases neurotransmitters in response to an impulse) takes place. The difference between the indirect uses of metaphor verses the direct use of language to explain the world. .

                      In some circles this is referred to tangential thinking, that approaching a subject from its edges without getting to the point. [12] Users can accept works which are vague, inane, and non-descript, evasive, and disorienting as between micro and macro metaphors and the way they can inform one another as the form of design may refer to its program, or a connector may reflect the concept of articulation as a design concept. Both machines and people have this capability, however the unpredictable human range is far more diverse and original. The macro metaphor drives the micro while they both inform one another. Metaphors work by “reference to analogies that are known to relate to the two domains”. In other words there is apriori knowledge of these before they are spoken and when heard they are immediately found. [13] Metaphors are formed in the human discovery of the obvious where one analogy begets another which may or may not be relevant but be interesting enough to explore and find a new referent. AI may receive, store and associate these to its existing but has only its artificial collective repertoire. Hover, as Microsoft spell-check it can learn, assimilate and recreate. A” problem of the metaphor concerns the relations between the word and sentence meaning, on the one hand, and speaker’s meaning or utterance meaning, on the other”

                    [14] “Whenever we talk about the metaphorical meaning of a word, expression, or sentence, we are talking about what a speaker might utter it to mean, in a way it that departs from what the word, expression or sentence actually means”. Without apparent rhyme of reason metaphors of all arts have a way of recalling other metaphors of other times and places. ‘Human cognition is fundamentally shaped by various processes of figuration”. “The ease with which many figurative utterances are comprehended are has often been attributed to the constraining influence of the context” ………..Including [15] “the common ground of knowledge, beliefs, and attitudes recognized as being shared by speakers and listeners (architects and users(clients, public) As speakers architects, designers and makers “can’t help but employ tropes in every day conversation (design) because they conceptualize (design) much of their experience through the figurative schemes of metaphor (design). A metaphor involves a nonliteral use of language”. [16] A non-literal use of language means that what is said is for affect and not for specificity. Minimizing risk, metaphor is an abbreviated simile and to appreciate similarities and analogies which is called “appreciation”.

                    [17] In psychology “appreciation” (Herbert (1898) was a general term for those mental process whereby an attached experience is brought into relation with an already acquired and familiar conceptual system (Encoding, mapping, categorizing, inference, assimilation and accommodation, attribution, etc). Metaphors build an image in the (AI system) mind, that is to say we (AI) “appreciate” what we already know. I have always contended that we do not learn anything we already do not know. We learn in terms of already established knowledge and concepts. We converse reiterating what we presume the other knows, otherwise the other party would not understand. The other party understands only because he already knows. In this way humans and machines are alike. Early stages of AI architectural application with architectural metaphoric axioms is best executed on commercial, industrial projects with little interior detail and which can be closely monitored. The risk of failure or mis-design my be simply mitigated after the completion but before submittal to building officials for code reviewed. The risks are fairly minimal since there are already phases in the design process which firewall the errors being transferred from one stage of development to the other. This in and of itself minimizes risk; Along with AI/BIM checking risk will be controlled. The architect who assembles thousands of bits of information , resifts and converts form words to graphics and specification documents communicates the new proposed (the strange new thing) in terms of the known and familiar. The first recipients are the owner, building officials; contractors must read seeking confirmations of known and confirm its adherence to expectations. After its construction the users read familiar signs, apparatus, spaces, volumes, shapes and forms.

                  The bridge carries over from one to another what is already known .Even the strange that becomes familiar are both known but not in the current relationship. For example when we apply a technology used on ships to a building or a room which is commonly associated with tombs as a bank, etc. Both are generally known but not in that specific context. We could not appreciate it if it were not known .It is what Weiss calls commonalties and is the selection between commonalties and differences that makes a metaphor. About understanding and discerning between what is” true in fact” and “true in the model”. Since the gathering, assimilation and observation of human contributions is often exx43nstiol this is best done by humans to humans in teams known by some architects as “squatting”. [bb] Prototype theory is a mode of graded categorization in cognitive science, where some members of a category are more central than others. For example, when asked to give an example of the concept furniture, chair is more frequently cited than, say, stool.” [18] “Metaphors are generally used to describe something new by references to something familiar not just in conversation, but in such diverse areas as science and psychotherapy. For both men and machines metaphors are not just nice, they are necessary. They are necessary for casting abstract concepts in terms of the apprehendable, as we do, for example, when we metaphorically extend spatial concepts and spatial terms to the realms of temporal concepts and temporal terms. Metaphor is reasoning using abstract characters whereas reason by analogy is a straight forward extension of its use in commonplace reasoning.

                        [19] All this to say and as if there was a choice that architects have a choice where to make a new building by analogy or by metaphor. Analogies may be the ticky-tacks, office building, church, school building, fire station analogies to a first model verses an abstraction of a program into a new prototype. Is the analogy any less a work of architecture? Or do we only mean that works of architecture are works of art when they make abstractions? Humans are able to design by metaphor whereas an artificial intelligence designs by analogy. [M] “In processing analogy, people implicitly focus on certain kinds of commonalities and ignore others”. Noting these things an industry was created called the “housing industry’ churning out analogies rather than individual metaphors, leaving the metaphor to the context or theme of the development. It is famous architects who are mostly famous because they made metaphors and from them analogies were drawn. The analogous phenomenon has resulted in the nineteenth century Sears offering pre-designed and package barns ready to ship form Wisconsin to any where by mail order. Pre-engineered metal being and manufactured homes are all part of the analogous scheme of reasoning the built environment. Users have access to either and are able to shift perceptions. In commonplace users wanting to be fed by metaphorical architecture go to Disney, European, or urban entertainment and recreation centers. Las Vegas thrives on what I call "metaphoric analogies” abstractions of analogous building types. It is that synapse which attracts and beguiles the visitor hungry for authenticity and reality. Living in analogous urban replicas city dweller migrated to the suburbs in search of the metaphor of “a man’s home is his castle”. Today this metaphor has become an analogy as the metaphor proliferates and analogies are transferred from one to another state and country. We may be told a “cell is like a factory” which gives us a framework for analogy and similarity. [19] An analogy is a kind of highly selective similarity where we focus on certain commonalities and ignore others.

                  The commonality is no that they are both built out of bricks but that they both take in resources to operate and to generate their products. On the creative and architect’s side: “the central idea is that an analogy is a mapping of knowledge from one domain (the base) into another (the target) such that a system of relations that holds among the base objects also holds among the target objects”. On the user’s side in interpreting an analogy, people seek to put objects of the base in one-to-one correspondence with the objects of the targets as to obtain the maximum structural match”. Confronting a Bedouin village of tents a westerner faced with apparent differences looks for similarities. “The corresponding objects in the base and target need not resemble each other; rather object correspondences are determined by the like roles in the matching relational structures.” Cushions for seats, carpets for flooring, stretched fabric for walls, roof, and cable for beams and columns, etc. “Thus, an analogy is a way of aligning and focusing on rational commonalities independently of the objects in which those relationships are embedded.” However, there may be metaphors at work as well as the user reads the tent’s tension cable structure, banners and the entire assemblage in a “romantic” eclectic image of Arabness, metaphors beyond the imperial but of the realm of the abstract and inaccurate. Managing the process by quality checking the information from the fist domain minimizes risk assuring that analogy will be correct. “Central to the mapping process is the principle of “systematicity: people prefer to map systems of predicates favored by higher-order relations with inferential import (the Arab tent), rather that to map isolated predicates.

                    The systematicity principle reflects a tacit preference for coherence and inferential power in interpreting analogy”. Metaphors work by applying to the principle (literal) subject of the metaphor a system of “associated implications” [20] characteristic of the metaphorical secondary subject. These implications are typically provided by the received “commonplaces” (ordinary; undistinguished or uninteresting; without individuality: a commonplace person.) About the secondary subject ‘The success of the metaphor rests on its success in conveying to the listener (Reader) some quieter defines respects of similarity or analogy between the principle and secondary subject, “human and AI design by translating concepts into two dimensional graphics that which ultimately imply a multidimensional future reality. “Dubbing” (invest with any name, character, dignity, or title; style; name; call) and “epistemic access” (relating to, or involving knowledge; cognitive.), “when dubbing is abandoned the link between language and the world disappears”.

                 [21] Architectural metaphors are all about names, titles, and the access to that the work provides for the reader to learn and develop. At its best the vocabulary of the parts and whole of the work is an encyclopedia and cultural building block. The work incorporates the current state of man’s culture and society which is an open book for the reader. The freedom of both the creator and reader to dub and show is all part of the learning experience of the metaphor. As a good writer “shows” and not “tells” so a good designer manifests configurations without words. However objective, thorough and scientific; the designer, the design tools and the work gets dubbed with ideas (not techne) we may call style, personality, and identity above and beyond the program and its basic design (techne). It is additional controls, characterizations and guidelines engrafted into the form not necessarily overtly and expressly required. Dubbing may occur in the making of metaphors as a way in which the design itself is conceived and brought together. Dubbing may in fact be the process which created the work as an intuitive act. [22] Consider new concepts as being characterized in terms of old ones (plus logical conjunctives)” [E] As William J. Gordon points out we make the strange familiar by talking about one thing in terms of another.

                   [22] “Knowledge” would not itself be conceptual or be expressed in the medium of thought, and therefore it would not be cognitively structured, integrated with other knowledge, or even comprehended. Hence, it would be intellectually inaccessible”. In other words we would not know that we know. Where knowing is the Greek for suffer, or experience. This was the Greek ideal proved in Oedipus; “through suffering man learns”; we know that we know. Therefore, when we observe that architecture makes metaphors we mean that we know that we know that works exists and we can read authors messages. We learn the work and improve the more artificial intelligence impacts the future of architecture. While architectural metaphoric axioms are proactive the context is benign and does not pose the catastrophic calamity doomsday- sayers prophesied. Still, it is good to know that Axioms provide the checks and balances to a successful and safe AI performance. Postscript: Aesthetics, human to machine admixture and AI as complex design tool Today it is possible for AI to design complex structures making possible the use of materials and structures heretofore uneconomical, too costly and time consuming to ever be considered, for example the steel light weight truss system [cc] of a conventional roof .

               Not withstanding the work of Afrred I Tauber’s Elusive Synthesis: Aesthetics and Science and considering the five senses of human experience defining aesthetics at best warrants a negotiated and interdependency between man and his AI system. What can be systemically or specifically programmed will never reconstruct the human that directly senses and then with a sixth sense makes some illogical but yet pleasing redirection to himself feel, experience and enjoy the environment. Aesthetics is a guiding principle in matters of artistic beauty and taste, metaphor is the warrant to taste and is used to form works of art and architecture. Aesthetics is also reasoning matters having to do with understanding perceptions. While AI tools may be designed to replicate man’s abilities to navigate, perceive, and judge the environment, AI cannot enjoy the experience as one man (or the collective of all men).Then the AI device still refers back to its creator to make sense of the events. It is to this extent that AI thinking can intelligently, without the normative sense feedback, be involved in aesthetic experience, judgment and consciousness. It is its limitation of total sovereignty, autonomy and independence of AI. It is likewise questionable, as a design device, to replace human designers as the affects the quality of the aesthetics of the design outcomes.

                  But there is no doubt that the AI designer can change the paradigms of design outcomes where time, space and cost would otherwise be prohibited and therefore could potentially expand the, scope , breadth and depth of programs to fully design green buildings, solve environmental issues, optimize, use of space, materials and use materials in new ways. Multi-disciplinary access from arts, sciences, philosophies are economical and feasible with enough capacity and devises so that buildings and their systems can include the sculptors aesthetics for shapes and forms, the musicians ear for lyrical, harmony and the poets sense of rhyme, sense and inference, Not to mention behavior psychologist parameters of sequences and impacts of color, spaces, and distances, etc. AI design will also facilitate client, user and occupant participation in the design process. So while AI can perceive and act on signs of the senses the artificial is not natural and has no natural understanding of the senses. Aesthetically, as “beauty is in the eyes of the beholder” the AI does “be” but not “behold”. In fact, since the world in which man inhabits us actually design more and not les control of our habitations, that is while we wish our habitations to be designed more humanely than machine, meaning that ideally it would be designed by us. “Us” being natural man augmented by a device but not managed by that device.

                  We do not desire the aesthetic of machines. As example we don’t want to live in a factory, industrial park or warehouse. Even living in a space capsule can only be for limited times as it is devoid of nature. It is nature and free will which artificial lacks. AI is not a sinister possibility but an opportunity to optimize the efficiency of nature in human terms. Human architects both compose the program and manage to reify its contents from words to diagrams and diagrams to two dimensional graphics and three dimensional models to reify and bring- out (educate) the user’s mind and fulfillment of unspoken and hidden needs. Needs which may or may not have been programmed and intended; the metaphor is the final resolution until it is built and used. Then it is subject to further tests of time, audience, trends, social politics, demographic shifts, economics, and cultural changes.

                The aesthetics of the process and the product are indigenous to natural man metaphor and a can be metaphorically assimilated by artificial intelligence architects. Conclusion: The risks which AI architectural axioms mitigate are benign, local and parochial to the profession and pose little danger to the general public. However, as a model and safe to develop it may be the proving ground and fist small step to bolster public confidence to consider applying AI to other applications which pose more of a risk public welfare. 

 

 

Web Site: Managing the benefits and risks of architectural artificial intelligence



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