Pattern recognition in artificial and natural intelligence
Following the idea of intelligence is design invention. Integrated, built upon previous innovation, and driven toward a reconfiguration of the known or accepted, invention is like the patent office of possible design approaches; it is the repository of potentials from which we draw to produce alternative readings or formulations. Invention, for us, is embedded in architecture and urban design that rethink organizational systems, spatial and formal configurations, type, technology, and constructed and natural systems. These sorts of projects that invent, delight, and perhaps confound do so in various ways. Seemingly irrational programmatic laminations can produce a new rationality that reorients preconceived ideas of function and adjacency. Innovation in structure and materiality can change ideas of possible or accepted forms and dimensions of buildings. The work that we include in this book is presented in written, drawn, modeled, and built form. The projects range from the theoretical and speculative to the built, but building and the way things are put together is central to all the works. We test our beliefs as architects through the logics of built form. Issues of efficiency, technology, and constructability are integral to our design process, not externally applied upon later assessment. For us, a pattern-recognition scheme is incomplete without the material, physical, and phenomenological concerns that built form requires. And the work that we produce is an amalgam of the forces—both determined and imposed—and elements—both stable and unstable—in an architectural agglomeration. In this regard, the ultimate goal of pattern recognition, as we define it, is not mere pattern, but more building. Pattern Recognition in Artificial Intelligence In artificial-intelligence parlance, pattern recognition defines methodologies for the classification of information “observed” by machines. These machines use previously described or statistically derived patterns to make voice, face, and hand identifications, as well as text and graphic determinations, and the patterns they use conform to one of three basic categorical methodologies: syntactic, statistical, and neural. A syntactic classification scheme operates on the structural interrelationships among elements; a statistical classification scheme analyzes data, statistics, and numerical information in order to group and identify similarities and traits; a neural classification scheme is an adaptive, network-based 9
analysis that identifies organizational systems by means of nonlinear statistical data–modeling methodologies. The applications of these AI processes mainly appear in the programming of computers, directing the computers to do what human beings (and bees and starlings) innately do: extract organizations of constituent components that define points in particular multidimensional spaces. No surprise, then, that the term carries a fascination and an eerie sci-fi connotation, since humans are transferring identification and decision-making functions over to machines. Witness the burgeoning field of biometrics, in which machines are programmed to make realtime identifications of people by using differences as points of reference in a field of likeness. And, similarly, in architecture, where the use of computer scripting through data input and algorithmic procedures generates forms that are considered to be products of their data. Suffice it to say that we reject this school of thought for our practice and process, because it gives away the agency of the architect to artificially cognizant but nonsentient machines. For us, pattern recognition, in its observations, diagramming, and focus on construction methods, necessitates the architect as the driver of intentionality and, ultimately, form. In our work, we transform these three basic AI organizational constructs (syntactic, statistical, and neural) into dynamic and nonlinear methodologies. In this way, pattern recognition functions more as a filter for understanding and navigating a problem—as the means of a design (process), not the end (product). Pattern Recognition in Natural Intelligence Visual arts, scholastic and optics testing, the ability of birds to flock and of bees to recognize hive mates and human faces, are all non-AI examples of pattern recognition. The diversity of users of this process of classifying and acting on observations of patterns attests to the fundamental nature of pattern recognition as a navigational and survival trait in human and animal cognition. Pattern, as any traffic engineer will tell you, is about flows, bottlenecks, detours, and schedules. It is also about crime solving, weather modeling, finding cures for diseases, and making financial determinations. In William Gibson’s novel Pattern Recognition,1 the ability to see and interpret patterns is fundamental to survival in global subcultures of underground Internet video trading and “cool hunting” (a rarified art form whose high period peaked in the dot-com