/ P e o pl e / jul-sep 2019
ADQ3_018_021_AI Architecture_V1.indd 19
learn how to better display or interpret lakes of streaming data. The more data that computers are fed, the more capably they seem to crush complex tasks; partly through their suprahuman powers of pattern recognition. Machine vision scientists depend on open-source datasets comprising images of objects that are classified and labelled to allow comparisons with new images containing similar objects. The world’s largest object dataset, ImageNet, contains more than 14 million crowd-labelled thumbnails, which can be downloaded to help identify, for example, different types of natural places, buildings, rooms, products such as fridges or dishwashers, furniture, fabrics, clothes, and apparel such as hats or sunglasses. Vision boffins classify database images according to whether they depict ‘things’ (box-frameable objects like chairs, people or windows) or ‘stuff’ (matter with no clear boundaries, like a patch of sky, an office corridor, a wall, a hillside or a street). Ironically, the image databases now being assembled to support AI analytics all depend on the ‘artificial, artificial [natural] intelligence’ of humans working online to label and cross-check the images uploaded by database compilers. One busy conduit is Amazon’s Mechanical Turk (AMT) portal, which matches employers (such as public research groups) wanting freelancers to contribute to specific human intelligence tasks (HITs). One recent HIT, to assemble and correctly label 328,000 thumbnail images of ‘common objects in context’ for the Microsoft COCO dataset, required 70,000
Architecture & design
One of AI’s most promising uses is for robots to replace humans in performing extremely dangerous tasks: such as shimmying along narrow cavities to replace damaged wiring or record material stresses. Czech writer Karel Čapek first coined the term robot in his 1921 play R.U.R: Rossum’s Universal Robots – and today’s humanoid versions, such as Boston Dynamics’ Atlas and Honda’s Asimo, are astonishingly agile and sophisticated. Non-humanoid robots, swivelling from fixed bases or traversing across gantries, are already printing small dwellings in masonry or powder-resins, or assembling timberwork as adroitly as a master carpenter. Artificial intelligence was ignored by most built environment professionals until satellite-enabled telecoms caused widespread apprehensions during the 1990s, systemic disruption during the 2000s, and now inevitably, new ways of understanding and doing things. Today AI brings another wave of unfamiliar technologies and terms – including augmented intelligence, where machines are intended to improve human abilities to decide and perform. This seems less threatening than artificial intelligence, where machines increasingly replace humans, to a tipping point known as the Singularity (the term popularised by Ray Kurzweil). All intelligence, artificial or natural, flows from competent processing of information. Most AI researchers have abandoned their early reliance on pre-programmed rules to solve problems. Instead they are evolving machine learning, where computers use algorithms to
above Tianjin Library, Singapore. Photography by Ossip van Duivenbode BELOW Richard Buckminster Fuller’s 1928 vision of a ‘4D Air-Ocean World Town Plan’.
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26/7/19 4:49 pm