Bibliographic record
Abstract
How intelligent is the typical smart-city design approach? In an era when artificial intelligence and big data promise to improve urban life in unprecedented ways, are smart cities being imagined and designed in ways that are actually inspiring and truly innovative? This project examines the proposals and approaches of Sidewalk Labs’ designs for Quayside, located in Toronto Canada, and asks how intelligent, really, is the city the propose. Sidewalk Labs (the city-building subsidiary of Alphabet, Google’s parent company), in partnership with Waterfront Toronto (a government-appointed nonprofit development corporation), claims to be reimagining cities from the internet up.” But is their project more than a corporate optimization of the usual “smart” themes of sustainability, data collection, efficiency, economic development, and technology? A Smart Intelligent City poses the question, how could Google A.I. and Machine Learning technology, specifically Google Cloud Vision, be utilized as a design tool and source of design material to enhance or alter the conventional design and planning process used by Sidewalk Labs for Sidewalk Toronto at Quayside? An Intelligent Smart City utilizes Quayside’s current technologies and digital infrastructures to dynamically generate new visual environments within the built environment with the use of Google Cloud Vision technology as the foundation to speculate on a similar but different algorithm, an architectural one. The algorithm detects “architecture” from the ubiquitous influx of image uploads and stored into the Google Cloud by the individuals residing within the Quayside community, in addition, the algorithm also detects “architecture” within images that are searched through keywords and faceted navigation, a typical Google image search. Once detected, the algorithm then explores the architectural images, analyzing and examining the images’ contents, attributes, format, etc. to determine if the images qualify to move forward in the design process – this examination and approval of images is done in collaboration with the architect. Finally, the algorithm operates on a collection of qualified/approved images to generate a “new architecture.” Some of the operations that function within the algorithm consist of blend, collage, merge, stylize, crop, filter, etc. The new architecture is then generated in the form of a watch, incorporating an assemblage of images that contain an array of architectural elements. The algorithm is programmed to resemble methods and techniques in which architectural images are produced by contemporary architects – through a series of operations incorporated within architectural, engineering, animation, etc. computing and design software. As the algorithm continues to generate swatches over time, it learns which types of images generate the best swatches. The city visually responds to its complex environment, it’s multi-layered system, by processing an additional layer from its inhabitants – their image activity. Architecture in the city is no longer fixed; it is intelligent enough to constantly generate its own image.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".