An Intelligent Smart City
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,006 |
| Communication savante | 0,010 | 0,013 |
| Science ouverte | 0,001 | 0,008 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,033 | 0,012 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».