Artificial Intelligence and smart cities through the looking glass: real-time application challenges
Bibliographic record
Abstract
Currently, there are 3.6 billion people residing in urban areas, and it is projected that by 2050, 75% of the global population will be living in cities. This will result in approximately 80 billion interconnected devices by 2020i. The COVID-19 pandemic has caused significant global impact and disruption, prompting us to rethink the future of cities and consider the type of cities that can support humanity. In particular, armed conflicts and the aftermath of the pandemic raise important questions about the kind of cities needed in a predominantly urban world. How should we envision and reimagine the future of cities? What should our cities strive to become? What are the potential scenarios for growth and development? This study explores real-time applications and provides insights into the ongoing development of technology to support smart cities and create a better future in terms of outlook, systems, and services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".