Testing AI
in Smart Cities and
Communities

Services

Welcome to the services of the CitCom.AI Testing and Experimentation Facility. Our services range covers the testing and experimentation of AI services for smart cities.

Categories

Interested in hearing more? See below the "Categories"
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Physical services enable AI innovators to test in city infrastructure with guidance, street closure, and installation support, reducing deployment time and advancing Technology Readiness Levels (TRL). 

A select group of nodes facilitates faster collaboration by streamlining the interaction between AI innovators and cities. 

Access to real infrastructure enhances TRL by validating solutions against real-life scenarios. Citizen engagement involves ecosystem participation for tailored testing and validation. 

Physical infrastructure, combined with a regulatory sandbox, ensures a legally compliant testing zone, addressing regulations and adaptations. This comprehensive approach accelerates innovation, reduces deployment time, and fosters collaboration, benefiting both AI innovators and cities.

Photo: Joshua Sortino. Unsplash

Virtual facility services provide easy access to computing resources, supporting remote experiments and real-time data analysis. Data models enhance interoperability, offering domain-specific datasets. 

Physical facility services enable AI innovators to test in city infrastructure, receiving support for procedure guidance, street closure, and installation. 

This accelerates solution validation, advancing Technology Readiness Levels. Citizen engagement involves creating specific scenarios, and physical infrastructure, combined with a regulatory sandbox, ensures legal compliance for testing zones. This comprehensive approach closes gaps between AI innovators and cities, reducing deployment time and fostering collaboration, advancing innovation and creating value across domains.

Photo by Chris Ried on Unsplash

Algorithm creation and validation services, in collaboration with AI innovators. The co-created algorithms can validate others in a feedback loop, involving data collection, storage, preprocessing, machine learning frameworks, model training, evaluation, hyperparameter tuning, deployment, scalability, and monitoring. 

The process prioritizes security, privacy, and continuous improvement through a feedback loop, ensuring effective and efficient AI algorithm development aligned with specific project requirements and preferences. 

The comprehensive approach addresses data quality, infrastructure, and performance metrics, promoting seamless integration, encryption, and access control, enhancing the deployment and performance of AI algorithms.

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Assistance ensuring adherence to laws, regulations, and ethical standards. Compliance assistance involves audits, program development, and training to mitigate risks, with experts adapting practices to regulatory changes. Ethics assistance promotes ethical behaviour, guiding data use and developing codes of conduct. 

These efforts play a key role in proactive risk management, preventing legal and ethical risks, regulatory violations, and financial losses. These services are crucial for understanding and complying with evolving EU landscape requirements. In virtual facility services, security and compliance prioritize data protection through robust measures like encryption, access controls, and authentication. 

Overall, TEF supports responsible operations, mitigating risks, and upholding integrity.

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This service category focuses on the desirability and viability of AI innovation. 

While most TEF services assess feasibility, there's a crucial need to evaluate the actual impact on the environment, stakeholders, and end-users. 

Desirability relates to meeting stakeholder needs and solving problems, while viability considers the business model, revenue generation, costs, and benefits. 

TEF sites excel in answering these questions by tapping into local ecosystems, facilitating contact with relevant stakeholders, and addressing parameters linked to impact and outcomes.

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Services that help identify opportunities, define innovation scope, and align stakeholders, while also mitigating risks by evaluating financial viability, technical feasibility, and cities’ needs.

Namely: understanding target audiences, assessing organizational viability, and evaluating technical feasibility. Various activities are conducted based on idea maturity and customer requirements, including exploring, co-creating, and validating solutions to prepare for experiments.

These services - focussing on customer needs and maturity, range from (i) idea exploration, understanding problems, assessing resources (ii) co-creation to characterise solutions and (iii) preparatory activities to select the best technical approach, including data, infrastructure, and expertise needs.

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These services engage A Community and Ecosystem Engagement Service within the context of AI innovation focuses on creating a collaborative platform dedicated to AI innovation that brings together researchers, academics, institutions, industry partners, and other stakeholders. The goal is to facilitate knowledge exchange, collaboration on research & development projects, and the development of a vibrant R&D community.

Suppliers can showcase their technology and AI expertise, backed by capital investors looking for opportunities beyond state of the art.

Cities and other potential buyers can share their interests by presenting real-life use cases for which they seek technology innovations.