NEW YORK CITY - LISBON
NEW YORK CITY - LISBON
A Global Network Based in NYC to Study
and Accelerate Artificial Intelligence for All
ARCx fosters dialogues, builds international and multidisciplinary teams, and creates knowledge and tools to address the research, educational, methodological, organizational and societal needs necessary for high-impact and ethical AI innovation. We work closely with academics, entrepreneurs, investors, public authorities, civil society and industries. Some of our clients and partners include The City of New York, New Lab, NYC Economic Development Corporation, the New York Academy of Sciences, Columbia University, NYU Gov Lab, Cornell Tech, the Portuguese Parliament, the Municipality of Lisbon, Global Futures Group and the Kazakhstan’s Academy of Public Administration.
Our work follows a six-pillar methodology that allows us to operate in multiple regions of the world at the intersection of multidisciplinary approaches, artificial intelligence, and innovation.
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1: Research & Discover
2: Learn & Evolve
3: Decide & Innovate
4: Deploy & Add Value
5: Evaluate & Improve
6: Disseminate & Impact All
Research & Discover
Identify opportunities and challenges for AI innovation
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Industry-specific AI Innovation Ecosystem Reports
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Organization-specific AI Innovation Drivers Reports
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AI Algorithms Mapping Reports: Evaluations, Integration, and Governance
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AI Challenges for Business Solutions Scouting with Technology Firms and Academia
Learn how to augment AI opportunities, mitigate challenges and mobilize/upskill AI talent
Craft and decide on an AI innovation agenda and make strategic decisions (ex. implement vs. scale-up)
Deploy & Add Value
Train, Test, Validate and Deploy AI algorithms, build public and private partnerships and mobilize other resources
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Advisory Groups and Strategic Partnerships Report
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Stakeholders, Data, AI/ML techniques, Talent and Computational Power Report
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Research and development Collaborative AI Labs: from modeling to deployment
Disseminate & Impact All
Disseminate best practices, recommendations and lessons learned as well as assess system’s and community impact
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​Industry-wide Report
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Organization-specific case studies
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Papers series and Academic Journal
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AI Webinars
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Standards and benchmarks: transparency, fairness and trust
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Podcasts