Davos Agenda

National AI strategies can move up the ranks using value networks. Here's how

Yellow block letters spelling "Ai" sit across a green yellow and black blurred background: The key to a leading AI strategy is people.

The key to a leading AI strategy is people. Image: Unsplash/Jackson Sophat

Joseph Wehbe
Chief Artificial Intelligence Ecosystem Builder, DAIMLAS
Phani Nagarjuna
Tech Entrepreneur and Investor, Circlesecurity.ai
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Davos Agenda

This article is part of: World Economic Forum Annual Meeting

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  • In a government AI value network, there are eight stakeholders that include upstream resources, downstream channels and resource providers supporting a shared goal to build the next generation of talent to populate ecosystems.
  • To effectively execute government AI strategies, we must solve the AI education problem where those with and without an AI education need a pathway to achieve their goals.
  • A virtual platform can operationalize government and grassroots AI policies to build talent by creating connections and sharing knowledge that benefits the whole ecosystem.

According to the 2022 Global AI Index, the United States is ranked first in artificial intelligence (AI) based on talent, research and development (R&D) and commercial application.

The US government introduced several strategies and policies to achieve this ranking, such as:

  • The Congressional AI Caucus.
  • The National Security Commission on AI.
  • The National AI Research Resource Task Force.
  • The National AI Initiative Office.
  • The National AI Advisory Committee.
  • National Science Foundation AI programmes.

While each government agency may have its own plan to execute its policies, what we know is that the Office of the White House Science and Technology Policy focuses its AI initiatives on six main areas: R&D, research infrastructure, advancing governance, international cooperation, government use of AI and education.

However, if we believe that AI is about the people, how can federal, state and local governments do more to evaluate their policies’ effectiveness in that area? For example, are they building the next generation of talent, advancing workforce development and encouraging innovators from underrepresented backgrounds or underserved communities to engage with AI federal programmes and services?

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People-first approach

In the United States, the AI ecosystems in Boston and Silicon Valley are anchored by the educational institutions that graduate a pipeline of talent. In Canada, which launched the world’s first National AI Strategy, the ecosystem is anchored by scientific advisors and their host research centres of excellence. The common factor between the two North American neighbours’ AI ecosystems is that they revolve around people.

AI garners much public opinion and policy but students of AI rarely have a say in the progression of their discipline. At the same time, they bear the burden of delivering on and navigating the potential of the future. Perhaps the better way forward is to build an environment that brings in students’ voices within a culture of learning, innovation and research.

Centring AI students

An AI student is an individual who is enrolled in a degree-granting AI programme recognized by an AI centre of excellence or Ministry of Education or matriculated in any university degree programme that participates in AI research or projects connected to their university’s AI centre of excellence.

AI is now infiltrating all areas of life and while the educational barrier to an AI degree is extremely high and practitioners across different sectors – marketing, sales, legal, finance, operations, project management, public sector etc. – all want AI skills, they must find their own educational path in AI. But just like we hold the field of medicine to the highest standards, we must also keep the AI field to the highest academic standards.

The expectations of the next generation of AI talent are great – to uphold the highest ethical standards, address societal challenges and advance critical and emerging technologies. Yet, students cannot contribute to regulations, public opinion or policy. Seeding a culture of learning, innovation and research builds an environment bringing students into the stakeholder matrix on AI policy. To accomplish this, governments should create pathways for AI students to contribute to every level, from national to local.

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AI ecosystem vs AI value network

An AI ecosystem is made up of various stakeholders:

  • AI centres of excellence.
  • Scientific advisors.
  • Risk capital.
  • Institutional capital.
  • AI degree-granting universities.
  • Non-AI degree-granting universities.
  • AI students and alumni.
  • Non-AI students.
  • Practitioners (AI and others).
  • AI entrepreneurs.
  • AI opportunities.
  • AI research labs.
  • AI regulators.
  • AI funding.
  • AI ethicists.
  • AI projects, computation and hardware resources.
  • AI government grants.
  • AI data sources.
  • Government representation at federal, state and local levels.
  • AI think tanks and NGOs.

Consider the value network’s shared goal of building the next generation of AI talent, each resource and stakeholder’s activity adds value to the end goal of an ecosystem. All the resources are streamlined, not compartmentalized or one-to-one, because they are all in effect sitting at the same table and exchanging value.

A virtual platform

Few countries command trust that their AI policies are effectively accessible to their constituents. A new cross-government platform that enables verified stakeholders in a local ecosystem to have a pathway to achieve their AI goal is the future of how to execute a government AI strategy virtually.

If we believe former Google Brain co-founder and Stanford University’s Professor Andrew Ng’s claim that “AI is the new electricity,” just like electricity needs its infrastructure and network to function, AI needs the same.

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How is the World Economic Forum ensuring the responsible use of technology?

Utilizing a software layer that builds your virtual AI ecosystem avoids needing a physical interface. The goal of a virtual ecosystem is to spur cross-national inclusive and diverse AI ecosystems while intentionally focusing on hubs that don’t traditionally participate in these ecosystems.

This platform provides networks, tailored support structures and services vital to the ecosystem’s growth. It’s a human-centred approach of upstream resources and downstream channels that empowers governments with the skills, tools, networks and capital needed to thrive.

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The views expressed in this article are those of the author alone and not the World Economic Forum.

Related topics:
Davos AgendaArtificial Intelligence
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