Designing a National Hub on Artificial Intelligence (AI) for a country like Romania requires a well-thought-out strategy that takes into account the country’s resources and goals. Such a National Hub for AI would be a centralized platform for AI research, development, and innovation. Its purpose would be to facilitate collaboration between academic institutions, private companies, and the government, to advance the field of AI and promote its use in various sectors. The architecture of the National Hub for AI could include the following components:
- Data Management: This component would be responsible for collecting, storing, and managing the vast amounts of data required for AI development. It would also include data quality checks, data cleaning, and data processing functionalities.
- Machine Learning and AI Development: This component would include the tools and resources required for AI development, such as libraries, algorithms, and frameworks. It would provide a platform for researchers and developers to collaborate on AI projects.
- High-Performance Computing: This component would include a high-performance computing infrastructure to support large-scale AI computations. It would provide researchers and developers with the necessary computational resources to train and run AI models.
- Education and Training: This component would be responsible for providing education and training programs to equip individuals with the necessary skills and knowledge to work with AI. It would provide courses, workshops, and seminars on AI-related topics.
- Innovation and Entrepreneurship: This component would focus on fostering innovation and entrepreneurship in the field of AI. It would provide resources and support for startups and entrepreneurs to develop and commercialize AI-based products and services.
- Policy and Regulation: This component would be responsible for developing policies and regulations related to AI. It would work closely with the government to ensure that AI is developed and used ethically and responsibly.
In creating a cost-effective and multi-faceted hub we must consider the following aspects:
- Define the goals and objectives: The first step in designing a National AI Hub is to clearly define its goals and objectives. This includes identifying the areas where AI can have the biggest impact and the specific needs of the country.
- Involve stakeholders: Involve relevant stakeholders in the planning and design process, including government agencies, academia, industry, and the general public. This will ensure that the hub takes into account the needs of all relevant parties and that it is designed to meet the needs of all stakeholders.
- Leverage existing resources: Romania has a rich pool of human capital and resources that can be leveraged to create a National AI Hub. This includes universities, research institutions, and existing technology companies.
- Focus on interdisciplinary collaboration: AI is a multidisciplinary field that requires collaboration between experts in areas such as computer science, engineering, mathematics, and the social sciences. The National AI Hub should foster interdisciplinary collaboration and partnerships to maximize its impact.
- Foster entrepreneurship and innovation: Encourage entrepreneurship and innovation in the AI field by providing support and resources to startups and small businesses. This will help to create a thriving AI ecosystem in the country.
- Invest in infrastructure and talent: Investing in the right infrastructure and talent is crucial to the success of a National AI Hub. This includes investing in high-speed internet connectivity, cloud computing, and data centers, as well as attracting and retaining top AI talent.
- Foster international partnerships: Building international partnerships and collaborations with other AI hubs and organizations can help Romania to access new technologies, expertise, and best practices in the field.
The National AI Hub could be designed as follows:
- Hardware Infrastructure: The hardware infrastructure of the National AI Hub should be able to support a variety of AI models, including those for quantum computing, autonomous driving, and large-scale data processing and analysis. This could include high-performance computing systems, data storage systems, and cloud computing resources.
- Software Infrastructure: The software infrastructure of the National AI Hub should provide access to a wide range of AI development tools, including machine learning frameworks, data visualization tools, and programming languages. The hub should also provide access to pre-trained AI models, data sets, and APIs to support AI development and deployment.
- Organizational Structure: The National AI Hub could be organized as a central government-supported entity with departments focused on research and development, operations, business development, and marketing and outreach. A board of directors or advisory committee could provide oversight and guidance.
- Operational Staff: The operational staff of the National AI Hub should include a mix of AI researchers, engineers, and data scientists, as well as support staff for administration, finance, and HR. The size of the operational staff would depend on the scale and scope of the hub’s activities.
- Research Purposes: The National AI Hub should provide resources and support for AI research and development, including access to cutting-edge technologies and data sets. This could include a research and development department focused on advancing the state of the art in AI.
- Small to Medium-sized Businesses: The National AI Hub should provide resources and support for small to medium-sized businesses looking to adopt AI technologies. This could include access to pre-trained AI models, data sets, and APIs, as well as training and educational programs.
- Large Enterprises: The National AI Hub should provide resources and support for large enterprises looking to adopt AI technologies, including access to cutting-edge technologies, data sets, and pre-trained AI models.
- Service Providers: The National AI Hub should provide resources and support for service providers looking to integrate AI into their offerings. This could include access to pre-trained AI models, data sets, and APIs, as well as training and educational programs.
- Weather Forecasting, Forecasting Dynamic Social Processes, Monitoring and Predicting Cyberattacks for Governmental Structures: The National AI Hub should provide resources and support for AI-powered solutions in these areas, including access to data sets and AI models, as well as training and educational programs.
- Fighting Against Fake News, Terrorists, and Cyberattacks: The National AI Hub should provide resources and support for AI-powered solutions in these areas, including access to data sets and AI models, as well as training and educational programs.
- Quantum Computing, Autonomous Driving, Energy Management and Infrastructure Monitoring, Digital Twins and Predictive Maintenance, Traffic Management, Safety, and Security: The National AI Hub should provide resources and support for AI-powered solutions in these areas, including access to data sets and AI models, as well as training and educational programs.
- Health: The National AI Hub should provide resources and support for AI-powered solutions in healthcare, including access to data sets and AI models, as well as training and educational programs.
- Agriculture, Personalized Medicine, Food Security, Genomics, and Stem Cells: The National AI Hub should provide resources and support for AI-powered solutions in these areas, including access to data sets and AI models, as well as training and educational programs.
The cost of the initial investment and annual operational activities for a National Hub on Artificial Intelligence that serves all the use cases mentioned would be substantial. It would likely require a significant investment in hardware and software infrastructure, as well as staffing and resources to support the various AI models and applications. For example, the hardware infrastructure would likely include high-performance computing systems, storage systems, and networking equipment. Additionally, there would be a need for software infrastructure, including databases, cloud computing platforms, and development tools.
The staffing requirements would depend on the complexity of the use cases and the number of AI models being developed and maintained. A team of data scientists, software engineers, and AI experts would be needed to support the development, deployment, and maintenance of the AI models.
It is likely to be in the millions or tens of millions of dollars range for the initial investment, and hundreds of thousands to millions of dollars for annual operational activities.
Centralization vs. Decentralization: Finding the Optimal Infrastructure and Organization for AI Innovation
When it comes to building a national AI hub, three approaches can be taken: a centralized infrastructure and organization, a distributed one, or a hybrid one. All approaches have their pros and cons, as outlined below.
The choice of infrastructure ultimately depends on the specific needs and goals of the country. Each approach has its own strengths and weaknesses, and there is no one-size-fits-all solution.
A centralized infrastructure is ideal for situations where resources need to be concentrated in one location and managed consistently. This approach can foster collaboration and innovation among those who are physically located near the hub.
On the other hand, a distributed infrastructure is best for situations where resources need to be dispersed across multiple locations. This approach can provide greater access to resources for those who are not located near the hub but may create barriers to collaboration and resource utilization.
A hybrid infrastructure can provide the best of both worlds, combining the benefits of centralized and distributed systems. It can cater to the needs of a diverse range of users but may require additional resources to set up and manage both the centralized and distributed components.
The Challenge with the Right Decision: What is Missing in Selecting Between Several Variants, All with Pros and Cons?
In cases where multiple options have pros and cons, decision-making can be challenging. This is particularly true for decisions related to infrastructure investments, which often involve political factors and competing interests among stakeholders. In many cases, it may not be feasible or practical to consult with all stakeholders or to hold a formal vote to make the decision.
In such situations, it is important to take a strategic and holistic approach to decision-making, considering the potential benefits and drawbacks of each option, as well as the specific needs and goals of the organization or country. By prioritizing transparency, inclusivity, and adaptability, decision-makers can work to ensure that the chosen option is well-aligned with the needs and interests of all stakeholders, while also being feasible and cost-effective in the long term.
In my opinion, to overpass this dilemma, a set of clear strategic use cases is essential to determine which format of the organization is best. A strategic use case is a specific, high-level example that illustrates how the country can use AI to achieve a specific goal or objective. Strategic use cases can help to identify the requirements, constraints, and opportunities associated with different AI applications and use cases, and can help to determine which infrastructure format is best suited to achieve the desired outcomes.
In addition to strategic use cases, it’s also important to have a clear understanding of the resources and expertise available, as well as the budget and timeline for implementation. A comprehensive analysis of these factors can help to determine which infrastructure format is the most feasible and effective for achieving the desired outcomes.
Position for a Final Decision
In a developing country like Romania, it is essential to prioritize use cases that benefit all citizens. Therefore, the first priority should be to consider strategic use cases that have a broad impact, such as (a) forecasting dynamic social processes, (b) monitoring and predicting cyberattacks for governmental structures, (c) improving weather forecasting, (d) fighting against fake news, terrorism, and cyberattacks, (e) better energy management and infrastructure monitoring, (f) improving health, genomics, stem cells, and personalized medicine, and (g) enhancing agriculture and food security. By focusing on these use cases, the National AI Hub can contribute to improving the quality of life for all citizens in Romania.
If we accept this perspective, a centralized infrastructure would likely be the most efficient and effective option. Centralizing the hardware and software infrastructure in one location would provide the necessary computing power and data storage capacity to support the development and deployment of AI-powered solutions in various areas, such as weather forecasting, cybersecurity, healthcare, and agriculture. Additionally, a centralized infrastructure would make it easier to share data sets and AI models and to provide training and educational programs to a wider audience.
Also, it’s important to note that a hybrid infrastructure could also be a viable option, particularly if the centralized infrastructure is located in an area that is not easily accessible to all stakeholders. But a hybrid infrastructure is relevant only under some conditions. For example, if the distributed part of the infrastructure is located in six different locations, the annual operational cost may be higher than a centralized infrastructure. This is because each location would require its own hardware and software resources, as well as maintenance and support staff. The cost of maintaining multiple locations could be significantly higher than the cost of maintaining a single centralized location. In addition, a distributed infrastructure could be less efficient in terms of resource utilization. If the hardware and software resources are not fully utilized in each location, this could result in wasted resources and increased costs. It may also be more difficult to manage and monitor a distributed infrastructure, which could lead to higher operational costs.
Assuming an initial investment of 23.6 million euros on the infrastructure for the National AI hub, we can estimate the relative annual operational costs for the centralized and distributed infrastructures as follows:
- Estimated annual operational cost: 10% of the initial investment
- Annual operational cost = 0.1 x 23.6 million euros = 2.36 million euros
Distributed infrastructure (e.g., six different locations in several centers):
- Estimated annual operational cost: 15-20% of the initial investment
- For simplicity, let’s assume an average of 17.5% of the initial investment
- Annual operational cost = 0.175 x 23.6 million euros = 4.13 million euros (e.g. around 0.7 million euros/year/location)
Therefore, in this scenario, the estimated annual operating cost for the distributed infrastructure would be around 75% higher than that of the centralized infrastructure. If we add the cons and pros to this discussion, we can also add here the opportunity costs. Assuming the additional annual cost of the distributed infrastructure is 1.77 million euros (4.13 million euros – 2.36 million euros), the opportunity cost would depend on the potential benefits that could have been obtained with this amount. Some potential benefits that could be considered are investing in additional AI research and development, improving education and training programs, or investing in infrastructure development.
An important aspect of such kind of investments is the need to upgrade the infrastructure. For the case of an AI infrastructure, the time horizon for calculating its economic relevance is 10 years. As technology evolves rapidly, it is difficult to estimate a precise amount for upgrades. However, a general rule of thumb is that businesses typically reinvest 10-15% of their revenue in upgrading their infrastructure annually. Assuming a conservative 10% reinvestment rate, the amount needed to upgrade the AI infrastructure would be approximately 2.36 million euros annually for an initial investment of 23.6 million euros. For the initial investment of 23.6 million euros, assuming a 10-year period and a 10% annual reinvestment rate, and that in the first 3 years will be no investment in upgrading, the total amount invested in upgrading the infrastructure would be approximately 7 years x 2.36 million euros/year = 16.52 million euros for a centralized infrastructure and about 28.42 million euros for a decentralized infrastructure due to the replication of several cost chapters.
So, over a 10-year period, we have to consider the following budgets: initial investment + annual investments for upgrading x (time horizon -3 ) + annual operational costs x time horizon, which in our case results in about 23.6 million euros + (2.36 million euros x 7) + (2.36 million euros x 10) = 63.72 million euros for the centralized infrastructure, and 23.6 million euros + (4.06 million euros x 7) + (4.13 million euros x 10) = 93.32 million euros for a decentralized infrastructure; thus, about 29.6 million euros opportunity costs that can be otherwise invested in talents and research projects.
If we consider the key AI projects for the country the ones mentioned in some paragraphs above, we need also to find resources to support these projects. The estimates are (a) forecasting dynamic social processes: several hundred thousand to a few million euros, (b) monitoring and predicting cyberattacks for governmental structures: several million to tens of millions of euros, (c) improving weather forecasting: several hundred thousand to several million euros, (d) fighting against fake news, terrorism, and cyberattacks: several million to tens of millions of euros, (e) better energy management and infrastructure monitoring: several hundred thousand to several million euros, (f) improving health, genomics, stem cells, and personalized medicine: several million to hundreds of millions of euros, and (g) enhancing agriculture and food security: several hundred thousand to several million euros. In a pessimistic scenario, which is the most probable for a country such as Romania, the budgets would be: (a) 1 million euros; (b) 10 million euros; (c) 1 million euros; (d) 5 million euros; (e) 1 million euros, (f) 10 million euros, (g) 1 million euros, which in total is 29 million euros over a 10 year period, which in average would be about 2.9 million euros/year over 10 years.
However, we need to attract more money in 10 years to make this investment relevant. Taking into account the total costs of keeping the infrastructure operational and upgraded over a 10-year period, we would need to attract a minimum amount of money to achieve a positive NPV and an IRR of 9%. Assuming the same initial investment of 23.6 million euros, and using the formula for NPV and IRR, we can solve for the required cash inflow:
NPV = Σ(CF_t / (1+r)^t) – Initial Investment
IRR = the discount rate that makes NPV = 0
Where CF_t is the cash flow at time t, r is the discount rate, and t is the time period.
Using a conservative estimate of 5% annual growth in cash inflows, a 10-year time horizon, an initial investment of 23.6 million euros, and total costs of 63.72 million euros for the centralized infrastructure and 93.32 million euros for the decentralized infrastructure, we can solve for the required cash inflow:
For the centralized infrastructure:
NPV = 0, therefore:
23.6 + Σ(CF_t / (1+0.09)^t) = 63.72
Σ(CF_t / (1+0.09)^t) = 40.12
CF_1 = 23.6 * 1.05 = 24.78
Using the formula for the sum of a geometric series, we get:
CF_2 = 24.78 * 1.05 = 26.02
CF_3 = 26.02 * 1.05 = 27.33
CF_10 = 47.29 * 1.05 = 62.64
Therefore, the required cash inflow to achieve a positive NPV and an IRR of 9% for the centralized infrastructure is approximately 38.2 million euros.
For the decentralized infrastructure:
NPV = 0, therefore:
23.6 + Σ(CF_t / (1+0.09)^t) = 93.32
Σ(CF_t / (1+0.09)^t) = 69.72
CF_1 = 23.6 * 1.05 = 24.78
Using the formula for the sum of a geometric series, we get:
CF_2 = 24.78 * 1.05 = 26.02
CF_3 = 26.02 * 1.05 = 27.33
CF_10 = 47.29 * 1.05 = 62.64
Therefore, the required cash inflow to achieve a positive NPV and an IRR of 9% for the decentralized infrastructure is approximately 69.1 million euros.
One way to achieve this amount of money could be by attracting external funding or generating revenue through the use of the infrastructure. For example, the AI hub could offer services such as data analysis or machine learning to businesses or government agencies, or it could be a partner with universities or research institutions to offer training or research opportunities. Alternatively, the infrastructure could be used to drive economic growth in the region, attracting new businesses and talent to the area. This could result in increased tax revenue and economic activity, which could offset the costs of the investment.
If we need to attract money not only to cover the annual operating costs and upgrades but also to pay for 70% of the project personnel costs for researchers who develop the projects, the cash inflow will be affected. Let’s assume that the total cost of keeping the infrastructure operational and upgraded over a 10-year period is still estimated to be around 63.72 million euros for the centralized infrastructure and 93.32 million euros for the decentralized infrastructure. However, now we need to attract additional funds to cover 70% of the projects on human resources, which is estimated to be 17.64 million euros annually for a centralized infrastructure and 25.48 million euros annually for a decentralized infrastructure. This means that the total additional funding needed to cover the human resources projects over the 10-year period would be 176.4 million euros. So, the overall picture is the following: to make sense of the investment in the National AI Hub of 23.6 million euros, we need to think about how to attract in 10 years some other 40.12 million euros to keep the infrastructure functional and updated and other 176.4 million euros to create value added for the country by research and innovation (for the case of centralized infrastructure) and about 69.72 million euros for operating and upgrading, plus 254.8 million euros from research and innovation (decentralized infrastructure). Romania has succeeded to attract from H2020 about 250 million euros, in various fields of research. These figures indicate a big concern that Romania will be capable to attract about 200 million euros in 10 years only in AI-related projects. This indicates that the investment in the National AI Hub obtained from various public funds (mostly from the European Commission) will most probably lead to negative NPV.
A business plan is a critical component for the development of the National AI Hub. It is essential to have a business plan to ensure the sustainability of the project and to make sure that it can continue to operate in the long term. A business plan provides a detailed roadmap for the project, outlining the goals, objectives, strategies, and resources needed to achieve them. It also helps to identify potential risks and challenges and provides a framework for monitoring progress and making adjustments as needed.
In the case of the National AI hub, a business plan would help to ensure that the resources invested in the project are used effectively and efficiently. It would provide a clear understanding of the costs associated with running the hub, including operational expenses, salaries, equipment, and training, and help to identify potential sources of revenue or funding. This would ensure that the project is sustainable and can continue to operate even if there are changes in funding or support from the government or other stakeholders.
Additionally, a business plan would help to establish the value proposition of the National AI hub and communicate it to potential stakeholders, investors, or partners. It would provide a clear explanation of the benefits that the hub would bring, including its impact on various industries, the potential economic benefits, and its contribution to society as a whole. This would help to attract the necessary support and resources needed to develop and sustain the project.
Ideally, a team of experts in business strategy and infrastructure planning should be involved in developing the business plan. This team could include project managers, financial analysts, economists, and industry specialists who can provide insight into the specific needs and challenges of the project. The business plan should be developed in close collaboration with the infrastructure plan to ensure that both plans are aligned with the project’s goals and objectives. In terms of correlation, the business plan should be regularly reviewed and updated to ensure that it remains aligned with the infrastructure plan and any changes that may occur.
Unfortunately, there is a risk that even if the centralized or hybrid infrastructure is the better choice for Romania, the distributed option may be selected due to the potential for things to go wrong, as stated by Murphy’s law (“anything that can go wrong, will go wrong”). This highlights the importance of carefully considering all factors and potential challenges in making the decision on which infrastructure to adopt, rather than simply defaulting to the distributed option. One potential reason for the selection of the decentralized infrastructure for the AI hub could be a prioritization of local interests over strategic objectives. Additionally, a lack of clear commitment to achieving tangible outcomes may also contribute to the decision-making process. These factors may lead to a decision that is not necessarily aligned with the optimal solution for the overall goals of the citizens and the benefits of the country.
Credits: Stelian Brad