AI and changes in growth and decision-making

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   AI requires a set of special infrastructure modes; yet a targeted plan for national AI-computed capacities to facilitate socio-economic growth is a difficult issue to complete. Presently, complete data on such nation-wide AI infrastructure generally does not exist, which is naturally hindering economic goals. It is necessary to identifies tools and mechanisms to evaluate, manage and treat risks at each stage of the AI-system’s lifecycle.  

Artificial intelligence, AI is becoming a “game-changing” technology: its rapid progress raises complex challenges regarding present decision-making structures at national and local levels, as well as in the whole governance system.
Although it is still at the infancy stage of its development with expectations often set too high, AI could bring benefits if it is used to predict developmental risks and growth outcomes. However, software programs can malfunction and cause serious harm without being considered beforehand with certain drawbacks for decision-makers’ ability.

AI’s other options and use
The AI revolution has accelerated into almost all spheres of social development, every industry, as well as economics and politics, in general. Governing authorities around the world are grappling with the ideas of harnessing the AI’s transformative power.
Already at the end of 2022, over 2.5 million global viewers tuned in to watch the world’s most influential AI’s technologies in shaping the future of responsible innovation.
Modern governance have to keep a closer look at AI’s technical aspects and AI’s implications for using and preserving big data technologies in order to be better used in present rapidly crowing complexities in decision-making procedures; both in traditionally reactive approach to modern challenges and in more proactive outcomes in AI’s programs.
As soon as the necessary hardware is already available from the digital providers, main attention is to software and elaborated algorithms at different stages in decision-making: i.e. socio-economic, administrative and legal, to name a few, with closer look at all possible solutions for a “mental governance” to finally adopt a justified and most optimal option.
AI is penetrating numerous other spheres of people’s life, e.g. education, art and culture. A newly appeared application called Chat GPT is going to have a tremendous impact on education and learning processes.
The abbreviation GPT stays for “degenerative pre-trained transformer: although it does not say much about the app, its creators postulate that the Chabot is able, for example, to respond to questions in a human-like manner, and understand the context of follow-up queries much like in human conversations. The app is also being able compose assays on request and provide answers to various questions. However, there are some fears that the app could be used by students to complete their assignments often without teacher’s ability to verify the individual students’ work abilities.

AI in socio-economic development
Artificial intelligence is transforming economies and promising new opportunities for productivity, growth, and resilience. National governance is responsible for developing national AI strategies and to facilitate digital transformations. Lack of such AI-wide computation policies can jeopardize national planning and economic goals.
The OECD’s recent report provides the first blueprint for policy makers to help assess and plan national AI capacities to enable productivity and capture AI’s socio-economic potential. It provides guidance for policy makers on how to develop a national AI compute plan along three dimensions: capacity (availability and use), effectiveness (people, policy, innovation, access), and resilience (security, sovereignty, sustainability). The report also defines main AI indicators, datasets, and proxies for measuring national AI compute capacity, and identifies obstacles to measuring and benchmarking national AI compute capacity.

European examples
The EU-27 adopted in 2019 the European Chips Act to support the AI development in the member states with about € 15 billion in public and private investments. Spain, e.g. approved
a strategic plan of more than € 12 billion to design and develop national production capacities of the Spanish microelectronics and semiconductor industry, covering the value chain from design to chip manufacturing. In April 2021, the EU adopted an AI strategy through a “coordinated plan on Artificial Intelligence” with the goals to: a) accelerate investments in AI technologies to drive resilient economic and social recovery aided by the uptake of new digital solutions; b) fully and timely implement AI programs to ensure that the EU fully benefits from the AI’s advantages; and c) align AI policy to remove fragmentation and address global challenges.
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The EU regards AI is an “emerging concept facilitating intelligent and automated decision-making and is thus becoming a prerequisite for the deployment of IoT and Industry 4.0 scenarios as well as other application areas”. However, being clearly beneficial, the AI’s application to “automated decision-making”, specifically with safety’s concern, can create certain drawbacks and challenges to privacy.

Considering security in the context of AI, the duality of the concept shall be kept in mind: on one hand, the AI can be exploited to manipulate expected outcomes, but on the other hand AI techniques can be used to support security operations and even to decrease adversarial attacks. Before considering using AI as a tool to support cybersecurity, it is essential to understand what needs to be secured and to develop specific security measures to ensure that AI itself is secure and trustworthy. Hence, the EU’s ENISA is an agency working on mapping the AI cybersecurity system and providing security recommendations for the states.
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In cloud computing and connectivity the EU has numerous initiatives: thus, since 2016, the European Commission has been developing a blueprint for cloud-based services and data infrastructure, including the European Data Infrastructure and the European Open Science Cloud, which will deploy high-bandwidth networks, large scale storage, and supercomputer capacity for academic and industry users. E.g. in 2019, France and Germany launched GAIA-X, an EU cloud-based initiative that aims to establish an interoperable data exchange through which business and research partners can share data and access services at scale, including for AI.
In Spain, the Barcelona Supercomputing Centre (established in 2004) provides high-performance computing services (HPC) to scientists and industry, to be operational in 2023, which is a EU-wide leader in computer architectures research and HPC for AI applications.
The European High-Performance Computing Joint Undertaking (EuroHPC) was established in
2018 to share computing resources and coordinate efforts among EU countries and partners, with a 2021-27 budget of € 7 billion; it aims to develop wide-scale supercomputing capacities and data infrastructure to support European scientific and industrial research and innovation for scientific, industrial and public users, including for AI.

However, AI-computing hardware alone is not sufficient to enable the development and deployment of AI: numerous users (e.g. researchers and developers) have to be able to adequately access AI apps and related support services to efficiently and effectively utilise HPC clusters. Very specific skills are often needed, i.e. from engineers and specialised hardware specialists for AI. Perspectives from diverse disciplines and backgrounds are also critical to close compute divides between developed and emerging economies, as well as the public, academic and private sectors. Additional research is needed into the supply and demand for AI skills, training, and workforce composition to understand the need for investments and support effective use of national AI computing capacity.

Recent OECD report acknowledges that “AI is a general-purpose technology impacting nearly every facet of the global economy, prompting governments to formulate and publish national AI strategies”. It adds that a “successful implementation of national AI strategies could become one of the factors defining a country’s ability to deliver innovation, productivity gains, and long-term growth”, for which governments have to allocate additional budgets and investments to support implementation of AI’s strategies and programs.
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