Speaker
Description
The development and operation of private Artificial Intelligence (AI) capacity represents a strategic opportunity for research institutions and R&D-intensive companies to strengthen scientific excellence, technological independence, and innovation competitiveness.
Dedicated AI infrastructure enables secure processing of sensitive research data, development of domain-specific models, and acceleration of computational experiments, simulations, and scientific discoveries. Beyond computational resources, private AI capacity supports the creation of intellectual property, advanced research methodologies, and industrial partnerships.
In this context, I would like to discuss the necessity of investing in new AI hardware capacity and the key challenges associated with moving in this direction, including infrastructure costs, technological evolution, resource utilization, energy efficiency, and long-term sustainability. The discussion will explore strategic motivations, research opportunities, and potential approaches to developing sustainable and competitive private AI ecosystems.