Introduction and Background
The Secretaría de Ciencia, Humanidades, Tecnología e Innovación (Secihti) in Mexico has launched a permanent cycle of conferences to tackle national challenges in health, energy, and the environment using artificial intelligence (AI) and high-performance computing under an ethical, social, and integrative vision.
Objective and Scope
Secihti aims to establish technological independence and strengthen national development by positioning AI and high-performance computing as strategic axes to address Mexico’s most pressing problems. The initiative, facilitated by Dr. Feliú Sagols Troncoso, Director General of Public Research Centers, emphasizes that the focus will not only be technical but also ethical and legal, creating a high-level dialogue forum.
Monthly Gatherings of Experts
The Ciclo de Conferencias en Inteligencia Artificial will bring together academics, specialists, and students monthly to analyze how the convergence of AI and high-performance computing can transform critical sectors like health, energy, industry, and environmental preservation.
National Science with a State Vision
Through the National Network for Artificial Intelligence and High-Performance Computing, Secihti seeks to align human talent and the country’s infrastructure. The secretariat aims to ensure responsible, inclusive adoption of disruptive technologies with direct social impact benefiting the Mexican population.
Alignment with National Research Projects
This effort aligns with the creation of the National Research Projects for Impact (Pronaii), where AI will serve as a transversal tool to optimize various aspects, from medication distribution to water management in hydrologically stressed areas.
Inaugural Session: 40 Years of Evolutionary Algorithms
The first lecture was delivered by renowned Cinvestav researcher Carlos Artemio Coello Coello, who presented “Multi-objective Evolutionary Algorithms: A History That Began More Than 40 Years Ago.”
Evolutionary Algorithms and Their Applications
Coello, a Tulane University doctorate and international reference in the field, explained how these algorithms, inspired by natural evolution, optimize multiple conflicting objectives simultaneously. He highlighted their robustness as decision-making tools in engineering, logistics, and finance, identifying efficient solutions for complex problems.
Future Challenges
Key Areas for Improvement
- Interpretability: Better understanding how models reach their results.
- Efficiency: Reducing high computational costs.
- Linkage: Bridging the gap between fundamental research and real-world applications.
Coello emphasized that Mexico’s AI future hinges on overcoming these three fundamental challenges. Secihti intends to be the guiding body in the country’s scientific policy, building a solid knowledge base addressing contemporary Mexico’s needs.
Inclusive Impact
Secihti’s representative stressed that the implementation of these systems won’t be isolated; it will rely on a network of computing centers and human talent formed in public institutions, ensuring that the impact of innovation reaches Mexico’s most vulnerable communities.
