Speaker
Description
Mainstream artificial intelligence (AI) solutions, while achieving considerable success in areas such as classification, generation, and natural language understanding, still face several notorious, long-standing challenges. These include unexpected classification errors, hallucinations in generative models, and catastrophic forgetting, among others.
Addressing these issues requires rethinking our understanding of intelligence itself. Current AI solutions resemble intuitive, automatic responses more than true "thinking". True thinking involves structured knowledge representation, incorporating concepts such as relevant features, governing principles, abstraction, and generalization.
In this talk, I will explore our perspective on these challenges, outlining the logic and implementation of an algorithm designed as an initial step toward deeper, more thoughtful AI. This approach aims to address core limitations in current systems and lay a foundation for more robust intelligent systems.
