Efforts to address these shortcomings have branched in several directions. One influential line of work integrates structured knowledge and symbolic reasoning with neural learning. Neuro-symbolic architectures attempt to combine the pattern recognition strengths of deep networks with the compositional, rule-governed character of symbolic systems.…[Leer más]

However, attributing genuine understanding to these systems remains deeply contentious. The debate is not merely philosophical; it has practical implications for how these models are deployed, regulated, and trusted. Large language models generate outputs by sampling from probability distributions conditioned on input prompts. They have no…[Leer más]

At the core of modern AI lies the principle of learning representations from data. The shift from handcrafted features to learned embeddings transformed natural language processing, computer vision, and speech recognition. Word embeddings, then contextualized representations from models such as BERT and its successors, captured semantic…[Leer más]

@paulacastrosv24cuoz The Evolving Landscape of Artificial Intelligence: From Narrow Expertise to General Intelligence

The trajectory of artificial intelligence has long been defined by a paradox: systems that surpass human capability in narrowly defined tasks while remaining brittle and inflexible outside their training domains. Over the past…[Leer más]