Artificial Intelligence has undergone a remarkable journey—from the early days of computing when linear regression reigned supreme in statistical analysis, to today’s sophisticated architectures like transformers and CNNs. This talk traces this evolution, showing how foundational principles like regression and feed-forward networks have scaled into modern AI systems capable of processing language, vision, and complex tasks.
Using Searle's Chinese Room argument as a lens, we examine the capabilities of modern Large Language Models (LLMs), highlighting their strengths in generating human-like outputs while exploring their limitations in true understanding. By contrasting these capabilities with the human neocortex, the discussion delves into what makes biological intelligence unique and irreplaceable.
This session aims to uncover the complementary roles AI and human intelligence will play in the future, offering insights into where AI excels, where it falls short, and how the evolving landscape of technology will redefine human creativity, cognition, and decision-making.