Curving Cognition: Resilient AI Beyond Prediction Accuracy

The relentless pursuit of prediction accuracy in AI has led to a paradox: systems that perform well in controlled environments but fail catastrophically in real-world complexity. Despite advancements in fields like natural language processing and autonomous driving, AI remains brittle when faced with novelty, uncertainty, and change. Current AI models lack basic embodied learning, where intelligence evolves through dynamic, real-time interaction with the environment. In contrast, typical AI systems, such as vision models, classify new stimuli into predefined categories with misplaced confidence, unable to adapt or update their understanding. This "Brittle AI" is optimized for narrow prediction tasks but lacks true adaptability. Similar challenges arise in domains like autonomous driving, where AI often collapses to a single interpretation, unable to adjust to unexpected situations. The limitations of such systems, particularly in safety-critical applications, can have catastrophic consequences. This systemic flaw, termed the "Optimization Trap," results from a philosophical commitment to "disembodied prediction"—a view that intelligence is merely the maximization of prediction accuracy via internal computation. This project proposes a shift towards a paradigm that prioritizes resilience and adaptability, aiming to redefine AI as a dynamic, embodied system capable of adapting to change and complexity in real-world contexts.