Pseudo-Intelligence

Current AI systems, including advanced large language models and multimodal architectures, remain in the realm of pseudo-intelligence, sophisticated pattern-matching mechanisms that simulate intelligent behavior through statistical correlations without genuine understanding, self-awareness, or sentience. 

A whitepaper is presented that argues that conflating these capabilities with true consciousness creates significant ethical, societal, and regulatory risks, urging responsible development through clearer distinctions, transparency, and frameworks that prioritize human values over hype.

Current AI systems rely on statistical methods like neural networks and large language models, which excel at pattern matching but remain pseudo-intelligence due to their inherent opacity, lack of true understanding, and inability to achieve genuine sentience. While biomimetic approaches, advanced hardware like quantum computing, and future steps such as embodied cognition may enhance realism and performance, a fundamental paradigm shift beyond statistical correlation is required for sentience, underscoring the critical role of responsible AI in mitigating risks along the way.

xxyzz AGI

xxyzz Sentience