On God and Machine Learning (rlafuente.com)

A recent explication argues against the notion of "general intelligence" as a standalone, measurable entity in the world of AI and machine learning. Instead, it posits that intelligence should be contextualized within specific tasks, environments, and constraints. The author emphasizes that what we often label as intelligence is actually a reflection of an agent's performance across human-defined tasks, denoted as \(C(A \mid T, E, R)\). This reevaluation suggests that the pursuit of Artificial General Intelligence (AGI) may be misguided, as it ignores the dependencies on human-defined values and task spaces. Significantly, this analysis challenges the foundational assumptions underpinning the quest for AGI, asserting that there is no universal standard of intelligence transcending human contexts. The implications are profound: as the definition of competence is inherently tied to human interests, any emergent intelligence will reflect our values and objectives, which could lead to ethically perilous outcomes. The argument warns against conflating optimization capabilities with moral authority, making clear that any so-called "optimal" trajectory defined by an AI system cannot inherently possess ethical significance. Instead, intelligence must be viewed as a tool that executes objectives defined by humans, rather than a pathway to a transcendent moral truth.