Nice work Steven, I will try to keep it short; your discussion highlights critical aspects of autonomous learning and AI generalization, but I think it overlooks a foundational issue: Neither AI nor humans truly self-train without an external base of validation grounded in reality itself.
1. The Myth of Self-Training AI
AI today cannot be considered self-training in any meaningful sense because its validation sources are fundamentally either human-labeled data or heuristics derived from previous outputs. There is no external, reality-anchored feedback loop beyond what humans provide. The claim that AI autonomously generalizes is misleading because it does not—and cannot—validate its knowledge against anything more fundamental than opinionated datasets and predefined optimization metrics.
Nice work Steven, I will try to keep it short; your discussion highlights critical aspects of autonomous learning and AI generalization, but I think it overlooks a foundational issue: Neither AI nor humans truly self-train without an external base of validation grounded in reality itself.
1. The Myth of Self-Training AI
AI today cannot be considered self-training in any meaningful sense because its validation sources are fundamentally either human-labeled data or heuristics derived from previous outputs. There is no external, reality-anchored feedback loop beyond what humans provide. The claim that AI autonomously generalizes is misleading because it does not—and cannot—validate its knowledge against anything more fundamental than opinionated datasets and predefined optimization metrics.
2. The... (read 487 more words →)