// Insights on transparent AI, physics-inspired architecture, and the future of explainable systems
Why opacity in AI systems is not just a technical limitation, but a fundamental design flaw. Exploring the critical problems with black-box architectures and why post-hoc explanations aren't enough.
Beyond bits and bytes: exploring what information truly means in the context of AI systems. Why semantic grounding in physical relationships matters more than statistical patterns alone.
The fallacy of reverse-engineering a black box we don't understand. Why the path to artificial superintelligence lies in physics and cosmic intelligence, not neuroscience and biological mimicry.
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