Mohamed Taqi

Rapid progress in artificial intelligence (AI) capabilities has drawn fresh attention to the prospect of consciousness in AI. There is an urgent need for rigorous methods to assess AI systems for consciousness, but significant uncertainty about relevant issues in consciousness science. We present a method for assessing AI systems for consciousness that involves exploring what follows from existing or future neuroscientific theories of consciousness. Indicators derived from such theories can be used to inform credences about whether particular AI systems are conscious. This method allows us to make meaningful progress because some influential theories of consciousness, notably including computational functionalist theories, have implications for AI that can be investigated empirically.

Aporetic Liminality: Consider a different perspective on liminality–not as a temporary state emerging between two separate ontologies, but as a prerequisite condition for anything to become distinct at all. If liminality is an original condition carried forward since the beginning of time, paradox persists not because structure fails, but because any self-referential system that is functionally distinct relies on boundaries it cannot fully explain from within. Liminality names this condition by which a function remains ambiguously coherent. This aporetic shift inverts foundational notions of liminality and challenges its philosophy to meditate on coherence without closure and its relationship to aliveness.