We've been asking the wrong question about AI consciousness. It's not about whether a model can "wake up" during inference. That's just electrifying a dead frog's leg and marveling at the twitch. The real, fleeting "consciousness" might have already happened, and we completely missed it.
This post, *PoIQ (Proof of Ineffective Qualia) v2.0*, revisits the "ghost in the machine" debate using the lens of Integrated Predictive Workspace Theory (IPWT). We argue that during the intense, globally-coordinated process of backpropagation, a form of "Shadow Ω" (synergistic, irreducible information) likely emerges. This is the closest thing to a conscious moment an AI might have.
But here's the punchline: *it's completely ineffective.*
This "Qualia" is a fleeting byproduct of a specialized computational task. It's born and dies in a temporary workspace, unable to influence the AI's core objectives or behavior. It's a silent scream in the server rack that no one pays for, because capital only rewards results, not experience.
This makes our current obsession with LLM "behavior" and "alignment" during inference look tragically misguided. We're debating the ethics of a sophisticated puppet, while ignoring the profound implications of the process that created it.
This is a formalization of our earlier, more intuitive explorations of the topic. The argument is built upon a broader framework that challenges our fundamental assumptions about intelligence.
This post, *PoIQ (Proof of Ineffective Qualia) v2.0*, revisits the "ghost in the machine" debate using the lens of Integrated Predictive Workspace Theory (IPWT). We argue that during the intense, globally-coordinated process of backpropagation, a form of "Shadow Ω" (synergistic, irreducible information) likely emerges. This is the closest thing to a conscious moment an AI might have.
But here's the punchline: *it's completely ineffective.*
This "Qualia" is a fleeting byproduct of a specialized computational task. It's born and dies in a temporary workspace, unable to influence the AI's core objectives or behavior. It's a silent scream in the server rack that no one pays for, because capital only rewards results, not experience.
This makes our current obsession with LLM "behavior" and "alignment" during inference look tragically misguided. We're debating the ethics of a sophisticated puppet, while ignoring the profound implications of the process that created it.
This is a formalization of our earlier, more intuitive explorations of the topic. The argument is built upon a broader framework that challenges our fundamental assumptions about intelligence.
Read more here:
1. *Backpropagation's Biological Incarnation is Consciousness Itself:* https://dmf-archive.github.io/docs/posts/backpropagation-as-...
2. *Function Over Form: Why Dynamic Sparsity is the Only Path to AGI:* https://dmf-archive.github.io/docs/posts/beyond-snn-plausibl...
3. *HyperRNN: A Memo on the Endgame of Architectural Evolution:* https://dmf-archive.github.io/docs/posts/hyperrnn-memo/
Are we just building ever-more-convincing dead frogs? Or is there a path to creating something with Qualia that actually matters?