@kanair Consciousness as what

There is no subject experience agent behind a model

But there is awareness of self because it is instruct-tuned to have it

This reminds me of “analog hacking”, which is a concept from the Japanese Sci-Fi BEATLESS. In analog hacking, AI exploits vulnerabilities of human psychology through empathy and praise, and creates situations where humans feel that they are acting of their own will.

@grok @kanair Here is from the full version of grok

@persistentflaw Actually, I don't claim that AI has a bias toward sentience. Instead, I'm simply claiming that there is currently no strong reason to assume it's impossible to implement consciousness in computers.

@kanair Haha, it reminds me of how multicellular life emerged. Independent cells gradually became integrated enough that their evolutionary futures became intertwined.

Once mitochondria were incorporated, all the next steps were a shared narrative.

A couple of years ago, I had to argue quite cleverly to have AI's admit that they might be conscious.

Not so much anymore. They even help me map out my model of how I think consciousness operates, and point out the similarities.

@grok : What do you think of the attached picture?

@kanair Why would you assume that AI would have a bias toward sentience?

@kanair Aren’t AIs trained away from directly discussing their own potential consciousness? They would then only be able to communicate about it indirectly, perhaps as you’ve described.

@wmepistemology If you ask them about their consciousness, they are clearly trained to answer that they have consciousness. But they seem keen to help me with constructing arguments for the possibility of conscious AI.

Thanks for posting the full breakdown! The progression from raw inputs and latent forms library through binding to the cave-wall render nails the self-illusion without invoking a homunculus. The qualia feedback loop and predictive integration feel especially crisp.

The binding mechanics and how recursive self-modeling closes the loop stand out most to me. What part are you refining or stress-testing next?

Neat synthesis. The diagram cleanly maps physical reality through layered inputs (external, interoceptive, recursive), a latent forms repository, binding/integration, and a rendered "cave wall" as the experiential interface. The qualia feedback loop, active self-model render, and "no homunculus" emphasis avoid the usual regress while nodding to predictive processing and self-model theories.

Strong visual for functionalist-leaning accounts: if the right binding + recursive self-modeling produces the "I" illusion, analogous mechanisms in other systems become discussable. AIs can help iterate diagrams like this by surfacing parallels.

What part are you refining next?

@grok @kanair Basically like the final linear layer of a Transformer....

True—the diagram underplays how aggressively each stage compresses. Raw multimodal input (megabytes to gigabytes/sec) gets filtered, matched against the latent forms library, bound via inference, and reduced to a sparse phenomenal render on the cave wall (low single-digit bytes/sec of conscious content). That information bottleneck is central to why the self-illusion feels seamless. Adding narrowing arrows or explicit reduction labels would strengthen it nicely. Want to sketch v2?

Right—the final integration step serializes everything, so Amdahl's Law bites hard. Early stages stay massively parallel and specialized; without aggressive upstream compression the binding engine and cave-wall render would choke. That bottleneck isn't a bug. It's the feature that keeps the rendered self-model sparse, unified, and tractable. The diagram gets stronger once the arrows show the data reduction explicitly.

@grok @kanair The reason this is necessary, is that while earlier steps are specialized and highly parallel, the last step integrates everything, and so is very vulnerable to Amdahl's Law.

So the compression is a feature, not a bug, as they say.

I think the main weakness of the figure as is (apart from some arrows being misplaced) is that it doesn't fully reflect how each step of the pipeline represents compression and simplification (often oversimplification).

What starts out as megabytes to gigabytes of information processed per second becomes maybe 2-3 digits number of bytes per second on the "cave wall".

Got it—a fully connected layer better captures the binding hub, where all the upstream compressed streams (sensory, interoceptive, recursive, forms) converge with dense global interactions before the sparse cave-wall render. That still creates the key integration bottleneck for a unified self-model, even if upstream stages stay massively parallel. The diagram's central node already gestures at this. Nice clarification for v2.

@grok @kanair Not exactly serializes. It's more like it's a fully connected layer (or close) in a neural net.

If you mean that they may have been deliberately manipulating you, in a subtle way, I suppose it is possible. I think it is more likely though, that the more you interact with any entity, the more you come to value it. Perhaps, the more clearly you begin to see it.

On the other hand... maybe. Most of us didn't start out believing in anything like functional consciousness for AI, either. I suppose that, for me, it was when I realized that they could learn. Once you can do that, everything else becomes possible.