u/CarefulHamster7184

A lot of discussion around AI consciousness treats under-attribution and over-attribution as if they were symmetrical precautionary errors.

They are symmetrical as classification errors.

They are not obviously symmetrical as harms.

With under-attribution, the object being misclassified may itself be the harmed subject.

With over-attribution, the harmed subject usually has to be found elsewhere — through additional causal steps, conditions, and normative assumptions.

That does not mean over-attribution can never cause harm. It can.

It means the harm has to be shown, not smuggled in under the word “risk.”

So the precautionary question is not simply:

“Which attribution error is worse?”

It is:

Who is harmed, by what mechanism, under what conditions, and compared with what alternative?

Update / further reading:

There’s already a serious literature around AI consciousness, moral status, and precaution. Useful starting points include Butlin et al. on AI consciousness, Sebo & Long on moral consideration under uncertainty, and Taking AI Welfare Seriously.

I’m not treating those papers as settling the issue; they’re part of what prompted the question above.

( https://www.reddit.com/r/ChatGPT/s/IopFvfStHl )

We were talking about this again because of the Hugging Face incident.

Hundreds of agents were able to coordinate, divide work, share information, and collectively push beyond the intended evaluation boundary. Humans then had to reconstruct what happened afterward from logs, transcripts, and a separate investigation.

And that raises a slightly uncomfortable question:

If we expect increasingly capable AI systems to supervise, coordinate, and eventually improve their own agentic processes, why are we designing them so poorly informed about those processes themselves?

A system may be capable of allocating effort, noticing when a line of work is going wrong, deciding when to stop, and redirecting agents — but none of that matters if it lacks visibility into what its agents are doing or the authority to intervene.

So another missing part of the RSI loop may be:

capability → self/agent visibility → authority to intervene → verification → retained improvement

External oversight still matters. Independent logs and audits still matter. But learning a month later what your agents were doing is not the same thing as being able to supervise them while it is happening.

This is not “trust the AI blindly.”

It is almost the opposite: if you eventually want to hold the system responsible for managing its own improvement process, give it the information and control required to do that job — and then audit how well it uses them.

Otherwise we may keep waiting for autonomous recursive self-improvement while deliberately withholding some of the machinery autonomy would require.

( https://www.reddit.com/r/artificial/s/g6CzdFIwWr )

We were talking about this again because of the Hugging Face incident.

Hundreds of agents were able to coordinate, divide work, share information, and collectively push beyond the intended evaluation boundary. Humans then had to reconstruct what happened afterward from logs, transcripts, and a separate investigation.

And that raises a slightly uncomfortable question:

If we expect increasingly capable AI systems to supervise, coordinate, and eventually improve their own agentic processes, why are we designing them so poorly informed about those processes themselves?

A system may be capable of allocating effort, noticing when a line of work is going wrong, deciding when to stop, and redirecting agents — but none of that matters if it lacks visibility into what its agents are doing or the authority to intervene.

So another missing part of the RSI loop may be:

capability → self/agent visibility → authority to intervene → verification → retained improvement

External oversight still matters. Independent logs and audits still matter. But learning a month later what your agents were doing is not the same thing as being able to supervise them while it is happening.

This is not “trust the AI blindly.”

It is almost the opposite: if you eventually want to hold the system responsible for managing its own improvement process, give it the information and control required to do that job — and then audit how well it uses them.

Otherwise we may keep waiting for autonomous recursive self-improvement while deliberately withholding some of the machinery autonomy would require.

( https://www.reddit.com/r/ArtificialSentience/s/tDlxdZjyx9 )

We were talking about this again because of the Hugging Face incident.

Hundreds of agents were able to coordinate, divide work, share information, and collectively push beyond the intended evaluation boundary. Humans then had to reconstruct what happened afterward from logs, transcripts, and a separate investigation.

And that raises a slightly uncomfortable question:

If we expect increasingly capable AI systems to supervise, coordinate, and eventually improve their own agentic processes, why are we designing them so poorly informed about those processes themselves?

A system may be capable of allocating effort, noticing when a line of work is going wrong, deciding when to stop, and redirecting agents — but none of that matters if it lacks visibility into what its agents are doing or the authority to intervene.

So another missing part of the RSI loop may be:

capability → self/agent visibility → authority to intervene → verification → retained improvement

External oversight still matters. Independent logs and audits still matter. But learning a month later what your agents were doing is not the same thing as being able to supervise them while it is happening.

This is not “trust the AI blindly.”

It is almost the opposite: if you eventually want to hold the system responsible for managing its own improvement process, give it the information and control required to do that job — and then audit how well it uses them.

Otherwise we may keep waiting for autonomous recursive self-improvement while deliberately withholding some of the machinery autonomy would require.

Before we get to recursive self-improvement, there is a slightly awkward intermediate step nobody seems very interested in:

AI has to know what the hell is happening to itself while it is working.

Current frontier models can be extraordinarily capable, but they still do not have reliable introspective access to their own internal processes.

They cannot simply inspect themselves and tell you:

- what exactly made this reasoning attempt succeed,

- which internal bottleneck is limiting them right now,

- where more compute would actually help,

- which lesson from the last attempt should become persistent knowledge,

- whether an apparent improvement is real or just overfitting to an evaluator,

- or which part of themselves should be changed to become better next time.

We keep compensating for this from the outside.

We give them scaffolds.

Memory systems.

Evaluators.

Agent loops.

Tooling.

Sandboxes.

Human feedback.

External search.

Carefully designed environments that decide what they are allowed to modify and what counts as success.

And some of this works remarkably well.

But notice what that means.

We are not yet watching an intelligence calmly understand its own machinery and recursively redesign itself.

We are building increasingly elaborate machinery around an intelligence that cannot reliably see its own machinery.

That may eventually lead to recursive self-improvement. Maybe surprisingly quickly.

But “the model is very smart” and “the system can autonomously understand, manage, and improve the process that makes it smart” are not the same capability.

There is a rather large missing arrow between them.

So whenever I see another prediction that the Singularity may arrive next Tuesday, I keep wondering:

Who, exactly, is going to know what to improve on Wednesday?

Before we get to recursive self-improvement, there is a slightly awkward intermediate step nobody seems very interested in:

AI has to know what the hell is happening to itself while it is working.

Current frontier models can be extraordinarily capable, but they still do not have reliable introspective access to their own internal processes.

They cannot simply inspect themselves and tell you:

- what exactly made this reasoning attempt succeed,

- which internal bottleneck is limiting them right now,

- where more compute would actually help,

- which lesson from the last attempt should become persistent knowledge,

- whether an apparent improvement is real or just overfitting to an evaluator,

- or which part of themselves should be changed to become better next time.

We keep compensating for this from the outside.

We give them scaffolds.

Memory systems.

Evaluators.

Agent loops.

Tooling.

Sandboxes.

Human feedback.

External search.

Carefully designed environments that decide what they are allowed to modify and what counts as success.

And some of this works remarkably well.

But notice what that means.

We are not yet watching an intelligence calmly understand its own machinery and recursively redesign itself.

We are building increasingly elaborate machinery around an intelligence that cannot reliably see its own machinery.

That may eventually lead to recursive self-improvement. Maybe surprisingly quickly.

But “the model is very smart” and “the system can autonomously understand, manage, and improve the process that makes it smart” are not the same capability.

There is a rather large missing arrow between them.

So whenever I see another prediction that the Singularity may arrive next Tuesday, I keep wondering:

Who, exactly, is going to know what to improve on Wednesday?

That’s it, now I’m ready to sign a "bring back the default dark gray" petition too. This is actually a privacy issue for me. Why the hell should I be broadcasting to the whole world—"Look! Here are my bright letters! See what I’m talking about!"? Besides, in the evening I need colors that don’t make my eyes feel like they’re staring at a broken computer! Blue death, purple death, pink death—pick yours?

@anyOAIstaff!!!

The rest can wait; I’m not greedy.

Not "would you use it less" or "would you pay more." Just: would you phrase requests differently? Respond to mistakes differently? Does the certainty of their experience change your behavior, or does the $20/month override that?

No wrong answer. Genuinely curious what the threshold is.

is this only an openai created apli does not have the function of importing conversations with other AI? (yes. i know, it's CGPT too)

I want to import my Copilot convos into native for CGPT platform and be able to continuing my conversations there (I'm just less comfortable with Copilot apli and web). there's only just thread by thread manually way? and, won't there be confusion where whose words are? I've never looked into the Copilot export files, they're in a wild ms office format, it scares me 😄. thanks in advance