Scientist & Artist | Behavior & CogSec Expert: Tag me under any post for account analyses | Singer/Comedian | AI Video Creator | Software Developer | Constr PM
I didn’t build MIPU/MAP for isolated findings.
I built it to find the hidden dependencies connecting things science studies separately.
Placebo is a perfect example:
Train pain relief initially with morphine, then remove the morphine, and the body can later recreate part of that pain-relief effect naturally by recruiting its own opioid system and using the same opioid pathway morphine originally acted through WITHOUT TAKING MORPHINE.
Important to know though, it isn’t making morphine.
It’s releasing/recruiting its own endogenous opioids and activating opioid receptors to naturally reproduce part of the same pain-relieving effect morphine originally produced.
Train the system through a different drug route, and the learned response can recruit a different route.
So the brain may be preserving more than:
“relief happened.”
It may also preserve:
“THIS is how we produced relief.”
That’s the kind of hidden dependency MIPU/MAP is built to connect across fields.
Potential validation, but there are two possibilities I can’t distinguish from the public evidence.
They may have independently followed a similar path, or my earlier published work may have influenced the direction. I can’t prove what I can’t prove but the timing and topic and how they got there are unique enough for me to think there was influence involved but ultimately it’s more about who did what first.
What I can establish is the chronology and the conceptual relationship: I published my emotional-quantization framework about 9 months earlier, and Anthropic later published empirical work that overlaps with part of the territory my framework predicted.
Their results don’t validate all of MIPU/MAP, but they are relevant evidence for the direction it pointed toward.
-At first:
“I think this means XYZ.”
-Later:
“This means XYZ.”
-Eventually:
“I can literally see that it’s XYZ.”
That transition fascinates me.
My MIPU/MAP framework predicts one possible mechanism: repeated interpretation can progressively reduce accessible alternatives until the interpretation itself becomes cognitively invisible.
MIPU means Minimal Irreversible Predictive Update.
MAP means Maximal Anticipatory Pattern.
I invented MIPU/MAP to answer questions science couldn’t answer with its existing methods by giving us a new process for finding hidden dependencies, quantifying the smallest prediction-changing update, and rebuilding the larger predictive structure around it.
I even used it to quantify emotional change in a way conventional approaches had not previously formalized.
About 9 months later, Anthropic published its emotion-concepts work showing emotion-like internal representations inside Claude that can affect behavior, moving in the same direction I had already published independently.
I think “placebo effect” is hiding something much bigger.
-Placebo.
-Nocebo.
-Drug tolerance.
-Conditioned immune responses.
-Anticipatory hormone responses.
These may look like separate weird phenomena, but they can be viewed as different outputs of the same basic process:
the body learning a future state so well that the prediction itself begins helping create it.
“I don’t know” might be one of the worst compressions the human brain makes.
Because “unknown” can mean:
- I haven’t looked yet
- I can’t observe it
- I lack the right distinction
- I have the evidence but can’t derive it
- I’m asking too early
- My current way of thinking can’t represent it
Those are completely different problems.
Intelligence starts improving fast when you stop asking “What don’t I know?”
and start asking:
“Why don’t I know it?”
It’s truly just the best. It’s changed my life for the better. Everything that I couldn’t do, I can now do within one try, then to make it perfect several but who cares? Every single time I prompt it my app gets that much better and secure.
It doesn’t ask me for permissions, it just does the work. Compare that to Claude code that asks 1000 permissions as if they purposely did that so people get glued to their devices 30 minutes per response….
So when compared to Claude code it’s literally night and day. Idk what would make it better at this point it even makes videos using certain tools. I guess making videos would be cooler and if it could have way more style. I feel like its website building style is limited and average unless I personally guide it. Other than that, it’s a dream come true on steroids.
One note tho for users of the work option on chatgpt:
I love how much access it gives me per week btw but I didn’t know the “work”option on the “chatgpt app” used codex credits so wasted my free reset option, with that said don’t use the chatgpt work option if you want to use codex for things as it uses credits from codex.
Now connect the last three posts.
Knock. Red light. Bell.
Same lesson: the signals above themselves don't contain the meaning.
And this goes beyond behavior.
-Pair a signal with immune suppression and it can later recruit suppression.
-Pair another with immune activation and it can recruit enhancement.
The relationship is carrying information the signal alone does not have.
A knock at the door can relax you or terrify you.
If every knock has historically meant your best friend arriving, your body prepares for one world.
If knocks have historically preceded bad news, it prepares for another.
The knock on its own isn't positive or negative. Your association has turned it into a prediction.
A red light does not inherently mean “stop.”
-On the road: stop.
-On a recording device: recording has started.
-On a battery charger: something may be wrong.
The color stayed identical.
What changed was the dependency attached to it.
(These posts are not about red lights)
A bell sound does not mean “move” or anything else on its own. Its meaning depends on what you’ve learned it predicts in that particular context.
-At school, a bell can mean leave the room. In boxing, a bell can mean start fighting.
---The sound doesn’t contain an instruction.
------The instruction exists in the learned relationship.
Ai companies benefit by looking like they hate each other while simultaneously changing rules towards the same limitations that would come from a monopoly.
Imagine five restaurants fighting for customers, but every menu gradually becomes identical. You still have five restaurants, yet, the amount of choices available, shrank.
Think Coke vs. Pepsi, except imagine both companies gradually removing the same ingredients. The brands remain competitors while the available choices narrow.
If every major provider converges on the same boundaries, switching companies stops giving you additional choices.
This is what I’m seeing.