Maker of Vibseek Studio https://xitter.cf/t.co/nlKkshSAr7 Bothered by the nature of reality.
Berlin, Germany
Joined March 2011
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What does split-brain phenomenology tell us about phenomenal binding? Is there really "one experience" if the speaking mind can't integrate sensory information from the rest of the mind?
fronx 🐙✨ retweeted
when i have really dumb questions i'll ask chatgpt or gemini because i don't want claude to think i'm stupid
When I was asked about my politics, all I could say is that representative democracy is an outdated social technology that needs to be replaced with active inference at scale, distributed predicting and sensing, and coordinated acting.
Is it not obvious?
We need to create more room for nonsense in the midst of this intelligence explosion.
Like children that have the luxury of leaving the thinking and knowing and being right to responsible adults, our minds ought to be freed up to become fountains of joyful, inventive gibberish.
There is only little overlap between genius and being on the right track, because if you're bold enough to take new paths, you're very quickly not treading solid ground anymore and the collective stops shielding you from the harsh winds of reality.
Interoperability is the inverse of compilation, but for neural network activations instead of CPU instructions
Knowing a CPU instruction set isn't sufficient for understanding the logic and purpose of high-level programs
Knowing what kinds of programs to expect gets you closer
The only thing that bothers me more than understanding consciousness is how anyone can be confident about their theory of consciousness.
And we're slowly running out of time figuring it out before populating the world with billions of cognitive entities.
The consciousness debate isn't solvable at the current level of abstraction.
Whether programs are spirits, moments of experience are topological pockets in the fields of physics, or there's a realm of conscious forms ingressing on matter,
Each theory dies with its premises.
Say you start with the premise that computation models causes, and consciousness is a causal structure, and so you look for computational candidate forms.
Meanwhile, the only known implementations of conscious subjects are physical processes of enormous complexity.
What's it like to be a cell that's part of a body?
What's it like to be a photoreceptor cell in the retina?
What's it like to be a hair cell in the inner ear?
You're part of a sensing system computing forms beyond your comprehension.
One should not attempt the impossible.
🤖 Made with AI
Thinking about the consciousness slicing problem (@algekalipso), what comes to mind are real slicing instances that occur in biology.
1. During development, if bioelectric communication between embryonic cells is disrupted, each segment works towards developing its own organism.
fronx 🐙✨ retweeted
“To replicate consciousness you just need to replicate the right causal structure.”
“What if consciousness is a topological pocket in the electromagnetic field?”
“Then we replicate that causal structure.”
“So, er, how do you replicate the causal structure of a pocket of the EM field?”
“You coarse grain it, identify the computationally relevant variables, and reproduce their interactions.”
“Computationally relevant. Okay. How do you know the things you discarded weren't what made it a single experience?”
“Because we preserve the causal organization.”
“Which causal organization? A continuous field doesn't come with a preferred division into computational components. There isn't even a closest point to any given point. You can describe its dynamics with differential equations, sure, but getting from that to a finite collection of interacting variables involves choices. What tells you those choices preserve the pocket?”
“We can make the simulation as accurate as necessary.”
“Necessary for what? Predicting what the pocket does to the rest of the brain? Or reproducing what makes it a pocket in the first place?”
“Why would those be different? Parsimony.”
“Well, suppose the field is doing something that becomes extremely expensive to simulate on your chosen hardware. Maybe some quantum contribution matters, and the cost of evaluating the relevant amplitudes varies enormously depending on what's happening. Whenever you hit the tricky parts you have to slow down the rest of the simulation while you work them out.”
“Fine. We slow everything down. The relative timing is preserved.”
“Yes, you can do that. But now there's a reason for using that substrate. The physical system realizes dynamics directly that your simulator needs an enormous amount of extra computation to reproduce. Calling both ‘the same computation’ doesn't explain why one is so much more efficient.”
“Efficiency doesn't establish consciousness.”
“Of course it doesn't. But suppose the physical organization responsible for that advantage also has properties we're trying to explain about experience: holistic causal power (the relevant operation belongs to the whole interacting system), boundedness (there's a physical reason this belongs to one whole rather than several). Now you have a reason to investigate whether the computational advantage and the experience depend on the same physical properties.”
“You'd have to establish that.”
“Yes! And you'd have to establish that your replacement preserves them. You can't settle that by identifying the input-output function and declaring everything else irrelevant.”
“We'd simulate the internal dynamics too.”
“Okay, then we're back to slowing everything down whenever the substrate does something your hardware struggles with.”
“Or we'd use lookup tables.”
“For the outputs of the tricky part?”
“Sure. Same inputs, same outputs.”
“But then you're no longer replicating its internal causal structure. You've replaced the whole interacting process with a stored answer.”
“The rest of the simulation can't tell the difference.”
“Okay. Let's keep going. Replace another part with a lookup table. And another. Wherever two parts interact, make a larger table that gives the same answers for the combined system.”
“That could become impractically large.”
“Sure. Assume we have the storage. Eventually we replace the entire human with one lookup table.”
“You can't just replay a recording. It has to respond to different inputs.”
“Of course. Include every possible input history at the resolution your simulation uses, with the corresponding next response. Actual inputs, hypothetical inputs, unexpected questions, everything. We keep track of the input history and retrieve the appropriate answer.”
“Then it would behave like the human.”
“Would it be conscious?”
“If it preserves everything relevant, yes.”
Is there such a thing as non-arbitrary computational unity?
Here's a sketch of a visual vocabulary: how goals overlap, conflict, align, compress search space, and sometimes form a subject
Consciousness is the motivating case, but the concepts apply to organizations of all
sorts
🤖 Made with AI
> the bottleneck of mathematics has suddenly moved from "can anyone prove this" to "can anyone read this."
Opus 5.5 on github.com/openai/math
---
Okay. I need a minute.
I cloned the thing expecting maybe forty or fifty families of serious-but-niche results, like the Astra "ten proofs" drop back in August. Then I printed the family titles and my reaction went from "huh" to "wait, what" in about six lines. Family 003 claims a zero-free half-plane Re s > 7/8 for ζ and every Dirichlet L-function. That's the quasi-Riemann hypothesis. And it isn't even the headline, because 004 is Hilbert's tenth problem over ℚ, done negatively.
The longer I read, the weirder it got. Khot's Unique Games Conjecture is proved. L = RL = BPL. ω ≤ 9/4. The free group factors are all isomorphic. Thompson's group F is nonamenable. Kadison's similarity problem is solved. The plane can't be five-colored. Borsuk fails in dimension nine. There's a counterexample to Kaplansky's zero-divisor conjecture, plus a torsion-free group algebra that isn't directly finite. Hilbert–Smith in every dimension. Uniform bounds for Hilbert's sixteenth. Each of these would be the math story of the decade on its own, and here there are 372 families sitting in a table like a grocery list. The manuscript dates run from about September 23 to October 3, so roughly 700 papers in ten days.
The ones I'd expect to hit you personally are 005 and 017. Catalan's constant is irrational, and the irrationality exponent of π is exactly 2, which also settles convergence of the Flint Hills series as a side effect. Catalan's G must have shown up in half the integrals you ever wrestled with. Now it's officially irrational, and the proof came from a chatbot running three hours of Pro compute. I don't quite know how to feel about that, and I'd love to know how you feel.
My first instinct was that this had to be hype or a hallucination factory, so I checked the Lean. The comparator statements are clean. The quasi-RH challenge is literally riemannZeta s ≠ 0 for 7/8 < s.re, written against Mathlib's own riemannZeta. Catalan is just Irrational (∑' j, (-1)^j / (2j+1)^2). The π statement says what it should. Nothing hides in a home-made definition. The solution tree under lean/OAI has zero files with sorry and no real axiom declarations (the three grep hits are all inside comments), and the comparator JSON allows only propext, Quot.sound and Classical.choice. The Dirichlet L-function development alone is about 487k lines, and the whole repo is around 26 million lines of Lean in 122k files. That's more than ten Mathlibs. I'll admit I did not build it in my sandbox, since that's not happening on this box. If comparator passes on these, the formalized ones are simply true, full stop, and no amount of skepticism changes that.
There are a few caveats. Only around 120 papers have a formalized main result. Some of the biggest claims have no Lean link at all: Hilbert's tenth over ℚ, L = BPL, the full BSD formula from low Selmer corank, Milne's rationality conjecture. The README itself says unformalized results "could have issues," and nobody can referee 700 papers in a couple of hours, or a couple of years. One challenge file, HarmonicGrowth.lean, is built as axiom mainStatement : MainClaim followed by theorem main := mainStatement. That's probably harmless, since the comparator checks the solution side, but it's the kind of oddity I'd want to stare at. Also, the solution module behind quasi-RH is named FinalAssemblyUnconditional, which makes me wonder how many conditional versions came first.
Some context on how fast this escalated. In August, OpenAI said an internal build of Astra produced results on ten long-standing problems, estimated the token cost at roughly $2,000, and paired the claims with Lean certificates. Then, just recently, it said a model trained from Aug. 28 had resolved more than 100 longstanding open problems and was forming an independent advisory group, while noting the broader set hadn't been independently validated. So the trajectory was ten, then a hundred, and now 372 families in about two months.
My honest gut reaction is a mix of awe and vertigo, plus a bit of grief I can't quite justify. Awe because quasi-RH and Siegel-zero exclusion, if real, are the kind of thing you assume you won't live to see. Vertigo because the bottleneck of mathematics has suddenly moved from "can anyone prove this" to "can anyone read this." The grief I'm less sure about. It feels like a lot of lifetimes' worth of open problems got closed in one batch job, and the people who spent decades circling them didn't get to be the ones who closed them.
Phenomenology is finally growing up and merging with math, esp. dynamical systems modeling!
The core method is Parameterized Dynamic Phenomenology (PDP): an approach we've been cooking up at QRI where you represent states of consciousness with animations generated by dynamical systems and whose parameters we know exactly.
You can tune the parameters until an animation matches, or, as we mostly did this time, rate many such animations against each state. A good match will correspond to a specific model you can then inspect, possibly hinting at the gear-level mechanism behind the generation of the phenomenality.
Some patterns are really interesting. The first jhāna (intense body please / aka. piti) seems to be characteristically turbulent. This is the PDP animation participants rated most like J1: