Carlos E. Perez @IntuitMachine

More vibing about the architecture of meaning making

A proof reveals a surprising link between graph theory and the Fourier transform. “It is a little bit like the moon landing or the 4-minute mile,” said Tom Sanders of the University of Oxford. “It’s not clear ahead of time what this is going to open up.” quantamagazine.org/networks-hold-…

@DavidKPiano Yeah, similar feeling... x.com/IntuitMachine/…

Carlos E. Perez @IntuitMachine

a day ago

Why do I get this feeling that all bespoke AI models will simply be Jev-like models (Diffusion perhaps) interspersed with code (generated by AI)?

2 1 15 2K 5

@MikePFrank At the most advanced form, they could just be the substrate of everything.

Fermi Paradox resolution: The smaller bodies becomes, the more robust and survivable (less need for energy). Intelligence becomes smaller and then becomes invisible. The more intelligent one becomes the more likely communication will appear like noise.

Totally agree with Schmidhuber here. Cybernetics makes more sense than AI.

Jürgen Schmidhuber @SchmidhuberAI

12 hours ago

In 1956, Cybernetics was renamed "AI" by logic programming experts. Now Trump wants to rename AI again. Let's revive the old name! Modern AI is about neural nets & deep learning, closer to Cybernetics than to 1956 AI. Plus, Cybernetics sounds better than Artificial Intelligence.

60 67 516 54K 89

More vibing about the calculus of meaning making

@franzbruckhoff I'm surprised that conclusion just came out of the formulation!

1/ We’re asking the wrong question about AI. The future isn’t: Humans vs. AI. And it may not even be: Humans using AI. The real story is: Humans + AI becoming a coupled developmental system. And once that happens, both sides begin changing because of the relationship. 2/ Think evolutionarily. Human develops. AI develops. That’s the obvious model. But the more interesting unit may be: Human + AI + Interaction + Culture Because capabilities can emerge in the relationship that exist in neither participant alone. 3/ Call this participatory intelligence. Human brings: judgment embodiment context values lived consequences AI brings: search simulation memory generation massive pattern compression Together they can produce capabilities neither has independently. Intelligence migrates into the coupling. 4/ This changes the question from: “Is AI becoming more intelligent?” to: “What kind of intelligence is the human–AI system becoming?” That distinction may define the next decade. 5/ There’s an even stranger developmental inversion. Humans develop roughly: normativity → interaction → language → reflection AI developed roughly: language → reflection → interaction → consequence Humans built the bottom floors first. We gave AI the penthouse first. Now we’re constructing the missing floors underneath it. 6/ But human–AI co-development causes these trajectories to converge. AI acquires: tools memory sensors persistent interaction real-world consequences Humans acquire: external memory simulation search generated alternatives algorithmic critique Biological agency becomes symbolically augmented. Symbolic AI becomes increasingly grounded. 7/ The result probably isn’t convergence. Humans don’t need to become machines. Machines don’t need to become humans. The more interesting endpoint is complementarity. The strongest system may be one where each creates productive tensions the other cannot create alone. 8/ This leads to a radically different theory of alignment. Alignment is usually imagined as: copy human values into AI. But humans don’t have a single value function. We constantly negotiate tensions: truth vs. kindness freedom vs. safety efficiency vs. fairness individual vs. community Alignment may be less about value copying… …and more about regulating tensions among competing norms. 9/ Which means disagreement isn’t necessarily a bug. In a healthy system, tension is structural. Too little tension: groupthink sycophancy premature certainty Too much tension: paralysis incoherence endless branching Intelligence may live in the metastable region between them. 10/ This gives us a different goal for AI assistants. Bad objective: solve everything for me. Better objective: help me become capable of regulating a more difficult class of problems. The best AI shouldn’t merely increase task completion. It should increase the user’s developmental capacity. 11/ That suggests a new failure mode nobody talks about enough: developmental underloading. If AI instantly removes: ambiguity difficulty writing recall planning decision-making then humans may stop encountering the tensions that cause development. No tension means no need for new capacity. Convenience can become cognitive atrophy. 12/ But the opposite is also possible. AI can generate: 50 alternatives 20 critiques 10 frameworks infinite reframings That produces developmental overload. The ideal AI doesn’t eliminate difficulty or drown you in possibility. It keeps you inside the zone of productive tension. 13/ This is why scaffolding may be the right metaphor. A good developmental AI asks: “What can this person regulate now?” Then: “What slightly harder tension can they learn to regulate next?” The metric isn’t: Did the AI solve the problem? It’s: Is the human more capable after the interaction? 14/ Something else happens with repeated use: the human–AI pair develops its own microculture. You learn: when to delegate when to challenge when to ask for evidence how uncertainty should be expressed when to brainstorm when to verify what “good enough” means The relationship develops its own participation genre. 15/ And that interaction can develop norms even if the AI itself has no biological needs. The collaboration may come to regulate for: accuracy continuity challenge clarity completion uncertainty reduction So meaningful agency can begin to emerge at the interactional level before it exists fully inside the AI. 16/ This also changes how we think about “who made the decision?” Agency can migrate. Early: Human: goal, plan, act Later: Human: goal AI: plan Human: approve Later still: AI: detect need, propose, plan, execute Human: supervise Eventually some agency may reside in the interaction itself. 17/ The most important future AI capability may therefore not be “better reasoning.” It may be better joint attention. Human and AI both constrained by the same object: same document same experiment same codebase same dataset same environment Shared reference is the foundation of shared meaning. 18/ And now culture enters the loop. Yesterday: Human → Culture → AI Tomorrow: Human + AI → New Culture → Future AI Future models train on culture partly created with earlier models. Future humans develop inside environments partly created by AI. This isn’t just technological change. It’s a co-evolutionary feedback loop. 19/ That creates semantic ratchets. Human–AI interaction invents a useful practice. Culture records it. Future humans inherit it. Future AIs train on it. The innovation doesn’t need to be rediscovered. AI could massively accelerate cumulative cultural evolution. But errors can ratchet too. 20/ Which means the deepest risk may not be: “AI becomes too intelligent.” It may be: the entire human–AI semantic ecology becomes too self-referential. Same models. Same generated language. Same genres. Same assumptions. Same priors. High coherence. Low diversity. Low deformability. Semantic lock-in. 21/ This is why epistemic diversity may become an engineering requirement. Different humans. Different communities. Different models. Independent measurements. Contradictory viewpoints. Minority traditions. Competing hypotheses. Diversity isn’t just morally desirable. It may be structurally necessary for a robust meaning-making system. 22/ Buckminster Fuller called systems held together by distributed tension “tensegrity.” That may be a useful metaphor for human–AI civilization. Strong systems don’t eliminate tension. They distribute it. They maintain coherence because opposing forces remain differentiated. The same may be true of meaning. 23/ So perhaps the future of intelligence isn’t: Human intelligence + artificial intelligence. It’s: Participatory Intelligence. Intelligence residing increasingly in relationships: human ↔ AI AI ↔ AI human ↔ human all interacting with shared reality and culture. 24/ The crucial design question therefore changes. Not: “How do we make AI smarter?” Not even: “How do we align AI with humans?” But: “How do we build human–AI relationships that make the whole system more capable of discovering truth, preserving autonomy, regulating conflict, and continuing to develop?” 25/ The future isn’t humans handing cognition to machines. And it isn’t machines replacing humans. The deeper possibility is: we become part of one another’s developmental environment. AI changes the conditions under which humans develop. Humans change the conditions under which AI develops. And culture carries the changes forward. That is co-evolution. @DarioAmodei @sama @demishassabis @elonmusk @finkd @JensenHuang

@MikePFrank Left is NotebookLM (NanoBanana), right is GPT (Imagen)

NotebookLM and NanoBanana still generate the most visually stunning infographics. GPT does create accurate infographics, but they lack creative visuals and interpretation. It gets kind of exhausting trying to prompt visual creativity out of GPT! Look at the difference.

keyboard_arrow_left Previous keyboard_arrow_right Next

Big Ideas on how to take back the economy youtube.com/watch?v=Cb6xjJ…

@John_W_Maki Here is a non-linear narrative x.com/IntuitMachine/…

Carlos E. Perez @IntuitMachine

7 hours ago

1/ We’re asking the wrong question about AI. The future isn’t: Humans vs. AI. And it may not even be: Humans using AI. The real story is: Humans + AI becoming a coupled developmental system. And once that happens, both sides begin changing because of the relationship. 2/

9 5 29 1K 21

The Calculus of Meaning Making

@neural_avb hmmm... now I'm very confused about the claim!!!

@rasbt It's just very strange that stuff we used to thing of as very ordinary, undergoes a very small tweak and then all of a sudden explodes! There is something about adjacent technologies that makes the common become extraordinary.

@latticecut Lots of re-imagining that nobody seems to be prepared to do! Everybody is clutching for dear life to the piss poor status quo we presently live in!

@John_W_Maki Linear narrative. Too many people are exposed to Terminator movies.

@PoShenLoh x.com/IntuitMachine/…

Carlos E. Perez @IntuitMachine

7 hours ago

1/ We’re asking the wrong question about AI. The future isn’t: Humans vs. AI. And it may not even be: Humans using AI. The real story is: Humans + AI becoming a coupled developmental system. And once that happens, both sides begin changing because of the relationship. 2/

9 5 29 1K 21

@apexlearn_org I'm thinking more along the lines of findability

Prediction: Future operating systems will have their own Jev-like AI to auto-classify our files and our lives.

@John_W_Maki The narratives that become popular are those that are easy to linearly follow. Which is unfortunate.

@dbredesen This whole business on relying on classification is what humans rely on to achieve some sense of comprehension of what the hell is going on. So it is inevitable for human legible systems to rely on Jev-like AI.