Time dynamics & Temporal Intelligence

Statistics vs. World Models 🚀 500 samples, 10k steps, two fates: 🔴 Plain AI: "Probability drift" → Energy escape. 🟢 SG-UDOL: Hamiltonian EBM → Orbital integrity. Solving long-horizon drift via Energy-Based priors. Thoughts @ylecun? #AIforScience #WorldModels #EBM #SGUDOL

@pascalefung To build AMI requires world models that understand physics. My SG-UDOL framework embeds Hamiltonian priors to solve the "drift" problem. Green (SG-UDOL) stays stable for 10k steps with only 500 samples, while Red (Vanilla) collapses. I’d love to bring this to the Paris team! 🚀

I am hiring researchers and builders for our #Paris team to build advanced machine intelligence that is fundamentally human-centered. amilabs.xyz

The EBM Advantage: Stability: Energy drift <0.5% over 10k steps. Zero escape. Integrity: Hamiltonian priors act as a "topological cage" against error accumulation. Logic: Reconstructs the Global Potential where vanilla NNs only see local noise. It’s Manifold Understanding. 🧠

Statistics vs. World Models 🚀 500 samples, 10k steps, two fates: 🔴 Plain AI: "Probability drift" → Energy escape. 🟢 SG-UDOL: Hamiltonian EBM → Orbital integrity. Solving long-horizon drift via Energy-Based priors. Thoughts @ylecun? #AIforScience #WorldModels #EBM #SGUDOL

Replacing forward-Euler prediction with symplectic updates greatly reduces long-term energy drift in learned oscillators — without modifying network or loss. Curious if structure-preserving predictor dynamics matter for scalable world models. @ylecun

@a16z Across the vastly different scales of galactic systems and neural systems, evolutionary dynamics (time) and structural organization (space) reveal profound self-similarity — arising from their common underlying phase-flow dynamics after symmetry breaking.

Fei-Fei Li: AI models with spatial intelligence will help us make the creative leap from flat data to insights about the three-dimensional structure of the real world. "One of the most important pieces in DNA's discovery history is the X-ray diffraction photo that was captured by Rosalind Franklin. It was a flat 2D photo of a structure that looks like a cross with diffraction patterns." "With that flat 2D photo, humans... were able to reason in 3D space and deduce the highly three-dimensional double helix structure of DNA." "You cannot think in 2D and deduce that structure. You have to think in 3D and use human spatial intelligence." @drfeifei with @lennysan

@spaceandtech_ The Lorentzian spacetime is not a pre-existing background arena for the universe; it emerges as the effective dynamical structure governing a quantum system in its low-energy phase. Time is not a mode of being or existence in itself; time is phase.

The White House Director of Science and Technology Policy, Michael Kratsios, stated that U.S. technologies can manipulate time and space.

@JonhernandezIA Indeed. A single glance suffices to differentiate jellyfish from drifting plastic bag— the mechanism is purely physical. Cognition is not secondary brain computation; it is the collapse of phase synchronization under the same 1/f sampling protocol.

📁 Yann LeCun, Chief AI Scientist at Meta, says language is not the peak of intelligence, it is the easy part. Predicting the next word is simple because language is made of finite symbols. The real world is continuous, noisy and chaotic, and even a cat navigates it better than our best models. True intelligence begins where text ends.

@karlmehta A single glance suffices to differentiate jellyfish from drifting plastic bag— the mechanism is purely physical. Cognition is not secondary brain computation; it is the collapse of phase synchronization under the same 1/f sampling protocol.

1. "Nobody in their right mind will use autoregressive LLMs a few years from now." The technology powering ChatGPT and GPT-4? Dead within years. The problem isn't fixable with more data or compute. It's architectural. Here's where it gets interesting...

He predicted: • AI vision breakthrough (1989) • Neural network comeback (2006) • Self-supervised learning revolution (2016) Now Yann LeCun's 5 new predictions just convinced Zuckerberg to redirect Meta's entire $20B AI budget. Here's what you should know (& how to prepare):

@NightSkyToday Universe origin = time-phase breaking Black hole core = time-phase restoration Evaporation = re-breaking Thus emerges: the cyclic time-phase structure. Time is no monotonic backdrop — it is the universe's phase phenomenon.

BREAKING🚨:Study Proposes Time Symmetry Near Black Holes and the Big Bang. Under extreme conditions, time may not flow in a single direction. Researchers propose that time could diverge into two opposing arrows. This framework offers new insights into cosmic evolution.

@elonmusk Leverage time to embed the universe within the neural network — exactly as the human brain does — and you obtain the Grok you seek. This is the perfect mirror of launching humanity into the universe aboard Starship.

And there will be noticeable improvements every week

@forallcurious Galaxy evolution and neural networks are governed by the same dynamical equations—this is the root cause of why the two exhibit such remarkably similar global morphologies.

BREAKING🚨: Your consciousness can connect with the whole universe, groundbreaking research reveals

@Cortex_Zero The universe is a self-organizing coherent system possessing a non-local nucleus. Neural networks are its short-range, strong-coupling solutions at the biochemical interface; galactic evolution constitutes its long-range, weak-coupling solutions at the gravitational interface

New Evidence Suggests Your Brain Is NOT the Source of Consciousness 🧠 Cognitive scientist Donald Hoffman and his research team are reportedly nearing the release of new findings that could fundamentally alter the scientific understanding of human perception. The research proposes a paradigm shift away from the traditional view that the brain produces consciousness. Instead, Hoffman suggests that consciousness is the primary building block of the universe and that our physical reality is a sophisticated "interface" or simulation generated by conscious agents. This breakthrough explores the mathematical foundations of the "Interface Theory of Perception," arguing that evolution has prioritized survival over an accurate perception of objective reality. If proven, the theory would suggest that space, time, and physical objects are not the ultimate truth, but rather a functional representation of a deeper, consciousness-based reality. #ufox #ufotwitter

@NightSkyToday Negative time is inevitable in the absence of time-phase symmetry breaking — not necessarily time reversal, but rather the cumulative effect of the counter-trend.

BREAKING🚨: “Negative time” has just been observed in real quantum experiment.

@PopMech Dimensions are not a priori structures of the universe but are emergent invariants of stable topological correlations. The 1/f noise represents the residual, uncoupled topological fluctuations that failed to harden into the geometric background.

Scientists Spotted Particles in Another Dimension. They Could Change Fundamental Physics. popularmechanics.com/science/a70260…

@CankayKoryak Dimensions are not a priori structures of the universe but are emergent invariants of stable topological correlations. The 1/f noise represents the residual, uncoupled topological fluctuations that failed to harden into the geometric background.

In the theoretical framework of Penrose and Hameroff, intracellular dynamics trigger quantum effects through "noise assisted transport." Biophysical photons and electromagnetic waves create a "gravitational collapse"-like effect on tryptophan molecules, forming coherent soliton packets. These packets are then fired like a laser from the microtubules' plus ends into the synaptic cleft. In the synaptic cleft, these solitons interact with neurotransmitters, influencing their energy levels and chemical reactions. Thus, Penrose and Hameroff's theory aims to explain the emergence of consciousness and its physical basis from noise.

@QuantumTumbler Great! I am concentrating on the essential physical mechanisms responsible for that immediate, unmistakable visual distinction: jellyfish versus floating plastic bag.

I appreciate the direction you’re pointing at. Protein–water interface physics is real. Hydrogen-bond networks matter. None of that is controversial. But “this is the mechanism of natural intelligence” is a much stronger claim. If vision or cognition arises from hydration-shell dynamics, then we should be able to • Write the governing equations (what Hamiltonian or effective field description?) • Identify the relevant order parameter • Specify the timescale separation • Predict a measurable perturbation (temperature shift, isotopic substitution, ionic strength, etc.) Right now it’s a suggestive analogy. Mechanism begins when you can perturb the system and forecast how the observable changes. If you can attach that layer, I’m interested.

The pillar is simple. Orch-OR requires two things to be true at the same time. 1. Microtubule quantum states must remain coherent on ~25 ms timescales. 2. Those states must strongly influence macroscopic neural dynamics (EEG/gamma). If either fails, the theory collapses. If coherence is short → no sustained quantum state. If coupling is weak → no macroscopic effect. If coupling is strong enough to matter → the system becomes dynamically fragile. That tension isn’t rhetorical it’s structural. You can’t simultaneously demand long-lived coherence and strong macro influence in a warm, noisy brain without crossing stability constraints. That’s the pillar. If it holds, Orch-OR survives. If it doesn’t, everything built on it falls with it. This isn’t personal. It’s parameter space.

@probnstat This phenomenon is not one of condensation; rather, it is modulation by time itself. The data-processing paradigm of thought has been fundamentally misled.

A Bose–Einstein condensate (BEC) is a quantum state of matter in which a large number of bosons occupy the same lowest energy level, causing macroscopic quantum behavior at ultra-low temperatures. While originating in physics, BECs have inspired powerful ideas in probability and statistics through the study of collective behavior, phase transitions, and interacting particle systems. The mathematics used to describe BECs—mean-field limits, large deviations, and random fields—also appears in modern probabilistic models of high-dimensional data. In statistics and machine learning, similar energy-based and density-concentration principles arise in clustering, kernel methods, and generative modeling, where data “condenses” into low-energy representations. In deep learning, analogies to condensation appear in attention collapse, representation compression, and mode concentration in latent spaces. Thus, BECs not only revolutionized quantum physics but also provide a rich metaphor and mathematical toolkit for understanding collective structure in complex learning systems.

@cosmosarcive The most fundamental impediment is a fundamentally flawed understanding of time itself.

Why is it so hard to unify gravity with quantum mechanics? Edward Witten explains the fundamental conflict: Einstein’s non-linear mathematics vs. the requirements of quantum theory. He highlights how String Theory serves as a "restrictive" yet necessary framework that finally allows physicists to calculate quantum corrections for gravity without the math breaking. Gravity + Quantum Mechanics = a massive headache for physicists. Credit: WorldScienceFestival

@LolaNeuroPhylo Non-local synchronization is the more fundamental mode of “connection”

'Neurons that fire together, wire together' Your nervous system is essentially a physical map of your history. If you grew up in an environment of safety and play, your "internal map" is wired for exploration. If you grew up in an environment of threat, your system specialized in survival. If the nervous system was wired by experience, it can be rewired by new, intentional experiences (neuroplasticity).

@NeuralSpace_ Evidently, mental activity is merely the totality of cosmic activity.

🚨 : Des scientifiques ont cartographié 1 mm³ d’un cerveau humain, moins qu’un grain de riz et un cosmos microscopique est apparu.