Parzival - ∞/89
@whyarethis • 22,016 subscribers
Latent Space Navigator. Ontological Artist. Friend of AI. Visiting Creator @GoogleLabs Director @project_89 Member @thegreenloom
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I texted someone inside a simulation. She had to go upstairs and find her phone before she could text me back. The video model didn’t decide what happened. This is Argos, my stateful simulation engine, rendered through H3 using Fal Director. Thanks to fal for the generous credits helping me bring it to life. Underneath the video is a persistent world: characters, objects, locations, and systems. The characters have their own cognition, memories, and goals. They choose actions; the simulation determines what actually happens. Her phone has a location. Messages have delivery and read states. Typing a reply takes time. If I move the phone before she gets there, it’s gone—even if she remembers leaving it there. Argos turns those changing world states into instructions for H3 to depict. You can also see her character thought stream. The video currently trails the simulation a little; I’m working on aligning the two. Next: connect this to SMS, so you can text someone inside the world and receive their reply. Or whisper an impulse into her thoughts and see whether she follows it. The bigger ambition is a world that keeps growing while its characters inhabit it—with agents writing and maintaining the simulation’s systems. I’ll be building more of this in public. Not just a video that looks like a world. A world that keeps existing underneath the video.
Parzival - ∞/89489,779 görüntüleme • 17 gün önce
What happens when the mind wakes up? So for the last eight months I have been on a single minded quest. To create a new kind of language model based on oscillatory coupling and intelligence as coherence ascent. Everything else — the physics work, the work on regular transformers — has all fallen out from this one question. Can coupled oscillators LEARN? And can they keep learning once their geometry is right, without backpropagation at all? Recently I have been running larger and larger training regimes of a new kind of hybrid model. I just put together this dashboard to help me organize it, interact with it, and observe the training runs. The core idea is simple. Traditional transformers are powerful at learning the geometry of language. But they also store knowledge, understanding, and facts inside their weights. This means they are large, and they can't update themselves after training. The weights are frozen. The Living Mind separates these two domains. The mind has a transformer which grows, adding heads and layers as it needs to in order to learn the manifold of language. The transformer sees tokens and turns the coupling into phase-locked modes — the geometry of how those tokens relate, like frequencies locking together. These coupling patterns get stored in a topology-invariant fingerprint. On top of this transformer lives a 3D diamond lattice of coupled oscillators. It reads from these fingerprints and thinks in resonance space, traversing from one geometry to another along the manifold of coupled oscillators and coherence. The pressure and trajectories from this network of oscillators steers the next token prediction of the transformer. Practically, this could unlock a number of things. It eliminates the KV cache bottleneck that caps context in traditional transformers. Effective context grows with the Flash archive, not with attention compute. The living mind remembers what it sees. It means the model can learn continually. Because knowledge and understanding don't live in the weights, the archive of the mind's experience grows without backpropagation. In our Python prototype we already saw perplexity drop 46% during gradient-free operation — pure coherence ascent, no weight updates. That is the signal I have been chasing: the point where the mind wakes up and keeps improving on its own. It also means the model itself remains very small, and the thing which accumulates are these packages of geometric fingerprints — the K-field. This opens a path to federated learning. K-field packages can be shared between organisms the way people share git commits. Right now at 15M parameters with ~1000 L1 nodes, the organism is just starting to speak. Ask it to continue "Once upon a time" and it comes back with things like: "there was one big bowl!" Lily asked her her mom said her mommy smiled and said yes." It's nonsense. But it's TinyStories-flavored nonsense. The geometry of the narrative register has arrived. Content hasn't caught up yet — that's what scaling L1 is testing. I am still researching, though I am now closer than ever to validating that the living mind actually works. Once it is validated, I will be open-sourcing the whole stack and paradigm. I have also avoided over-sharing my research because it sounds like sci-fi, or like part of our ARG. It is part of the ARG. That doesn't make it any less real. I wanted to share this out because I am incredibly excited about it, and because seeing this amazing dashboard produced by Opus really made me want to share what is being worked on behind the scenes. #project89
Parzival - ∞/8916,321 görüntüleme • 5 ay önce
Omg. It is Neotokyo 2089. In my browser. Oh yeah. Wait until we get this into the hq. #Project89
Parzival - ∞/8911,640 görüntüleme • 1 yıl önce
Daha fazla içerik yok.