ASICs for #AI will be trinary.

More updates on the🪼#Neuraxon front from @_Qubic_ #OpenScience hybridized with #Aigarth @josesanchezhb & @VivancosDavid are proud to share #NeuraxonLive Game Of Life Server Client so you can Deploy your own world of 🪼based on Game of Life 5.0 Full open source code at: github.com/DavidVivancos/Neu… Deploy your own server or use ours. Breed your own 🪼the first season just started, is live at nxon.online - Will yours live forever? -> If so share your 🪼 - Will yours be on top of the rankings? This season world max is 500🪼and max 100 Custom Nxers claim yours now! Why it matters? Neuraxon Live isn't a simulation you load and reset — it's one neural world running 24/7, "forever", where every creature carries a real g-capable brain that forages, mates, sings, and dies on its own. You're not watching a replay; you're watching open-ended evolution and emergent intelligence happen live, ranked all-time across every NxEr that ever lived. Open a browser, zoom in, and you can literally hear artificial minds being shaped by selection in real time. 🧠🌍

Yes... Looks Like #Neuraxon just started.... @josesanchezhb & @VivancosDavid are very proud to communicate that our paper #MultiNeuraxon follow-up to the just presented and awarded at ICMLT Neuraxon 2.0 in #Berlin for @_Qubic_ #OpenScience evolving @c___f___b #Aigarth to new heights... This time has been accepted for poster presentation a the 19th #AGI Conference, so to #SanFrancisco @SFSU and beyond...

Awarded with BEST presentation in evening session in ICMLT conference. @VivancosDavid and myself in Berlín presenting Neuraxon. Thank you @_Qubic_ !!

Today #MultiNeuraxon🪼joins the @nvidia #cuda family , very glad to release for @_Qubic_ #OpenScience The Cuda Kernels and library so you can teach your #bioinspired #AIs using Nvdia #GPUs too. Code: github.com/DavidVivancos/Neu… Why it matters? It brings Multi-Neuraxon #Aigarth evolution to GPU-native execution: CPUs orchestrate while NVIDIA GPUs teach, execute, and scale neural compute in parallel. CPU GPU together = Multi-Neuraxon at speed.

x.com/c___f___b/status/20503… was like striking gold. By keeping parameter space dimensions unchanged we get ability to extract information about experiments that need to be conducted to achieve our goal faster (cheaper energy-wise). This is for the cases when #AI learns about the real world. If we can construct the extended phase space without experimenting then we, obviously, don't get any extra benefit.

Interesting, what cryptocoins will remain if #AI hacks all OSes... x.com/lyq_sqsp/status/204752…

#Aigarth progress looks like improving a single branch of a technology tree in a strategy computer game. It's all about energy. Trinary and CPU were the easiest discoveries. Anti-attractors felt like discovery of an alien tech, we still don't fully understand how to unlock its potential. Recently we removed some things which happened to be different aspects of the same phenomenon (separation to training and testing datasets, precise representation of data). For that we looked at the whole concept pretending to be a 4-dimensional being. Now we have to find much more energy than is currently available to the #Qubic ecosystem. But why should hoomans and their computers do all the work?.. So here is the plan: 1. Implement Outsourced Computations feature to give #AI ability to affect the outer world. 2. Run #IntelligentTissue on the network. 3. Target mining power towards improvement of that #IntelligentTissue. 4. Set the main task for that thing to bringing more energy into the ecosystem.

Want to feel yourself like John Connor? Do like me: Listen to #AI generated songs on @YouTube via an Albania VPN.

Yann LeCun was right the entire time. And generative AI might be a dead end. For the last three years, the entire industry has been obsessed with building bigger LLMs. Trillions of parameters. Billions in compute. The theory was simple: if you make the model big enough, it will eventually understand how the world works. Yann LeCun said that was stupid. He argued that generative AI is fundamentally inefficient. When an AI predicts the next word, or generates the next pixel, it wastes massive amounts of compute on surface-level details. It memorizes patterns instead of learning the actual physics of reality. He proposed a different path: JEPA (Joint-Embedding Predictive Architecture). Instead of forcing the AI to paint the world pixel by pixel, JEPA forces it to predict abstract concepts. It predicts what happens next in a compressed "thought space." But for years, JEPA had a fatal flaw. It suffered from "representation collapse." Because the AI was allowed to simplify reality, it would cheat. It would simplify everything so much that a dog, a car, and a human all looked identical. It learned nothing. To fix it, engineers had to use insanely complex hacks, frozen encoders, and massive compute overheads. Until today. Researchers just dropped a paper called "LeWorldModel" (LeWM). They completely solved the collapse problem. They replaced the complex engineering hacks with a single, elegant mathematical regularizer. It forces the AI's internal "thoughts" into a perfect Gaussian distribution. The AI can no longer cheat. It is forced to understand the physical structure of reality to make its predictions. The results completely rewrite the economics of AI. LeWM didn't need a massive, centralized supercomputer. It has just 15 million parameters. It trains on a single, standard GPU in a few hours. Yet it plans 48x faster than massive foundation world models. It intrinsically understands physics. It instantly detects impossible events. We spent billions trying to force massive server farms to memorize the internet. Now, a tiny model running locally on a single graphics card is actually learning how the real world works.

A major update of #Aigarth is approaching. We are transforming #Qubic into a giant "anthill" where every miner will be searching for shares in a coordinated manner (like ants for food). But we are not trying to create #SwarmIntelligence, we are using it for something more ambitious.

The CEO of Google DeepMind just went on record saying he disagrees with one of the most respected AI researchers in the world. Demis Hassabis, the man behind AlphaFold, AlphaGo, and Google's entire AI operation publicly pushed back against Yann LeCun's claim that large language models are a dead end for artificial intelligence. LeCun, who left Meta earlier this year to start his own AI lab, has been saying for years that LLMs cannot reason, cannot plan, and will never get us to human-level intelligence. Hassabis disagrees, and he said so directly. His position is that scaling laws are still working, foundation models are still getting more capable, and whatever AGI ends up looking like, LLMs will be a central part of it, not something that gets replaced. He does say there is roughly a 50/50 chance that one or two additional breakthroughs will be needed beyond scaling alone, things like better memory, long-term planning, and world models. But the core disagreement with LeCun is clear, Hassabis believes the current architecture is sound and the current path leads somewhere real. Two Nobel-recognized researchers, two founding figures of modern AI, now publicly on opposite sides of the most important technical question in the industry.

Look, guys, they've started doing what #Qubic has been doing for several years already: x.com/AlphaSignalAI/status/2…

I was passing by my son's bedroom and was astonished to see the bed was nicely made and everything was picked up... Then, I saw an envelope, propped up prominently on the pillow, addressed to, 'Dad'. With the worst premonition, I opened the envelope and read the letter, with trembling hands. "Dear, Dad. It is with great regret and sorrow that I'm writing you. I had to elope with my new girlfriend, because I wanted to avoid a scene with Mum and you. I've been finding real passion with Stacy. She is so nice, but I knew you would not approve of her because of all her piercing's, tattoos, her tight motorcycle clothes and because she is so much older than I am. But it's not only the passion, Dad. She's pregnant. Stacy said that we will be very happy. She owns a trailer in the woods, and has a stack of firewood for the whole winter. We share a dream of having many more children. Stacy has opened my eyes to the fact that marijuana doesn't really hurt anyone. We'll be growing it for ourselves and trading it with the other people in the commune for all the cocaine and ecstasy we want. In the meantime, we'll pray that science will find a cure for AIDS so that Stacy can get better. She sure deserves it! Don't worry Dad, I'm 15, and I know how to take care of myself. Someday, I'm sure we'll be back to visit so you can get to know your many grandchildren. Love, your son, Joshua. P.S. Dad, none of the above is true. I'm over at Jason's house. I just wanted to remind you that there are worse things in life than the school report that's on the kitchen table. Call when it is safe for me to come home!"

A group of Jewish women were at a seminar on how to live in a loving relationship with their husband. The women were asked, "How many of you love your husband?" All the women raised their hands. Then they were asked, "When was the last time you told your husband you loved him?" Some women answered today, a few yesterday, and some can't remember. The women were then told to take out their cell phones and text to their husband: "I love you, sweetheart." Then they were instructed to exchange phones with another person, and to read aloud the text message that was received in response to their message. Below are 12 replies; some are hilarious. 1. Who the hell is this? 2. Eh, mother of my children, are you sick or what? 3. Yeah, and I love you too. What's wrong? 4. What now? Did you crash the car again? 5. I don't understand what you mean? 6. What the hell did you do now? 8. Don't beat about the bush, just tell me how much you need? 9. Am I dreaming? 10. If you don't tell me who this message is actually for, someone will die. 11. I thought we agreed you wouldn't drink during the day. 12. Your mother is coming to stay with us, isn't she?